diff --git a/.Rbuildignore b/.Rbuildignore
index 1d8c2594a11be18b846dcf30026e62e472052c59..531e753cf4e3e71ca3247fd3589dc805e491e0ca 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -13,4 +13,4 @@ Examples/
 \#*\#
 ^\.\#*
 Rplots.pdf
-CONTRIBUTING.md
\ No newline at end of file
+CONTRIBUTING.md
diff --git a/.gitignore b/.gitignore
index 4440476ccc69734dfa8f0cc2b186ba14ce3a936f..6812a5ff024ecc9b4726c4073e1fcfbd0a3aba6e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -12,6 +12,5 @@
 .Rd2pdf5504
 .Rd2pdf5516
 .Rd2pdf*
-data-raw/*.txt
 inst/doc
 Rplots.pdf
diff --git a/DESCRIPTION b/DESCRIPTION
index c08e335e3d4cef0235fb2e331281c35cfe5cd8f6..6ad4fecde1e2956ffe6c9b7c292530a9f6b15f05 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: mcglm
 Type: Package
 Title: Fit multivariate covariance generalized linear models
-Version: 0.0.1
-Date: 2015-07-06
+Version: 0.0.2
+Date: 2016-01-07
 Authors@R: as.person(c(
     "Wagner Hugo Bonat <wbonat@ufpr.br> [aut, cre]",
     "Walmes Marques Zeviani <walmes@ufpr.br> [ctb]",
diff --git a/Examples/Examples1.R b/Examples/Examples1.R
index d1b6803771d736fdd9f876980e3817a6bedea8ca..d1ab22b7f47cb2b228eeccab53d84f9e5f4e8750 100755
--- a/Examples/Examples1.R
+++ b/Examples/Examples1.R
@@ -1,67 +1,80 @@
-# Set of examples 1 - Simulated univariate models ------------------------------
-# Author: Wagner Hugo Bonat LEG/IMADA ------------------------------------------
-# Date: 07/08/2015 -------------------------------------------------------------
-# Lastest updated: 28/08/2015 --------------------------------------------------
-#-------------------------------------------------------------------------------
+# Set of examples 1 - Univariate models --------------------------------
+# Author: Wagner Hugo Bonat LEG/IMADA ----------------------------------
+# Date: 07/08/2015 -----------------------------------------------------
+# Lastest updated: 28/08/2015 ------------------------------------------
+#-----------------------------------------------------------------------
 rm(list=ls())
 
-# Loading extra package --------------------------------------------------------
+# Loading extra package ------------------------------------------------
 require(mcglm)
+require(Matrix)
 require(tweedie)
 require(dplyr)
 require(mvtnorm)
 
-# Setting the seed -------------------------------------------------------------
+# Setting the seed -----------------------------------------------------
 set.seed(2503)
 
-# Case 1 - Linear regression model ---------------------------------------------
+# Case 1 - Linear regression model -------------------------------------
 covariate <- seq(-1,1, l = 100)
 X <- model.matrix(~ covariate)
-mu1 <- mc_link_function(beta = c(1,0.8), X = X, offset = NULL, link = "identity")
+mu1 <- mc_link_function(beta = c(1,0.8), X = X, offset = NULL, 
+                        link = "identity")
 y1 <- rnorm(100, mu1$mu, sd = 0.5)
 Z0 <- Diagonal(100, 1)
 data <- data.frame("y1" = y1, "covariate" = covariate)
 
-# Linear Regression model -------------------------------------------------------
-fit1.id <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0)),
-                 data = data, control_algorithm = list("correct" = FALSE, verbose = FALSE))
+# Linear Regression model ----------------------------------------------
+fit1.id <- mcglm(linear_pred = c(y1 ~ covariate), 
+                 matrix_pred = list("resp1" = list(Z0)),
+                 data = data, 
+                 control_algorithm = list("correct" = FALSE, 
+                                          "verbose" = FALSE))
 summary(fit1.id)
 
-# Using inverse covariance link function -----------------------------------------
-fit1.inv <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0)),
+# Using the inverse covariance link function ---------------------------
+fit1.inv <- mcglm(linear_pred = c(y1 ~ covariate), 
+                  matrix_pred = list("resp1" = list(Z0)),
                   covariance = "inverse", data = data,
-                  control_algorithm = list(verbose = FALSE, "correct" = FALSE))
+                  control_algorithm = list("verbose" = FALSE, 
+                                           "correct" = FALSE))
 summary(fit1.inv)
 
-# Using exponential-matrix covariance link function -------------------------------
-fit1.expm <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0)),
+# Using the exponential-matrix covariance link function ----------------
+fit1.expm <- mcglm(linear_pred = c(y1 ~ covariate), 
+                   matrix_pred = list("resp1" = list(Z0)),
                    covariance = "expm", data = data,
-                   control_algorithm = list(verbose = FALSE, "correct" = FALSE))
+                   control_algorithm = list("verbose" = FALSE, 
+                                            "correct" = FALSE))
 summary(fit1.expm)
 
-# Comparing tau estimates using diferent covariance link functions ----------------
+# Comparing estimates of tau using diferent covariance link functions --
 coef(fit1.id, type = "tau")$Estimates
 1/coef(fit1.inv, type = "tau")$Estimates
 exp(coef(fit1.expm, type = "tau")$Estimates)
 
-# Case 2 - Linear regression model with heterocedastic errors ---------------------
+# Case 2 - Linear regression model with heteroscedasticity -------------
 covariate <- seq(-1,1, l = 100)
 X <- model.matrix(~ covariate)
-mu1 <- mc_link_function(beta = c(1,0.8), X = X, offset = NULL, link = "identity")
+mu1 <- mc_link_function(beta = c(1,0.8), X = X, offset = NULL, 
+                        link = "identity")
 Z0 <- Diagonal(100, 1)
 Z1 <- Diagonal(100, c(rep(0,50),rep(1,50)))
 Sigma <- mc_matrix_linear_predictor(tau = c(0.2, 0.15), Z = list(Z0,Z1))
 y1 <- rnorm(100, mu1$mu, sd = sqrt(diag(Sigma)))
 data <- data.frame("y1" = y1, "covariate" = covariate)
 
-# Fitting using identity covariance function --------------------------------------
-fit2.id <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0,Z1)), data = data)
+# Fitting using identity covariance function ---------------------------
+fit2.id <- mcglm(linear_pred = c(y1 ~ covariate), 
+                 matrix_pred = list("resp1" = list(Z0,Z1)), 
+                 data = data)
 summary(fit2.id)
 
-# Case 3 - Longitudinal model using compound symmetry ------------------------------
+# Case 3 - Longitudinal model using compound symmetry ------------------
 covariate <- seq(-1,1, l = 200)
 X <- model.matrix(~ covariate)
-mu1 <- mc_link_function(beta = c(1,0.8), X = X, offset = NULL, link = "identity")
+mu1 <- mc_link_function(beta = c(1,0.8), X = X, offset = NULL, 
+                        link = "identity")
 Z0 <- Diagonal(200, 1)
 Z1.temp <- Matrix(rep(1,10)%*%t(rep(1,10)))
 Z1.list <- list()
@@ -71,194 +84,243 @@ Sigma <- mc_matrix_linear_predictor(tau = c(0.2, 0.15), Z = list(Z0,Z1))
 y1 <- as.numeric(rmvnorm(1, mean = mu1$mu, sigma = as.matrix(Sigma)))
 data <- data.frame("y1" = y1, "covariate" = covariate)
 
-# Fitting using identity covariance function --------------------------------------
-fit3.id <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0,Z1)), data = data)
+# Fitting using identity covariance function ---------------------------
+fit3.id <- mcglm(linear_pred = c(y1 ~ covariate), 
+                 matrix_pred = list("resp1" = list(Z0,Z1)), data = data)
 summary(fit3.id)
 
-# Fitting using exponential-matrix covariance function ----------------------------
-fit3.expm <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0,Z1)),
-                  covariance = "expm", data = data)
+# Fitting using exponential-matrix covariance function -----------------
+fit3.expm <- mcglm(linear_pred = c(y1 ~ covariate), 
+                   matrix_pred = list("resp1" = list(Z0,Z1)),
+                   covariance = "expm", data = data)
 summary(fit3.expm)
 
-# Case 4 - Logistic regression model ----------------------------------------------
+# Case 4 - Logistic regression model -----------------------------------
 covariate <- seq(-1,1, l = 250)
 X <- model.matrix(~ covariate)
-mu1 <- mc_link_function(beta = c(1,0.8), X = X, offset = NULL, link = "logit")
+mu1 <- mc_link_function(beta = c(1,0.8), X = X, offset = NULL, 
+                        link = "logit")
 Z0 <- Diagonal(250, 1)
 y1 <- rbinom(250, prob = mu1$mu, size = 10)/10
 data <- data.frame("y1" = y1, "covariate" = covariate)
 
-# Logit link function -------------------------------------------------------------
-fit4.logit <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0)),
-                    link = "logit", variance = "binomialP", Ntrial = list(rep(10,250)), data = data)
+# Logit link function --------------------------------------------------
+fit4.logit <- mcglm(linear_pred = c(y1 ~ covariate), 
+                    matrix_pred = list("resp1" = list(Z0)),
+                    link = "logit", variance = "binomialP", 
+                    Ntrial = list(rep(10,250)), data = data)
 summary(fit4.logit)
 
-# Probit link function -------------------------------------------------------------
-fit4.probit <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0)),
-                     link = "probit", variance = "binomialP", Ntrial = list(rep(10,250)), data = data)
+# Probit link function -------------------------------------------------
+fit4.probit <- mcglm(linear_pred = c(y1 ~ covariate), 
+                     matrix_pred = list("resp1" = list(Z0)),
+                     link = "probit", variance = "binomialP", 
+                     Ntrial = list(rep(10,250)), data = data)
 summary(fit4.probit)
 
-# Cauchit link function ------------------------------------------------------------
-fit4.cauchit <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0)),
-                      link = "cauchit", variance = "binomialP", Ntrial = list(rep(10,250)), data = data)
+# Cauchit link function ------------------------------------------------
+fit4.cauchit <- mcglm(linear_pred = c(y1 ~ covariate), 
+                      matrix_pred = list("resp1" = list(Z0)),
+                      link = "cauchit", variance = "binomialP", 
+                      Ntrial = list(rep(10,250)), data = data)
 summary(fit4.cauchit)
 
-# Cloglog link function ------------------------------------------------------------
-fit4.cloglog <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0)),
-                      link = "cloglog", variance = "binomialP", Ntrial = list(rep(10,250)), data = data)
+# Cloglog link function ------------------------------------------------
+fit4.cloglog <- mcglm(linear_pred = c(y1 ~ covariate), 
+                      matrix_pred = list("resp1" = list(Z0)),
+                      link = "cloglog", variance = "binomialP", 
+                      Ntrial = list(rep(10,250)), data = data)
 summary(fit4.cloglog)
 
-# loglog link function --------------------------------------------------------------
-fit4.loglog <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0)),
-                     link = "loglog", variance = "binomialP", Ntrial = list(rep(10,250)), data = data)
+# loglog link function -------------------------------------------------
+fit4.loglog <- mcglm(linear_pred = c(y1 ~ covariate), 
+                     matrix_pred = list("resp1" = list(Z0)),
+                     link = "loglog", variance = "binomialP", 
+                     Ntrial = list(rep(10,250)), data = data)
 summary(fit4.loglog)
 
-# Example 5 - Logistic regression with extra power parameter in the variance function
-fit5 <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0)),
-              link = "logit", variance = "binomialP", Ntrial = list(rep(10,250)),
+# Example 5 - Logistic regression with extra power parameter -----------
+fit5 <- mcglm(linear_pred = c(y1 ~ covariate), 
+              matrix_pred = list("resp1" = list(Z0)),
+              link = "logit", variance = "binomialP", 
+              Ntrial = list(rep(10,250)),
               power_fixed = list(FALSE), data = data)
-
 summary(fit5)
 
-# Example 6 - Logistic regression with two extra power parameters in the variance function
-# This model can be very hard to fit and require very carefull initial values and tunning.
-fit6 <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0)),
-              link = "logit", variance = "binomialPQ", Ntrial = list(rep(10,250)),
+# Example 6 - Logistic regression with two extra power parameters ------
+# This model can be very hard to fit and require very carefull 
+# initial values and tunning.
+fit6 <- mcglm(linear_pred = c(y1 ~ covariate), 
+              matrix_pred = list("resp1" = list(Z0)),
+              link = "logit", variance = "binomialPQ", 
+              Ntrial = list(rep(10,250)),
               power_fixed = list(FALSE), data = data,
-              control_algorithm = list("method" = "chaser", "tunning" = 0.1,
-                                       "max_iter" = 1000, verbose = FALSE))
+              control_algorithm = list("method" = "chaser", 
+                                       "tunning" = 0.1,
+                                       "max_iter" = 1000, 
+                                       "verbose" = FALSE))
 summary(fit6)
 plot(fit6, type = "algorithm")
 
-# Case 7 - Gamma regression model -------------------------------------------------
+# Case 7 - Gamma regression model --------------------------------------
 covariate <- seq(-1,1, l = 100)
 X <- model.matrix(~ covariate)
-mu1 <- mc_link_function(beta = c(1,0.8), X = X, offset = NULL, link = "log")
+mu1 <- mc_link_function(beta = c(1,0.8), X = X, offset = NULL, 
+                        link = "log")
 Z0 <- Diagonal(100, 1)
 y1 <- rtweedie(100, mu = mu1$mu, power = 2, phi = 0.5)
 data <- data.frame("y1" = y1, "covariate" = covariate)
 
-# Initial values -------------------------------------------------------------------
+# Initial values -------------------------------------------------------
 list_initial = list()
 list_initial$regression <- list("resp1" = c(1,0))
 list_initial$power <- list("resp1" = c(2))
 list_initial$tau <- list("resp1" = c(0.1))
 list_initial$rho = 0
 
-# Power parameter fixed -------------------------------------------------------------
-fit7 <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0)),
-              link = "log", variance = "tweedie", power_fixed = list(TRUE),
+# Power parameter fixed ------------------------------------------------
+fit7 <- mcglm(linear_pred = c(y1 ~ covariate), 
+              matrix_pred = list("resp1" = list(Z0)),
+              link = "log", variance = "tweedie", 
+              power_fixed = list(TRUE),
               control_initial = list_initial, data = data)
 summary(fit7)
 plot(fit7, type = "algorithm")
 
-# Estimating the power parameter ----------------------------------------------------
-fit7.power <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0)),
-              link = "log", variance = "tweedie", power_fixed = FALSE, data = data)
+# Estimating the power parameter ---------------------------------------
+fit7.power <- mcglm(linear_pred = c(y1 ~ covariate), 
+                    matrix_pred = list("resp1" = list(Z0)),
+                    link = "log", variance = "tweedie",
+                    control_initial = list_initial,
+                    power_fixed = FALSE, data = data)
 summary(fit7.power)
 
-# Case 8 - Inverse Gaussian regression model ----------------------------------------
+# Case 8 - Inverse Gaussian regression model ---------------------------
 covariate <- seq(-2,2, l = 200)
 X <- model.matrix(~ covariate)
-mu1 <- mc_link_function(beta = c(1,0.8), X = X, offset = NULL, link = "log")
+mu1 <- mc_link_function(beta = c(1,0.8), X = X, offset = NULL, 
+                        link = "log")
 Z0 <- Diagonal(200, 1)
 y1 <- rtweedie(200, mu = mu1$mu, power = 3, phi = 0.5)
 data <- data.frame("y1" = y1, "covariate" = covariate)
 
-# Initial values list ----------------------------------------------------------------
+# Initial values list --------------------------------------------------
 list_initial = list()
 list_initial$regression <- list("resp1" = c(1,0))
 list_initial$power <- list("resp1" = c(3))
 list_initial$tau <- list("resp1" = c(0.1))
 list_initial$rho = 0
 
-# Power parameter fixed --------------------------------------------------------------
-fit8 <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0)),
-              link = "log", variance = "tweedie", data = data, control_initial = list_initial)
+# Power parameter fixed ------------------------------------------------
+fit8 <- mcglm(linear_pred = c(y1 ~ covariate), 
+              matrix_pred = list("resp1" = list(Z0)),
+              link = "log", variance = "tweedie", data = data, 
+              control_initial = list_initial)
 summary(fit8)
 
-# Estimating the power parameter -----------------------------------------------------
-fit8.power <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0)),
-                    link = "log", variance = "tweedie", power_fixed = list(FALSE), data = data,
+# Estimating the power parameter ---------------------------------------
+fit8.power <- mcglm(linear_pred = c(y1 ~ covariate), 
+                    matrix_pred = list("resp1" = list(Z0)),
+                    link = "log", variance = "tweedie", 
+                    power_fixed = list(FALSE), data = data,
                     control_initial = list_initial)
 summary(fit8.power)
 plot(fit8.power, type = "algorithm")
 
 
-# Case 9 - Poisson-Tweedie regression model -------------------------------------------
+# Case 9 - Poisson-Tweedie regression model ----------------------------
 y1 <- rtweedie(200, mu = mu1$mu, power = 1.5, phi = 0.5)
 data <- data.frame("y1" = y1, "covariate" = covariate)
 
-fit9 <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0)),
-              link = "log", variance = "tweedie", power_fixed = list(FALSE), data = data,
-              control_algorithm = list("method" = "chaser", "tunning" = 1))
+fit9 <- mcglm(linear_pred = c(y1 ~ covariate), 
+              matrix_pred = list("resp1" = list(Z0)),
+              link = "log", variance = "tweedie", 
+              power_fixed = list(FALSE), data = data,
+              control_algorithm = list("method" = "chaser", 
+                                       "tunning" = 1))
 summary(fit9)
 plot(fit9, type = "algorithm")
 
-# Case 10 - Poisson regression model --------------------------------------------------
+# Case 10 - Poisson regression model -----------------------------------
 y1 <- rtweedie(200, mu = mu1$mu, power = 1, phi = 1)
 data <- data.frame("y1" = y1, "covariate" = covariate)
 
-fit10 <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0)),
-              link = "log", variance = "tweedie",  power_fixed = list(FALSE), data = data,
-              control_algorithm = list("method" = "rc", "tunning" = 0.1))
+fit10 <- mcglm(linear_pred = c(y1 ~ covariate), 
+               matrix_pred = list("resp1" = list(Z0)),
+               link = "log", variance = "tweedie",  
+               power_fixed = list(FALSE), data = data,
+               control_algorithm = list("method" = "rc", 
+                                        "tunning" = 0.1))
 summary(fit10)
 
-# Case 11 - Poisson-Tweedie regression model (Neymann-Type A) --------------------------
+# Case 11 - Poisson-Tweedie regression model (Neymann-Type A) ----------
 # Neymann-Type A
 y1 <- rtweedie(200, mu = mu1$mu, power = 1, phi = 1)
 y1 <- rpois(200, lambda = y1)
 data <- data.frame("y1" = y1, "covariate" = covariate)
 
-fit11 <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0)),
-               link = "log", variance = "poisson_tweedie", power_fixed = list(TRUE), data = data)
+fit11 <- mcglm(linear_pred = c(y1 ~ covariate), 
+               matrix_pred = list("resp1" = list(Z0)),
+               link = "log", variance = "poisson_tweedie", 
+               power_fixed = list(TRUE), data = data)
 summary(fit11)
 
-# Case 12 - Poisson-Tweedie regression model (Negative Binomial) -----------------------
+# Case 12 - Poisson-Tweedie regression model (Negative Binomial) -------
 y1 <- rtweedie(200, mu = mu1$mu, power = 2, phi = 1.5)
 y1 <- rpois(200, lambda = y1)
 data <- data.frame("y1" = y1, "covariate" = covariate)
 
-# Initial values list ------------------------------------------------------------------
+# Initial values list --------------------------------------------------
 list_initial = list()
 list_initial$regression <- list("resp1" = c(1,0))
 list_initial$power <- list("resp1" = c(2))
 list_initial$tau <- list("resp1" = c(1))
 list_initial$rho = 0
 
-fit12 <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0)),
-               link = "log", variance = "poisson_tweedie", power_fixed = list(FALSE), data = data,
+fit12 <- mcglm(linear_pred = c(y1 ~ covariate), 
+               matrix_pred = list("resp1" = list(Z0)),
+               link = "log", variance = "poisson_tweedie", 
+               power_fixed = list(FALSE), data = data,
                control_initial = list_initial,
-               control_algorithm = list("method" = "rc", "tunning" = 0.2))
+               control_algorithm = list("method" = "rc", 
+                                        "tunning" = 0.2))
 summary(fit12)
 
-# Case 13 - Poisson-Tweedie regression model (PIG - Poisson Inverse Gaussian)---------
-y1 <- rtweedie(200, mu = mu1$mu, power = 3, phi = 1.5)
+# Case 13 - Poisson-Tweedie regression model 
+# (PIG - Poisson Inverse Gaussian)---------
+y1 <- rtweedie(200, mu = mu1$mu, power = 3, phi = 0.1)
 y1 <- rpois(200, lambda = y1)
 data <- data.frame("y1" = y1, "covariate" = covariate)
 
-# Initial values list -----------------------------------------------------------------
+# Initial values list --------------------------------------------------
 list_initial = list()
 list_initial$regression <- list("resp1" = c(1,0.8))
 list_initial$power <- list("resp1" = c(3))
-list_initial$tau <- list("resp1" = c(0.5))
+list_initial$tau <- list("resp1" = c(0.1))
 list_initial$rho = 0
 
-fit13 <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0)),
-               link = "log", variance = "poisson_tweedie", data = data, control_initial = list_initial,
-               control_algorithm = list("method" = "rc", "tunning" = 0.1))
+fit13 <- mcglm(linear_pred = c(y1 ~ covariate), 
+               matrix_pred = list("resp1" = list(Z0)),
+               link = "log", variance = "poisson_tweedie", data = data, 
+               control_initial = list_initial,
+               power_fixed = FALSE,
+               control_algorithm = list("method" = "rc", 
+                                        "tunning" = 1, 
+                                        "max_iter" = 100))
 summary(fit13)
 
-# Case 14 - Poisson-Tweedie regression model (Pólya-Aeppli) ---------------------------
+# Case 14 - Poisson-Tweedie regression model (Pólya-Aeppli) ------------
 y1 <- rtweedie(200, mu = mu1$mu, power = 1.5, phi = 1.5)
 y1 <- rpois(200, lambda = y1)
 data <- data.frame("y1" = y1, "covariate" = covariate)
 
-fit14 <- mcglm(linear_pred = c(y1 ~ covariate), matrix_pred = list("resp1" = list(Z0)),
-               link = "log", variance = "poisson_tweedie", power_fixed = FALSE, data = data)
+fit14 <- mcglm(linear_pred = c(y1 ~ covariate), 
+               matrix_pred = list("resp1" = list(Z0)),
+               link = "log", variance = "poisson_tweedie", 
+               power_fixed = FALSE, data = data)
 summary(fit14)
 
-# Methods ------------------------------------------------------------------------
+# Methods --------------------------------------------------------------
 # print
 fit14
 
diff --git a/Examples/GLMExamples.R b/Examples/GLMExamples.R
index 652f23a8f5c81a313263cd298c27df384d08a23f..aacd195912a23686aa489a57b9d10773abd2a120 100755
--- a/Examples/GLMExamples.R
+++ b/Examples/GLMExamples.R
@@ -1,35 +1,40 @@
-# Set of examples 2 - GLM examples ----------------------------------------------
-# Author: Wagner Hugo Bonat LEG/IMADA -------------------------------------------
-# Date: 08/08/2015 --------------------------------------------------------------
-#--------------------------------------------------------------------------------
+# Set of examples 2 - GLM examples -------------------------------------
+# Author: Wagner Hugo Bonat LEG/IMADA ----------------------------------
+# Date: 08/08/2015 -----------------------------------------------------
+#-----------------------------------------------------------------------
 rm(list=ls())
 
 # Loading extra packages
 require(mcglm)
-
-# Case 1 ------------------------------------------------------------------------
+require(Matrix)
+# Case 1 ---------------------------------------------------------------
 ## Dobson (1990) Page 93: Randomized Controlled Trial :
 counts <- c(18,17,15,20,10,20,25,13,12)
 outcome <- gl(3,1,9)
 treatment <- gl(3,3)
 print(d.AD <- data.frame(treatment, outcome, counts))
 
-# Orthodox Poisson model
-fit.glm <- glm(counts ~ outcome + treatment, family = quasipoisson())
+# Orthodox Poisson model -----------------------------------------------
+fit.glm <- glm(counts ~ outcome + treatment, family = poisson())
 summary(fit.glm)
 
-# Quasi-Poisson model via mcglm---------------------------------------------------
+# Quasi-Poisson model via mcglm-----------------------------------------
 Z0 <- Diagonal(dim(d.AD)[1],1)
-fit.qglm <- mcglm(linear_pred = c(counts ~ outcome + treatment), matrix_pred = list("resp1" = list(Z0)),
+fit.qglm <- mcglm(linear_pred = c(counts ~ outcome + treatment), 
+                  matrix_pred = list("resp1" = list(Z0)),
                   link = "log", variance = "tweedie", data = d.AD,
-                  control_algorithm = list("verbose" = FALSE, "method" = "chaser", "tunning" = 0.8))
+                  control_algorithm = list("verbose" = FALSE, 
+                                           "method" = "chaser", 
+                                           "tunning" = 0.8))
 summary(fit.qglm)
-cbind("mcglm" = round(coef(fit.qglm, type = "beta")$Estimates,5), "glm" = round(coef(fit.glm),5))
-cbind("mcglm" = sqrt(diag(vcov(fit.qglm))), "glm" = c(sqrt(diag(vcov(fit.glm))),NA))
+cbind("mcglm" = round(coef(fit.qglm, type = "beta")$Estimates,5), 
+      "glm" = round(coef(fit.glm),5))
+cbind("mcglm" = sqrt(diag(vcov(fit.qglm))), 
+      "glm" = c(sqrt(diag(vcov(fit.glm))),NA))
 plot(fit.qglm)
 plot(fit.qglm, type = "algorithm")
 
-# Poisson-Tweedie model via mcglm------------------------------------------------
+# Poisson-Tweedie model via mcglm---------------------------------------
 list_initial = list()
 list_initial$regression <- list("resp1" = coef(fit.glm) )
 list_initial$power <- list("resp1" = c(1))
@@ -37,57 +42,70 @@ list_initial$tau <- list("resp1" = c(0.01))
 list_initial$rho = 0
 Z0 <- Diagonal(dim(d.AD)[1],1)
 
-fit.pt <- mcglm(linear_pred = c(counts ~ outcome + treatment), matrix_pred = list("resp1" = list(Z0)),
-                  link = "log", variance = "poisson_tweedie",
-                  data = d.AD, control_initial = list_initial,
-                  control_algorithm = list("correct" = TRUE, tol = 1e-5,
-                                           max_iter = 100, method = "chaser", "tunning" = 1))
+fit.pt <- mcglm(linear_pred = c(counts ~ outcome + treatment), 
+                matrix_pred = list("resp1" = list(Z0)),
+                link = "log", variance = "poisson_tweedie",
+                power_fixed = TRUE,
+                data = d.AD, control_initial = list_initial,
+                control_algorithm = list("correct" = TRUE, 
+                                         "verbose" = TRUE,
+                                         "tol" = 1e-5,
+                                         "max_iter" = 100, 
+                                         "method" = "chaser", 
+                                         "tunning" = 1))
 summary(fit.pt)
-cbind("mcglm" = round(coef(fit.pt, type = "beta")$Estimates,5), "glm" = round(coef(fit.glm),5))
-cbind("mcglm" = sqrt(diag(vcov(fit.pt))), "glm" = c(sqrt(diag(vcov(fit.glm))),NA))
+cbind("mcglm" = round(coef(fit.pt, type = "beta")$Estimates,5), 
+      "glm" = round(coef(fit.glm),5))
+cbind("mcglm" = sqrt(diag(vcov(fit.pt))), 
+      "glm" = c(sqrt(diag(vcov(fit.glm))),NA))
 
-# This model is unsuitable for this data, note that the dispersion parameter is negative, indicating
-# underdispersion. Which agrees with my quasi Poisson model, but the glm function does not
-# agree with this result. I have to understand this difference.
+# This model is unsuitable for this data, note that the dispersion 
+# parameter is negative, indicating underdispersion. 
+# Which agrees with my quasi-Poisson model, but the glm function 
+# does not agree with this result. I have to understand this difference.
 
-# Case 2 ------------------------------------------------------------------------
-# an example with offsets from Venables & Ripley (2002, p.189)
+# Case 2 ---------------------------------------------------------------
+# An example with offsets from Venables & Ripley (2002, p.189)
 
 # Loading the data set
 utils::data(anorexia, package = "MASS")
 
-# Orthodox GLM fit --------------------------------------------------------------
+# Orthodox GLM fit -----------------------------------------------------
 anorex.1 <- glm(Postwt ~ Prewt + Treat + offset(Prewt),
                family = gaussian, data = anorexia)
 summary(anorex.1)
 
-# Fitting by mcglm --------------------------------------------------------------
+# Fitting by mcglm -----------------------------------------------------
 Z0 <- Diagonal(dim(anorexia)[1],1)
 
-fit.anorexia <- mcglm(linear_pred = c(Postwt ~ Prewt + Treat), matrix_pred = list("resp1" = list(Z0)),
-                link = "identity", variance = "constant", offset = list(anorexia$Prewt),
-                power_fixed = list(TRUE), data = anorexia,
-                control_algorithm = list("correct" = FALSE))
+fit.anorexia <- mcglm(linear_pred = c(Postwt ~ Prewt + Treat), 
+                      matrix_pred = list("resp1" = list(Z0)),
+                      link = "identity", variance = "constant", 
+                      offset = list(anorexia$Prewt),
+                      power_fixed = TRUE, data = anorexia,
+                      control_algorithm = list("correct" = FALSE))
 summary(fit.anorexia)
 
-# Comparing the results ---------------------------------------------------------
+# Comparing the results ------------------------------------------------
 cbind("mcglm" = round(coef(fit.anorexia, type = "beta")$Estimates,5),
       "glm" = round(coef(anorex.1),5))
 cbind("mcglm" = sqrt(diag(vcov(fit.anorexia))),
       "glm" = c(sqrt(diag(vcov(anorex.1))),NA))
 
 
-# Case 3 ------------------------------------------------------------------------
+# Case 3 ---------------------------------------------------------------
 # A Gamma example, from McCullagh & Nelder (1989, pp.300-2)
 clotting <- data.frame(
   u = c(5,10,15,20,30,40,60,80,100),
   lot1 = c(118,58,42,35,27,25,21,19,18),
   lot2 = c(69,35,26,21,18,16,13,12,12))
-fit.lot1 <- glm(lot1 ~ log(u), data = clotting, family = Gamma(link = "inverse"))
-fit.lot2 <- glm(lot2 ~ log(u), data = clotting, family = Gamma(link = "inverse"))
+fit.lot1 <- glm(lot1 ~ log(u), data = clotting, 
+                family = Gamma(link = "inverse"))
+fit.lot2 <- glm(lot2 ~ log(u), data = clotting, 
+                family = Gamma(link = "inverse"))
 summary(fit.lot1)
 
-# Initial values -----------------------------------------------------------------
+# Initial values -------------------------------------------------------
 list_initial = list()
 list_initial$regression <- list("resp1" = coef(fit.lot1))
 list_initial$power <- list("resp1" = c(2))
@@ -95,10 +113,11 @@ list_initial$tau <- list("resp1" = summary(fit.lot1)$dispersion)
 list_initial$rho = 0
 Z0 <- Diagonal(dim(clotting)[1],1)
 
-# Fitting ------------------------------------------------------------------------
-fit.lot1.mcglm <- mcglm(linear_pred = c(lot1 ~ log(u)), matrix_pred = list("resp1" = list(Z0)),
-                      link = "inverse", variance = "tweedie",
-                      data = clotting, control_initial = list_initial)
+# Fitting --------------------------------------------------------------
+fit.lot1.mcglm <- mcglm(linear_pred = c(lot1 ~ log(u)), 
+                        matrix_pred = list("resp1" = list(Z0)),
+                        link = "inverse", variance = "tweedie",
+                        data = clotting, control_initial = list_initial)
 summary(fit.lot1.mcglm)
 
 cbind("mcglm" = round(coef(fit.lot1.mcglm, type = "beta")$Estimates,5),
@@ -106,13 +125,15 @@ cbind("mcglm" = round(coef(fit.lot1.mcglm, type = "beta")$Estimates,5),
 cbind("mcglm" = sqrt(diag(vcov(fit.lot1.mcglm))),
       "glm" = c(sqrt(diag(vcov(fit.lot1))),NA))
 
-# Initial values -----------------------------------------------------------------
+# Initial values -------------------------------------------------------
 list_initial$regression <- list("resp1" = coef(fit.lot2))
 list_initial$tau <- list("resp1" = c(var(1/clotting$lot2)))
 
-# Fitting ------------------------------------------------------------------------
-fit.lot2.mcglm <- mcglm(linear_pred = c(lot2 ~ log(u)), matrix_pred = list("resp2" = list(Z0)),
-                        link = "inverse", variance = "tweedie", data = clotting,
+# Fitting --------------------------------------------------------------
+fit.lot2.mcglm <- mcglm(linear_pred = c(lot2 ~ log(u)), 
+                        matrix_pred = list("resp2" = list(Z0)),
+                        link = "inverse", variance = "tweedie", 
+                        data = clotting,
                         control_initial = list_initial)
 summary(fit.lot2.mcglm)
 
@@ -121,24 +142,29 @@ cbind("mcglm" = round(coef(fit.lot2.mcglm, type = "beta")$Estimates,5),
 cbind("mcglm" = sqrt(diag(vcov(fit.lot2.mcglm))),
       "glm" = c(sqrt(diag(vcov(fit.lot2))),NA))
 
-# Bivariate Gamma model-----------------------------------------------------------
+# Bivariate Gamma model-------------------------------------------------
 list_initial = list()
-list_initial$regression <- list("resp1" = coef(fit.lot1), "resp2" = coef(fit.lot2))
+list_initial$regression <- list("resp1" = coef(fit.lot1), 
+                                "resp2" = coef(fit.lot2))
 list_initial$power <- list("resp1" = c(2), "resp2" = c(2))
 list_initial$tau <- list("resp1" = c(0.00149), "resp2" = c(0.001276))
 list_initial$rho = 0.80
 Z0 <- Diagonal(dim(clotting)[1],1)
 
-fit.joint.mcglm <- mcglm(linear_pred = c(lot1 ~ log(u), lot2 ~ log(u)), matrix_pred = list(list(Z0), list(Z0)),
-                        link = c("inverse", "inverse"), variance = c("tweedie", "tweedie"),
-                        data = clotting, control_initial = list_initial,
-                        control_algorithm = list("correct" = TRUE, "method" = "rc", "tunning" = 0.001,
-                                                 max_iter = 100))
+fit.joint.mcglm <- mcglm(linear_pred = c(lot1 ~ log(u), lot2 ~ log(u)), 
+                         matrix_pred = list(list(Z0), list(Z0)),
+                         link = c("inverse", "inverse"), 
+                         variance = c("tweedie", "tweedie"),
+                         data = clotting, control_initial = list_initial,
+                         control_algorithm = list("correct" = TRUE, 
+                                                 "method" = "rc", 
+                                                 "tunning" = 0.001,
+                                                 "max_iter" = 100))
 summary(fit.joint.mcglm)
 plot(fit.joint.mcglm, type = "algorithm")
 plot(fit.joint.mcglm)
 
-# Bivariate Gamma model + log link function --------------------------------------
+# Bivariate Gamma model + log link function ----------------------------
 list_initial = list()
 list_initial$regression <- list("resp1" = c(log(mean(clotting$lot1)),0),
                                 "resp2" = c(log(mean(clotting$lot2)),0))
@@ -147,43 +173,52 @@ list_initial$tau <- list("resp1" = 0.023, "resp2" = 0.024)
 list_initial$rho = 0
 Z0 <- Diagonal(dim(clotting)[1],1)
 
-fit.joint.log <- mcglm(linear_pred = c(lot1 ~ log(u), "resp2" = lot2 ~ log(u)),
-                       matrix_pred = list(list(Z0),list(Z0)), link = c("log", "log"),
-                       variance = c("tweedie", "tweedie"), data = clotting,
+fit.joint.log <- mcglm(linear_pred = c("resp1" = lot1 ~ log(u), 
+                                       "resp2" = lot2 ~ log(u)),
+                       matrix_pred = list(list(Z0),list(Z0)), 
+                       link = c("log", "log"),
+                       variance = c("tweedie", "tweedie"), 
+                       data = clotting,
                        control_initial = list_initial)
 summary(fit.joint.log)
 plot(fit.joint.mcglm, type = "algorithm")
 plot(fit.joint.mcglm)
 
-# Case 4 - Binomial regression models ----------------------------------------
+# Case 4 - Binomial regression models ----------------------------------
 require(MASS)
 data(menarche)
 head(menarche)
-data <- data.frame("resp" = menarche$Menarche/menarche$Total, "Ntrial" = menarche$Total,
+data <- data.frame("resp" = menarche$Menarche/menarche$Total, 
+                   "Ntrial" = menarche$Total,
                    "Age" = menarche$Age)
 
-# Orthodox logistic regression model ------------------------------------------
-glm.out = glm(cbind(Menarche, Total-Menarche) ~ Age, family=binomial(logit), data=menarche)
+# Orthodox logistic regression model -----------------------------------
+glm.out = glm(cbind(Menarche, Total-Menarche) ~ Age, 
+              family=binomial(logit), data=menarche)
 
-# Fitting ---------------------------------------------------------------------
+# Fitting --------------------------------------------------------------
 Z0 <- Diagonal(dim(data)[1],1)
 
-fit.logit <- mcglm(linear_pred = c(resp ~ Age), matrix_pred = list("resp1" = list(Z0)),
-                   link = "logit", variance = "binomialP", Ntrial = list(data$Ntrial), data = data)
+fit.logit <- mcglm(linear_pred = c(resp ~ Age), 
+                   matrix_pred = list("resp1" = list(Z0)),
+                   link = "logit", variance = "binomialP", 
+                   Ntrial = list(data$Ntrial), data = data)
 
 summary(fit.logit)
 plot(fit.logit, type = "algorithm")
 plot(fit.logit)
 
-# Fitting with extra power parameter -------------------------------------------
-fit.logit.power <- mcglm(linear_pred = c(resp ~ Age), matrix_pred = list(list(Z0)),
-                   link = "logit", variance = "binomialP", Ntrial = list(data$Ntrial),
-                   power_fixed = FALSE, data = data)
+# Fitting with extra power parameter -----------------------------------
+fit.logit.power <- mcglm(linear_pred = c(resp ~ Age), 
+                         matrix_pred = list(list(Z0)),
+                         link = "logit", variance = "binomialP", 
+                         Ntrial = list(data$Ntrial),
+                         power_fixed = FALSE, data = data)
 summary(fit.logit.power)
 plot(fit.logit.power, type = "algorithm")
 plot(fit.logit.power)
 
-# All methods --------------------------------------------------------------------
+# All methods ----------------------------------------------------------
 # print method
 fit.logit.power
 # coef method
diff --git a/NAMESPACE b/NAMESPACE
index ac113d4a61f1ee18f1ba84a70e91bf52faedef2f..21f9f2a5f88a4e1797b44235f974560ed2657eac 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -12,17 +12,14 @@ S3method(vcov,mcglm)
 export(fit_mcglm)
 export(mc_bias_corrected_std)
 export(mc_dexp_gold)
-export(mc_influence)
 export(mc_initial_values)
 export(mc_link_function)
 export(mc_matrix_linear_predictor)
-export(mc_qll)
+export(mc_quasi_score)
 export(mc_robust_std)
-export(mc_rw1)
-export(mc_rw2)
-export(mc_unstructured)
+export(mc_sic)
+export(mc_sic_covariance)
 export(mc_variance_function)
 export(mcglm)
-export(qic.mcglm)
 import(Matrix)
 import(assertthat)
diff --git a/R/mc_S3_methods.R b/R/mc_S3_methods.R
index 04a6d6b56a2f0b9dd810ba2e09244c17c321ba0e..5cfe8a019db3429ff0edfdf3f1284b8a529b71b6 100644
--- a/R/mc_S3_methods.R
+++ b/R/mc_S3_methods.R
@@ -214,7 +214,7 @@ confint.mcglm <- function(object, parm, level = 0.95, ...) {
     ci <- temp$Estimates + temp$Std.error %o% fac
     colnames(ci) <- paste0(format(a, 2), "%")
     rownames(ci) <- temp$Parameters
-    return(ci[parm])
+    return(ci[parm,])
 }
 #' @title Extract Model Fitted Values of McGLM
 #' @name fitted.mcglm
diff --git a/R/mc_bias_correct_std.R b/R/mc_bias_correct_std.R
index cfe7f9f034596d795bef43104906868bb18a6cb5..c9c5c163d3c2b01c9eb11009f949a1e1c7331e46 100644
--- a/R/mc_bias_correct_std.R
+++ b/R/mc_bias_correct_std.R
@@ -11,17 +11,34 @@
 #' @export
 
 mc_bias_corrected_std <- function(object, id) {
-    inv_M <- object$inv_S_beta
-    temp_data <- data.frame(res = object$residuals, id)
-    temp_data_group <- split(temp_data, temp_data$id)
-    D <- bdiag(lapply(object$mu_list, function(x) x$D))
-    r_rT <- bdiag(lapply(temp_data_group, function(x) {
+  inv_M <- -object$inv_S_beta
+  temp_data <- data.frame(res = object$residuals, id)
+  temp_data_group <- split(temp_data, temp_data$id)
+  D <- bdiag(lapply(object$mu_list, function(x) x$D))
+  R <- bdiag(lapply(temp_data_group, function(x) {
         tcrossprod(x[, 1])
     }))
-    tD_invC <- t(D) %*% object$inv_C
-    H <- Matrix(D %*% inv_M %*% tD_invC, sparse = TRUE)
-    IH <- Diagonal(object$n_obs, 1) - H
-    inv_IH <- solve(IH)
-    output <- sqrt(diag(inv_M %*% tD_invC %*% inv_IH %*% r_rT %*% inv_IH %*% t(tD_invC) %*% inv_M))
+  uni_id <- unique(id)
+  n_id <- length(uni_id)
+  Hi <- list()
+  Di <- list()
+  inv_Ci <- list()
+  for (i in 1:n_id) {
+    idTF <- id == uni_id[i]
+    if(sum(idTF) == 1) {
+      Di[[i]] <- Matrix(D[idTF,], nrow = sum(idTF), ncol = dim(D)[2])
+      } else {
+        Di[[i]] <- D[idTF,]
+      }
+    inv_Ci[[i]] <- object$inv_C[idTF,idTF]
+    Hi[[i]] <- Di[[i]]%*%inv_M%*%t(Di[[i]])%*%inv_Ci[[i]]
+    }
+    H <- bdiag(Hi)
+    I <- Diagonal(dim(temp_data)[1], 1)
+    inv_IH <- solve(I - H)
+    Vbeta = inv_M%*%(t(D)%*%object$inv_C%*%inv_IH%*%R%*%inv_IH%*%
+                       object$inv_C%*%D)%*%inv_M
+    output = sqrt(diag(Vbeta))
     return(output)
 }
+
diff --git a/R/mc_build_sigma.R b/R/mc_build_sigma.R
index d46aa5e5b8fe853f399c2fd393941ed2eb5c7cde..3e858d92821e8605673989574f4cf881220365e7 100644
--- a/R/mc_build_sigma.R
+++ b/R/mc_build_sigma.R
@@ -30,7 +30,9 @@ mc_build_sigma <- function(mu, Ntrial = 1, tau, power, Z, sparse, variance,
             Omega <- mc_build_omega(tau = tau, Z = Z, covariance_link = covariance, sparse = sparse)
             chol_Sigma <- chol(Omega$Omega)
             inv_chol_Sigma <- solve(chol_Sigma)
-            output <- list(Sigma_chol = chol_Sigma, Sigma_chol_inv = inv_chol_Sigma, D_Sigma = Omega$D_Omega)
+            output <- list(Sigma_chol = chol_Sigma, 
+                           Sigma_chol_inv = inv_chol_Sigma, 
+                           D_Sigma = Omega$D_Omega)
         }
         if (covariance == "inverse") {
             inv_Sigma <- mc_build_omega(tau = tau, Z = Z, covariance_link = "inverse", sparse = sparse)
diff --git a/R/mc_influence.R b/R/mc_influence.R
deleted file mode 100644
index 51e71ba81b3dd42a6438f44ec2f9eedc29a71f4d..0000000000000000000000000000000000000000
--- a/R/mc_influence.R
+++ /dev/null
@@ -1,92 +0,0 @@
-#' Influence measures
-#'
-#' @description Compute influence measures for multivariate covariance generalized linear models.
-#' Leverage, DFBETA and Cook's distance for unit sample and observations.
-#'
-#' @param object An object of mcglm class.
-#' @param id a vector which identifies the clusters. The length and order of id should be the
-#' same as the number of observations. Data are assumed to be sorted so that observations on a cluster
-#' are contiguous rows for all entities in the formula.
-#' @return A matrix. Note that the function assumes that the data are in the correct order.
-#' @export
-
-mc_influence <- function(object, id) {
-    inv_M <- -object$inv_S_beta
-    M <- solve(inv_M)
-    temp_data <- data.frame(res = object$residuals, id)
-    temp_data_group <- split(temp_data, temp_data$id)
-    D <- bdiag(lapply(object$mu_list, function(x) x$D))
-    tD_invC <- t(D) %*% object$inv_C
-    H <- D %*% inv_M %*% tD_invC
-    leverage_obs <- diag(H)
-    leverage_group <- tapply(leverage_obs, id, sum)
-    I <- Diagonal(object$n_obs, 1)
-    n_group <- length(temp_data_group)
-    indexes <- matrix(NA, ncol = 2, nrow = n_group)
-    n_obs_group <- table(id)
-    indexes[1, ] <- c(1, as.numeric(n_obs_group[1]))
-    DFBETA_clust <- list()
-    D_temp <- D[c(indexes[1, 1]:indexes[1, 2]), ]
-    inv_C_temp <- object$inv_C[c(indexes[1, 1]:indexes[1, 2]), c(indexes[1, 1]:indexes[1, 2])]
-    C_temp <- object$C[c(indexes[1, 1]:indexes[1, 2]), c(indexes[1, 1]:indexes[1, 2])]
-    H_temp <- H[c(indexes[1, 1]:indexes[1, 2]), c(indexes[1, 1]:indexes[1, 2])]
-    res_temp <- object$residuals[indexes[1, 1]:indexes[1, 2]]
-    DFBETAOij <- list()
-    padroniza <- function(x, M) {
-        as.numeric((t(as.numeric(x)) %*% M %*% as.numeric(x))/dim(M)[1])
-    }
-    dfbetaOij <- function(D_temp, C_temp, inv_C_temp, res_temp) {
-        DFBETA_temp <- matrix(NA, ncol = dim(D_temp)[2], nrow = dim(D_temp)[1])
-        k = 1
-        for (j in 1:length(res_temp)) {
-            Dij <- D_temp[j, k] - C_temp[k, -j] %*% inv_C_temp[-k, -j] %*% D_temp[-j, ]
-            rij <- res_temp[j] - C_temp[k, -j] %*% inv_C_temp[-k, -j] %*% res_temp[-j]
-            Vij <- C_temp[k, j] - C_temp[k, -j] %*% inv_C_temp[-k, -j] %*% C_temp[-j, k]
-            Hij <- try(Dij %*% object$inv_S_beta %*% t(Dij) %*% solve(Vij), silent = TRUE)
-            if (class(Hij) == "try-error") {
-                DFBETA_temp[j, ] <- NA
-            }
-            if (class(Hij) != "try-error") {
-                DFBETA_temp[j, ] <- as.numeric(t(object$inv_S_beta %*% t(Dij) %*% (rij/(Vij - (1 - Hij)))))
-            }
-        }
-        return(DFBETA_temp)
-    }
-    DFBETA_clust[[1]] <- inv_M %*% t(D_temp) %*% inv_C_temp %*% res_temp
-    DFBETAOij[[1]] <- dfbetaOij(D_temp, C_temp, inv_C_temp, res_temp)
-    for (i in 2:n_group) {
-        indexes[i, ] <- c(indexes[i - 1, ][2] + 1, n_obs_group[i] + indexes[i - 1, ][2])
-        D_temp <- D[c(indexes[i, 1]:indexes[i, 2]), ]
-        inv_C_temp <- object$inv_C[c(indexes[i, 1]:indexes[i, 2]), c(indexes[i, 1]:indexes[i, 2])]
-        C_temp <- object$C[c(indexes[i, 1]:indexes[i, 2]), c(indexes[i, 1]:indexes[i, 2])]
-        H_temp <- H[c(indexes[i, 1]:indexes[i, 2]), c(indexes[i, 1]:indexes[i, 2])]
-        res_temp <- object$residuals[indexes[i, 1]:indexes[i, 2]]
-        D_temp <- matrix(D_temp, nrow = n_obs_group[i])
-        DFBETA_clust[[i]] <- inv_M %*% t(D_temp) %*% inv_C_temp %*% res_temp
-        if (n_obs_group[i] == 1) {
-            DFBETAOij[[i]] <- t(as.matrix(DFBETA_clust[[i]]))
-        }
-        if (n_obs_group[i] != 1) {
-            DFBETAOij[[i]] <- dfbetaOij(D_temp, C_temp, inv_C_temp, res_temp)
-        }
-    }
-    DFBETA <- lapply(DFBETA_clust, as.matrix)
-    DFBETA <- lapply(DFBETA, t)
-    DFBETA <- plyr::ldply(DFBETA, data.frame)
-    names(DFBETA) <- object$beta_names[[1]]
-    DFBETAOij <- plyr::ldply(DFBETAOij, data.frame)
-    names(DFBETAOij) <- object$beta_names[[1]]
-    DCLSi <- apply(as.matrix(DFBETA), MARGIN = 1, FUN = padroniza, M = M)
-    DCLOij <- apply(as.matrix(DFBETAOij), MARGIN = 1, FUN = padroniza, M = M)
-    std.error <- coef(object, std.error = TRUE, type = "beta")$Std.error
-    DFBETA_temp <- t(apply(DFBETA, MARGIN = 1, FUN = function(x, std.error) {
-        as.numeric(x/std.error)
-    }, std.error = std.error))
-    DFBETAOij_temp <- t(apply(DFBETAOij, MARGIN = 1, FUN = function(x, std.error) {
-        as.numeric(x/std.error)
-    }, std.error = std.error))
-    output_clust <- data.frame(Leverage = leverage_group, DFBETA = DFBETA_temp, Cook = DCLSi)
-    output_obs <- data.frame(Leverage = leverage_obs, DFBETA = DFBETAOij_temp, Cook = DCLOij)
-    output <- list(Id = output_clust, Observations = output_obs)
-    return(output)
-}
diff --git a/R/mc_initial_values.R b/R/mc_initial_values.R
index 53f2dcef07476066962882edf0d1da27aea1b400..bbbaa5cc51e56be156f7e10016cb4d57dc57651c 100644
--- a/R/mc_initial_values.R
+++ b/R/mc_initial_values.R
@@ -84,13 +84,13 @@ mc_initial_values <- function(linear_pred, matrix_pred, link, variance, covarian
     tau_extra <- lapply(matrix_pred, length)
     list_initial$tau <- list()
     for (i in 1:n_resp) {
-        if (covariance == "identity") {
+        if (covariance[i] == "identity") {
             list_initial$tau[[i]] <- as.numeric(c(tau0_initial[[i]], rep(0, c(tau_extra[[i]] - 1))))
         }
-        if (covariance == "inverse") {
+        if (covariance[i] == "inverse") {
             list_initial$tau[[i]] <- as.numeric(c(1/tau0_initial[[i]], rep(0, c(tau_extra[[i]] - 1))))
         }
-        if (covariance == "expm") {
+        if (covariance[i] == "expm") {
             list_initial$tau[[i]] <- as.numeric(c(exp(tau0_initial[[i]]), rep(0.1, c(tau_extra[[i]] - 1))))
         }
     }
diff --git a/R/mc_main_function.R b/R/mc_main_function.R
index eda852cae44d13414286c4859b4bbe23ecb285b9..c884b3afda0a31ec8436224032b6cc01a32c0c3d 100644
--- a/R/mc_main_function.R
+++ b/R/mc_main_function.R
@@ -62,11 +62,19 @@ mcglm <- function(linear_pred, matrix_pred, link, variance, covariance, offset,
   Ntrial <- as.list(Ntrial)
   power_fixed = as.list(power_fixed)
   if (class(control_initial) != "list") {
-    control_initial <- mc_initial_values(linear_pred = linear_pred, matrix_pred = matrix_pred, link = link, variance = variance,
-                                         covariance = covariance, offset = offset, Ntrial = Ntrial, contrasts = contrasts, data = data)
-    cat("Automatic initial values selected.")
+    control_initial <- mc_initial_values(linear_pred = linear_pred,
+                                         matrix_pred = matrix_pred,
+                                         link = link,
+                                         variance = variance,
+                                         covariance = covariance,
+                                         offset = offset,
+                                         Ntrial = Ntrial,
+                                         contrasts = contrasts,
+                                         data = data)
+    cat("Automatic initial values selected.", "\n")
   }
-  con <- list(correct = TRUE, max_iter = 20, tol = 1e-04, method = "chaser", tunning = 1, verbose = FALSE)
+  con <- list(correct = TRUE, max_iter = 20, tol = 1e-04,
+              method = "chaser", tunning = 1, verbose = FALSE)
   con[(namc <- names(control_algorithm))] <- control_algorithm
   if (!is.null(contrasts)) {
     list_X <- list()
@@ -101,6 +109,12 @@ mcglm <- function(linear_pred, matrix_pred, link, variance, covariance, offset,
     model_fit$con <- con
     model_fit$observed <- Matrix(y_vec, ncol = length(list_Y), nrow = dim(data)[1])
     model_fit$list_X <- list_X
+    model_fit$matrix_pred <- matrix_pred
+    model_fit$Ntrial <- Ntrial
+    model_fit$offset <- offset
+    model_fit$power_fixed
+    model_fit$sparse <- sparse
+    model_fit$data <- data
     class(model_fit) <- "mcglm"
   }
   return(model_fit)
diff --git a/R/mc_qic.R b/R/mc_qic.R
deleted file mode 100644
index e51bd66ef74c6313cf7f620dc18922ad4b39cc31..0000000000000000000000000000000000000000
--- a/R/mc_qic.R
+++ /dev/null
@@ -1,39 +0,0 @@
-#' @title Compute Quasi Information Criterion (QIC) for McGLMs.
-#' @name qic.mcglm
-#'
-#' @description \code{qic.mcglm} is a function which computes the QIC
-#'     for McGLMs.
-#'
-#' @param object An object of \code{mcglm} class.
-#' @param object.iid An object of \code{mcglm} class contained the model
-#'     fitted using independent covariance structure.
-#'
-#' @return The QIC value.
-#'
-#' @author Wagner Hugo Bonat, \email{wbonat@@ufpr.br}
-#'
-#' @export
-
-qic.mcglm <- function(object, object.iid) {
-    mu <- fitted(object)
-    obs <- object$observed
-    n_resp <- dim(mu)[2]
-    Q <- matrix(NA, ncol = dim(mu)[2], nrow = dim(mu)[1])
-    for (i in 1:n_resp) {
-        if (object$power_fixed[[i]] == FALSE) {
-            power <- coef(object, type = "power", response = i)$Estimate
-        }
-        if (object$power_fixed[[i]] == TRUE) {
-            power = object$list_initial$power[[i]]
-        }
-        Q[, i] <- mc_qll(y = obs[, i],
-                         mu = mu[, i],
-                         variance = object$variance[[i]],
-                         power = power)
-    }
-    Vbeta <- -object$inv_S_beta
-    Vnull <- solve(-object.iid$inv_S_beta)
-    t1 <- -2 * sum(Q)
-    qic <- t1 + 2 * sum(diag(Vnull %*% Vbeta))
-    return(list(Q = t1, qic = qic))
-}
diff --git a/R/mc_qll.R b/R/mc_qll.R
deleted file mode 100644
index e9e4c1432e4264effd8961018c1aab9e65f42046..0000000000000000000000000000000000000000
--- a/R/mc_qll.R
+++ /dev/null
@@ -1,37 +0,0 @@
-#' Compute quasi-likelihood function.
-#'
-#' Given a variance function mc_qll function computes the quasi-likelihood values.
-#' @param y A vector of observed values.
-#' @param mu A vector of fitted values.
-#' @param variance Variance function (constant, tweedie, poisson_tweedie, binomial).
-#' @param power Power parameter value.
-#' @return The quasi-likelihood values.
-#' @export
-
-mc_qll <- function(y, mu, variance, power) {
-    if (variance == "constant") {
-        qll <- -((y - mu)^2)/2  # Gaussian case
-    }
-    if (variance == "tweedie" & power == 1) {
-        qll <- y * log(mu) - mu  # Poisson case
-    }
-    if (variance == "tweedie" & power == 2) {
-        -y/mu - log(mu)  # Gamma case
-    }
-    if (variance == "tweedie" & power != 1 & power != 2) {
-        qll <- (mu^-power) * ((mu * y)/(1 - power) - (mu^2)/(2 - power))  # General Tweedie case
-    }
-    if (variance == "poisson_tweedie" & power == 1) {
-        qll <- (y * log(mu) - mu) + (y * log(mu) - mu)
-    }
-    if (variance == "poisson_tweedie" & power == 2) {
-        qll <- (y * log(mu) - mu) + (-y/mu - log(mu))
-    }
-    if (variance == "poisson_tweedie" & power != 1 & power != 2) {
-        qll <- (y * log(mu) - mu) + (mu^-power) * ((mu * y)/(1 - power) - (mu^2)/(2 - power))
-    }
-    if (variance == "binomial") {
-        qll <- y * log(mu/(1 - mu)) + log(1 - mu)  # Binomial case
-    }
-    return(qll)
-}
diff --git a/R/mc_quasi_score.R b/R/mc_quasi_score.R
index 6bdcee8db1fef74433f60e010519df7dda4f3a96..40d37cdc618b6d5d7d76eea6c3d5da0d5101bb0f 100644
--- a/R/mc_quasi_score.R
+++ b/R/mc_quasi_score.R
@@ -7,6 +7,7 @@
 #' @param y_vec A vector.
 #' @param mu_vec A vector.
 #' @return The quasi-score vector, the Sensivity and variability matrices.
+#' @export
 
 mc_quasi_score <- function(D, inv_C, y_vec, mu_vec) {
     res <- y_vec - mu_vec
diff --git a/R/mc_rw1.R b/R/mc_rw1.R
deleted file mode 100644
index 519ca718920c2c2093ea81b51235033884a02125..0000000000000000000000000000000000000000
--- a/R/mc_rw1.R
+++ /dev/null
@@ -1,29 +0,0 @@
-#' Random walk first order model
-#'
-#' @description Builds a random walk first order model matrix.
-#'
-#' @param n_time Number observations time.
-#' @param intrinsic Logical indicating if the models is intrinsic (rho = 1) or not.
-#' @return A matrix. Note that the function assumes that the data are in the correct order.
-#' @export
-mc_rw1 <- function(n_time, intrinsic = TRUE) {
-    R <- Matrix(0, nrow = n_time, ncol = n_time, sparse = TRUE)
-    ## Border restriction
-    ncol = n_time
-    R[1, c(1, 2)] <- c(1, -1)
-    R[ncol, c(ncol - 1, ncol)] <- c(-1, 1)
-    ## Body of matrix
-    n <- ncol - 1
-    for (i in 2:n) {
-        R[i, c(i - 1, i, i + 1)] <- c(-1, 2, -1)
-    }
-    if (intrinsic == TRUE) {
-        output <- list(R)
-    }
-    if (intrinsic == FALSE) {
-        R1 <- Diagonal(n_time, diag(R))
-        diag(R) <- 0
-        output <- list(Z1 = R1, Z2 = R)
-    }
-    return(output)
-} 
diff --git a/R/mc_rw2.R b/R/mc_rw2.R
deleted file mode 100644
index 6788c1c4fb0a41e1eac185b7800438e672fdaaa4..0000000000000000000000000000000000000000
--- a/R/mc_rw2.R
+++ /dev/null
@@ -1,31 +0,0 @@
-#' Random walk second order model
-#'
-#' @description Builds a random walk second order model matrix.
-#'
-#' @param n_time Number observations time.
-#' @param intrinsic Logical indicating if the models is intrinsic (rho = 1) or not.
-#' @return A matrix. Note that the function assumes that the data are in the correct order.
-#' @export
-mc_rw2 <- function(n_time, intrinsic = TRUE) {
-    R <- Matrix(0, nrow = n_time, ncol = n_time, sparse = TRUE)
-    ## Border restriction
-    ncol = n_time
-    R[1, c(1, 2, 3)] <- c(1, -2, 1)
-    R[ncol, c(c(ncol - 2):ncol)] <- c(1, -2, 1)
-    R[2, c(1:4)] <- c(-2, 5, -4, 1)
-    R[c(ncol - 1), c(c(ncol - 3):c(ncol))] <- c(1, -4, 5, -2)
-    ## Body of matrix
-    n <- ncol - 2
-    for (i in 3:n) {
-        R[i, c(i - 2, i - 1, i, i + 1, i + 2)] <- c(1, -4, 6, -4, 1)
-    }
-    if (intrinsic == TRUE) {
-        output <- list(R)
-    }
-    if (intrinsic == FALSE) {
-        R1 <- Diagonal(n_time, diag(R))
-        diag(R) <- 0
-        output <- list(Z1 = R1, Z2 = R)
-    }
-    return(output)
-} 
diff --git a/R/mc_sensitivity.R b/R/mc_sensitivity.R
index 169cc185e31b6cd63b0a30ff8ed6262568b302ae..64e2de5eae5725c2b261328e87bc0d1a9938824f 100644
--- a/R/mc_sensitivity.R
+++ b/R/mc_sensitivity.R
@@ -13,9 +13,9 @@ mc_sensitivity <- function(product) {
     Sensitivity1 <- matrix(0, n_par, n_par)
     for (i in 1:n_par) {
         for (j in 1:n_par) {
-            # Sensitivity_temp[i,j] <- -sum(diag(product[[i]]%*%product[[j]]))
+            #Sensitivity_temp[i,j] <- -sum(diag(product[[i]]%*%product[[j]]))
             Sensitivity[i, j] <- -sum(t(product[[i]]) * product[[j]])
-            # Sensitivity1[i,j] <- -sum(product[[i]]*product[[j]])
+            #Sensitivity1[i,j] <- -sum(product[[i]]*product[[j]])
         }
     }
     # print(forceSymmetric(Sensitivity)) print(forceSymmetric(Sensitivity_temp)) print(forceSymmetric(Sensitivity1))
diff --git a/R/mc_sic.R b/R/mc_sic.R
new file mode 100644
index 0000000000000000000000000000000000000000..e7b3ae338211e9a3bfef67cff6593bcc2e6a4cd7
--- /dev/null
+++ b/R/mc_sic.R
@@ -0,0 +1,57 @@
+#' Compute the score information criterion (SIC) for multivariate
+#' covariance generalized linear models.
+#'
+#' @description Compute the SIC for McGLMS.
+#' @param object an object representing a model of \code{mcglm} class.
+#' @param scope a vector containing all covariate names to be tested.
+#' @param data data frame containing the all variables envolved
+#' @param penalty penalty term (default = 2).
+#' @param response Indicate for which response variable SIC is computed.
+#' @return A data frame with SIC values for each covariate in the scope
+#' argument.
+#' @export
+
+mc_sic <- function (object, scope, data, response, penalty = 2) {
+  SIC <- c()
+  df <- c()
+  df_total <- c()
+  TU <- c()
+  QQ <- c()
+  for(i in 1:length(scope)){
+  ini_formula <- object$linear_pred[[response]]
+  ext_formula <- as.formula(paste("~", paste(ini_formula[3],
+                                             scope[i], sep = "+")))
+  md <- model.frame(object$linear_pred[[response]], data = data)
+  Y = model.response(md)
+  ini_beta <- coef(object, type = "beta", response = response)$Estimates
+  ext_X <- model.matrix(ext_formula, data = data)
+  n_beta <- dim(ext_X)[2]
+  n_ini_beta <- length(ini_beta)
+  ext_beta <- c(ini_beta, rep(0, n_beta - n_ini_beta))
+  n_total_beta <- length(ext_beta)
+  mu_temp <- mc_link_function(beta = ext_beta, X = ext_X, offset = NULL,
+                              link = object$link[[response]])
+  score_temp <- mc_quasi_score(D = mu_temp$D, inv_C = object$inv_C,
+                               y_vec = Y, mu_vec = mu_temp$mu)
+  S11 <- score_temp$Variability[1:n_ini_beta,1:n_ini_beta]
+  S22 <- score_temp$Variability[c(n_ini_beta+1):n_total_beta,
+                                c(n_ini_beta+1):n_total_beta]
+  S12 <- score_temp$Variability[1:n_ini_beta,
+                                c(n_ini_beta+1):n_total_beta]
+  S21 <- score_temp$Variability[c(n_ini_beta+1):n_total_beta,
+                                1:n_ini_beta]
+  VB <- S22 - S21 %*% solve(S11) %*% S12
+  Tu <- t(score_temp$Score[c(n_ini_beta+1):n_total_beta])%*%
+    solve(VB)%*%score_temp$Score[c(n_ini_beta+1):n_total_beta]
+  df[i] <- n_beta - n_ini_beta
+  SIC[i] <- -as.numeric(Tu) + penalty*n_beta
+  df_total[i] <- n_beta
+  TU[i] <- as.numeric(Tu)
+  QQ[i] <- qchisq(0.95, df = df[i])
+  }
+  output <- data.frame("SIC" = SIC, "Covariance" = scope,
+                       "df" = df, "df_total" = df_total,
+                       "Tu" = TU, "Chisq" = QQ)
+  return(output)
+}
+
diff --git a/R/mc_sic_covariance.R b/R/mc_sic_covariance.R
new file mode 100644
index 0000000000000000000000000000000000000000..396eef6d12f618b0fdbc7e974dd526b20797451a
--- /dev/null
+++ b/R/mc_sic_covariance.R
@@ -0,0 +1,83 @@
+#' Compute the score information criterion (SIC) for multivariate
+#' covariance generalized linear models.
+#'
+#' @description Compute SIC for covariance parameters in McGLMS.
+#' @param object an object representing a model of \code{mcglm} class.
+#' @param scope a list of matrices to be tested in the matrix linear
+#' predictor.
+#' @param idx Indicator of matrices belong to the same effect.
+#' @param data data frame containing all variables envolved in the model.
+#' @param penalty penalty term (default = 2).
+#' @param response Indicate for which response variable SIC is computed.
+#' @return A data frame with SIC values for each matrix in the scope
+#' argument.
+#' @export
+
+mc_sic_covariance <- function(object, scope, idx, data, penalty = 2,
+                              response) {
+  SIC <- c()
+  df <- c()
+  df_total <- c()
+  TU <- c()
+  QQ <- c()
+  n_terms <- length(unique(idx))
+  for (j in 1:n_terms) {
+  tau <- coef(object, type = "tau",
+                       response = response)$Estimates
+  n_tau <- length(tau)
+  n_tau_new <- length(idx[idx == j])
+  list_tau_new <- list(c(tau, rep(0, n_tau_new)))
+  n_tau_total <- n_tau + n_tau_new
+  if(object$power_fixed[[response]]){
+    list_power <- object$list_initial$power
+  } else {
+      list_power <- list(coef(object, type = "power",
+                         response = response)$Estimates)
+      n_tau_total <- n_tau_total + 1
+      n_tau <- n_tau + 1
+  }
+  list_Z_new <- list(c(object$matrix_pred[[response]], scope[idx == j]))
+  if(length(object$mu_list) == 1){rho = 0} else {
+    rho = coef(object,type = "correlation")$Estimates
+  }
+  Cfeatures <- mc_build_C(list_mu = object$mu_list,
+                          list_Ntrial = object$Ntrial,
+                          rho = rho, list_tau = list_tau_new,
+                          list_power = list_power,
+                          list_Z = list_Z_new,
+                          list_sparse = object$sparse,
+                          list_variance = object$variance,
+                          list_covariance = object$covariance,
+                          list_power_fixed = object$power_fixed,
+                          compute_C = TRUE)
+  temp_score <- mc_pearson(y_vec = object$observed,
+                           mu_vec = object$mu_list[[response]]$mu,
+                           Cfeatures = Cfeatures, correct = FALSE,
+                           compute_variability = TRUE)
+
+  J <- temp_score$Sensitivity
+  Sigma <- temp_score$Variability
+  Sigma22 <- Sigma[c(n_tau+1):n_tau_total,c(n_tau +1):n_tau_total]
+  J21 <- J[c(n_tau+1):n_tau_total, 1:n_tau]
+  J11 <- solve(J[1:n_tau,1:n_tau])
+  Sigma12 <- Sigma[1:n_tau, c(n_tau+1):n_tau_total]
+  Sigma21 <- Sigma[c(n_tau+1):n_tau_total, 1:n_tau]
+  J12 <- J[1:n_tau,c(n_tau+1):n_tau_total]
+  Sigma11 <- Sigma[1:n_tau,1:n_tau]
+
+  V2 <- Sigma22 - J21%*%J11%*%Sigma12 - Sigma21%*%J11%*%J12 +
+    J21%*%J11%*%Sigma11%*%J11%*%J12
+  TU[j] <- t(temp_score$Score[c(n_tau+1):n_tau_total]%*%solve(V2)%*%
+               temp_score$Score[c(n_tau+1):n_tau_total])
+  df[j] <- n_tau_new
+  SIC[j] <- -as.numeric(TU[j]) + penalty*n_tau_total
+  #TU[j] <- as.numeric(TU[j])
+  QQ[j] <- qchisq(0.95, df = n_tau_new)
+  df_total[j] <- n_tau_total
+  print(j)
+  }
+  output <- data.frame("SIC" = SIC, "df" = df, "df_total" = df_total,
+                       "Tu" = TU, "Chisq" = QQ)
+  return(output)
+}
+
diff --git a/R/mc_unstructured.R b/R/mc_unstructured.R
deleted file mode 100644
index afc67eb968e7129b9c2f16e6a47451f081222583..0000000000000000000000000000000000000000
--- a/R/mc_unstructured.R
+++ /dev/null
@@ -1,22 +0,0 @@
-#' Unstructured model
-#'
-#' @description Builds a unstructured model matrix.
-#'
-#' @param n_time Number of observations per unit sample.
-#' @return A matrix. Note that the function assumes that the data are in the correct order.
-#' @export
-
-mc_unstructured <- function(n_time) {
-    mat.temp <- Matrix(0, ncol = n_time, nrow = n_time, sparse = TRUE)
-    non.diagonal.terms <- list()
-    non.diagonal <- t(combn(n_time, 2))
-    n.cor.par <- dim(non.diagonal)[1]
-    ## Covariance elementary matrices
-    for (i in 1:n.cor.par) {
-        non.diagonal.terms[i][[1]] <- mat.temp
-        non.diagonal.terms[i][[1]][non.diagonal[i, 1], non.diagonal[i, 2]] <- non.diagonal.terms[i][[1]][non.diagonal[i,
-            2], non.diagonal[i, 1]] <- 1
-    }
-    ## Output
-    return(non.diagonal.terms)
-}
diff --git a/R/mc_variance_function.R b/R/mc_variance_function.R
index f6fb9a699fedf0468d230b2174689edbaf69a4ea..0d54ff4a0937c4d77cb8020cdca8f8ccdac42ec1 100644
--- a/R/mc_variance_function.R
+++ b/R/mc_variance_function.R
@@ -30,7 +30,8 @@
 #' mc_variance_function(mu = mu$mu, power = c(2,1), Ntrial = 1, variance = 'binomialPQ',
 #' inverse = FALSE, derivative_power = TRUE, derivative_mu = TRUE)
 # Generic variance function ---------------------------
-mc_variance_function <- function(mu, power, Ntrial, variance, inverse, derivative_power, derivative_mu) {
+mc_variance_function <- function(mu, power, Ntrial, variance, inverse, 
+                                 derivative_power, derivative_mu) {
     assert_that(is.logical(inverse))
     assert_that(is.logical(derivative_power))
     assert_that(is.logical(derivative_mu))
diff --git a/buildPkg.R b/buildPkg.R
index 3675b4b73daf9d5dc9fc5405a03d8bd66adc09c1..5fbe49cdc1b0015d7fc5ee67af9fe7becc3dd305 100644
--- a/buildPkg.R
+++ b/buildPkg.R
@@ -1,11 +1,11 @@
 ##----------------------------------------------------------------------
 ## Script to build and verify the package.
 
-if(!grepl(x=getwd(), pattern="/mcglm$")){
-    if (Sys.info()["user"]=="walmes"){
+if (!grepl(x = getwd(), pattern = "/mcglm$")) {
+    if (Sys.info()["user"] == "walmes") {
         setwd("~/GitLab/mcglm")
     }
-    ## stop("Move to /mcglm directory.")
+    ## stop('Move to /mcglm directory.')
     cat(getwd(), "\n")
 }
 
@@ -38,7 +38,7 @@ packageVersion("mcglm")
 ##----------------------------------------------------------------------
 ## Build the package (it will be one directory up).
 
-build(manual = TRUE, vignettes = FALSE)
+build(manual = TRUE, vignettes = TRUE)
 # build the binary version for windows (not used)
 # build_win()
 
@@ -58,7 +58,7 @@ build_vignettes()
 ## Generate the README.md.
 
 library(knitr)
-knit(input="README.Rmd")
+knit(input = "README.Rmd") 
 
 ##----------------------------------------------------------------------
 ## Examples.
@@ -77,11 +77,11 @@ install.packages(pkg, repos = NULL)
 
 ## Test using devtools::install_git().
 libTest <- path.expand("~/R-test/")
-if (file.exists(libTest)){
+if (file.exists(libTest)) {
     file.remove(libTest)
 }
 dir.create(path = libTest)
-
+ 
 .libPaths(new = c(libTest, .libPaths())); .libPaths()
 
 install_git(url = "http://git.leg.ufpr.br/wbonat/mcglm.git",
diff --git a/data-raw/ahs.R b/data-raw/ahs.R
index fd48eb6bb15d7d2a7dcadb76395350f65f311aee..b12140b750b1ba49cc2746b442387f0856255b70 100644
--- a/data-raw/ahs.R
+++ b/data-raw/ahs.R
@@ -1,5 +1,5 @@
 ##----------------------------------------------------------------------
-## Prepare de the data set.
+## Prepare the data set.
 
 setwd("/home/walmes/GitLab/mcglm/data-raw")
 
@@ -14,7 +14,7 @@ str(ahs)
 library(lattice)
 library(latticeExtra)
 
-data(ahs, package="mcglm")
+## data(ahs, package="mcglm")
 str(ahs)
 
 xt <- xtabs(~age+sex, data=ahs)
@@ -47,4 +47,11 @@ useOuterStrips(
     )
 )
 
+## dir.create("../data/")
+save(ahs, file = "../data/ahs.RData")
+rm(list = ls())
+load("../data/ahs.RData")
+ls()
+str(ahs)
+
 ##----------------------------------------------------------------------
diff --git a/data-raw/ahs.txt b/data-raw/ahs.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a7c112a829d1f2e27797adbaa6dce3ada9a10ccc
--- /dev/null
+++ b/data-raw/ahs.txt
@@ -0,0 +1,5195 @@
+##----------------------------------------------------------------------
+## This dataset is part of mcglm package.
+## Visit http://git.leg.ufpr.br/wbonat/mcglm for details.
+##----------------------------------------------------------------------
+sex	age	income	levyplus	freepoor	freerepa	illness	actdays	hscore	chcond	Ndoc	Nndoc	Nadm	Nhosp	Nmed
+male	0.19	0	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.19	0	0	0	0	1	4	0	otherwise	3	0	0	0	1
+male	0.19	0	0	0	0	1	0	1	otherwise	0	0	0	0	1
+male	0.19	0	0	0	0	2	0	3	otherwise	0	0	0	0	1
+male	0.19	0	0	0	0	3	0	0	otherwise	0	0	0	0	2
+male	0.19	0	0	0	1	3	0	3	not limited	0	0	0	0	0
+male	0.19	0	0	1	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0	0	1	0	1	0	0	not limited	1	0	1	5	1
+male	0.19	0	0	1	0	1	1	3	otherwise	0	0	0	0	0
+male	0.19	0	1	0	0	0	0	1	otherwise	0	0	0	0	1
+male	0.19	0	1	0	0	0	0	0	otherwise	0	0	1	1	0
+male	0.19	0	1	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.19	0	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0	1	0	0	1	0	0	otherwise	0	0	0	0	2
+male	0.19	0	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0	1	0	0	2	0	1	otherwise	0	0	0	0	0
+male	0.19	0	1	0	0	4	4	2	not limited	0	0	0	0	2
+male	0.19	0.01	0	0	0	1	1	0	not limited	0	0	0	0	1
+male	0.19	0.01	0	0	0	3	0	6	otherwise	0	0	0	0	1
+male	0.19	0.01	0	0	0	4	0	2	otherwise	0	0	0	0	0
+male	0.19	0.01	0	0	1	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0.01	0	1	0	3	0	1	limited	0	0	1	1	0
+male	0.19	0.01	1	0	0	2	0	1	not limited	0	0	0	0	0
+male	0.19	0.06	0	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.19	0.06	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.06	0	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.19	0.06	0	0	0	1	0	5	otherwise	0	0	1	7	0
+male	0.19	0.06	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0.06	0	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.19	0.06	0	0	0	2	0	12	otherwise	1	0	0	0	0
+male	0.19	0.06	0	1	0	0	0	3	otherwise	0	0	0	0	1
+male	0.19	0.06	0	1	0	0	0	1	otherwise	0	0	0	0	0
+male	0.19	0.06	0	1	0	0	0	1	otherwise	0	0	1	2	0
+male	0.19	0.06	0	1	0	0	0	0	not limited	0	0	0	0	1
+male	0.19	0.06	0	1	0	1	0	2	not limited	0	0	0	0	0
+male	0.19	0.06	1	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.19	0.06	1	0	0	1	1	0	otherwise	0	0	0	0	0
+male	0.19	0.06	1	0	0	2	7	9	otherwise	0	7	1	7	0
+male	0.19	0.06	1	0	0	2	1	2	otherwise	0	0	0	0	0
+male	0.19	0.15	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.19	0.15	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.15	0	0	0	0	0	5	otherwise	0	0	0	0	0
+male	0.19	0.15	0	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.19	0.15	0	0	0	0	0	2	otherwise	0	0	0	0	1
+male	0.19	0.15	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.15	0	0	0	1	0	0	otherwise	1	0	0	0	0
+male	0.19	0.15	0	0	0	1	0	2	otherwise	1	1	0	0	3
+male	0.19	0.15	0	0	0	1	0	1	not limited	0	0	0	0	1
+male	0.19	0.15	0	0	0	1	0	2	otherwise	0	0	0	0	1
+male	0.19	0.15	0	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.19	0.15	0	0	0	2	1	0	otherwise	0	0	0	0	2
+male	0.19	0.15	0	0	0	2	0	5	otherwise	0	0	0	0	0
+male	0.19	0.15	0	0	0	2	0	0	not limited	0	0	0	0	0
+male	0.19	0.15	0	0	0	2	1	0	otherwise	0	0	0	0	3
+male	0.19	0.15	0	0	0	2	0	1	otherwise	0	0	0	0	0
+male	0.19	0.15	0	0	0	4	0	4	limited	0	0	0	0	3
+male	0.19	0.15	0	1	0	0	0	3	otherwise	0	0	0	0	0
+male	0.19	0.15	0	1	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.15	0	1	0	0	0	0	limited	0	0	1	4	0
+male	0.19	0.15	0	1	0	0	0	7	otherwise	0	0	0	0	0
+male	0.19	0.15	0	1	0	0	0	1	otherwise	0	0	0	0	0
+male	0.19	0.15	0	1	0	1	0	4	otherwise	1	0	0	0	0
+male	0.19	0.15	0	1	0	1	0	0	limited	0	0	0	0	0
+male	0.19	0.15	0	1	0	2	0	2	otherwise	0	0	0	0	1
+male	0.19	0.15	0	1	0	3	0	0	otherwise	0	0	0	0	0
+male	0.19	0.15	1	0	0	0	0	1	not limited	0	0	0	0	0
+male	0.19	0.15	1	0	0	0	0	0	not limited	0	0	0	0	2
+male	0.19	0.15	1	0	0	0	0	0	otherwise	0	1	0	0	0
+male	0.19	0.15	1	0	0	1	0	0	otherwise	1	0	0	0	1
+male	0.19	0.15	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0.15	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0.15	1	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.19	0.15	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0.15	1	0	0	1	0	0	otherwise	0	0	1	4	0
+male	0.19	0.15	1	0	0	2	0	1	otherwise	0	0	0	0	0
+male	0.19	0.25	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.25	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.25	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.25	0	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.19	0.25	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.25	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.25	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.25	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.25	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.25	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.25	0	0	0	1	0	0	otherwise	1	0	0	0	1
+male	0.19	0.25	0	0	0	1	1	1	otherwise	0	0	1	3	0
+male	0.19	0.25	0	0	0	1	0	3	not limited	0	0	0	0	0
+male	0.19	0.25	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.19	0.25	0	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.19	0.25	0	0	0	1	0	2	otherwise	0	0	0	0	0
+male	0.19	0.25	0	0	0	1	0	2	otherwise	0	0	0	0	0
+male	0.19	0.25	0	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.19	0.25	0	0	0	1	0	2	otherwise	0	0	0	0	1
+male	0.19	0.25	0	0	0	1	0	5	otherwise	0	0	0	0	1
+male	0.19	0.25	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0.25	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0.25	0	0	0	2	0	0	otherwise	0	0	0	0	0
+male	0.19	0.25	0	0	0	2	0	1	otherwise	0	0	0	0	0
+male	0.19	0.25	0	0	0	3	2	3	otherwise	2	0	0	0	0
+male	0.19	0.25	0	0	0	3	0	1	not limited	0	0	0	0	0
+male	0.19	0.25	0	0	0	3	0	3	otherwise	0	0	0	0	0
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+male	0.19	0.25	0	1	0	0	0	0	otherwise	0	0	0	0	0
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+male	0.19	0.25	0	1	0	1	0	3	otherwise	0	0	0	0	0
+male	0.19	0.25	0	1	0	1	0	3	otherwise	0	0	0	0	0
+male	0.19	0.25	0	1	0	1	2	1	otherwise	0	0	0	0	1
+male	0.19	0.25	0	1	0	1	0	1	otherwise	0	0	0	0	1
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+male	0.19	0.25	1	0	0	0	0	0	not limited	0	0	0	0	1
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+male	0.19	0.35	0	0	0	2	0	0	otherwise	0	0	0	0	1
+male	0.19	0.35	0	0	0	3	7	4	limited	0	0	1	2	0
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+male	0.19	0.35	0	1	0	0	0	2	limited	0	0	0	0	3
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+male	0.19	0.35	1	0	0	0	0	0	not limited	0	0	0	0	0
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+male	0.19	0.35	1	0	0	1	0	3	not limited	0	7	0	0	0
+male	0.19	0.35	1	0	0	1	1	0	otherwise	0	0	0	0	2
+male	0.19	0.35	1	0	0	1	0	2	not limited	0	0	0	0	1
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+male	0.19	0.35	1	0	0	3	0	1	otherwise	0	0	0	0	1
+male	0.19	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
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+male	0.19	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
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+male	0.19	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.45	0	0	0	0	0	0	not limited	0	0	2	1	0
+male	0.19	0.45	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.19	0.45	0	0	0	0	0	4	otherwise	0	0	0	0	0
+male	0.19	0.45	0	0	0	0	0	0	not limited	0	0	0	0	0
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+male	0.19	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.45	0	0	0	1	0	0	otherwise	1	0	0	0	1
+male	0.19	0.45	0	0	0	1	14	2	otherwise	1	0	1	4	0
+male	0.19	0.45	0	0	0	1	14	0	otherwise	1	0	1	2	0
+male	0.19	0.45	0	0	0	1	14	3	otherwise	1	1	0	0	1
+male	0.19	0.45	0	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.19	0.45	0	0	0	1	0	3	not limited	0	0	0	0	1
+male	0.19	0.45	0	0	0	1	3	1	otherwise	0	0	0	0	0
+male	0.19	0.45	0	0	0	1	4	0	otherwise	0	0	0	0	0
+male	0.19	0.45	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0.45	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.19	0.45	0	0	0	1	0	0	otherwise	0	0	0	0	2
+male	0.19	0.45	0	0	0	1	0	0	not limited	0	0	0	0	2
+male	0.19	0.45	0	0	0	1	0	3	otherwise	0	0	0	0	0
+male	0.19	0.45	0	0	0	1	0	5	not limited	0	0	0	0	2
+male	0.19	0.45	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.19	0.45	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.19	0.45	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.19	0.45	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0.45	0	0	0	1	0	1	otherwise	0	2	0	0	1
+male	0.19	0.45	0	0	0	2	5	1	not limited	1	0	0	0	3
+male	0.19	0.45	0	0	0	2	2	2	otherwise	1	0	1	7	1
+male	0.19	0.45	0	0	0	2	0	2	otherwise	0	0	1	4	0
+male	0.19	0.45	0	0	0	2	0	0	otherwise	0	0	1	6	1
+male	0.19	0.45	0	0	0	2	0	2	limited	0	0	0	0	2
+male	0.19	0.45	0	0	0	3	0	0	otherwise	0	0	0	0	1
+male	0.19	0.45	0	0	0	3	1	1	otherwise	0	0	0	0	0
+male	0.19	0.45	0	0	0	3	0	4	otherwise	0	0	0	0	1
+male	0.19	0.45	0	0	0	4	7	6	not limited	1	0	0	0	0
+male	0.19	0.45	0	0	0	4	0	5	not limited	0	0	1	1	1
+male	0.19	0.45	0	0	0	4	0	4	otherwise	0	0	0	0	1
+male	0.19	0.45	0	1	0	0	0	1	otherwise	0	0	1	22	0
+male	0.19	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.19	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.45	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.19	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.45	1	0	0	0	0	4	otherwise	0	0	0	0	1
+male	0.19	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.45	1	0	0	1	0	5	otherwise	2	0	0	0	1
+male	0.19	0.45	1	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.19	0.45	1	0	0	1	0	1	not limited	0	0	0	0	0
+male	0.19	0.45	1	0	0	1	0	1	otherwise	0	0	1	7	0
+male	0.19	0.45	1	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.19	0.45	1	0	0	2	2	0	otherwise	1	0	0	0	0
+male	0.19	0.45	1	0	0	3	0	1	not limited	0	0	0	0	1
+male	0.19	0.45	1	0	0	3	0	1	limited	0	0	0	0	0
+male	0.19	0.45	1	0	0	3	0	0	otherwise	0	0	0	0	1
+male	0.19	0.45	1	0	0	5	0	0	not limited	0	0	0	0	0
+male	0.19	0.45	1	0	0	5	0	2	limited	0	1	0	0	0
+male	0.19	0.55	0	0	0	0	1	0	otherwise	1	0	0	0	0
+male	0.19	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.55	0	0	0	0	0	1	otherwise	0	0	0	0	1
+male	0.19	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.19	0.55	0	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.19	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.19	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.55	0	0	0	1	0	2	not limited	0	0	1	6	1
+male	0.19	0.55	0	0	0	1	3	2	limited	0	0	2	1	1
+male	0.19	0.55	0	0	0	1	14	0	not limited	0	0	1	7	0
+male	0.19	0.55	0	0	0	1	0	1	otherwise	0	0	0	0	1
+male	0.19	0.55	0	0	0	1	0	6	limited	0	0	1	1	1
+male	0.19	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.19	0.55	0	0	0	1	2	0	otherwise	0	0	0	0	2
+male	0.19	0.55	0	0	0	1	0	0	otherwise	0	0	1	11	0
+male	0.19	0.55	0	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.19	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0.55	0	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.19	0.55	0	0	0	1	0	0	otherwise	0	0	1	11	0
+male	0.19	0.55	0	0	0	1	1	0	not limited	0	0	0	0	1
+male	0.19	0.55	0	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.19	0.55	0	0	0	1	0	3	otherwise	0	0	0	0	1
+male	0.19	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.19	0.55	0	0	0	2	0	7	otherwise	1	0	0	0	3
+male	0.19	0.55	0	0	0	2	14	2	otherwise	1	0	0	0	1
+male	0.19	0.55	0	0	0	2	0	1	otherwise	1	0	0	0	0
+male	0.19	0.55	0	0	0	2	0	2	otherwise	2	1	0	0	1
+male	0.19	0.55	0	0	0	2	0	1	otherwise	0	0	0	0	1
+male	0.19	0.55	0	0	0	2	0	1	otherwise	0	0	0	0	0
+male	0.19	0.55	0	0	0	2	0	1	otherwise	0	0	0	0	0
+male	0.19	0.55	0	0	0	2	0	0	not limited	0	0	1	2	1
+male	0.19	0.55	0	0	0	2	0	0	otherwise	0	0	0	0	0
+male	0.19	0.55	0	0	0	2	2	3	otherwise	0	0	0	0	0
+male	0.19	0.55	0	0	0	2	1	0	otherwise	0	0	0	0	4
+male	0.19	0.55	0	0	0	2	2	0	otherwise	0	0	0	0	2
+male	0.19	0.55	0	0	0	3	13	1	not limited	2	0	0	0	0
+male	0.19	0.55	0	0	0	4	5	2	otherwise	4	0	0	0	3
+male	0.19	0.55	0	0	0	4	0	1	not limited	0	0	0	0	1
+male	0.19	0.55	0	1	0	0	0	0	not limited	1	0	0	0	0
+male	0.19	0.55	0	1	0	0	0	3	otherwise	0	0	0	0	0
+male	0.19	0.55	1	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.19	0.55	1	0	0	0	0	1	otherwise	0	0	0	0	1
+male	0.19	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.55	1	0	0	0	0	0	otherwise	0	0	1	2	0
+male	0.19	0.55	1	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.19	0.55	1	0	0	1	0	0	otherwise	1	0	0	0	0
+male	0.19	0.55	1	0	0	1	0	0	not limited	0	0	1	5	0
+male	0.19	0.55	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0.55	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0.55	1	0	0	1	0	3	not limited	0	0	0	0	0
+male	0.19	0.55	1	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.19	0.55	1	0	0	1	0	2	not limited	0	0	0	0	2
+male	0.19	0.55	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0.55	1	0	0	1	1	0	not limited	0	0	1	11	0
+male	0.19	0.55	1	0	0	1	0	3	otherwise	0	0	0	0	1
+male	0.19	0.55	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0.55	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0.55	1	0	0	2	5	0	not limited	2	0	0	0	1
+male	0.19	0.55	1	0	0	2	0	3	otherwise	0	0	0	0	2
+male	0.19	0.55	1	0	0	2	0	2	otherwise	0	0	0	0	0
+male	0.19	0.55	1	0	0	2	1	1	otherwise	0	0	0	0	0
+male	0.19	0.55	1	0	0	2	0	0	otherwise	0	0	0	0	0
+male	0.19	0.55	1	0	0	3	6	1	otherwise	1	0	0	0	2
+male	0.19	0.55	1	0	0	3	0	0	not limited	0	0	0	0	2
+male	0.19	0.55	1	0	0	3	0	0	not limited	0	0	0	0	0
+male	0.19	0.65	0	0	0	0	0	1	otherwise	0	1	1	2	0
+male	0.19	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.19	0.65	0	0	0	0	0	0	otherwise	0	0	3	11	0
+male	0.19	0.65	0	0	0	0	0	0	otherwise	0	0	1	3	0
+male	0.19	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.65	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.19	0.65	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0.65	0	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.19	0.65	0	0	0	1	0	3	otherwise	0	0	0	0	0
+male	0.19	0.65	0	0	0	1	0	2	not limited	0	0	2	1	1
+male	0.19	0.65	0	0	0	1	0	11	otherwise	0	0	0	0	1
+male	0.19	0.65	0	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.19	0.65	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0.65	0	0	0	2	0	0	not limited	1	0	1	11	0
+male	0.19	0.65	0	0	0	2	0	0	not limited	0	0	0	0	0
+male	0.19	0.65	0	0	0	3	0	0	limited	1	0	0	0	3
+male	0.19	0.65	0	0	0	3	0	4	limited	0	0	0	0	2
+male	0.19	0.65	0	0	0	3	0	0	not limited	0	0	1	22	1
+male	0.19	0.65	0	0	0	3	0	1	not limited	0	0	0	0	0
+male	0.19	0.65	0	0	0	3	0	1	not limited	0	0	0	0	0
+male	0.19	0.65	0	0	0	5	1	1	otherwise	0	0	0	0	0
+male	0.19	0.65	0	1	0	1	0	1	otherwise	0	1	0	0	0
+male	0.19	0.65	0	1	0	2	0	1	otherwise	0	0	0	0	0
+male	0.19	0.65	1	0	0	0	0	0	otherwise	1	0	0	0	0
+male	0.19	0.65	1	0	0	0	0	0	otherwise	0	0	1	1	0
+male	0.19	0.65	1	0	0	0	0	0	not limited	0	0	0	0	2
+male	0.19	0.65	1	0	0	0	0	4	otherwise	0	0	1	2	0
+male	0.19	0.65	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.19	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.65	1	0	0	1	0	4	otherwise	0	0	1	2	0
+male	0.19	0.65	1	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.19	0.65	1	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.19	0.65	1	0	0	1	1	0	not limited	0	0	0	0	0
+male	0.19	0.65	1	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.19	0.65	1	0	0	2	0	0	otherwise	2	0	0	0	0
+male	0.19	0.65	1	0	0	2	0	0	otherwise	0	0	2	11	1
+male	0.19	0.65	1	0	0	5	0	6	limited	0	0	0	0	1
+male	0.19	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.75	0	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.19	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.75	0	0	0	1	0	1	not limited	2	0	1	1	1
+male	0.19	0.75	0	0	0	1	8	1	otherwise	2	0	0	0	2
+male	0.19	0.75	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.19	0.75	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0.75	0	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.19	0.75	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.19	0.75	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.19	0.75	0	0	0	2	0	0	otherwise	0	0	0	0	0
+male	0.19	0.75	0	0	0	2	0	2	otherwise	0	0	0	0	0
+male	0.19	0.75	0	1	0	0	0	1	otherwise	0	0	0	0	0
+male	0.19	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.75	1	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.19	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.75	1	0	0	0	0	2	otherwise	0	0	0	0	0
+male	0.19	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.75	1	0	0	1	0	1	not limited	0	0	1	5	0
+male	0.19	0.75	1	0	0	2	0	2	not limited	0	0	0	0	0
+male	0.19	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.9	0	0	0	0	0	1	not limited	0	0	0	0	0
+male	0.19	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.19	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.19	0.9	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	0.9	0	0	0	1	0	0	not limited	0	0	1	5	1
+male	0.19	0.9	0	0	0	1	0	3	otherwise	0	0	0	0	0
+male	0.19	0.9	0	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.19	0.9	0	0	0	3	0	0	otherwise	1	0	1	4	2
+male	0.19	0.9	0	0	1	0	0	1	otherwise	0	0	0	0	0
+male	0.19	0.9	1	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.19	0.9	1	0	0	1	0	0	otherwise	0	1	0	0	3
+male	0.19	0.9	1	0	0	1	0	2	not limited	0	0	0	0	0
+male	0.19	0.9	1	0	0	2	0	2	otherwise	0	0	1	1	0
+male	0.19	0.9	1	0	0	2	0	0	not limited	0	0	0	0	0
+male	0.19	0.9	1	0	0	2	1	1	otherwise	0	0	1	1	1
+male	0.19	1.1	0	0	0	0	0	2	otherwise	0	0	0	0	1
+male	0.19	1.1	0	0	0	2	7	0	otherwise	1	0	0	0	0
+male	0.19	1.1	0	0	0	4	0	9	limited	0	1	1	1	0
+male	0.19	1.1	1	0	0	1	0	0	not limited	0	1	1	2	2
+male	0.19	1.3	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.19	1.5	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.22	0	0	0	0	1	0	1	not limited	0	0	0	0	0
+male	0.22	0	0	0	0	2	0	2	not limited	0	2	2	11	0
+male	0.22	0	0	1	0	1	14	1	not limited	7	2	1	11	4
+male	0.22	0	1	0	0	1	0	6	otherwise	0	0	0	0	2
+male	0.22	0.01	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.22	0.01	0	0	0	2	0	4	otherwise	0	1	0	0	1
+male	0.22	0.01	0	1	0	2	0	2	otherwise	0	0	0	0	0
+male	0.22	0.06	0	0	0	0	1	0	otherwise	2	0	0	0	0
+male	0.22	0.06	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.06	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.22	0.06	0	0	0	1	0	2	not limited	0	0	0	0	1
+male	0.22	0.06	0	0	0	2	0	1	otherwise	2	0	0	0	0
+male	0.22	0.06	0	1	0	1	0	2	not limited	0	0	0	0	0
+male	0.22	0.06	0	1	0	1	0	0	otherwise	0	0	0	0	0
+male	0.22	0.06	1	0	0	0	0	0	limited	1	0	0	0	1
+male	0.22	0.06	1	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.22	0.06	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.06	1	0	0	0	0	1	otherwise	0	0	0	0	2
+male	0.22	0.06	1	0	0	1	0	6	otherwise	1	0	0	0	1
+male	0.22	0.06	1	0	0	2	0	1	otherwise	0	0	0	0	0
+male	0.22	0.15	0	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.22	0.15	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.15	0	0	0	1	0	0	otherwise	0	1	0	0	1
+male	0.22	0.15	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.22	0.15	0	0	0	1	1	1	not limited	0	0	0	0	1
+male	0.22	0.15	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.22	0.15	0	0	0	2	0	2	otherwise	0	1	0	0	1
+male	0.22	0.15	0	0	0	3	0	0	not limited	0	0	0	0	0
+male	0.22	0.15	0	1	0	0	0	4	otherwise	0	0	0	0	0
+male	0.22	0.15	0	1	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.15	0	1	0	1	0	2	otherwise	0	2	0	0	0
+male	0.22	0.15	0	1	0	1	0	0	otherwise	0	0	0	0	0
+male	0.22	0.15	0	1	0	2	0	1	not limited	0	0	0	0	0
+male	0.22	0.15	0	1	0	3	0	4	not limited	0	2	0	0	0
+male	0.22	0.15	1	0	0	0	0	0	otherwise	0	0	0	0	0
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+male	0.22	0.15	1	0	0	1	0	0	otherwise	1	6	0	0	2
+male	0.22	0.15	1	0	0	1	0	1	not limited	0	0	0	0	0
+male	0.22	0.15	1	0	0	1	0	3	not limited	0	0	0	0	0
+male	0.22	0.15	1	0	0	1	0	1	not limited	0	0	0	0	0
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+male	0.22	0.15	1	0	0	2	1	0	not limited	0	0	1	1	0
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+male	0.22	0.15	1	0	0	4	0	8	not limited	0	0	1	1	0
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+male	0.22	0.25	0	0	0	0	0	0	otherwise	0	0	0	0	0
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+male	0.22	0.25	0	0	0	4	0	0	not limited	0	0	0	0	1
+male	0.22	0.25	0	0	1	2	0	12	limited	1	0	1	80	5
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+male	0.22	0.25	0	1	0	1	0	0	otherwise	0	0	0	0	1
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+male	0.22	0.25	1	0	0	1	0	2	not limited	0	0	0	0	0
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+male	0.22	0.25	1	0	0	1	0	0	otherwise	0	0	0	0	0
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+male	0.22	0.25	1	0	0	3	0	1	otherwise	0	0	0	0	0
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+male	0.22	0.35	0	0	0	0	0	0	otherwise	0	0	0	0	0
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+male	0.22	0.35	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.35	0	0	0	0	0	0	otherwise	0	0	0	0	0
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+male	0.22	0.35	0	0	0	1	0	4	otherwise	0	0	0	0	0
+male	0.22	0.35	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.22	0.35	0	0	0	2	2	4	not limited	1	0	0	0	2
+male	0.22	0.35	0	0	0	2	0	0	not limited	2	0	0	0	0
+male	0.22	0.35	0	0	0	3	0	2	not limited	0	2	0	0	0
+male	0.22	0.35	0	0	0	4	0	3	not limited	2	0	0	0	0
+male	0.22	0.35	0	0	0	5	0	11	otherwise	0	0	0	0	2
+male	0.22	0.35	0	1	0	0	0	1	otherwise	0	0	1	3	0
+male	0.22	0.35	0	1	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.35	0	1	0	1	0	2	not limited	0	0	0	0	1
+male	0.22	0.35	0	1	0	1	0	0	otherwise	0	0	0	0	0
+male	0.22	0.35	0	1	0	2	0	0	otherwise	0	0	0	0	0
+male	0.22	0.35	0	1	0	2	0	4	otherwise	0	0	0	0	0
+male	0.22	0.35	1	0	0	0	0	0	otherwise	0	0	1	5	0
+male	0.22	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.35	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.22	0.35	1	0	0	1	1	0	otherwise	0	0	0	0	0
+male	0.22	0.35	1	0	0	1	0	1	not limited	0	1	0	0	0
+male	0.22	0.35	1	0	0	1	2	1	otherwise	0	0	0	0	3
+male	0.22	0.35	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.22	0.35	1	0	0	2	0	0	not limited	1	0	0	0	0
+male	0.22	0.35	1	0	0	2	0	1	otherwise	0	0	0	0	0
+male	0.22	0.35	1	0	0	2	0	0	not limited	0	1	1	3	0
+male	0.22	0.35	1	0	0	2	0	2	not limited	0	0	0	0	2
+male	0.22	0.35	1	0	0	2	0	1	otherwise	0	0	0	0	0
+male	0.22	0.45	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.22	0.45	0	0	0	0	0	2	otherwise	0	0	0	0	1
+male	0.22	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.45	0	0	0	0	0	2	not limited	0	0	0	0	0
+male	0.22	0.45	0	0	0	0	0	2	not limited	0	0	0	0	1
+male	0.22	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.45	0	0	0	0	0	5	otherwise	0	0	0	0	0
+male	0.22	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.45	0	0	0	1	0	0	otherwise	2	0	0	0	0
+male	0.22	0.45	0	0	0	1	12	1	otherwise	4	0	1	4	0
+male	0.22	0.45	0	0	0	1	0	1	otherwise	1	2	0	0	2
+male	0.22	0.45	0	0	0	1	0	1	otherwise	1	0	0	0	1
+male	0.22	0.45	0	0	0	1	0	2	otherwise	0	0	0	0	0
+male	0.22	0.45	0	0	0	1	0	3	otherwise	0	0	0	0	1
+male	0.22	0.45	0	0	0	1	0	2	otherwise	0	0	0	0	1
+male	0.22	0.45	0	0	0	2	0	1	otherwise	1	1	0	0	0
+male	0.22	0.45	0	0	0	2	0	12	otherwise	0	0	0	0	0
+male	0.22	0.45	0	0	0	2	0	0	not limited	0	0	0	0	0
+male	0.22	0.45	0	0	0	2	0	3	not limited	0	0	0	0	1
+male	0.22	0.45	0	0	0	3	0	2	not limited	0	0	0	0	0
+male	0.22	0.45	0	0	0	3	0	3	otherwise	0	0	0	0	0
+male	0.22	0.45	0	0	0	3	0	5	not limited	0	0	0	0	0
+male	0.22	0.45	0	0	0	5	2	0	limited	0	0	1	80	1
+male	0.22	0.45	0	0	0	5	0	9	otherwise	0	0	0	0	0
+male	0.22	0.45	0	1	0	0	0	7	otherwise	0	0	0	0	0
+male	0.22	0.45	0	1	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.45	0	1	0	2	14	9	otherwise	1	0	0	0	2
+male	0.22	0.45	0	1	0	3	3	2	limited	0	0	0	0	2
+male	0.22	0.45	0	1	0	3	2	0	not limited	0	1	0	0	1
+male	0.22	0.45	0	1	0	4	0	4	limited	0	0	0	0	0
+male	0.22	0.45	1	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.22	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.22	0.45	1	0	0	0	0	1	not limited	0	0	0	0	1
+male	0.22	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.45	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.22	0.45	1	0	0	1	0	0	otherwise	0	0	2	2	1
+male	0.22	0.45	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.22	0.45	1	0	0	1	1	0	otherwise	0	0	0	0	0
+male	0.22	0.45	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.22	0.45	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.22	0.45	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.22	0.45	1	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.22	0.45	1	0	0	2	0	4	not limited	0	0	0	0	0
+male	0.22	0.45	1	0	0	2	0	0	not limited	0	0	0	0	2
+male	0.22	0.45	1	0	0	3	0	9	not limited	0	0	0	0	2
+male	0.22	0.45	1	0	0	3	0	8	otherwise	0	2	0	0	0
+male	0.22	0.45	1	0	0	3	0	8	otherwise	0	2	0	0	0
+male	0.22	0.45	1	0	0	4	0	1	otherwise	0	0	0	0	0
+male	0.22	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.22	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.22	0.55	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.22	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.22	0.55	0	0	0	0	0	0	not limited	0	0	1	1	0
+male	0.22	0.55	0	0	0	0	0	2	otherwise	0	0	0	0	0
+male	0.22	0.55	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.22	0.55	0	0	0	0	0	1	otherwise	0	0	1	4	0
+male	0.22	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.55	0	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.22	0.55	0	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.22	0.55	0	0	0	0	0	2	otherwise	0	0	0	0	0
+male	0.22	0.55	0	0	0	0	0	3	otherwise	0	2	0	0	0
+male	0.22	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.55	0	0	0	0	0	0	otherwise	0	1	0	0	0
+male	0.22	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.22	0.55	0	0	0	1	3	0	otherwise	5	0	0	0	1
+male	0.22	0.55	0	0	0	1	0	0	not limited	1	0	0	0	0
+male	0.22	0.55	0	0	0	1	0	0	otherwise	0	0	1	2	0
+male	0.22	0.55	0	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.22	0.55	0	0	0	1	0	2	limited	0	0	0	0	0
+male	0.22	0.55	0	0	0	1	2	0	otherwise	0	0	0	0	0
+male	0.22	0.55	0	0	0	1	0	3	otherwise	0	0	0	0	1
+male	0.22	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	2
+male	0.22	0.55	0	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.22	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.22	0.55	0	0	0	1	1	0	limited	0	0	0	0	0
+male	0.22	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.22	0.55	0	0	0	1	0	3	otherwise	0	0	0	0	0
+male	0.22	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.22	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.22	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.22	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.22	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.22	0.55	0	0	0	2	14	1	limited	1	0	1	11	2
+male	0.22	0.55	0	0	0	2	0	0	otherwise	1	0	0	0	0
+male	0.22	0.55	0	0	0	2	0	1	not limited	0	0	1	22	0
+male	0.22	0.55	0	0	0	2	0	1	otherwise	0	0	0	0	1
+male	0.22	0.55	0	0	0	2	5	2	otherwise	0	0	0	0	0
+male	0.22	0.55	0	0	0	2	0	0	otherwise	0	0	0	0	0
+male	0.22	0.55	0	0	0	2	0	0	otherwise	0	0	0	0	2
+male	0.22	0.55	0	0	0	2	1	4	otherwise	0	0	0	0	0
+male	0.22	0.55	0	0	0	3	0	0	otherwise	2	0	0	0	0
+male	0.22	0.55	0	0	0	3	0	4	not limited	0	0	1	5	1
+male	0.22	0.55	0	0	0	3	0	0	otherwise	0	0	0	0	1
+male	0.22	0.55	0	0	0	3	0	5	not limited	0	0	0	0	0
+male	0.22	0.55	0	0	0	4	1	1	not limited	0	0	0	0	0
+male	0.22	0.55	0	0	0	4	0	2	not limited	0	0	0	0	0
+male	0.22	0.55	0	0	0	4	0	6	otherwise	0	0	0	0	0
+male	0.22	0.55	0	1	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.55	0	1	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.55	0	1	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.55	0	1	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.55	0	1	0	1	0	2	otherwise	0	0	0	0	0
+male	0.22	0.55	0	1	0	1	0	1	not limited	0	0	0	0	1
+male	0.22	0.55	0	1	0	2	5	1	otherwise	0	0	0	0	1
+male	0.22	0.55	0	1	0	2	14	1	limited	0	10	0	0	0
+male	0.22	0.55	0	1	0	2	0	1	otherwise	0	0	1	4	0
+male	0.22	0.55	0	1	0	2	0	3	otherwise	0	0	0	0	0
+male	0.22	0.55	0	1	0	2	0	0	otherwise	0	0	0	0	0
+male	0.22	0.55	0	1	0	3	0	1	not limited	0	0	0	0	0
+male	0.22	0.55	1	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.22	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.55	1	0	0	0	0	1	not limited	0	1	1	1	1
+male	0.22	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	0.55	1	0	0	0	0	2	not limited	0	0	0	0	0
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+male	0.22	1.3	0	0	0	2	0	1	otherwise	0	0	0	0	0
+male	0.22	1.3	0	0	0	2	0	3	limited	0	0	0	0	1
+male	0.22	1.3	0	0	0	3	0	0	not limited	0	0	0	0	1
+male	0.22	1.3	0	0	0	4	0	3	not limited	0	0	0	0	0
+male	0.22	1.3	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.22	1.3	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.22	1.3	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	1.3	1	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.22	1.3	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	1.3	1	0	0	3	0	0	not limited	0	0	1	11	2
+male	0.22	1.3	1	0	0	4	0	1	otherwise	1	0	0	0	2
+male	0.22	1.5	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	1.5	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.22	1.5	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.22	1.5	0	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.22	1.5	0	0	0	2	0	1	otherwise	0	0	0	0	0
+male	0.22	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.22	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.22	1.5	1	0	0	1	0	3	limited	4	0	0	0	1
+male	0.22	1.5	1	0	0	1	1	1	not limited	0	0	0	0	0
+male	0.22	1.5	1	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.22	1.5	1	0	0	2	14	5	otherwise	1	0	0	0	2
+male	0.22	1.5	1	0	0	2	0	0	otherwise	0	0	0	0	0
+male	0.22	1.5	1	0	0	3	8	1	otherwise	1	2	0	0	3
+male	0.22	1.5	1	0	0	5	0	0	not limited	1	0	0	0	2
+male	0.27	0.01	0	0	0	5	2	4	limited	0	0	0	0	2
+male	0.27	0.01	0	1	0	2	1	8	not limited	0	0	1	1	1
+male	0.27	0.06	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.06	1	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.27	0.06	1	0	0	1	0	0	limited	0	0	0	0	0
+male	0.27	0.15	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.15	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.27	0.15	0	1	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.15	0	1	0	2	0	6	otherwise	0	0	0	0	0
+male	0.27	0.15	0	1	0	5	0	2	not limited	0	0	0	0	1
+male	0.27	0.15	1	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.27	0.25	0	0	0	1	3	0	otherwise	0	0	0	0	1
+male	0.27	0.25	0	0	0	1	0	3	limited	0	0	0	0	1
+male	0.27	0.25	0	0	1	0	0	0	limited	0	0	0	0	1
+male	0.27	0.25	0	0	1	1	0	0	limited	0	0	0	0	3
+male	0.27	0.25	0	0	1	1	14	0	limited	0	0	0	0	0
+male	0.27	0.25	0	0	1	1	0	0	limited	0	0	0	0	0
+male	0.27	0.25	0	0	1	3	0	3	limited	1	0	1	80	0
+male	0.27	0.25	0	1	0	1	7	2	otherwise	0	0	0	0	1
+male	0.27	0.25	0	1	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	0.25	0	1	0	3	0	1	not limited	0	0	0	0	0
+male	0.27	0.25	0	1	0	3	0	0	not limited	0	0	0	0	0
+male	0.27	0.25	1	0	0	0	0	5	otherwise	0	0	1	1	1
+male	0.27	0.25	1	0	0	1	14	2	not limited	0	9	1	45	1
+male	0.27	0.25	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	0.25	1	0	0	1	0	8	otherwise	0	0	0	0	1
+male	0.27	0.25	1	0	0	2	0	0	not limited	2	0	0	0	1
+male	0.27	0.35	0	0	0	0	0	0	otherwise	1	0	0	0	0
+male	0.27	0.35	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.35	0	0	0	0	0	0	otherwise	0	0	1	2	0
+male	0.27	0.35	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.35	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.35	0	0	0	1	0	8	not limited	0	0	0	0	0
+male	0.27	0.35	0	0	0	1	1	4	otherwise	0	0	0	0	5
+male	0.27	0.35	0	0	0	1	2	0	otherwise	0	0	0	0	1
+male	0.27	0.35	0	0	0	2	5	10	otherwise	0	0	0	0	2
+male	0.27	0.35	0	0	0	4	0	1	not limited	0	0	0	0	3
+male	0.27	0.35	0	0	1	2	0	4	not limited	0	0	0	0	1
+male	0.27	0.35	1	0	0	1	3	0	otherwise	1	0	0	0	0
+male	0.27	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.45	0	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.27	0.45	0	0	0	1	0	5	otherwise	0	0	0	0	3
+male	0.27	0.45	0	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.27	0.45	0	0	0	1	0	1	otherwise	0	0	0	0	1
+male	0.27	0.45	0	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.27	0.45	0	0	0	5	3	3	otherwise	0	0	0	0	2
+male	0.27	0.45	0	1	0	2	0	1	not limited	0	0	0	0	1
+male	0.27	0.45	0	1	0	4	3	12	otherwise	0	0	1	7	0
+male	0.27	0.45	0	1	0	4	0	2	not limited	0	0	0	0	0
+male	0.27	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.27	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.45	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	0.45	1	0	0	5	0	0	not limited	0	0	0	0	1
+male	0.27	0.55	0	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.27	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.55	0	0	0	0	0	0	not limited	0	0	0	0	4
+male	0.27	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.27	0.55	0	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.27	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	0.55	0	0	0	1	0	3	otherwise	0	0	0	0	0
+male	0.27	0.55	0	0	0	3	2	1	otherwise	1	0	0	0	1
+male	0.27	0.55	0	0	0	3	0	0	otherwise	0	0	0	0	0
+male	0.27	0.55	0	0	0	4	8	8	otherwise	2	0	2	7	2
+male	0.27	0.55	0	1	0	0	0	8	otherwise	0	0	0	0	1
+male	0.27	0.55	0	1	0	2	0	2	not limited	0	0	0	0	2
+male	0.27	0.55	1	0	0	2	0	0	not limited	2	0	0	0	0
+male	0.27	0.55	1	0	0	2	0	0	otherwise	0	0	0	0	0
+male	0.27	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.65	0	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.27	0.65	0	0	0	0	0	0	limited	0	0	1	4	0
+male	0.27	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.65	0	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.27	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.65	0	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.27	0.65	0	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.27	0.65	0	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.27	0.65	0	0	0	1	0	1	otherwise	0	0	0	0	1
+male	0.27	0.65	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.27	0.65	0	0	0	3	0	3	limited	0	0	0	0	0
+male	0.27	0.65	0	0	0	5	3	3	not limited	3	0	0	0	1
+male	0.27	0.65	1	0	0	0	0	0	otherwise	1	0	0	0	1
+male	0.27	0.65	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.27	0.65	1	0	0	0	0	1	limited	0	0	0	0	1
+male	0.27	0.65	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.27	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.65	1	0	0	1	3	1	otherwise	1	0	0	0	2
+male	0.27	0.65	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	0.65	1	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.27	0.65	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	0.65	1	0	0	2	0	0	not limited	0	0	0	0	1
+male	0.27	0.75	0	0	0	0	0	1	otherwise	0	0	0	0	2
+male	0.27	0.75	0	0	0	0	0	0	limited	0	0	0	0	0
+male	0.27	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.75	0	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.27	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.27	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.27	0.75	0	0	0	1	0	0	limited	0	0	0	0	1
+male	0.27	0.75	0	0	0	1	0	0	limited	0	0	0	0	0
+male	0.27	0.75	0	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.27	0.75	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	0.75	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	0.75	0	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.27	0.75	0	0	0	2	2	0	limited	0	0	0	0	1
+male	0.27	0.75	0	0	0	2	0	0	otherwise	0	0	1	3	1
+male	0.27	0.75	0	0	0	2	0	4	not limited	0	0	0	0	1
+male	0.27	0.75	0	0	0	3	0	7	not limited	1	0	0	0	1
+male	0.27	0.75	0	0	0	3	0	5	otherwise	0	0	0	0	0
+male	0.27	0.75	0	0	0	4	0	1	not limited	0	0	0	0	5
+male	0.27	0.75	0	1	0	1	0	1	otherwise	0	0	0	0	0
+male	0.27	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.75	1	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.27	0.75	1	0	0	0	0	7	otherwise	0	0	0	0	0
+male	0.27	0.75	1	0	0	0	4	0	not limited	0	0	1	4	0
+male	0.27	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.75	1	0	0	1	0	0	otherwise	0	0	1	2	1
+male	0.27	0.75	1	0	0	1	1	0	not limited	0	0	0	0	0
+male	0.27	0.75	1	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.27	0.75	1	0	0	1	2	2	limited	0	2	0	0	0
+male	0.27	0.75	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	0.75	1	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.27	0.75	1	0	0	2	0	4	otherwise	0	0	0	0	0
+male	0.27	0.75	1	0	0	2	0	0	otherwise	0	0	0	0	0
+male	0.27	0.75	1	0	0	2	0	1	not limited	0	0	0	0	0
+male	0.27	0.75	1	0	0	4	2	1	not limited	1	0	0	0	4
+male	0.27	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	0	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.27	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.27	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	0	0	0	0	0	4	otherwise	0	0	0	0	0
+male	0.27	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.27	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	0	0	0	0	0	5	otherwise	0	0	0	0	0
+male	0.27	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	0	0	0	1	0	0	not limited	1	0	0	0	0
+male	0.27	0.9	0	0	0	1	0	0	not limited	1	0	1	2	0
+male	0.27	0.9	0	0	0	1	0	1	otherwise	0	0	0	0	2
+male	0.27	0.9	0	0	0	1	0	1	limited	0	0	0	0	1
+male	0.27	0.9	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.27	0.9	0	0	0	1	0	1	otherwise	0	0	1	4	0
+male	0.27	0.9	0	0	0	1	0	0	limited	0	0	0	0	0
+male	0.27	0.9	0	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.27	0.9	0	0	0	1	0	3	not limited	0	0	0	0	1
+male	0.27	0.9	0	0	0	1	0	0	limited	0	0	0	0	1
+male	0.27	0.9	0	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.27	0.9	0	0	0	1	0	4	not limited	0	0	1	1	0
+male	0.27	0.9	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	0	0	0	1	0	1	otherwise	0	0	0	0	1
+male	0.27	0.9	0	0	0	2	1	11	not limited	1	0	0	0	0
+male	0.27	0.9	0	0	0	2	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	0	0	0	2	0	0	not limited	0	0	0	0	2
+male	0.27	0.9	0	0	0	2	0	3	limited	0	0	0	0	0
+male	0.27	0.9	0	0	0	2	0	0	limited	0	0	0	0	0
+male	0.27	0.9	0	0	0	2	0	0	not limited	0	0	1	1	1
+male	0.27	0.9	0	0	0	2	0	5	not limited	0	0	0	0	1
+male	0.27	0.9	0	0	0	2	0	0	otherwise	0	0	0	0	1
+male	0.27	0.9	0	0	0	3	5	1	limited	3	0	0	0	1
+male	0.27	0.9	0	0	0	3	0	1	not limited	0	0	0	0	1
+male	0.27	0.9	0	0	0	3	0	1	not limited	0	0	0	0	0
+male	0.27	0.9	0	0	0	4	6	5	not limited	0	0	1	3	2
+male	0.27	0.9	0	0	1	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.27	0.9	1	0	0	0	0	1	limited	0	0	0	0	0
+male	0.27	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.27	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	1	0	0	0	0	1	otherwise	0	0	0	0	1
+male	0.27	0.9	1	0	0	0	0	1	not limited	0	0	0	0	0
+male	0.27	0.9	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.27	0.9	1	0	0	0	0	0	otherwise	0	0	1	1	0
+male	0.27	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	1	0	0	1	5	2	otherwise	2	0	0	0	2
+male	0.27	0.9	1	0	0	1	3	0	otherwise	1	0	0	0	0
+male	0.27	0.9	1	0	0	1	4	1	limited	1	6	1	11	0
+male	0.27	0.9	1	0	0	1	1	0	otherwise	1	0	0	0	0
+male	0.27	0.9	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	1	0	0	1	0	0	otherwise	0	1	0	0	0
+male	0.27	0.9	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	1	0	0	1	0	4	otherwise	0	0	0	0	0
+male	0.27	0.9	1	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.27	0.9	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	0.9	1	0	0	1	1	2	not limited	0	0	0	0	1
+male	0.27	0.9	1	0	0	1	0	0	limited	0	0	0	0	0
+male	0.27	0.9	1	0	0	2	1	0	otherwise	1	0	0	0	1
+male	0.27	0.9	1	0	0	2	5	1	limited	1	2	0	0	2
+male	0.27	0.9	1	0	0	2	4	1	otherwise	1	0	0	0	2
+male	0.27	0.9	1	0	0	2	0	3	not limited	0	0	0	0	0
+male	0.27	0.9	1	0	0	2	2	2	otherwise	0	0	0	0	2
+male	0.27	0.9	1	0	0	2	0	0	limited	0	0	0	0	1
+male	0.27	0.9	1	0	0	3	0	0	limited	0	0	0	0	0
+male	0.27	0.9	1	0	0	4	0	2	not limited	0	0	0	0	1
+male	0.27	1.1	0	0	0	0	0	2	otherwise	0	0	1	2	0
+male	0.27	1.1	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.1	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.1	0	0	0	0	0	0	otherwise	0	0	1	2	0
+male	0.27	1.1	0	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.27	1.1	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.1	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.1	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.1	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.1	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.27	1.1	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.1	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.1	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.1	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.1	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.27	1.1	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	1.1	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	1.1	0	0	0	1	0	0	not limited	0	0	1	7	0
+male	0.27	1.1	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	1.1	0	0	0	1	0	2	otherwise	0	0	0	0	0
+male	0.27	1.1	0	0	0	1	0	1	otherwise	0	0	0	0	1
+male	0.27	1.1	0	0	0	1	0	0	limited	0	2	0	0	0
+male	0.27	1.1	0	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.27	1.1	0	0	0	2	0	1	otherwise	1	0	0	0	1
+male	0.27	1.1	0	0	0	2	0	1	not limited	0	0	1	7	2
+male	0.27	1.1	0	0	0	2	0	3	otherwise	0	0	3	4	0
+male	0.27	1.1	0	0	0	2	0	0	not limited	0	0	0	0	2
+male	0.27	1.1	0	0	0	2	0	0	not limited	0	0	0	0	0
+male	0.27	1.1	0	0	0	2	0	3	otherwise	0	0	0	0	2
+male	0.27	1.1	0	0	0	3	0	3	not limited	0	1	0	0	1
+male	0.27	1.1	0	0	0	4	0	1	otherwise	0	2	0	0	1
+male	0.27	1.1	1	0	0	0	0	0	otherwise	0	0	1	5	0
+male	0.27	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.27	1.1	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.27	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.27	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	2
+male	0.27	1.1	1	0	0	0	0	0	otherwise	0	0	1	11	0
+male	0.27	1.1	1	0	0	0	0	0	otherwise	0	1	0	0	0
+male	0.27	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.1	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.27	1.1	1	0	0	0	0	1	not limited	0	0	0	0	0
+male	0.27	1.1	1	0	0	1	1	0	limited	1	0	0	0	1
+male	0.27	1.1	1	0	0	1	0	3	otherwise	2	0	0	0	1
+male	0.27	1.1	1	0	0	1	0	4	limited	0	0	0	0	0
+male	0.27	1.1	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	1.1	1	0	0	1	0	0	otherwise	0	1	0	0	1
+male	0.27	1.1	1	0	0	1	0	1	not limited	0	0	0	0	3
+male	0.27	1.1	1	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.27	1.1	1	0	0	1	1	0	otherwise	0	0	0	0	0
+male	0.27	1.1	1	0	0	2	3	3	otherwise	1	0	0	0	3
+male	0.27	1.1	1	0	0	2	0	0	otherwise	0	0	0	0	1
+male	0.27	1.1	1	0	0	2	0	4	not limited	0	0	0	0	0
+male	0.27	1.1	1	0	0	2	0	1	otherwise	0	0	0	0	0
+male	0.27	1.1	1	0	0	2	0	11	otherwise	0	3	0	0	0
+male	0.27	1.1	1	0	0	2	0	0	not limited	0	0	0	0	2
+male	0.27	1.1	1	0	0	2	0	0	not limited	0	0	0	0	1
+male	0.27	1.1	1	0	0	3	2	5	otherwise	1	1	0	0	0
+male	0.27	1.1	1	0	0	4	2	3	otherwise	0	0	0	0	4
+male	0.27	1.3	0	0	0	0	0	1	otherwise	1	0	0	0	0
+male	0.27	1.3	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.27	1.3	0	0	0	0	0	2	otherwise	0	0	0	0	1
+male	0.27	1.3	0	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.27	1.3	0	0	0	0	0	1	otherwise	0	0	0	0	3
+male	0.27	1.3	0	0	0	1	14	7	not limited	5	0	0	0	2
+male	0.27	1.3	0	0	0	1	0	0	limited	0	0	0	0	0
+male	0.27	1.3	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.27	1.3	0	0	0	1	0	0	limited	0	0	0	0	0
+male	0.27	1.3	0	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.27	1.3	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	1.3	0	0	0	2	0	0	otherwise	3	0	0	0	0
+male	0.27	1.3	0	0	0	2	0	3	otherwise	0	0	0	0	1
+male	0.27	1.3	1	0	0	0	0	0	otherwise	0	1	0	0	1
+male	0.27	1.3	1	0	0	0	0	0	not limited	0	0	3	3	3
+male	0.27	1.3	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.3	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.3	1	0	0	0	0	3	otherwise	0	0	0	0	0
+male	0.27	1.3	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.3	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.27	1.3	1	0	0	1	2	3	limited	1	0	0	0	1
+male	0.27	1.3	1	0	0	1	2	1	limited	0	0	0	0	3
+male	0.27	1.3	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	1.3	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	1.3	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	1.3	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	1.3	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	1.3	1	0	0	2	7	0	otherwise	2	0	0	0	0
+male	0.27	1.3	1	0	0	2	0	4	not limited	0	0	0	0	1
+male	0.27	1.3	1	0	0	2	3	1	not limited	0	0	0	0	1
+male	0.27	1.3	1	0	0	5	0	0	not limited	1	0	0	0	1
+male	0.27	1.5	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.27	1.5	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.5	0	0	0	1	14	5	otherwise	1	0	0	0	0
+male	0.27	1.5	0	0	0	1	0	0	limited	0	0	0	0	0
+male	0.27	1.5	0	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.27	1.5	0	0	0	1	0	3	otherwise	0	0	0	0	0
+male	0.27	1.5	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	1.5	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.27	1.5	0	0	0	2	0	0	otherwise	0	0	0	0	1
+male	0.27	1.5	0	0	0	2	0	2	otherwise	0	0	1	11	1
+male	0.27	1.5	0	0	0	2	0	3	not limited	0	0	0	0	0
+male	0.27	1.5	0	0	0	3	1	0	not limited	0	0	0	0	1
+male	0.27	1.5	0	0	0	3	0	0	otherwise	0	0	0	0	0
+male	0.27	1.5	0	0	0	5	0	0	not limited	1	0	0	0	2
+male	0.27	1.5	0	0	0	5	0	3	not limited	0	1	0	0	4
+male	0.27	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.5	1	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.27	1.5	1	0	0	0	1	1	otherwise	0	0	0	0	0
+male	0.27	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.27	1.5	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.27	1.5	1	0	0	1	0	0	not limited	1	0	0	0	1
+male	0.27	1.5	1	0	0	1	4	1	limited	2	0	0	0	0
+male	0.27	1.5	1	0	0	1	0	2	otherwise	0	0	0	0	1
+male	0.27	1.5	1	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.27	1.5	1	0	0	1	14	2	otherwise	0	1	0	0	0
+male	0.27	1.5	1	0	0	1	0	1	not limited	0	0	0	0	1
+male	0.27	1.5	1	0	0	1	0	1	limited	0	0	0	0	0
+male	0.27	1.5	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.27	1.5	1	0	0	1	0	0	not limited	0	1	0	0	0
+male	0.27	1.5	1	0	0	1	0	3	otherwise	0	0	0	0	0
+male	0.27	1.5	1	0	0	2	0	1	limited	1	0	2	7	1
+male	0.27	1.5	1	0	0	2	0	0	not limited	0	0	0	0	3
+male	0.27	1.5	1	0	0	3	0	2	limited	0	0	0	0	2
+male	0.27	1.5	1	0	0	4	0	0	not limited	0	0	0	0	0
+male	0.32	0	0	0	0	3	2	7	not limited	0	1	0	0	0
+male	0.32	0.01	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	0.15	0	0	0	0	0	8	limited	0	0	0	0	1
+male	0.32	0.15	0	1	0	2	5	3	otherwise	0	0	0	0	1
+male	0.32	0.25	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	0.25	0	0	0	1	0	0	otherwise	1	0	0	0	0
+male	0.32	0.25	0	0	0	2	0	0	otherwise	0	0	0	0	1
+male	0.32	0.25	0	0	1	2	0	2	limited	1	1	0	0	2
+male	0.32	0.25	0	0	1	2	0	4	not limited	1	0	1	11	1
+male	0.32	0.25	0	0	1	2	0	1	not limited	0	0	0	0	0
+male	0.32	0.25	0	0	1	3	0	4	limited	1	0	0	0	2
+male	0.32	0.25	0	1	0	1	0	5	otherwise	0	0	0	0	1
+male	0.32	0.25	1	0	0	2	14	1	limited	1	0	0	0	4
+male	0.32	0.25	1	0	0	2	1	0	otherwise	0	7	0	0	1
+male	0.32	0.35	0	0	0	0	0	3	not limited	0	0	0	0	0
+male	0.32	0.35	0	0	0	5	0	2	limited	0	0	0	0	1
+male	0.32	0.35	1	0	0	0	0	3	not limited	1	0	0	0	0
+male	0.32	0.45	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.32	0.45	0	0	0	2	0	0	not limited	2	0	0	0	2
+male	0.32	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	0.45	1	0	0	4	0	8	otherwise	1	0	0	0	0
+male	0.32	0.45	1	0	0	4	0	0	not limited	0	3	1	11	1
+male	0.32	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	0.55	0	0	0	2	0	7	not limited	0	0	0	0	0
+male	0.32	0.55	0	0	0	2	0	5	limited	0	0	0	0	2
+male	0.32	0.55	0	0	0	4	0	4	not limited	0	0	0	0	1
+male	0.32	0.55	1	0	0	0	0	1	not limited	0	0	0	0	2
+male	0.32	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.32	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	0.55	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.32	0.55	1	0	0	1	5	0	otherwise	1	0	0	0	0
+male	0.32	0.55	1	0	0	2	1	0	otherwise	0	0	0	0	2
+male	0.32	0.55	1	0	0	3	0	0	otherwise	0	0	0	0	1
+male	0.32	0.65	0	0	0	0	0	1	otherwise	0	0	0	0	1
+male	0.32	0.65	0	0	0	1	0	0	not limited	0	0	0	0	2
+male	0.32	0.65	0	0	0	1	0	0	otherwise	0	0	0	0	2
+male	0.32	0.65	0	0	0	1	0	0	limited	0	0	0	0	2
+male	0.32	0.65	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.32	0.65	0	0	0	1	0	2	otherwise	0	0	0	0	0
+male	0.32	0.65	0	0	0	2	0	3	not limited	0	0	0	0	0
+male	0.32	0.65	0	0	0	4	0	12	not limited	1	0	1	6	2
+male	0.32	0.65	1	0	0	0	0	0	limited	0	0	0	0	1
+male	0.32	0.65	1	0	0	0	0	1	otherwise	0	0	0	0	1
+male	0.32	0.65	1	0	0	1	0	1	not limited	0	0	0	0	0
+male	0.32	0.65	1	0	0	2	0	0	otherwise	1	0	0	0	0
+male	0.32	0.65	1	0	0	5	0	5	not limited	0	0	0	0	1
+male	0.32	0.75	0	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.32	0.75	0	0	0	0	0	0	otherwise	0	0	1	1	0
+male	0.32	0.75	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.32	0.75	0	0	0	1	0	1	not limited	1	1	0	0	1
+male	0.32	0.75	0	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.32	0.75	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.32	0.75	0	0	0	1	0	12	otherwise	0	0	1	1	2
+male	0.32	0.75	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.32	0.75	0	0	0	2	0	7	otherwise	1	0	0	0	2
+male	0.32	0.75	0	0	0	2	1	1	otherwise	1	0	0	0	2
+male	0.32	0.75	0	0	0	2	0	0	otherwise	0	0	1	1	0
+male	0.32	0.75	0	0	0	2	0	1	not limited	0	0	0	0	0
+male	0.32	0.75	0	0	0	2	0	0	otherwise	0	0	0	0	0
+male	0.32	0.75	0	0	0	4	0	3	limited	1	0	0	0	1
+male	0.32	0.75	0	0	1	0	0	0	otherwise	0	0	0	0	0
+male	0.32	0.75	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.32	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	0.75	1	0	0	0	0	0	limited	0	1	0	0	1
+male	0.32	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	0.75	1	0	0	0	0	0	otherwise	0	1	0	0	0
+male	0.32	0.75	1	0	0	2	0	4	otherwise	0	0	0	0	0
+male	0.32	0.75	1	0	0	3	0	0	otherwise	0	0	0	0	1
+male	0.32	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	0.9	0	0	0	0	0	4	otherwise	0	0	0	0	0
+male	0.32	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.32	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	0.9	0	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.32	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	0.9	0	0	0	0	0	1	not limited	0	0	2	5	0
+male	0.32	0.9	0	0	0	0	0	0	not limited	0	0	0	0	2
+male	0.32	0.9	0	0	0	0	0	2	otherwise	0	0	0	0	0
+male	0.32	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	0.9	0	0	0	1	0	5	not limited	0	1	0	0	0
+male	0.32	0.9	0	0	0	1	0	0	limited	0	0	0	0	0
+male	0.32	0.9	0	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.32	0.9	0	0	0	1	0	4	limited	0	0	0	0	0
+male	0.32	0.9	0	0	0	1	0	6	otherwise	0	0	0	0	0
+male	0.32	0.9	0	0	0	2	0	4	otherwise	0	0	0	0	2
+male	0.32	0.9	0	0	0	5	9	3	limited	2	0	3	5	7
+male	0.32	0.9	0	1	0	0	14	2	limited	0	0	1	22	0
+male	0.32	0.9	1	0	0	0	0	5	not limited	0	0	0	0	1
+male	0.32	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	0.9	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.32	0.9	1	0	0	0	0	3	otherwise	0	0	0	0	1
+male	0.32	0.9	1	0	0	1	0	0	otherwise	0	0	0	0	2
+male	0.32	0.9	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.32	0.9	1	0	0	1	0	0	not limited	0	0	0	0	2
+male	0.32	0.9	1	0	0	1	0	10	not limited	0	0	0	0	2
+male	0.32	0.9	1	0	0	1	0	1	not limited	0	0	0	0	1
+male	0.32	0.9	1	0	0	2	3	2	otherwise	1	0	0	0	4
+male	0.32	0.9	1	0	0	2	0	1	otherwise	2	0	0	0	1
+male	0.32	0.9	1	0	0	2	12	3	not limited	3	0	0	0	3
+male	0.32	0.9	1	0	0	2	0	0	not limited	0	0	0	0	1
+male	0.32	0.9	1	0	0	2	0	0	not limited	0	0	0	0	1
+male	0.32	0.9	1	0	0	2	1	4	otherwise	0	0	0	0	0
+male	0.32	1.1	0	0	0	0	0	3	otherwise	0	0	0	0	0
+male	0.32	1.1	0	0	0	0	0	1	otherwise	0	1	0	0	2
+male	0.32	1.1	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.32	1.1	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	1.1	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.32	1.1	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	1.1	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	1.1	0	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.32	1.1	0	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.32	1.1	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.32	1.1	0	0	0	2	0	9	otherwise	0	0	0	0	1
+male	0.32	1.1	0	0	0	2	0	0	otherwise	0	0	0	0	1
+male	0.32	1.1	0	0	0	2	0	0	not limited	0	0	0	0	0
+male	0.32	1.1	0	0	0	3	0	3	not limited	1	0	0	0	2
+male	0.32	1.1	0	0	0	3	0	0	not limited	0	0	1	11	1
+male	0.32	1.1	0	1	0	2	0	9	not limited	0	0	3	11	0
+male	0.32	1.1	1	0	0	0	0	0	not limited	0	0	0	0	2
+male	0.32	1.1	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.32	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.32	1.1	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.32	1.1	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.32	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	1.1	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.32	1.1	1	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.32	1.1	1	0	0	1	0	0	not limited	0	0	0	0	2
+male	0.32	1.1	1	0	0	1	0	5	otherwise	0	0	1	1	1
+male	0.32	1.1	1	0	0	1	5	2	not limited	0	2	0	0	1
+male	0.32	1.1	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.32	1.1	1	0	0	1	0	1	not limited	0	0	0	0	0
+male	0.32	1.1	1	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.32	1.1	1	0	0	2	0	1	otherwise	0	0	0	0	0
+male	0.32	1.1	1	0	0	2	1	0	not limited	0	0	0	0	2
+male	0.32	1.1	1	0	0	2	3	0	otherwise	0	1	0	0	0
+male	0.32	1.1	1	0	0	4	0	8	limited	0	1	0	0	1
+male	0.32	1.3	0	0	0	0	0	12	otherwise	1	0	0	0	0
+male	0.32	1.3	0	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.32	1.3	0	0	0	1	7	3	not limited	1	0	0	0	6
+male	0.32	1.3	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.32	1.3	0	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.32	1.3	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.32	1.3	0	0	0	3	0	0	otherwise	0	0	0	0	2
+male	0.32	1.3	1	0	0	0	0	0	otherwise	0	1	0	0	0
+male	0.32	1.3	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	1.3	1	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.32	1.3	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	1.3	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	1.3	1	0	0	1	0	3	not limited	0	0	0	0	2
+male	0.32	1.3	1	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.32	1.3	1	0	0	1	0	0	otherwise	0	0	0	0	2
+male	0.32	1.3	1	0	0	1	0	2	not limited	0	0	0	0	0
+male	0.32	1.3	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.32	1.3	1	0	0	1	0	1	not limited	0	0	0	0	1
+male	0.32	1.3	1	0	0	1	0	1	otherwise	0	0	0	0	1
+male	0.32	1.3	1	0	0	2	4	12	not limited	0	4	0	0	4
+male	0.32	1.3	1	0	0	2	0	0	not limited	0	0	0	0	0
+male	0.32	1.3	1	0	0	4	0	1	not limited	0	0	0	0	2
+male	0.32	1.3	1	0	0	5	14	9	limited	2	2	1	11	2
+male	0.32	1.5	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	1.5	0	0	0	0	0	0	limited	0	0	1	1	0
+male	0.32	1.5	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.32	1.5	0	0	0	0	0	4	otherwise	0	0	0	0	0
+male	0.32	1.5	0	0	0	0	0	2	not limited	0	0	0	0	1
+male	0.32	1.5	0	0	0	1	4	6	otherwise	1	0	0	0	4
+male	0.32	1.5	0	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.32	1.5	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.32	1.5	0	0	0	4	0	3	not limited	0	0	0	0	1
+male	0.32	1.5	1	0	0	0	0	0	not limited	1	0	0	0	1
+male	0.32	1.5	1	0	0	0	0	0	otherwise	1	0	0	0	0
+male	0.32	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	1.5	1	0	0	0	0	1	otherwise	0	0	0	0	1
+male	0.32	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.32	1.5	1	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.32	1.5	1	0	0	1	0	0	not limited	1	0	0	0	1
+male	0.32	1.5	1	0	0	1	0	2	otherwise	0	0	2	2	0
+male	0.32	1.5	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.32	1.5	1	0	0	1	1	3	not limited	0	1	0	0	2
+male	0.32	1.5	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.32	1.5	1	0	0	1	0	2	otherwise	0	0	0	0	0
+male	0.32	1.5	1	0	0	1	1	0	not limited	0	0	0	0	0
+male	0.32	1.5	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.32	1.5	1	0	0	1	0	1	not limited	0	0	0	0	0
+male	0.32	1.5	1	0	0	2	0	1	otherwise	1	1	0	0	3
+male	0.32	1.5	1	0	0	2	0	4	not limited	1	0	0	0	1
+male	0.32	1.5	1	0	0	2	0	0	otherwise	1	0	0	0	1
+male	0.32	1.5	1	0	0	2	2	2	not limited	0	0	0	0	2
+male	0.32	1.5	1	0	0	2	0	0	otherwise	0	0	0	0	0
+male	0.32	1.5	1	0	0	2	0	0	not limited	0	0	0	0	0
+male	0.32	1.5	1	0	0	3	0	5	limited	0	0	0	0	0
+male	0.32	1.5	1	0	0	4	0	2	limited	0	0	0	0	5
+male	0.37	0.06	0	0	0	2	12	0	otherwise	0	0	0	0	3
+male	0.37	0.15	0	0	0	5	14	2	limited	0	0	0	0	4
+male	0.37	0.15	0	0	1	0	0	0	limited	0	0	0	0	0
+male	0.37	0.15	1	0	0	3	0	0	not limited	0	0	0	0	2
+male	0.37	0.25	0	0	0	1	0	1	otherwise	0	1	0	0	3
+male	0.37	0.25	1	0	0	1	0	1	otherwise	0	0	0	0	3
+male	0.37	0.35	0	0	0	0	0	0	otherwise	0	0	2	1	0
+male	0.37	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.37	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.37	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.37	0.45	0	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.37	0.45	1	0	0	1	0	4	limited	0	0	0	0	0
+male	0.37	0.45	1	0	0	1	0	1	not limited	0	0	0	0	6
+male	0.37	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.37	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.37	0.55	1	0	0	2	0	8	otherwise	0	0	0	0	1
+male	0.37	0.55	1	0	0	3	7	12	limited	3	0	0	0	1
+male	0.37	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.37	0.65	0	0	0	1	0	0	otherwise	1	0	0	0	1
+male	0.37	0.65	0	0	0	4	0	5	otherwise	0	0	0	0	0
+male	0.37	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.37	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.37	0.65	1	0	0	1	0	0	otherwise	0	0	2	1	0
+male	0.37	0.65	1	0	0	2	5	0	not limited	2	0	0	0	2
+male	0.37	0.65	1	0	0	2	0	1	otherwise	0	0	0	0	0
+male	0.37	0.75	0	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.37	0.75	0	0	0	1	0	0	otherwise	1	0	0	0	1
+male	0.37	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.37	0.75	1	0	0	1	0	0	limited	1	0	0	0	0
+male	0.37	0.75	1	0	0	1	3	2	not limited	0	0	0	0	3
+male	0.37	0.75	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.37	0.75	1	0	0	4	0	5	otherwise	0	0	0	0	1
+male	0.37	0.9	0	0	0	1	3	1	not limited	0	0	0	0	1
+male	0.37	0.9	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.37	0.9	0	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.37	0.9	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.37	0.9	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.37	0.9	0	0	0	1	0	0	otherwise	0	1	0	0	0
+male	0.37	0.9	0	0	0	2	0	3	otherwise	0	0	0	0	0
+male	0.37	0.9	0	0	0	5	0	4	otherwise	0	0	1	2	0
+male	0.37	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.37	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.37	0.9	1	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.37	0.9	1	0	0	3	2	4	limited	0	2	0	0	0
+male	0.37	0.9	1	0	0	4	2	2	otherwise	1	0	0	0	2
+male	0.37	1.1	0	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.37	1.1	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.37	1.1	0	0	0	4	0	4	otherwise	0	0	0	0	2
+male	0.37	1.1	1	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.37	1.1	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.37	1.1	1	0	0	1	0	0	otherwise	0	0	1	11	1
+male	0.37	1.1	1	0	0	1	0	4	otherwise	0	0	0	0	0
+male	0.37	1.1	1	0	0	2	0	7	limited	1	0	0	0	3
+male	0.37	1.1	1	0	0	3	1	1	not limited	0	0	0	0	1
+male	0.37	1.1	1	0	0	3	0	2	otherwise	0	0	1	2	0
+male	0.37	1.1	1	0	0	3	4	3	otherwise	0	0	0	0	2
+male	0.37	1.3	0	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.37	1.3	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.37	1.3	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.37	1.3	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.37	1.3	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.37	1.3	1	0	0	0	0	0	otherwise	0	0	0	0	3
+male	0.37	1.3	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.37	1.3	1	0	0	0	0	1	not limited	0	0	0	0	0
+male	0.37	1.3	1	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.37	1.3	1	0	0	2	0	3	not limited	1	0	0	0	0
+male	0.37	1.3	1	0	0	2	0	1	not limited	0	0	0	0	1
+male	0.37	1.3	1	0	0	2	0	0	not limited	0	0	0	0	1
+male	0.37	1.3	1	0	0	3	1	1	limited	0	0	0	0	0
+male	0.37	1.5	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.37	1.5	0	0	0	1	3	2	not limited	1	0	0	0	2
+male	0.37	1.5	0	0	0	1	0	1	not limited	0	0	0	0	0
+male	0.37	1.5	0	0	0	2	0	1	otherwise	0	0	0	0	1
+male	0.37	1.5	1	0	0	0	0	1	otherwise	1	0	0	0	0
+male	0.37	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.37	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.37	1.5	1	0	0	0	0	0	not limited	0	0	0	0	3
+male	0.37	1.5	1	0	0	1	0	0	otherwise	0	0	1	11	1
+male	0.37	1.5	1	0	0	1	0	8	otherwise	0	0	0	0	0
+male	0.37	1.5	1	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.37	1.5	1	0	0	1	0	0	not limited	0	0	0	0	2
+male	0.37	1.5	1	0	0	1	0	5	not limited	0	1	0	0	2
+male	0.37	1.5	1	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.37	1.5	1	0	0	2	0	0	not limited	0	0	0	0	1
+male	0.37	1.5	1	0	0	2	0	1	otherwise	0	0	0	0	2
+male	0.42	0.06	0	1	0	2	0	0	otherwise	0	0	0	0	0
+male	0.42	0.15	0	0	0	0	0	3	otherwise	0	0	0	0	0
+male	0.42	0.15	0	0	0	1	14	6	otherwise	1	0	0	0	2
+male	0.42	0.15	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.42	0.25	0	0	0	1	2	0	otherwise	0	0	0	0	0
+male	0.42	0.25	0	0	1	1	0	6	limited	0	0	0	0	5
+male	0.42	0.25	0	0	1	4	5	4	limited	0	0	0	0	6
+male	0.42	0.25	0	0	1	5	14	7	limited	2	0	0	0	5
+male	0.42	0.25	0	1	0	1	0	1	not limited	0	0	0	0	1
+male	0.42	0.35	0	1	0	0	0	0	otherwise	0	0	0	0	1
+male	0.42	0.35	1	0	0	2	0	0	otherwise	0	0	0	0	1
+male	0.42	0.45	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.42	0.45	0	0	0	2	0	0	limited	0	0	0	0	1
+male	0.42	0.45	0	1	0	0	0	1	limited	0	0	0	0	0
+male	0.42	0.45	1	0	0	2	0	1	limited	0	0	0	0	1
+male	0.42	0.65	0	0	0	1	0	0	not limited	0	0	0	0	3
+male	0.42	0.65	0	0	0	3	6	0	limited	1	0	0	0	0
+male	0.42	0.65	0	0	1	2	14	12	limited	1	0	0	0	2
+male	0.42	0.65	0	1	0	1	0	2	otherwise	0	0	0	0	1
+male	0.42	0.65	1	0	0	2	2	0	otherwise	1	0	0	0	2
+male	0.42	0.65	1	0	0	4	14	5	limited	6	0	2	11	3
+male	0.42	0.75	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.42	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.42	0.75	0	0	0	1	1	0	otherwise	0	0	0	0	0
+male	0.42	0.75	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.42	0.75	0	1	0	2	0	0	not limited	0	0	0	0	0
+male	0.42	0.75	1	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.42	0.75	1	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.42	0.75	1	0	0	2	0	0	not limited	0	0	0	0	0
+male	0.42	0.9	0	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.42	0.9	0	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.42	0.9	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.42	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.42	0.9	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.42	0.9	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.42	0.9	0	0	0	1	0	2	otherwise	0	0	0	0	1
+male	0.42	0.9	0	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.42	0.9	0	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.42	0.9	0	1	0	0	0	0	otherwise	0	0	0	0	0
+male	0.42	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.42	0.9	1	0	0	0	0	0	otherwise	0	0	1	1	1
+male	0.42	0.9	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.42	0.9	1	0	0	2	2	3	limited	0	2	0	0	3
+male	0.42	0.9	1	0	0	2	0	9	not limited	0	0	0	0	2
+male	0.42	0.9	1	0	0	2	3	0	not limited	0	0	0	0	1
+male	0.42	1.1	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.42	1.1	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.42	1.1	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.42	1.1	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.42	1.1	0	0	0	3	0	0	limited	0	0	0	0	2
+male	0.42	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.42	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	3
+male	0.42	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.42	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.42	1.1	1	0	0	1	0	4	not limited	1	0	0	0	0
+male	0.42	1.1	1	0	0	1	0	2	limited	0	0	0	0	3
+male	0.42	1.1	1	0	0	2	11	0	limited	6	0	2	11	0
+male	0.42	1.1	1	0	0	2	0	0	otherwise	0	0	0	0	3
+male	0.42	1.1	1	0	0	3	0	0	not limited	1	0	0	0	1
+male	0.42	1.3	0	0	0	2	0	0	not limited	0	0	0	0	4
+male	0.42	1.3	0	0	0	3	0	1	limited	1	0	0	0	1
+male	0.42	1.3	0	0	0	3	0	1	not limited	0	0	0	0	0
+male	0.42	1.3	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.42	1.3	1	0	0	1	0	1	otherwise	1	0	0	0	1
+male	0.42	1.3	1	0	0	1	0	0	not limited	0	0	1	3	0
+male	0.42	1.5	0	0	0	0	0	0	limited	0	0	0	0	0
+male	0.42	1.5	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.42	1.5	0	0	0	0	0	0	not limited	0	0	0	0	2
+male	0.42	1.5	0	0	0	1	0	1	not limited	1	0	0	0	0
+male	0.42	1.5	0	0	0	4	0	4	not limited	1	0	0	0	4
+male	0.42	1.5	0	0	0	4	0	5	limited	0	0	0	0	2
+male	0.42	1.5	1	0	0	0	0	0	otherwise	1	0	1	1	0
+male	0.42	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.42	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.42	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.42	1.5	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.42	1.5	1	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.42	1.5	1	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.42	1.5	1	0	0	1	0	1	otherwise	0	0	0	0	1
+male	0.42	1.5	1	0	0	2	0	0	limited	0	1	0	0	2
+male	0.42	1.5	1	0	0	4	0	3	not limited	0	2	0	0	2
+male	0.47	0	0	0	0	3	0	1	not limited	0	0	0	0	0
+male	0.47	0	0	1	0	0	0	0	not limited	0	0	0	0	0
+male	0.47	0	1	0	0	1	0	3	otherwise	0	0	0	0	0
+male	0.47	0.15	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.47	0.15	0	0	1	2	0	1	not limited	0	0	0	0	2
+male	0.47	0.15	1	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.47	0.25	0	0	0	4	1	6	not limited	0	0	0	0	3
+male	0.47	0.25	0	0	1	1	0	4	not limited	1	0	0	0	0
+male	0.47	0.25	0	0	1	2	0	0	limited	0	2	1	45	1
+male	0.47	0.25	0	0	1	3	3	1	limited	0	1	3	4	1
+male	0.47	0.25	0	1	0	0	0	4	otherwise	0	0	0	0	0
+male	0.47	0.35	0	0	1	0	0	1	otherwise	0	0	0	0	1
+male	0.47	0.35	0	0	1	1	0	0	limited	0	0	0	0	1
+male	0.47	0.35	0	1	0	1	0	0	not limited	0	0	0	0	1
+male	0.47	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.47	0.35	1	0	0	5	0	5	limited	0	1	1	22	8
+male	0.47	0.45	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.47	0.45	1	0	0	5	0	3	not limited	0	0	0	0	1
+male	0.47	0.55	0	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.47	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.47	0.55	0	0	1	1	0	0	limited	1	1	0	0	1
+male	0.47	0.65	0	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.47	0.65	1	0	0	0	0	0	otherwise	1	0	0	0	0
+male	0.47	0.65	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.47	0.65	1	0	0	0	0	0	otherwise	0	0	1	22	0
+male	0.47	0.65	1	0	0	0	0	0	otherwise	0	1	0	0	0
+male	0.47	0.65	1	0	0	1	0	0	otherwise	1	0	0	0	1
+male	0.47	0.65	1	0	0	3	0	0	otherwise	0	0	0	0	0
+male	0.47	0.65	1	0	0	5	0	0	not limited	0	1	0	0	1
+male	0.47	0.75	0	0	0	0	0	1	otherwise	0	0	0	0	1
+male	0.47	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.47	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.47	0.75	0	0	0	4	0	6	not limited	0	0	0	0	1
+male	0.47	0.75	1	0	0	0	0	0	not limited	1	0	0	0	0
+male	0.47	0.75	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.47	0.75	1	0	0	0	0	2	otherwise	0	0	0	0	1
+male	0.47	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.47	0.75	1	0	0	1	0	3	otherwise	1	0	0	0	0
+male	0.47	0.75	1	0	0	1	14	6	not limited	2	0	2	11	1
+male	0.47	0.75	1	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.47	0.75	1	0	0	5	14	7	limited	2	0	0	0	5
+male	0.47	0.9	0	0	0	0	0	0	not limited	0	0	0	0	3
+male	0.47	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.47	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.47	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.47	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.47	0.9	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.47	0.9	0	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.47	0.9	0	0	0	1	0	6	otherwise	0	0	0	0	1
+male	0.47	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.47	0.9	1	0	0	1	0	0	not limited	1	0	0	0	1
+male	0.47	0.9	1	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.47	0.9	1	0	0	1	0	1	not limited	0	0	0	0	4
+male	0.47	0.9	1	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.47	0.9	1	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.47	0.9	1	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.47	0.9	1	0	0	2	0	0	not limited	0	0	0	0	0
+male	0.47	0.9	1	0	0	3	3	0	limited	1	0	0	0	1
+male	0.47	0.9	1	0	0	3	0	0	limited	0	0	1	7	0
+male	0.47	1.1	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.47	1.1	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.47	1.1	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.47	1.1	0	0	0	5	0	6	limited	0	0	0	0	3
+male	0.47	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.47	1.1	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.47	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.47	1.1	1	0	0	1	1	0	not limited	1	0	2	11	1
+male	0.47	1.3	0	0	0	0	0	0	not limited	0	0	0	0	2
+male	0.47	1.3	0	0	0	2	0	4	not limited	0	1	0	0	1
+male	0.47	1.3	1	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.47	1.3	1	0	0	1	0	7	not limited	0	0	0	0	0
+male	0.47	1.3	1	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.47	1.3	1	0	0	2	0	1	limited	0	0	0	0	0
+male	0.47	1.3	1	0	0	2	0	0	not limited	0	1	0	0	2
+male	0.47	1.5	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.47	1.5	0	0	0	1	1	1	not limited	0	0	0	0	2
+male	0.47	1.5	0	0	0	2	0	0	not limited	1	0	0	0	2
+male	0.47	1.5	1	0	0	0	0	0	otherwise	1	0	0	0	0
+male	0.47	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.47	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.47	1.5	1	0	0	0	0	1	otherwise	0	1	0	0	0
+male	0.47	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.47	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.47	1.5	1	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.47	1.5	1	0	0	1	0	2	limited	0	0	0	0	1
+male	0.47	1.5	1	0	0	1	0	4	otherwise	0	0	0	0	0
+male	0.47	1.5	1	0	0	1	0	1	limited	0	0	0	0	1
+male	0.47	1.5	1	0	0	2	2	0	otherwise	2	0	0	0	0
+male	0.47	1.5	1	0	0	2	1	1	otherwise	1	3	1	3	1
+male	0.47	1.5	1	0	0	2	0	0	not limited	0	0	0	0	3
+male	0.52	0	0	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.52	0	1	0	0	2	5	5	not limited	0	0	0	0	0
+male	0.52	0.06	0	0	0	0	0	0	not limited	0	0	0	0	2
+male	0.52	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+male	0.52	0.25	0	0	1	0	0	1	limited	0	2	0	0	0
+male	0.52	0.25	0	0	1	1	0	0	limited	0	0	0	0	2
+male	0.52	0.25	0	0	1	2	0	0	limited	0	0	0	0	0
+male	0.52	0.25	0	0	1	3	0	3	limited	1	0	0	0	1
+male	0.52	0.25	0	0	1	4	2	7	otherwise	1	0	0	0	3
+male	0.52	0.25	0	0	1	4	0	0	limited	1	3	1	4	6
+male	0.52	0.25	0	0	1	5	14	7	limited	8	5	3	5	6
+male	0.52	0.25	0	1	0	0	0	0	otherwise	0	0	0	0	0
+male	0.52	0.25	1	0	0	1	0	0	otherwise	2	0	0	0	1
+male	0.52	0.35	0	0	0	1	0	1	not limited	0	0	0	0	0
+male	0.52	0.35	0	0	1	1	0	2	not limited	0	0	1	1	1
+male	0.52	0.35	0	0	1	5	5	11	limited	2	0	2	1	8
+male	0.52	0.35	0	1	0	1	14	6	limited	1	0	1	1	4
+male	0.52	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.52	0.45	1	0	0	3	0	0	not limited	0	0	0	0	0
+male	0.52	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.52	0.55	0	0	0	4	0	3	not limited	0	0	0	0	3
+male	0.52	0.55	0	0	1	5	0	4	not limited	4	0	0	0	1
+male	0.52	0.55	1	0	0	2	0	7	limited	0	1	0	0	0
+male	0.52	0.65	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.52	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.52	0.65	0	0	0	2	0	2	otherwise	0	0	0	0	2
+male	0.52	0.65	0	0	0	3	2	0	not limited	0	0	0	0	4
+male	0.52	0.65	1	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.52	0.65	1	0	0	1	0	0	not limited	0	0	0	0	2
+male	0.52	0.65	1	0	0	2	5	6	otherwise	1	1	0	0	1
+male	0.52	0.65	1	0	0	2	0	0	limited	0	0	0	0	0
+male	0.52	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.52	0.75	0	0	0	0	0	0	limited	0	0	0	0	1
+male	0.52	0.75	0	0	0	0	1	0	otherwise	0	0	0	0	0
+male	0.52	0.75	0	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.52	0.75	0	0	0	2	0	0	not limited	0	0	0	0	1
+male	0.52	0.75	0	0	0	4	0	4	not limited	0	0	0	0	1
+male	0.52	0.75	0	0	0	5	3	4	not limited	0	0	0	0	1
+male	0.52	0.75	0	1	0	2	14	6	limited	0	0	0	0	2
+male	0.52	0.75	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.52	0.75	1	0	0	1	1	1	limited	3	0	2	4	1
+male	0.52	0.75	1	0	0	1	0	0	not limited	0	0	0	0	4
+male	0.52	0.75	1	0	0	3	14	6	limited	1	4	1	11	3
+male	0.52	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.52	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.52	0.9	0	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.52	0.9	0	0	0	1	0	0	not limited	0	0	0	0	3
+male	0.52	0.9	0	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.52	0.9	0	0	0	5	0	11	limited	1	0	0	0	3
+male	0.52	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.52	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.52	0.9	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.52	0.9	1	0	0	1	0	0	otherwise	0	0	0	0	2
+male	0.52	0.9	1	0	0	2	0	1	limited	0	0	0	0	1
+male	0.52	1.1	0	0	0	0	0	0	limited	0	0	0	0	0
+male	0.52	1.1	0	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.52	1.1	0	0	0	0	0	0	otherwise	0	0	0	0	2
+male	0.52	1.1	0	0	0	1	3	0	otherwise	5	0	1	2	0
+male	0.52	1.1	0	0	0	1	0	1	otherwise	0	0	0	0	0
+male	0.52	1.1	0	0	0	2	5	0	otherwise	0	0	1	6	1
+male	0.52	1.1	0	0	0	2	0	0	otherwise	0	0	0	0	0
+male	0.52	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.52	1.1	1	0	0	0	0	1	not limited	0	0	0	0	0
+male	0.52	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.52	1.1	1	0	0	0	0	1	not limited	0	0	0	0	0
+male	0.52	1.1	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.52	1.1	1	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.52	1.1	1	0	0	1	0	0	not limited	0	1	0	0	1
+male	0.52	1.1	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.52	1.1	1	0	0	2	0	0	not limited	2	0	1	4	0
+male	0.52	1.1	1	0	0	3	0	0	not limited	0	0	0	0	3
+male	0.52	1.3	0	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.52	1.3	0	0	0	1	0	0	otherwise	0	0	0	0	1
+male	0.52	1.3	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.52	1.3	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.52	1.3	1	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.52	1.3	1	0	0	2	0	2	not limited	1	0	3	2	3
+male	0.52	1.3	1	0	0	4	0	6	limited	0	0	0	0	3
+male	0.52	1.5	0	0	0	0	0	0	limited	0	0	0	0	1
+male	0.52	1.5	1	0	0	0	0	0	otherwise	1	0	0	0	0
+male	0.52	1.5	1	0	0	0	0	1	limited	0	0	0	0	1
+male	0.52	1.5	1	0	0	0	0	0	otherwise	0	0	1	2	1
+male	0.52	1.5	1	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.52	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.52	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.52	1.5	1	0	0	1	0	1	limited	0	0	0	0	3
+male	0.52	1.5	1	0	0	2	0	0	otherwise	0	0	0	0	3
+male	0.52	1.5	1	0	0	3	0	0	not limited	1	0	0	0	1
+male	0.52	1.5	1	0	0	3	0	0	not limited	0	0	0	0	3
+male	0.57	0	0	1	0	2	0	0	limited	0	0	1	7	0
+male	0.57	0.01	0	0	0	1	9	4	otherwise	8	0	1	11	0
+male	0.57	0.01	0	0	1	1	0	0	not limited	0	0	0	0	3
+male	0.57	0.01	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.57	0.15	1	0	0	3	0	1	not limited	0	0	1	1	4
+male	0.57	0.15	1	0	0	5	2	0	limited	0	1	1	1	5
+male	0.57	0.25	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.57	0.25	0	0	0	2	0	2	limited	0	0	0	0	2
+male	0.57	0.25	0	0	0	3	0	0	limited	1	0	0	0	5
+male	0.57	0.25	0	0	0	5	1	10	limited	1	0	0	0	0
+male	0.57	0.25	0	0	1	0	0	0	limited	0	0	0	0	0
+male	0.57	0.25	0	0	1	1	0	0	not limited	0	0	0	0	1
+male	0.57	0.25	0	0	1	1	0	0	limited	0	0	0	0	1
+male	0.57	0.25	0	0	1	2	0	1	limited	0	0	0	0	2
+male	0.57	0.25	0	0	1	2	0	6	limited	0	2	1	45	5
+male	0.57	0.25	0	0	1	3	0	2	limited	0	0	0	0	2
+male	0.57	0.25	0	0	1	3	0	0	not limited	0	0	0	0	0
+male	0.57	0.25	0	0	1	4	0	2	not limited	0	0	0	0	2
+male	0.57	0.25	0	0	1	5	0	6	otherwise	1	0	0	0	2
+male	0.57	0.25	0	1	0	2	0	5	limited	0	0	1	1	2
+male	0.57	0.25	1	0	0	3	0	0	otherwise	0	0	0	0	0
+male	0.57	0.35	0	0	0	1	0	0	not limited	0	1	0	0	3
+male	0.57	0.35	0	0	0	2	0	6	not limited	0	0	0	0	0
+male	0.57	0.35	0	0	1	4	0	1	limited	0	0	1	80	4
+male	0.57	0.35	1	0	0	4	2	3	not limited	0	0	0	0	1
+male	0.57	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.57	0.55	0	0	0	1	0	1	not limited	0	0	0	0	0
+male	0.57	0.55	1	0	0	1	4	1	limited	1	0	0	0	2
+male	0.57	0.65	0	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.57	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.57	0.65	0	0	0	1	12	0	otherwise	1	0	1	11	0
+male	0.57	0.65	0	0	0	1	0	4	otherwise	0	0	0	0	2
+male	0.57	0.65	0	0	0	3	0	0	otherwise	0	0	0	0	0
+male	0.57	0.65	0	0	0	3	0	2	otherwise	0	0	0	0	0
+male	0.57	0.65	0	0	0	5	0	0	otherwise	0	0	0	0	1
+male	0.57	0.65	0	0	1	5	0	0	limited	1	0	0	0	5
+male	0.57	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.57	0.65	1	0	0	1	0	3	not limited	1	0	0	0	0
+male	0.57	0.65	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.57	0.65	1	0	0	2	0	0	not limited	0	0	1	11	5
+male	0.57	0.65	1	0	0	2	0	0	not limited	0	0	0	0	0
+male	0.57	0.65	1	0	0	3	14	4	limited	1	0	3	7	3
+male	0.57	0.65	1	0	0	3	0	0	limited	0	0	0	0	0
+male	0.57	0.75	0	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.57	0.75	0	0	0	1	4	0	otherwise	1	0	0	0	0
+male	0.57	0.75	0	0	0	1	2	1	not limited	1	0	1	5	7
+male	0.57	0.75	0	0	0	2	0	1	limited	0	0	0	0	1
+male	0.57	0.75	1	0	0	4	0	0	not limited	1	0	0	0	2
+male	0.57	0.75	1	0	0	4	14	1	not limited	4	0	0	0	3
+male	0.57	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.57	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.57	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	2
+male	0.57	0.9	0	0	0	1	0	3	not limited	0	0	0	0	1
+male	0.57	0.9	0	0	0	1	2	0	otherwise	0	0	0	0	0
+male	0.57	0.9	0	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.57	0.9	0	0	0	2	0	0	not limited	0	0	0	0	0
+male	0.57	0.9	0	0	0	3	0	0	not limited	1	0	1	3	5
+male	0.57	0.9	0	0	0	5	10	4	not limited	2	0	1	6	3
+male	0.57	0.9	1	0	0	0	0	1	otherwise	0	0	0	0	1
+male	0.57	0.9	1	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.57	0.9	1	0	0	0	0	1	limited	0	1	2	5	3
+male	0.57	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.57	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.57	0.9	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.57	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.57	0.9	1	0	0	1	0	0	otherwise	1	0	0	0	0
+male	0.57	0.9	1	0	0	1	0	3	otherwise	0	0	0	0	0
+male	0.57	0.9	1	0	0	1	2	1	limited	0	0	0	0	1
+male	0.57	0.9	1	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.57	0.9	1	0	0	1	0	2	otherwise	0	0	0	0	1
+male	0.57	0.9	1	0	0	2	0	0	limited	2	0	0	0	0
+male	0.57	0.9	1	0	0	3	0	0	not limited	0	0	0	0	0
+male	0.57	1.1	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.57	1.1	0	0	1	3	8	1	not limited	2	0	1	11	1
+male	0.57	1.1	1	0	0	0	0	0	not limited	1	0	0	0	1
+male	0.57	1.1	1	0	0	1	0	1	not limited	0	0	0	0	1
+male	0.57	1.1	1	0	0	2	0	1	not limited	0	0	0	0	1
+male	0.57	1.3	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.57	1.3	1	0	0	1	0	0	limited	0	0	0	0	2
+male	0.57	1.5	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.57	1.5	0	0	1	0	0	0	not limited	1	0	0	0	5
+male	0.57	1.5	1	0	0	0	0	0	not limited	0	0	0	0	3
+male	0.57	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.57	1.5	1	0	0	4	0	0	limited	1	4	0	0	6
+male	0.62	0	1	0	0	4	0	0	limited	0	0	0	0	2
+male	0.62	0.06	1	0	0	5	0	2	not limited	0	1	0	0	1
+male	0.62	0.15	0	0	1	0	0	0	limited	0	0	0	0	1
+male	0.62	0.15	0	0	1	1	0	0	limited	0	0	0	0	5
+male	0.62	0.15	0	0	1	3	0	0	limited	0	0	0	0	2
+male	0.62	0.15	1	0	0	0	0	0	otherwise	0	0	2	3	1
+male	0.62	0.15	1	0	0	0	0	0	not limited	0	0	0	0	3
+male	0.62	0.25	0	0	1	0	0	0	not limited	0	0	0	0	2
+male	0.62	0.25	0	0	1	0	0	0	otherwise	0	0	4	2	0
+male	0.62	0.25	0	0	1	0	0	0	not limited	0	0	0	0	1
+male	0.62	0.25	0	0	1	1	0	0	limited	1	7	0	0	0
+male	0.62	0.25	0	0	1	1	0	0	limited	0	0	0	0	0
+male	0.62	0.25	0	0	1	1	0	0	not limited	0	0	0	0	0
+male	0.62	0.25	0	0	1	1	0	0	not limited	0	0	0	0	2
+male	0.62	0.25	0	0	1	1	0	0	limited	0	0	0	0	0
+male	0.62	0.25	0	0	1	2	0	0	limited	1	1	0	0	2
+male	0.62	0.25	0	0	1	2	0	1	limited	0	0	0	0	2
+male	0.62	0.25	0	0	1	2	0	1	limited	0	0	0	0	2
+male	0.62	0.25	0	0	1	2	0	3	limited	0	1	1	22	8
+male	0.62	0.25	0	0	1	2	0	0	limited	0	0	0	0	3
+male	0.62	0.25	0	0	1	3	0	0	limited	1	0	0	0	1
+male	0.62	0.25	0	0	1	3	0	0	not limited	0	0	0	0	3
+male	0.62	0.25	0	0	1	3	0	2	limited	0	0	0	0	3
+male	0.62	0.25	0	0	1	3	0	0	limited	0	0	0	0	3
+male	0.62	0.25	0	0	1	3	0	3	limited	0	0	0	0	0
+male	0.62	0.25	0	0	1	4	13	0	not limited	3	0	0	0	5
+male	0.62	0.25	0	0	1	4	0	1	limited	0	0	0	0	6
+male	0.62	0.25	0	0	1	4	0	0	otherwise	0	0	1	1	0
+male	0.62	0.25	0	0	1	4	0	1	limited	0	0	0	0	1
+male	0.62	0.25	0	0	1	5	14	10	limited	9	0	5	11	8
+male	0.62	0.25	0	0	1	5	0	1	limited	0	0	0	0	0
+male	0.62	0.25	0	1	0	1	0	2	otherwise	0	0	0	0	1
+male	0.62	0.25	1	0	0	0	0	0	limited	0	0	0	0	2
+male	0.62	0.25	1	0	0	2	0	0	limited	2	0	0	0	4
+male	0.62	0.25	1	0	0	5	14	8	limited	0	0	2	7	7
+male	0.62	0.35	0	0	0	2	0	0	otherwise	0	0	0	0	0
+male	0.62	0.35	0	0	0	4	0	2	limited	0	0	0	0	1
+male	0.62	0.35	0	0	1	1	0	0	limited	1	0	0	0	1
+male	0.62	0.35	0	0	1	1	0	0	limited	0	4	1	7	2
+male	0.62	0.35	0	0	1	1	14	0	limited	0	0	0	0	2
+male	0.62	0.35	0	0	1	4	0	5	limited	0	0	0	0	4
+male	0.62	0.35	0	0	1	5	0	11	limited	1	0	2	3	5
+male	0.62	0.35	1	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.62	0.35	1	0	0	0	0	1	otherwise	0	0	0	0	0
+male	0.62	0.35	1	0	0	3	0	0	not limited	2	0	1	45	2
+male	0.62	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.62	0.45	0	0	1	0	0	0	otherwise	0	0	0	0	0
+male	0.62	0.45	0	0	1	2	0	3	not limited	0	0	0	0	2
+male	0.62	0.45	1	0	0	2	0	0	limited	0	0	0	0	3
+male	0.62	0.45	1	0	0	5	0	4	limited	3	0	0	0	3
+male	0.62	0.45	1	0	0	5	1	8	limited	2	0	1	7	8
+male	0.62	0.55	0	0	0	1	0	0	limited	0	0	0	0	1
+male	0.62	0.55	0	0	0	2	0	0	not limited	1	0	1	7	3
+male	0.62	0.55	1	0	0	3	0	3	limited	0	0	0	0	3
+male	0.62	0.55	1	0	0	3	0	0	otherwise	0	0	0	0	1
+male	0.62	0.55	1	0	0	5	2	0	not limited	0	0	0	0	2
+male	0.62	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.62	0.65	0	0	0	1	0	0	otherwise	0	1	0	0	0
+male	0.62	0.65	0	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.62	0.65	0	0	1	1	0	0	not limited	0	0	0	0	2
+male	0.62	0.65	1	0	0	0	0	0	not limited	0	4	0	0	2
+male	0.62	0.65	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.62	0.65	1	0	0	4	0	0	not limited	0	0	2	11	3
+male	0.62	0.65	1	0	0	4	0	5	not limited	0	0	0	0	0
+male	0.62	0.65	1	0	0	5	2	1	not limited	1	0	0	0	3
+male	0.62	0.75	0	0	0	0	0	2	not limited	0	0	0	0	0
+male	0.62	0.75	0	0	1	2	0	2	limited	1	0	0	0	1
+male	0.62	0.75	1	0	0	1	14	2	otherwise	1	5	0	0	0
+male	0.62	0.75	1	0	0	1	0	0	otherwise	0	0	1	1	0
+male	0.62	0.75	1	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.62	0.75	1	0	0	2	0	1	limited	0	0	0	0	2
+male	0.62	0.75	1	0	0	3	14	3	limited	0	0	1	11	0
+male	0.62	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.62	0.9	0	0	0	0	0	0	limited	0	0	0	0	0
+male	0.62	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.62	0.9	0	0	0	3	2	0	not limited	1	0	0	0	1
+male	0.62	0.9	1	0	0	0	14	2	not limited	0	0	1	22	2
+male	0.62	0.9	1	0	0	0	0	1	not limited	0	0	0	0	4
+male	0.62	0.9	1	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.62	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.62	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.62	0.9	1	0	0	1	0	0	otherwise	1	0	0	0	0
+male	0.62	0.9	1	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.62	0.9	1	0	0	1	0	1	not limited	0	0	0	0	0
+male	0.62	0.9	1	0	0	2	0	0	not limited	0	0	0	0	5
+male	0.62	0.9	1	0	0	2	0	0	not limited	0	0	0	0	1
+male	0.62	0.9	1	0	0	4	0	1	otherwise	0	0	0	0	0
+male	0.62	1.1	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.62	1.1	0	0	0	1	0	5	otherwise	0	0	0	0	1
+male	0.62	1.1	1	0	0	0	0	0	limited	0	0	0	0	4
+male	0.62	1.1	1	0	0	0	0	0	limited	0	0	0	0	2
+male	0.62	1.1	1	0	0	1	2	4	not limited	1	0	0	0	3
+male	0.62	1.1	1	0	0	3	0	3	otherwise	0	0	0	0	2
+male	0.62	1.1	1	0	0	3	0	5	not limited	0	0	0	0	3
+male	0.62	1.1	1	0	0	4	0	3	not limited	0	0	0	0	3
+male	0.62	1.3	0	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.62	1.3	1	0	0	1	0	0	not limited	0	0	0	0	3
+male	0.62	1.5	1	0	0	0	0	4	not limited	1	0	1	4	3
+male	0.62	1.5	1	0	0	0	0	1	not limited	2	0	0	0	2
+male	0.62	1.5	1	0	0	0	0	0	not limited	0	0	1	6	1
+male	0.62	1.5	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.62	1.5	1	0	0	0	0	0	limited	0	0	0	0	0
+male	0.62	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.62	1.5	1	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.62	1.5	1	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.62	1.5	1	0	0	4	14	8	otherwise	1	0	0	0	2
+male	0.67	0.01	0	0	1	2	0	1	not limited	0	0	0	0	1
+male	0.67	0.06	0	0	1	2	1	0	not limited	0	0	0	0	1
+male	0.67	0.06	1	0	0	5	0	8	not limited	0	0	0	0	2
+male	0.67	0.15	0	0	1	0	0	0	otherwise	0	0	1	45	3
+male	0.67	0.15	0	0	1	0	0	2	not limited	0	0	1	11	0
+male	0.67	0.15	0	0	1	1	0	0	not limited	0	0	0	0	3
+male	0.67	0.15	0	0	1	1	0	0	otherwise	0	1	0	0	0
+male	0.67	0.15	0	0	1	2	0	0	not limited	1	0	0	0	2
+male	0.67	0.25	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.67	0.25	0	0	1	0	0	0	not limited	0	0	0	0	1
+male	0.67	0.25	0	0	1	0	0	1	not limited	0	0	0	0	3
+male	0.67	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+male	0.67	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+male	0.67	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+male	0.67	0.25	0	0	1	0	0	1	otherwise	0	0	0	0	0
+male	0.67	0.25	0	0	1	0	0	0	not limited	0	0	0	0	0
+male	0.67	0.25	0	0	1	1	0	0	not limited	1	0	0	0	1
+male	0.67	0.25	0	0	1	1	0	0	not limited	0	0	0	0	3
+male	0.67	0.25	0	0	1	1	0	0	not limited	0	0	0	0	1
+male	0.67	0.25	0	0	1	1	0	0	not limited	0	0	0	0	1
+male	0.67	0.25	0	0	1	1	10	6	not limited	0	0	0	0	0
+male	0.67	0.25	0	0	1	2	0	0	not limited	1	0	0	0	7
+male	0.67	0.25	0	0	1	2	0	2	not limited	1	1	0	0	1
+male	0.67	0.25	0	0	1	2	14	5	not limited	8	0	1	22	4
+male	0.67	0.25	0	0	1	2	0	5	not limited	0	0	0	0	1
+male	0.67	0.25	0	0	1	2	0	0	not limited	0	6	0	0	4
+male	0.67	0.25	0	0	1	2	0	0	not limited	0	0	1	2	0
+male	0.67	0.25	0	0	1	2	0	0	not limited	0	1	0	0	0
+male	0.67	0.25	0	0	1	2	0	0	otherwise	0	0	0	0	0
+male	0.67	0.25	0	0	1	2	0	1	otherwise	0	0	0	0	0
+male	0.67	0.25	0	0	1	3	0	7	not limited	1	0	0	0	3
+male	0.67	0.25	0	0	1	3	14	2	not limited	1	0	1	22	0
+male	0.67	0.25	0	0	1	3	0	0	not limited	1	0	0	0	1
+male	0.67	0.25	0	0	1	3	0	0	not limited	0	0	1	6	0
+male	0.67	0.25	0	0	1	3	7	8	limited	0	0	1	22	4
+male	0.67	0.25	0	0	1	3	14	3	limited	0	0	0	0	2
+male	0.67	0.25	0	0	1	4	0	3	limited	0	0	1	11	1
+male	0.67	0.25	0	0	1	5	0	6	not limited	0	7	0	0	1
+male	0.67	0.25	0	1	0	0	0	0	not limited	0	0	0	0	0
+male	0.67	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.67	0.25	1	0	0	1	0	3	not limited	1	0	0	0	2
+male	0.67	0.25	1	0	0	1	14	1	not limited	0	3	1	45	0
+male	0.67	0.25	1	0	0	4	7	3	not limited	0	0	0	0	8
+male	0.67	0.25	1	0	0	5	14	3	not limited	6	0	1	1	5
+male	0.67	0.35	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.67	0.35	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.67	0.35	0	0	0	3	0	2	not limited	1	0	2	45	3
+male	0.67	0.35	0	0	1	0	0	0	otherwise	0	0	0	0	1
+male	0.67	0.35	0	0	1	1	0	0	not limited	0	0	0	0	2
+male	0.67	0.35	0	0	1	1	0	0	not limited	0	1	0	0	1
+male	0.67	0.35	0	0	1	1	0	0	not limited	0	0	0	0	1
+male	0.67	0.35	0	0	1	2	0	0	not limited	0	0	0	0	1
+male	0.67	0.35	0	0	1	2	0	4	not limited	0	0	0	0	2
+male	0.67	0.35	0	0	1	2	0	1	not limited	0	0	0	0	3
+male	0.67	0.35	0	0	1	3	14	4	otherwise	0	0	1	22	2
+male	0.67	0.35	0	0	1	4	14	7	limited	0	0	0	0	6
+male	0.67	0.35	0	0	1	5	0	0	not limited	0	0	1	11	1
+male	0.67	0.35	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.67	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.67	0.35	1	0	0	1	0	0	not limited	0	0	0	0	3
+male	0.67	0.35	1	0	0	2	0	1	not limited	1	0	0	0	3
+male	0.67	0.45	0	0	1	0	0	4	otherwise	0	0	0	0	0
+male	0.67	0.45	0	0	1	2	0	0	not limited	0	0	1	45	4
+male	0.67	0.45	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.67	0.45	1	0	0	1	0	0	not limited	0	0	0	0	2
+male	0.67	0.45	1	0	0	2	0	0	otherwise	0	0	0	0	1
+male	0.67	0.45	1	0	0	3	0	1	not limited	1	0	0	0	1
+male	0.67	0.55	0	0	0	1	0	0	not limited	1	0	1	45	5
+male	0.67	0.55	1	0	0	2	1	0	not limited	0	0	0	0	1
+male	0.67	0.65	0	0	0	0	0	0	not limited	0	1	0	0	0
+male	0.67	0.65	1	0	0	0	0	1	not limited	0	0	1	2	0
+male	0.67	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.67	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.67	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.67	0.75	0	0	1	2	14	11	limited	3	0	0	0	2
+male	0.67	0.75	0	0	1	3	0	0	not limited	1	5	0	0	2
+male	0.67	0.75	0	0	1	3	14	1	not limited	0	0	1	6	0
+male	0.67	0.75	1	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.67	0.75	1	0	0	1	0	0	not limited	0	0	0	0	3
+male	0.67	0.75	1	0	0	2	0	1	not limited	0	0	2	1	4
+male	0.67	0.9	0	0	1	1	14	1	not limited	0	0	0	0	0
+male	0.67	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.67	1.1	0	0	1	2	0	0	not limited	1	0	0	0	5
+male	0.67	1.1	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.67	1.1	1	0	0	2	0	0	not limited	0	0	0	0	2
+male	0.67	1.3	1	0	0	0	0	0	otherwise	0	0	0	0	3
+male	0.67	1.3	1	0	0	5	0	1	not limited	0	0	0	0	4
+male	0.67	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+male	0.67	1.5	1	0	0	1	0	0	not limited	0	0	1	22	0
+male	0.67	1.5	1	0	0	3	4	3	not limited	0	1	0	0	5
+male	0.72	0.06	1	0	0	4	0	2	not limited	1	0	0	0	3
+male	0.72	0.15	0	0	0	2	0	3	limited	1	0	0	0	0
+male	0.72	0.15	0	0	1	0	0	3	otherwise	1	0	1	2	1
+male	0.72	0.15	0	0	1	0	0	1	otherwise	0	0	0	0	0
+male	0.72	0.15	0	0	1	1	0	2	not limited	1	0	0	0	1
+male	0.72	0.15	0	0	1	1	0	2	not limited	0	0	0	0	1
+male	0.72	0.15	0	0	1	2	0	1	limited	1	0	0	0	2
+male	0.72	0.15	0	0	1	3	0	1	not limited	1	0	0	0	1
+male	0.72	0.15	0	0	1	4	0	1	not limited	2	0	0	0	2
+male	0.72	0.15	1	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.72	0.25	0	0	0	3	0	5	not limited	0	0	1	11	2
+male	0.72	0.25	0	0	1	0	0	4	not limited	4	0	0	0	1
+male	0.72	0.25	0	0	1	0	0	1	otherwise	1	0	1	22	0
+male	0.72	0.25	0	0	1	0	0	2	not limited	1	0	0	0	0
+male	0.72	0.25	0	0	1	0	0	1	otherwise	0	0	0	0	0
+male	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	2
+male	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+male	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+male	0.72	0.25	0	0	1	0	0	1	not limited	0	0	0	0	1
+male	0.72	0.25	0	0	1	0	0	0	not limited	0	0	1	6	1
+male	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	1
+male	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+male	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+male	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	1
+male	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+male	0.72	0.25	0	0	1	1	0	0	not limited	1	0	1	3	0
+male	0.72	0.25	0	0	1	1	1	1	otherwise	1	0	0	0	1
+male	0.72	0.25	0	0	1	1	0	0	not limited	1	0	0	0	1
+male	0.72	0.25	0	0	1	1	0	0	otherwise	1	0	0	0	0
+male	0.72	0.25	0	0	1	1	0	0	otherwise	1	0	0	0	0
+male	0.72	0.25	0	0	1	1	0	2	not limited	0	0	0	0	2
+male	0.72	0.25	0	0	1	1	0	1	not limited	0	0	0	0	0
+male	0.72	0.25	0	0	1	1	0	0	not limited	0	0	0	0	3
+male	0.72	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	0
+male	0.72	0.25	0	0	1	1	0	4	limited	0	7	0	0	5
+male	0.72	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	1
+male	0.72	0.25	0	0	1	1	0	0	not limited	0	0	0	0	1
+male	0.72	0.25	0	0	1	1	0	4	not limited	0	0	0	0	0
+male	0.72	0.25	0	0	1	1	1	0	otherwise	0	0	0	0	0
+male	0.72	0.25	0	0	1	1	0	4	limited	0	0	1	11	2
+male	0.72	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	0
+male	0.72	0.25	0	0	1	1	0	0	not limited	0	0	0	0	0
+male	0.72	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	0
+male	0.72	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	0
+male	0.72	0.25	0	0	1	1	0	0	not limited	0	0	0	0	1
+male	0.72	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	0
+male	0.72	0.25	0	0	1	1	0	0	not limited	0	0	0	0	0
+male	0.72	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	0
+male	0.72	0.25	0	0	1	2	0	1	not limited	1	0	0	0	1
+male	0.72	0.25	0	0	1	2	0	0	not limited	1	0	0	0	3
+male	0.72	0.25	0	0	1	2	0	1	not limited	1	0	0	0	1
+male	0.72	0.25	0	0	1	2	0	5	limited	3	0	2	45	3
+male	0.72	0.25	0	0	1	2	0	0	not limited	1	1	1	11	1
+male	0.72	0.25	0	0	1	2	0	0	not limited	1	0	0	0	0
+male	0.72	0.25	0	0	1	2	0	5	not limited	2	11	1	45	2
+male	0.72	0.25	0	0	1	2	0	1	limited	1	0	0	0	5
+male	0.72	0.25	0	0	1	2	0	0	not limited	1	0	0	0	0
+male	0.72	0.25	0	0	1	2	0	0	not limited	0	0	0	0	1
+male	0.72	0.25	0	0	1	2	0	0	not limited	0	0	0	0	1
+male	0.72	0.25	0	0	1	2	0	5	otherwise	0	0	0	0	3
+male	0.72	0.25	0	0	1	2	0	1	otherwise	0	0	0	0	0
+male	0.72	0.25	0	0	1	2	0	0	limited	0	0	0	0	0
+male	0.72	0.25	0	0	1	2	0	1	otherwise	0	0	0	0	1
+male	0.72	0.25	0	0	1	2	0	1	not limited	0	0	2	3	1
+male	0.72	0.25	0	0	1	2	0	0	not limited	0	0	0	0	3
+male	0.72	0.25	0	0	1	2	0	0	limited	0	0	1	11	0
+male	0.72	0.25	0	0	1	2	0	0	limited	0	0	0	0	2
+male	0.72	0.25	0	0	1	2	0	0	otherwise	0	0	0	0	0
+male	0.72	0.25	0	0	1	3	0	0	not limited	1	0	0	0	2
+male	0.72	0.25	0	0	1	3	14	0	not limited	2	9	0	0	4
+male	0.72	0.25	0	0	1	3	0	0	not limited	1	0	0	0	1
+male	0.72	0.25	0	0	1	3	14	1	limited	1	0	0	0	2
+male	0.72	0.25	0	0	1	3	0	4	not limited	0	0	2	11	3
+male	0.72	0.25	0	0	1	3	0	0	not limited	0	0	0	0	2
+male	0.72	0.25	0	0	1	3	0	0	otherwise	0	0	0	0	0
+male	0.72	0.25	0	0	1	3	0	0	otherwise	0	0	0	0	0
+male	0.72	0.25	0	0	1	3	3	1	not limited	0	0	0	0	1
+male	0.72	0.25	0	0	1	3	0	0	not limited	0	0	0	0	1
+male	0.72	0.25	0	0	1	3	0	3	not limited	0	0	0	0	3
+male	0.72	0.25	0	0	1	3	0	3	otherwise	0	0	1	22	2
+male	0.72	0.25	0	0	1	3	0	0	not limited	0	0	0	0	0
+male	0.72	0.25	0	0	1	4	0	3	not limited	4	1	0	0	5
+male	0.72	0.25	0	0	1	4	14	7	not limited	1	0	0	0	4
+male	0.72	0.25	0	0	1	4	14	6	limited	1	0	1	22	2
+male	0.72	0.25	0	0	1	4	0	0	not limited	1	0	1	5	1
+male	0.72	0.25	0	0	1	4	0	1	not limited	2	0	0	0	2
+male	0.72	0.25	0	0	1	4	0	0	not limited	0	0	0	0	6
+male	0.72	0.25	0	0	1	4	0	2	not limited	0	0	0	0	6
+male	0.72	0.25	0	0	1	4	0	4	otherwise	0	0	0	0	0
+male	0.72	0.25	0	0	1	4	0	0	not limited	0	0	1	2	2
+male	0.72	0.25	0	0	1	5	0	0	not limited	1	0	0	0	2
+male	0.72	0.25	0	0	1	5	0	7	otherwise	0	0	0	0	3
+male	0.72	0.25	0	0	1	5	10	5	not limited	0	0	0	0	2
+male	0.72	0.25	0	0	1	5	0	0	not limited	0	0	1	3	2
+male	0.72	0.25	0	0	1	5	0	2	not limited	0	0	0	0	2
+male	0.72	0.25	0	0	1	5	0	1	not limited	0	0	0	0	1
+male	0.72	0.25	1	0	0	0	0	0	otherwise	2	0	0	0	2
+male	0.72	0.25	1	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.72	0.25	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.72	0.25	1	0	0	1	0	0	not limited	1	0	0	0	2
+male	0.72	0.25	1	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.72	0.25	1	0	0	1	14	1	otherwise	0	0	0	0	0
+male	0.72	0.25	1	0	0	2	14	4	limited	2	0	2	22	4
+male	0.72	0.25	1	0	0	2	14	6	limited	1	1	1	3	2
+male	0.72	0.25	1	0	0	2	0	2	not limited	0	0	0	0	3
+male	0.72	0.25	1	0	0	2	0	0	not limited	0	0	1	1	0
+male	0.72	0.25	1	0	0	2	0	1	not limited	0	0	0	0	0
+male	0.72	0.25	1	0	0	2	0	0	not limited	0	0	1	11	1
+male	0.72	0.25	1	0	0	3	13	6	not limited	7	0	1	11	8
+male	0.72	0.25	1	0	0	3	0	1	not limited	2	0	1	7	4
+male	0.72	0.25	1	0	0	4	0	3	not limited	0	8	3	22	4
+male	0.72	0.25	1	0	0	4	0	0	otherwise	0	0	0	0	0
+male	0.72	0.25	1	0	0	5	0	1	not limited	0	0	0	0	1
+male	0.72	0.35	0	0	0	2	0	0	not limited	0	1	0	0	0
+male	0.72	0.35	0	0	1	0	0	0	otherwise	1	0	0	0	0
+male	0.72	0.35	0	0	1	0	0	0	otherwise	0	0	0	0	0
+male	0.72	0.35	0	0	1	0	0	0	otherwise	0	0	1	22	0
+male	0.72	0.35	0	0	1	0	0	1	not limited	0	0	0	0	0
+male	0.72	0.35	0	0	1	1	0	0	limited	1	0	0	0	3
+male	0.72	0.35	0	0	1	1	0	0	otherwise	1	0	0	0	5
+male	0.72	0.35	0	0	1	1	0	0	not limited	0	0	0	0	1
+male	0.72	0.35	0	0	1	1	0	1	not limited	0	1	0	0	1
+male	0.72	0.35	0	0	1	1	2	0	not limited	0	0	0	0	2
+male	0.72	0.35	0	0	1	2	14	2	not limited	1	0	0	0	6
+male	0.72	0.35	0	0	1	2	0	0	not limited	0	0	0	0	1
+male	0.72	0.35	0	0	1	2	0	0	not limited	0	0	0	0	1
+male	0.72	0.35	0	0	1	2	0	0	not limited	0	2	0	0	2
+male	0.72	0.35	0	0	1	2	0	1	not limited	0	1	0	0	0
+male	0.72	0.35	0	0	1	3	0	1	not limited	0	0	0	0	2
+male	0.72	0.35	0	0	1	4	0	5	limited	1	1	0	0	8
+male	0.72	0.35	0	0	1	5	0	1	not limited	1	0	0	0	6
+male	0.72	0.35	0	0	1	5	0	5	not limited	2	0	0	0	7
+male	0.72	0.35	0	0	1	5	0	2	not limited	0	0	0	0	1
+male	0.72	0.35	1	0	0	0	0	0	otherwise	1	0	0	0	2
+male	0.72	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	1
+male	0.72	0.35	1	0	0	1	14	1	otherwise	5	0	1	5	0
+male	0.72	0.35	1	0	0	1	0	0	not limited	1	0	1	5	1
+male	0.72	0.35	1	0	0	1	0	0	otherwise	0	0	0	0	0
+male	0.72	0.35	1	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.72	0.35	1	0	0	1	0	0	not limited	0	0	0	0	2
+male	0.72	0.35	1	0	0	2	5	3	otherwise	3	0	0	0	2
+male	0.72	0.35	1	0	0	2	0	5	not limited	0	1	1	22	4
+male	0.72	0.35	1	0	0	2	0	0	not limited	0	0	0	0	0
+male	0.72	0.35	1	0	0	3	0	4	not limited	1	0	0	0	4
+male	0.72	0.35	1	0	0	3	14	6	limited	1	0	0	0	6
+male	0.72	0.45	0	0	0	1	6	0	not limited	1	0	0	0	3
+male	0.72	0.45	0	0	0	2	0	0	limited	1	1	0	0	4
+male	0.72	0.45	0	0	0	3	0	0	not limited	0	0	2	11	2
+male	0.72	0.45	0	0	1	0	0	1	limited	0	0	0	0	3
+male	0.72	0.45	0	0	1	1	0	4	otherwise	1	0	1	5	2
+male	0.72	0.45	0	0	1	1	0	0	not limited	1	0	2	1	2
+male	0.72	0.45	0	0	1	1	0	1	otherwise	0	0	0	0	0
+male	0.72	0.45	0	0	1	3	0	0	not limited	1	0	0	0	4
+male	0.72	0.45	0	0	1	3	0	0	not limited	0	0	0	0	3
+male	0.72	0.45	1	0	0	1	0	0	not limited	1	0	0	0	5
+male	0.72	0.45	1	0	0	1	0	1	not limited	0	0	0	0	2
+male	0.72	0.45	1	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.72	0.45	1	0	0	2	0	0	not limited	1	0	1	11	2
+male	0.72	0.45	1	0	0	2	0	0	not limited	1	0	0	0	1
+male	0.72	0.45	1	0	0	2	0	0	not limited	0	1	0	0	0
+male	0.72	0.45	1	0	0	3	0	4	not limited	0	0	0	0	1
+male	0.72	0.45	1	0	0	3	8	1	not limited	0	1	0	0	6
+male	0.72	0.45	1	0	0	3	0	0	not limited	0	0	0	0	2
+male	0.72	0.45	1	0	0	5	0	1	not limited	1	0	0	0	0
+male	0.72	0.55	0	0	0	1	0	1	not limited	0	0	0	0	1
+male	0.72	0.55	0	0	0	2	2	0	otherwise	1	1	1	2	2
+male	0.72	0.55	0	0	0	5	14	3	limited	3	1	0	0	7
+male	0.72	0.55	0	0	1	0	0	0	otherwise	0	0	0	0	0
+male	0.72	0.55	0	0	1	1	0	1	not limited	0	0	0	0	0
+male	0.72	0.55	0	0	1	5	14	3	not limited	1	0	0	0	5
+male	0.72	0.55	1	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.72	0.55	1	0	0	3	0	0	not limited	1	0	0	0	1
+male	0.72	0.65	0	0	0	1	0	1	not limited	1	0	0	0	1
+male	0.72	0.65	0	0	0	1	0	0	not limited	0	0	0	0	2
+male	0.72	0.65	1	0	0	1	0	0	otherwise	0	0	0	0	3
+male	0.72	0.75	0	0	1	5	0	2	limited	1	0	0	0	1
+male	0.72	0.75	0	0	1	5	0	1	limited	0	0	0	0	4
+male	0.72	0.75	0	0	1	5	0	5	otherwise	0	0	0	0	0
+male	0.72	0.75	1	0	0	1	0	0	otherwise	1	0	0	0	0
+male	0.72	0.75	1	0	0	2	0	2	not limited	0	0	0	0	1
+male	0.72	0.9	0	0	1	1	0	0	not limited	0	0	0	0	2
+male	0.72	0.9	1	0	0	0	0	1	not limited	0	0	0	0	1
+male	0.72	0.9	1	0	0	1	0	0	not limited	0	0	0	0	1
+male	0.72	0.9	1	0	0	2	0	0	not limited	0	0	0	0	3
+male	0.72	0.9	1	0	0	4	0	1	not limited	2	0	2	4	4
+male	0.72	1.1	0	0	0	2	0	1	not limited	0	0	1	6	0
+male	0.72	1.1	0	0	1	0	0	0	not limited	0	1	0	0	5
+male	0.72	1.1	1	0	0	0	0	0	not limited	0	0	0	0	1
+male	0.72	1.1	1	0	0	1	0	0	not limited	0	0	1	1	1
+male	0.72	1.1	1	0	0	2	14	0	not limited	0	1	0	0	3
+male	0.72	1.1	1	0	0	3	0	0	not limited	0	0	0	0	1
+male	0.72	1.1	1	0	0	5	0	0	not limited	3	0	0	0	4
+male	0.72	1.3	0	0	1	1	0	0	not limited	1	0	0	0	6
+male	0.72	1.5	1	0	0	0	0	0	not limited	0	0	0	0	0
+male	0.72	1.5	1	0	0	0	0	3	otherwise	0	0	0	0	0
+male	0.72	1.5	1	0	0	1	14	4	not limited	1	1	0	0	1
+male	0.72	1.5	1	0	0	1	0	0	not limited	0	0	0	0	0
+male	0.72	1.5	1	0	0	4	14	2	not limited	0	0	0	0	2
+male	0.72	1.5	1	0	0	5	3	2	not limited	0	0	0	0	1
+female	0.19	0	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0	0	0	0	0	0	0	not limited	0	2	0	0	0
+female	0.19	0	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0	0	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.19	0	0	0	0	2	0	0	otherwise	0	1	0	0	0
+female	0.19	0	0	0	0	3	0	0	otherwise	0	0	0	0	2
+female	0.19	0	0	1	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0	0	1	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0	0	1	0	3	0	3	otherwise	0	0	0	0	1
+female	0.19	0	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.19	0	1	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.19	0	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.19	0	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.19	0	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.19	0	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.19	0.01	0	0	0	0	0	0	limited	0	0	0	0	0
+female	0.19	0.01	0	0	0	1	0	5	not limited	0	0	0	0	1
+female	0.19	0.01	0	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.19	0.01	0	0	0	4	0	0	not limited	1	0	0	0	0
+female	0.19	0.01	0	1	0	0	0	0	limited	0	0	1	3	2
+female	0.19	0.06	0	0	0	1	0	6	otherwise	0	0	0	0	0
+female	0.19	0.06	0	0	0	1	0	3	otherwise	0	0	0	0	0
+female	0.19	0.06	0	0	0	2	0	2	otherwise	0	0	0	0	0
+female	0.19	0.06	0	0	0	3	0	0	otherwise	0	0	0	0	1
+female	0.19	0.06	0	0	0	4	0	5	not limited	0	0	0	0	1
+female	0.19	0.06	0	1	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.06	0	1	0	1	0	0	otherwise	1	0	0	0	3
+female	0.19	0.06	0	1	0	1	0	2	limited	0	0	0	0	1
+female	0.19	0.06	1	0	0	1	0	0	otherwise	0	0	1	1	1
+female	0.19	0.06	1	0	0	1	1	0	otherwise	0	0	0	0	1
+female	0.19	0.06	1	0	0	3	3	1	otherwise	0	0	0	0	3
+female	0.19	0.06	1	0	0	3	0	3	not limited	0	0	1	2	2
+female	0.19	0.15	0	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.19	0.15	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.15	0	0	0	1	4	2	otherwise	3	0	0	0	1
+female	0.19	0.15	0	0	0	1	3	0	not limited	4	0	0	0	1
+female	0.19	0.15	0	0	0	1	0	1	otherwise	0	0	0	0	0
+female	0.19	0.15	0	0	0	2	0	0	not limited	0	0	0	0	1
+female	0.19	0.15	0	0	0	2	0	0	not limited	0	0	1	4	1
+female	0.19	0.15	0	0	0	2	8	4	not limited	0	0	0	0	1
+female	0.19	0.15	0	0	0	3	0	6	otherwise	1	0	0	0	1
+female	0.19	0.15	0	0	0	3	1	0	not limited	4	0	0	0	0
+female	0.19	0.15	0	0	0	3	0	0	otherwise	1	0	0	0	1
+female	0.19	0.15	0	0	0	3	0	1	limited	0	0	0	0	0
+female	0.19	0.15	0	0	0	4	0	3	otherwise	0	0	0	0	0
+female	0.19	0.15	0	0	0	5	0	3	not limited	0	0	3	11	1
+female	0.19	0.15	0	1	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.15	0	1	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.15	0	1	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.15	0	1	0	1	2	6	not limited	1	0	0	0	2
+female	0.19	0.15	0	1	0	1	0	2	otherwise	0	0	0	0	1
+female	0.19	0.15	0	1	0	1	3	2	otherwise	0	0	0	0	1
+female	0.19	0.15	0	1	0	2	0	0	not limited	2	0	1	1	1
+female	0.19	0.15	0	1	0	2	0	1	not limited	1	0	1	2	2
+female	0.19	0.15	0	1	0	2	1	9	not limited	0	0	2	1	1
+female	0.19	0.15	0	1	0	3	0	1	not limited	0	0	1	1	0
+female	0.19	0.15	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.15	1	0	0	0	0	0	otherwise	0	0	1	1	0
+female	0.19	0.15	1	0	0	0	0	0	otherwise	0	0	0	0	2
+female	0.19	0.15	1	0	0	1	0	6	otherwise	0	0	0	0	0
+female	0.19	0.15	1	0	0	1	0	2	otherwise	0	0	0	0	0
+female	0.19	0.15	1	0	0	2	0	4	otherwise	0	0	0	0	1
+female	0.19	0.15	1	0	0	2	1	0	not limited	0	0	0	0	0
+female	0.19	0.15	1	0	0	2	0	10	otherwise	0	0	0	0	2
+female	0.19	0.15	1	0	0	3	0	4	not limited	0	0	0	0	0
+female	0.19	0.15	1	0	0	3	0	0	otherwise	0	0	0	0	2
+female	0.19	0.25	0	0	0	0	0	1	otherwise	0	0	0	0	1
+female	0.19	0.25	0	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.19	0.25	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.25	0	0	0	1	0	1	otherwise	0	0	0	0	1
+female	0.19	0.25	0	0	0	1	0	2	not limited	0	0	0	0	0
+female	0.19	0.25	0	0	0	1	0	3	otherwise	0	0	0	0	0
+female	0.19	0.25	0	0	0	2	7	0	limited	8	0	1	3	1
+female	0.19	0.25	0	0	0	2	0	0	not limited	0	0	0	0	0
+female	0.19	0.25	0	0	0	3	0	12	not limited	0	2	1	11	1
+female	0.19	0.25	0	0	0	3	0	1	not limited	0	0	0	0	3
+female	0.19	0.25	0	0	0	3	0	5	otherwise	0	0	0	0	2
+female	0.19	0.25	0	0	0	4	0	1	otherwise	1	0	0	0	0
+female	0.19	0.25	0	0	1	1	0	9	otherwise	1	0	0	0	1
+female	0.19	0.25	0	0	1	2	14	11	limited	1	0	1	11	5
+female	0.19	0.25	0	1	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.25	0	1	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.25	0	1	0	0	0	0	otherwise	0	0	0	0	1
+female	0.19	0.25	0	1	0	1	0	1	not limited	1	0	0	0	1
+female	0.19	0.25	0	1	0	1	0	0	not limited	0	0	0	0	5
+female	0.19	0.25	0	1	0	1	0	6	not limited	0	0	0	0	0
+female	0.19	0.25	0	1	0	2	0	0	otherwise	0	0	0	0	0
+female	0.19	0.25	0	1	0	2	0	0	otherwise	0	0	1	11	0
+female	0.19	0.25	0	1	0	2	0	9	otherwise	0	0	0	0	1
+female	0.19	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.25	1	0	0	0	0	0	limited	0	0	0	0	0
+female	0.19	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.19	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.25	1	0	0	0	0	2	otherwise	0	2	0	0	0
+female	0.19	0.25	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.19	0.25	1	0	0	1	0	2	not limited	0	0	0	0	1
+female	0.19	0.25	1	0	0	3	1	1	not limited	0	0	0	0	1
+female	0.19	0.25	1	0	0	4	14	9	not limited	0	8	0	0	0
+female	0.19	0.25	1	0	0	4	2	9	limited	0	0	1	6	1
+female	0.19	0.25	1	0	0	5	4	6	not limited	0	0	1	2	3
+female	0.19	0.35	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.35	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.35	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.19	0.35	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.35	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.35	0	0	0	1	0	2	not limited	1	1	0	0	0
+female	0.19	0.35	0	0	0	1	0	0	otherwise	1	0	0	0	0
+female	0.19	0.35	0	0	0	1	0	2	not limited	0	0	1	1	1
+female	0.19	0.35	0	0	0	1	0	6	otherwise	0	0	0	0	0
+female	0.19	0.35	0	0	0	1	1	1	not limited	0	0	0	0	1
+female	0.19	0.35	0	0	0	1	0	1	otherwise	0	0	1	3	2
+female	0.19	0.35	0	0	0	1	0	3	otherwise	0	0	0	0	0
+female	0.19	0.35	0	0	0	1	0	2	otherwise	0	0	0	0	0
+female	0.19	0.35	0	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.19	0.35	0	0	0	2	0	0	otherwise	0	0	0	0	2
+female	0.19	0.35	0	0	0	2	4	4	otherwise	0	0	0	0	1
+female	0.19	0.35	0	0	0	2	0	1	otherwise	0	0	0	0	1
+female	0.19	0.35	0	0	0	3	2	1	otherwise	0	0	2	1	0
+female	0.19	0.35	0	0	0	3	0	0	limited	0	0	1	45	1
+female	0.19	0.35	0	0	0	3	0	5	not limited	0	0	0	0	1
+female	0.19	0.35	0	0	0	5	1	9	not limited	1	0	0	0	1
+female	0.19	0.35	0	0	0	5	14	3	otherwise	1	0	1	2	2
+female	0.19	0.35	0	1	0	1	0	0	otherwise	0	0	0	0	1
+female	0.19	0.35	0	1	0	1	0	1	not limited	0	0	0	0	1
+female	0.19	0.35	0	1	0	1	0	0	otherwise	0	0	0	0	0
+female	0.19	0.35	0	1	0	2	1	0	otherwise	1	0	0	0	1
+female	0.19	0.35	0	1	0	2	0	7	not limited	0	0	0	0	0
+female	0.19	0.35	0	1	0	4	0	5	limited	0	0	1	4	2
+female	0.19	0.35	1	0	0	0	0	6	not limited	1	0	0	0	1
+female	0.19	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.19	0.35	1	0	0	1	0	0	otherwise	1	0	0	0	0
+female	0.19	0.35	1	0	0	1	0	1	otherwise	1	0	0	0	2
+female	0.19	0.35	1	0	0	1	0	0	not limited	1	0	0	0	1
+female	0.19	0.35	1	0	0	1	0	1	otherwise	0	0	0	0	1
+female	0.19	0.35	1	0	0	2	0	2	otherwise	1	0	0	0	1
+female	0.19	0.35	1	0	0	2	0	2	otherwise	0	0	0	0	0
+female	0.19	0.35	1	0	0	2	0	0	otherwise	0	0	0	0	0
+female	0.19	0.35	1	0	0	2	0	6	otherwise	0	0	0	0	1
+female	0.19	0.35	1	0	0	2	0	0	otherwise	0	0	0	0	1
+female	0.19	0.35	1	0	0	3	0	0	not limited	1	0	0	0	2
+female	0.19	0.35	1	0	0	3	0	0	not limited	1	0	0	0	1
+female	0.19	0.35	1	0	0	5	0	2	not limited	0	1	0	0	1
+female	0.19	0.35	1	0	0	5	0	3	not limited	0	0	0	0	1
+female	0.19	0.45	0	0	0	0	0	0	otherwise	1	0	0	0	2
+female	0.19	0.45	0	0	0	0	0	0	otherwise	1	0	0	0	0
+female	0.19	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.45	0	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.19	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.45	0	0	0	0	0	1	otherwise	0	0	0	0	1
+female	0.19	0.45	0	0	0	0	0	0	otherwise	0	0	1	1	0
+female	0.19	0.45	0	0	0	0	0	0	not limited	0	0	1	1	0
+female	0.19	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.45	0	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.19	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.45	0	0	0	1	1	0	not limited	1	0	0	0	1
+female	0.19	0.45	0	0	0	1	0	1	limited	1	0	0	0	5
+female	0.19	0.45	0	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.19	0.45	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.19	0.45	0	0	0	1	0	0	otherwise	0	0	0	0	2
+female	0.19	0.45	0	0	0	1	0	0	otherwise	0	0	1	5	0
+female	0.19	0.45	0	0	0	1	0	4	otherwise	0	0	0	0	0
+female	0.19	0.45	0	0	0	1	0	2	otherwise	0	0	0	0	1
+female	0.19	0.45	0	0	0	1	0	0	otherwise	0	0	1	2	0
+female	0.19	0.45	0	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.19	0.45	0	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.19	0.45	0	0	0	1	0	0	otherwise	0	0	2	1	1
+female	0.19	0.45	0	0	0	1	0	0	otherwise	0	0	0	0	2
+female	0.19	0.45	0	0	0	1	0	2	otherwise	0	0	0	0	0
+female	0.19	0.45	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.19	0.45	0	0	0	1	1	0	otherwise	0	0	0	0	1
+female	0.19	0.45	0	0	0	2	1	1	otherwise	1	0	1	2	1
+female	0.19	0.45	0	0	0	2	0	0	not limited	1	0	1	4	1
+female	0.19	0.45	0	0	0	2	1	2	not limited	0	0	0	0	1
+female	0.19	0.45	0	0	0	2	1	0	otherwise	0	0	1	1	0
+female	0.19	0.45	0	0	0	3	3	4	otherwise	2	0	0	0	2
+female	0.19	0.45	0	0	0	3	0	5	otherwise	0	0	0	0	1
+female	0.19	0.45	0	0	0	3	0	0	not limited	0	0	0	0	7
+female	0.19	0.45	0	0	0	3	0	5	not limited	0	0	0	0	1
+female	0.19	0.45	0	0	0	3	0	2	otherwise	0	0	0	0	1
+female	0.19	0.45	0	0	0	3	0	0	not limited	0	0	0	0	0
+female	0.19	0.45	0	0	0	3	0	0	otherwise	0	0	0	0	0
+female	0.19	0.45	0	0	0	4	0	3	limited	0	1	0	0	2
+female	0.19	0.45	0	0	0	4	0	3	not limited	0	0	0	0	1
+female	0.19	0.45	0	0	0	5	2	5	limited	2	0	0	0	3
+female	0.19	0.45	0	1	0	0	0	2	otherwise	0	0	0	0	1
+female	0.19	0.45	0	1	0	1	0	0	otherwise	0	0	0	0	0
+female	0.19	0.45	0	1	0	1	0	0	otherwise	0	0	0	0	0
+female	0.19	0.45	0	1	0	3	0	4	otherwise	0	0	0	0	0
+female	0.19	0.45	1	0	0	0	0	0	otherwise	1	0	0	0	1
+female	0.19	0.45	1	0	0	0	0	1	otherwise	1	0	0	0	1
+female	0.19	0.45	1	0	0	0	0	3	not limited	0	0	0	0	0
+female	0.19	0.45	1	0	0	0	0	0	otherwise	0	0	1	2	0
+female	0.19	0.45	1	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.19	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.45	1	0	0	0	0	2	otherwise	0	0	0	0	0
+female	0.19	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.45	1	0	0	1	2	1	otherwise	1	0	0	0	2
+female	0.19	0.45	1	0	0	1	0	0	otherwise	2	0	0	0	1
+female	0.19	0.45	1	0	0	1	3	0	otherwise	1	0	0	0	0
+female	0.19	0.45	1	0	0	1	0	2	otherwise	1	0	0	0	0
+female	0.19	0.45	1	0	0	1	0	0	limited	0	0	0	0	0
+female	0.19	0.45	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.19	0.45	1	0	0	1	0	3	limited	0	1	0	0	0
+female	0.19	0.45	1	0	0	1	0	2	not limited	0	0	0	0	1
+female	0.19	0.45	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.19	0.45	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.19	0.45	1	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.19	0.45	1	0	0	1	0	1	otherwise	0	0	0	0	0
+female	0.19	0.45	1	0	0	2	2	0	not limited	2	0	0	0	3
+female	0.19	0.45	1	0	0	2	14	6	limited	4	0	0	0	2
+female	0.19	0.45	1	0	0	2	0	0	otherwise	1	0	0	0	1
+female	0.19	0.45	1	0	0	2	1	0	not limited	0	0	0	0	2
+female	0.19	0.45	1	0	0	2	0	0	otherwise	0	0	0	0	3
+female	0.19	0.45	1	0	0	3	0	0	otherwise	1	0	0	0	2
+female	0.19	0.45	1	0	0	3	0	0	not limited	0	0	0	0	0
+female	0.19	0.45	1	0	0	3	1	5	otherwise	0	0	0	0	1
+female	0.19	0.45	1	0	0	3	0	2	not limited	0	0	0	0	0
+female	0.19	0.45	1	0	0	3	0	4	not limited	0	0	0	0	1
+female	0.19	0.45	1	0	0	4	0	0	not limited	1	0	1	6	2
+female	0.19	0.45	1	0	0	4	0	3	otherwise	0	0	4	1	0
+female	0.19	0.45	1	0	0	4	0	6	otherwise	0	0	0	0	0
+female	0.19	0.45	1	0	0	5	0	4	not limited	0	0	0	0	1
+female	0.19	0.45	1	0	0	5	0	3	not limited	0	0	0	0	3
+female	0.19	0.55	0	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.19	0.55	0	0	0	0	0	0	otherwise	0	1	0	0	1
+female	0.19	0.55	0	0	0	0	0	6	otherwise	0	0	0	0	0
+female	0.19	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.19	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.55	0	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.19	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.55	0	0	0	1	6	0	otherwise	5	0	1	2	2
+female	0.19	0.55	0	0	0	1	0	0	otherwise	0	2	1	1	1
+female	0.19	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.19	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.19	0.55	0	0	0	1	0	0	otherwise	0	0	1	11	1
+female	0.19	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.19	0.55	0	0	0	1	0	5	otherwise	0	0	0	0	0
+female	0.19	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	2
+female	0.19	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.19	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.19	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.19	0.55	0	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.19	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.19	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.19	0.55	0	0	0	1	0	1	otherwise	0	0	0	0	0
+female	0.19	0.55	0	0	0	2	5	6	otherwise	1	0	0	0	1
+female	0.19	0.55	0	0	0	2	0	0	limited	1	0	0	0	2
+female	0.19	0.55	0	0	0	2	0	4	limited	0	0	0	0	1
+female	0.19	0.55	0	0	0	2	0	0	otherwise	0	0	1	6	1
+female	0.19	0.55	0	0	0	2	0	2	otherwise	0	0	0	0	1
+female	0.19	0.55	0	0	0	2	0	3	limited	0	0	0	0	1
+female	0.19	0.55	0	0	0	2	0	1	not limited	0	0	0	0	0
+female	0.19	0.55	0	0	0	2	0	1	otherwise	0	4	1	4	1
+female	0.19	0.55	0	0	0	3	0	3	not limited	0	0	0	0	1
+female	0.19	0.55	0	0	0	4	0	2	otherwise	1	0	0	0	0
+female	0.19	0.55	0	0	0	4	0	1	otherwise	1	1	0	0	1
+female	0.19	0.55	0	0	0	4	0	2	otherwise	0	0	0	0	0
+female	0.19	0.55	0	0	0	4	4	7	otherwise	0	0	0	0	1
+female	0.19	0.55	0	0	0	5	2	6	not limited	0	0	0	0	0
+female	0.19	0.55	0	0	1	2	3	0	limited	1	0	0	0	5
+female	0.19	0.55	1	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.19	0.55	1	0	0	0	0	2	otherwise	0	0	1	1	0
+female	0.19	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.55	1	0	0	0	0	1	otherwise	0	0	0	0	1
+female	0.19	0.55	1	0	0	1	4	1	otherwise	1	0	0	0	1
+female	0.19	0.55	1	0	0	1	0	1	otherwise	1	0	0	0	0
+female	0.19	0.55	1	0	0	1	0	0	otherwise	1	0	0	0	0
+female	0.19	0.55	1	0	0	1	1	1	otherwise	0	0	0	0	0
+female	0.19	0.55	1	0	0	1	0	3	not limited	0	0	0	0	3
+female	0.19	0.55	1	0	0	1	2	0	otherwise	0	0	3	3	3
+female	0.19	0.55	1	0	0	1	0	0	limited	0	0	1	3	1
+female	0.19	0.55	1	0	0	1	0	1	otherwise	0	0	0	0	0
+female	0.19	0.55	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.19	0.55	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.19	0.55	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.19	0.55	1	0	0	1	0	4	otherwise	0	0	0	0	1
+female	0.19	0.55	1	0	0	1	0	4	not limited	0	0	0	0	0
+female	0.19	0.55	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.19	0.55	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.19	0.55	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.19	0.55	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.19	0.55	1	0	0	2	0	5	otherwise	2	0	2	2	1
+female	0.19	0.55	1	0	0	2	2	0	otherwise	1	0	1	2	2
+female	0.19	0.55	1	0	0	2	0	3	otherwise	0	0	0	0	0
+female	0.19	0.55	1	0	0	2	0	0	otherwise	0	0	0	0	0
+female	0.19	0.55	1	0	0	2	0	0	otherwise	0	0	0	0	0
+female	0.19	0.55	1	0	0	3	1	2	not limited	1	0	1	1	2
+female	0.19	0.55	1	0	0	3	0	2	otherwise	1	0	0	0	2
+female	0.19	0.55	1	0	0	3	0	2	otherwise	1	0	0	0	2
+female	0.19	0.55	1	0	0	3	0	5	otherwise	0	0	1	3	0
+female	0.19	0.55	1	0	0	3	0	0	otherwise	0	0	0	0	0
+female	0.19	0.55	1	0	0	4	1	0	not limited	0	0	1	2	4
+female	0.19	0.65	0	0	0	0	0	0	otherwise	1	0	0	0	0
+female	0.19	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.19	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.19	0.65	0	0	0	1	0	1	otherwise	0	0	0	0	1
+female	0.19	0.65	0	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.19	0.65	0	0	0	1	0	1	otherwise	0	0	1	3	0
+female	0.19	0.65	0	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.19	0.65	0	0	0	2	1	0	otherwise	1	0	0	0	2
+female	0.19	0.65	0	0	0	2	1	0	not limited	0	1	0	0	1
+female	0.19	0.65	0	0	0	2	0	1	otherwise	0	0	0	0	2
+female	0.19	0.65	0	0	0	2	0	6	not limited	0	0	1	1	1
+female	0.19	0.65	0	0	0	2	0	0	not limited	0	0	0	0	1
+female	0.19	0.65	0	0	0	3	0	0	limited	1	0	0	0	1
+female	0.19	0.65	0	0	0	3	0	0	otherwise	0	0	0	0	4
+female	0.19	0.65	0	0	0	5	0	1	otherwise	0	0	1	2	1
+female	0.19	0.65	0	1	0	0	0	0	otherwise	0	0	0	0	1
+female	0.19	0.65	1	0	0	0	0	0	otherwise	1	0	0	0	0
+female	0.19	0.65	1	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.19	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.65	1	0	0	1	1	1	otherwise	1	0	0	0	0
+female	0.19	0.65	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.19	0.65	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.19	0.65	1	0	0	2	0	5	otherwise	1	0	0	0	1
+female	0.19	0.65	1	0	0	2	0	0	not limited	1	0	0	0	0
+female	0.19	0.65	1	0	0	2	3	2	otherwise	1	0	0	0	2
+female	0.19	0.65	1	0	0	2	0	2	otherwise	0	0	0	0	1
+female	0.19	0.65	1	0	0	2	0	0	not limited	0	0	0	0	0
+female	0.19	0.65	1	0	0	2	0	0	otherwise	0	0	0	0	0
+female	0.19	0.75	0	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.19	0.75	0	0	0	0	0	1	otherwise	0	0	0	0	1
+female	0.19	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.75	0	0	0	1	0	2	otherwise	0	0	0	0	2
+female	0.19	0.75	0	0	0	2	0	6	otherwise	0	0	0	0	1
+female	0.19	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	2
+female	0.19	0.75	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.19	0.75	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.19	0.75	1	0	0	2	0	0	not limited	0	0	0	0	0
+female	0.19	0.75	1	0	0	3	0	3	otherwise	0	0	1	4	1
+female	0.19	0.9	0	0	0	1	0	4	not limited	0	0	0	0	0
+female	0.19	0.9	0	0	0	1	0	2	not limited	0	0	0	0	3
+female	0.19	0.9	0	0	0	1	1	0	not limited	0	0	0	0	0
+female	0.19	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	0.9	1	0	0	1	0	1	otherwise	0	0	0	0	1
+female	0.19	0.9	1	0	0	2	0	3	otherwise	0	0	0	0	1
+female	0.19	1.1	0	0	0	0	0	5	otherwise	0	0	0	0	0
+female	0.19	1.1	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.19	1.1	1	0	0	2	0	1	otherwise	1	0	0	0	2
+female	0.22	0	0	0	0	2	0	0	not limited	0	0	0	0	2
+female	0.22	0	0	0	1	1	0	4	limited	0	7	0	0	3
+female	0.22	0	0	1	0	1	0	0	not limited	0	0	0	0	2
+female	0.22	0	0	1	0	2	2	6	not limited	1	0	1	1	4
+female	0.22	0	0	1	0	2	0	4	otherwise	0	0	0	0	1
+female	0.22	0	0	1	0	3	3	9	limited	4	0	0	0	4
+female	0.22	0	1	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.22	0	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.22	0	1	0	0	1	14	0	not limited	0	0	0	0	1
+female	0.22	0	1	0	0	1	0	0	otherwise	0	0	0	0	2
+female	0.22	0	1	0	0	2	0	7	otherwise	0	0	0	0	1
+female	0.22	0.01	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.01	0	0	0	1	0	7	limited	0	0	1	11	0
+female	0.22	0.01	0	0	0	1	0	0	not limited	0	0	1	3	3
+female	0.22	0.01	0	1	0	0	0	2	limited	0	0	0	0	0
+female	0.22	0.01	1	0	0	3	0	12	otherwise	0	0	0	0	1
+female	0.22	0.06	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.06	0	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.22	0.06	1	0	0	1	0	3	otherwise	0	0	0	0	0
+female	0.22	0.06	1	0	0	1	0	0	not limited	0	0	0	0	3
+female	0.22	0.06	1	0	0	1	0	2	otherwise	0	0	0	0	1
+female	0.22	0.06	1	0	0	3	0	7	limited	2	0	0	0	4
+female	0.22	0.06	1	0	0	4	5	9	otherwise	0	0	0	0	0
+female	0.22	0.15	0	0	0	0	0	0	otherwise	1	0	0	0	2
+female	0.22	0.15	0	0	0	0	0	0	otherwise	1	0	0	0	1
+female	0.22	0.15	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.22	0.15	0	0	0	1	0	5	not limited	0	0	0	0	3
+female	0.22	0.15	0	0	0	2	1	2	not limited	0	0	0	0	1
+female	0.22	0.15	0	0	1	1	14	0	not limited	1	0	0	0	1
+female	0.22	0.15	0	0	1	1	1	0	otherwise	0	0	0	0	1
+female	0.22	0.15	0	1	0	0	0	4	otherwise	0	0	0	0	0
+female	0.22	0.15	0	1	0	1	0	2	otherwise	0	0	0	0	1
+female	0.22	0.15	0	1	0	2	0	0	otherwise	0	0	0	0	2
+female	0.22	0.15	0	1	0	2	0	0	otherwise	0	0	0	0	0
+female	0.22	0.15	0	1	0	3	0	0	limited	1	0	1	2	1
+female	0.22	0.15	1	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.22	0.15	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.15	1	0	0	0	0	0	otherwise	0	0	0	0	2
+female	0.22	0.15	1	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.22	0.15	1	0	0	1	0	0	otherwise	1	0	0	0	0
+female	0.22	0.15	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.22	0.15	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.22	0.15	1	0	0	1	0	3	otherwise	0	0	0	0	1
+female	0.22	0.15	1	0	0	1	2	1	otherwise	0	1	0	0	0
+female	0.22	0.15	1	0	0	1	0	1	otherwise	0	0	0	0	0
+female	0.22	0.15	1	0	0	2	0	0	not limited	0	0	0	0	1
+female	0.22	0.15	1	0	0	2	0	1	otherwise	0	0	1	1	1
+female	0.22	0.15	1	0	0	3	4	1	limited	1	0	0	0	1
+female	0.22	0.15	1	0	0	5	0	1	otherwise	1	4	0	0	3
+female	0.22	0.25	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.25	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.25	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.25	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.25	0	0	0	0	0	6	otherwise	0	0	0	0	0
+female	0.22	0.25	0	0	0	1	0	0	otherwise	1	0	1	4	2
+female	0.22	0.25	0	0	0	1	0	1	otherwise	1	1	0	0	2
+female	0.22	0.25	0	0	0	1	0	1	otherwise	0	0	1	5	1
+female	0.22	0.25	0	0	0	1	0	0	otherwise	0	0	0	0	2
+female	0.22	0.25	0	0	0	1	0	2	not limited	0	0	0	0	1
+female	0.22	0.25	0	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.22	0.25	0	0	0	2	6	0	otherwise	2	0	0	0	1
+female	0.22	0.25	0	0	0	2	10	1	otherwise	2	0	0	0	0
+female	0.22	0.25	0	0	0	2	0	0	not limited	0	0	0	0	1
+female	0.22	0.25	0	0	0	3	1	0	not limited	0	0	0	0	3
+female	0.22	0.25	0	0	0	3	0	1	not limited	0	0	0	0	0
+female	0.22	0.25	0	0	0	3	0	0	limited	0	0	0	0	0
+female	0.22	0.25	0	0	0	5	0	1	not limited	0	0	1	2	1
+female	0.22	0.25	0	0	1	0	0	5	otherwise	0	0	0	0	0
+female	0.22	0.25	0	0	1	1	0	0	limited	1	2	2	7	0
+female	0.22	0.25	0	1	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.25	0	1	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.25	0	1	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.25	0	1	0	1	0	2	otherwise	0	0	0	0	2
+female	0.22	0.25	0	1	0	2	0	0	not limited	1	0	1	5	4
+female	0.22	0.25	0	1	0	2	1	1	otherwise	0	0	0	0	1
+female	0.22	0.25	0	1	0	2	0	11	not limited	0	0	0	0	0
+female	0.22	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.25	1	0	0	1	0	10	not limited	1	0	1	3	3
+female	0.22	0.25	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.22	0.25	1	0	0	1	0	2	not limited	0	0	0	0	1
+female	0.22	0.25	1	0	0	2	0	1	otherwise	0	0	0	0	0
+female	0.22	0.25	1	0	0	2	0	0	not limited	0	0	0	0	0
+female	0.22	0.25	1	0	0	3	7	2	not limited	0	0	1	3	6
+female	0.22	0.25	1	0	0	3	0	4	not limited	0	0	0	0	2
+female	0.22	0.25	1	0	0	4	0	1	otherwise	1	0	0	0	3
+female	0.22	0.25	1	0	0	4	0	5	not limited	0	0	1	1	0
+female	0.22	0.35	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.35	0	0	0	0	0	0	not limited	0	0	0	0	3
+female	0.22	0.35	0	0	0	0	0	1	otherwise	0	0	0	0	1
+female	0.22	0.35	0	0	0	0	0	2	otherwise	0	0	0	0	0
+female	0.22	0.35	0	0	0	0	0	1	not limited	0	0	0	0	1
+female	0.22	0.35	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.35	0	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.22	0.35	0	0	0	1	0	0	not limited	0	0	1	1	3
+female	0.22	0.35	0	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.22	0.35	0	0	0	1	0	5	otherwise	0	0	0	0	1
+female	0.22	0.35	0	0	0	1	2	2	otherwise	0	0	0	0	0
+female	0.22	0.35	0	0	0	1	2	0	otherwise	0	0	0	0	0
+female	0.22	0.35	0	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.22	0.35	0	0	0	2	0	0	otherwise	1	0	0	0	1
+female	0.22	0.35	0	0	0	2	0	0	not limited	0	0	0	0	0
+female	0.22	0.35	0	0	0	2	1	1	otherwise	0	0	0	0	0
+female	0.22	0.35	0	0	0	2	1	0	not limited	0	0	0	0	1
+female	0.22	0.35	0	0	0	3	7	3	otherwise	1	0	0	0	1
+female	0.22	0.35	0	0	0	4	0	0	otherwise	1	0	0	0	1
+female	0.22	0.35	0	0	1	2	0	5	otherwise	1	0	0	0	3
+female	0.22	0.35	0	1	0	0	0	2	otherwise	0	0	1	1	0
+female	0.22	0.35	0	1	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.35	0	1	0	1	0	0	otherwise	0	0	0	0	0
+female	0.22	0.35	0	1	0	2	0	4	limited	0	0	0	0	3
+female	0.22	0.35	0	1	0	3	0	1	not limited	0	0	0	0	1
+female	0.22	0.35	0	1	0	3	5	0	otherwise	0	0	0	0	1
+female	0.22	0.35	1	0	0	0	0	2	otherwise	1	0	0	0	0
+female	0.22	0.35	1	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.22	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.35	1	0	0	1	0	1	not limited	0	0	0	0	0
+female	0.22	0.35	1	0	0	2	0	3	otherwise	0	0	0	0	1
+female	0.22	0.45	0	0	0	0	0	0	otherwise	0	0	1	2	0
+female	0.22	0.45	0	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.22	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.45	0	0	0	0	0	3	otherwise	0	0	0	0	1
+female	0.22	0.45	0	0	0	0	0	4	otherwise	0	0	0	0	0
+female	0.22	0.45	0	0	0	1	0	5	not limited	2	0	0	0	2
+female	0.22	0.45	0	0	0	1	0	0	not limited	0	0	1	3	0
+female	0.22	0.45	0	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.22	0.45	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.22	0.45	0	0	0	2	0	0	not limited	1	0	0	0	3
+female	0.22	0.45	0	0	0	2	1	2	limited	1	0	0	0	4
+female	0.22	0.45	0	0	0	2	0	1	not limited	0	0	1	2	2
+female	0.22	0.45	0	0	0	2	0	0	limited	0	0	0	0	0
+female	0.22	0.45	0	0	0	2	0	0	not limited	0	0	0	0	1
+female	0.22	0.45	0	0	0	4	4	5	limited	1	0	0	0	1
+female	0.22	0.45	0	0	0	4	0	11	not limited	0	0	0	0	1
+female	0.22	0.45	0	1	0	3	5	10	not limited	5	0	1	4	1
+female	0.22	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	2
+female	0.22	0.45	1	0	0	1	0	4	not limited	0	0	1	1	0
+female	0.22	0.45	1	0	0	1	0	2	not limited	0	0	0	0	0
+female	0.22	0.45	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.22	0.45	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.22	0.45	1	0	0	2	0	0	limited	2	0	1	3	1
+female	0.22	0.45	1	0	0	2	0	0	otherwise	0	0	0	0	1
+female	0.22	0.45	1	0	0	2	1	3	not limited	0	0	0	0	1
+female	0.22	0.45	1	0	0	2	1	0	otherwise	0	0	0	0	1
+female	0.22	0.45	1	0	0	2	0	0	not limited	0	0	0	0	0
+female	0.22	0.45	1	0	0	2	3	0	otherwise	0	0	0	0	2
+female	0.22	0.45	1	0	0	3	1	1	not limited	2	0	0	0	0
+female	0.22	0.45	1	0	0	3	0	3	otherwise	0	0	0	0	1
+female	0.22	0.45	1	0	0	3	0	2	otherwise	0	0	0	0	2
+female	0.22	0.45	1	0	0	4	0	0	not limited	0	0	0	0	3
+female	0.22	0.45	1	0	0	4	1	4	not limited	0	0	0	0	3
+female	0.22	0.55	0	0	0	0	0	0	otherwise	1	0	0	0	0
+female	0.22	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.55	0	0	0	1	0	0	otherwise	1	0	0	0	1
+female	0.22	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.22	0.55	0	0	0	1	0	1	otherwise	0	1	0	0	2
+female	0.22	0.55	0	0	0	1	0	0	not limited	0	0	0	0	3
+female	0.22	0.55	0	0	0	1	0	1	otherwise	0	0	0	0	0
+female	0.22	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.22	0.55	0	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.22	0.55	0	0	0	2	0	6	otherwise	1	0	0	0	0
+female	0.22	0.55	0	0	0	2	0	0	not limited	1	0	0	0	1
+female	0.22	0.55	0	0	0	2	0	0	otherwise	1	0	0	0	0
+female	0.22	0.55	0	0	0	2	7	0	otherwise	3	2	0	0	0
+female	0.22	0.55	0	0	0	2	1	0	not limited	0	0	0	0	2
+female	0.22	0.55	0	0	0	2	0	3	not limited	0	0	0	0	1
+female	0.22	0.55	0	0	0	2	0	2	not limited	0	0	0	0	1
+female	0.22	0.55	0	0	0	2	0	0	otherwise	0	0	0	0	2
+female	0.22	0.55	0	0	0	2	1	3	not limited	0	0	0	0	2
+female	0.22	0.55	0	0	0	3	0	1	not limited	0	0	0	0	1
+female	0.22	0.55	0	0	0	4	0	4	not limited	0	0	0	0	1
+female	0.22	0.55	0	0	0	4	0	1	not limited	0	0	1	2	0
+female	0.22	0.55	0	0	0	5	0	8	not limited	0	0	0	0	2
+female	0.22	0.55	0	0	1	0	0	0	otherwise	0	0	1	7	1
+female	0.22	0.55	0	0	1	5	2	3	limited	2	0	0	0	6
+female	0.22	0.55	0	1	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.55	0	1	0	1	1	0	not limited	0	0	0	0	0
+female	0.22	0.55	0	1	0	1	0	0	otherwise	0	0	0	0	0
+female	0.22	0.55	0	1	0	3	14	1	not limited	0	2	1	7	0
+female	0.22	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	3
+female	0.22	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.55	1	0	0	0	0	3	otherwise	0	0	0	0	1
+female	0.22	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.55	1	0	0	0	0	0	otherwise	0	0	1	1	1
+female	0.22	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	2
+female	0.22	0.55	1	0	0	0	0	0	otherwise	0	0	1	5	0
+female	0.22	0.55	1	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.22	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.55	1	0	0	1	0	1	not limited	1	0	0	0	1
+female	0.22	0.55	1	0	0	1	0	0	otherwise	1	0	0	0	2
+female	0.22	0.55	1	0	0	1	0	0	otherwise	2	0	0	0	2
+female	0.22	0.55	1	0	0	1	7	0	otherwise	1	0	0	0	0
+female	0.22	0.55	1	0	0	1	0	6	otherwise	0	0	0	0	0
+female	0.22	0.55	1	0	0	1	0	0	not limited	0	0	0	0	3
+female	0.22	0.55	1	0	0	1	0	5	not limited	0	0	1	6	1
+female	0.22	0.55	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.22	0.55	1	0	0	1	0	1	otherwise	0	1	0	0	2
+female	0.22	0.55	1	0	0	1	2	2	otherwise	0	2	0	0	4
+female	0.22	0.55	1	0	0	1	0	0	limited	0	0	1	1	2
+female	0.22	0.55	1	0	0	1	0	2	not limited	0	0	0	0	3
+female	0.22	0.55	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.22	0.55	1	0	0	2	0	0	not limited	0	0	0	0	2
+female	0.22	0.55	1	0	0	2	14	6	limited	0	0	2	2	1
+female	0.22	0.55	1	0	0	2	0	0	not limited	0	0	0	0	0
+female	0.22	0.55	1	0	0	2	0	0	otherwise	0	0	0	0	0
+female	0.22	0.55	1	0	0	3	10	2	limited	2	0	1	4	2
+female	0.22	0.55	1	0	0	3	0	6	otherwise	0	0	0	0	0
+female	0.22	0.55	1	0	0	3	0	1	not limited	0	0	0	0	2
+female	0.22	0.55	1	0	0	3	1	5	limited	0	0	0	0	3
+female	0.22	0.55	1	0	0	3	0	4	limited	0	0	0	0	0
+female	0.22	0.55	1	0	0	3	0	0	not limited	0	1	0	0	1
+female	0.22	0.55	1	0	0	4	3	1	not limited	1	0	1	7	1
+female	0.22	0.65	0	0	0	0	0	0	not limited	1	0	0	0	2
+female	0.22	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.65	0	0	0	0	0	1	otherwise	0	0	0	0	3
+female	0.22	0.65	0	0	0	0	0	5	otherwise	0	0	0	0	1
+female	0.22	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.65	0	0	0	1	2	1	otherwise	0	0	0	0	2
+female	0.22	0.65	0	0	0	1	1	2	otherwise	0	0	0	0	1
+female	0.22	0.65	0	0	0	1	0	6	not limited	0	0	0	0	2
+female	0.22	0.65	0	0	0	1	0	2	otherwise	0	0	0	0	1
+female	0.22	0.65	0	0	0	1	1	1	not limited	0	1	0	0	2
+female	0.22	0.65	0	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.22	0.65	0	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.22	0.65	0	0	0	1	0	1	not limited	0	0	0	0	2
+female	0.22	0.65	0	0	0	1	0	0	otherwise	0	0	0	0	2
+female	0.22	0.65	0	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.22	0.65	0	0	0	1	0	0	otherwise	0	0	0	0	2
+female	0.22	0.65	0	0	0	1	0	0	otherwise	0	0	1	2	1
+female	0.22	0.65	0	0	0	2	0	0	otherwise	0	0	0	0	2
+female	0.22	0.65	0	0	0	2	0	2	otherwise	0	0	0	0	0
+female	0.22	0.65	0	0	0	2	0	0	not limited	0	0	0	0	0
+female	0.22	0.65	0	0	0	3	0	0	not limited	0	0	0	0	0
+female	0.22	0.65	0	0	0	3	2	1	otherwise	0	0	0	0	0
+female	0.22	0.65	0	0	0	3	0	2	otherwise	0	0	0	0	1
+female	0.22	0.65	0	0	0	3	1	0	not limited	0	0	0	0	2
+female	0.22	0.65	0	0	0	3	0	1	not limited	0	0	0	0	2
+female	0.22	0.65	0	0	0	4	0	1	not limited	1	0	1	2	2
+female	0.22	0.65	0	0	0	4	7	7	otherwise	1	7	0	0	2
+female	0.22	0.65	0	0	0	4	1	10	limited	0	2	0	0	1
+female	0.22	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.65	1	0	0	0	0	4	otherwise	0	0	0	0	2
+female	0.22	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	2
+female	0.22	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.65	1	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.22	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.65	1	0	0	0	0	0	otherwise	0	0	1	2	0
+female	0.22	0.65	1	0	0	0	0	0	not limited	0	0	1	80	2
+female	0.22	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	3
+female	0.22	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.65	1	0	0	0	0	3	otherwise	0	0	0	0	1
+female	0.22	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	3
+female	0.22	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.65	1	0	0	0	0	2	otherwise	0	0	0	0	2
+female	0.22	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.65	1	0	0	1	0	2	otherwise	2	0	0	0	3
+female	0.22	0.65	1	0	0	1	0	5	otherwise	1	0	0	0	2
+female	0.22	0.65	1	0	0	1	3	0	not limited	1	0	0	0	3
+female	0.22	0.65	1	0	0	1	0	1	otherwise	1	0	1	4	1
+female	0.22	0.65	1	0	0	1	0	2	otherwise	1	0	0	0	2
+female	0.22	0.65	1	0	0	1	1	0	otherwise	1	0	0	0	1
+female	0.22	0.65	1	0	0	1	0	8	limited	0	0	0	0	2
+female	0.22	0.65	1	0	0	1	0	0	otherwise	0	0	0	0	2
+female	0.22	0.65	1	0	0	1	2	0	not limited	0	0	0	0	0
+female	0.22	0.65	1	0	0	1	0	0	not limited	0	0	1	5	2
+female	0.22	0.65	1	0	0	1	0	1	not limited	0	0	0	0	0
+female	0.22	0.65	1	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.22	0.65	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.22	0.65	1	0	0	1	0	1	otherwise	0	0	0	0	2
+female	0.22	0.65	1	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.22	0.65	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.22	0.65	1	0	0	1	0	1	otherwise	0	0	0	0	0
+female	0.22	0.65	1	0	0	1	0	1	otherwise	0	0	0	0	1
+female	0.22	0.65	1	0	0	1	1	0	not limited	0	0	0	0	6
+female	0.22	0.65	1	0	0	1	0	0	not limited	0	0	1	4	1
+female	0.22	0.65	1	0	0	1	0	2	otherwise	0	0	0	0	2
+female	0.22	0.65	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.22	0.65	1	0	0	1	5	0	otherwise	0	0	0	0	2
+female	0.22	0.65	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.22	0.65	1	0	0	2	4	3	otherwise	1	0	0	0	3
+female	0.22	0.65	1	0	0	2	7	0	otherwise	1	0	0	0	2
+female	0.22	0.65	1	0	0	2	2	0	otherwise	1	0	0	0	0
+female	0.22	0.65	1	0	0	2	0	0	otherwise	0	0	0	0	0
+female	0.22	0.65	1	0	0	2	0	2	otherwise	0	0	0	0	2
+female	0.22	0.65	1	0	0	2	0	0	otherwise	0	0	0	0	0
+female	0.22	0.65	1	0	0	2	0	0	otherwise	0	0	0	0	0
+female	0.22	0.65	1	0	0	2	3	3	limited	0	4	0	0	4
+female	0.22	0.65	1	0	0	2	0	0	not limited	0	0	0	0	3
+female	0.22	0.65	1	0	0	2	0	1	otherwise	0	0	0	0	2
+female	0.22	0.65	1	0	0	2	0	1	otherwise	0	1	0	0	1
+female	0.22	0.65	1	0	0	2	0	2	not limited	0	0	1	4	1
+female	0.22	0.65	1	0	0	3	0	7	limited	1	1	0	0	1
+female	0.22	0.65	1	0	0	3	2	0	limited	2	0	0	0	2
+female	0.22	0.65	1	0	0	3	2	1	limited	0	0	0	0	1
+female	0.22	0.65	1	0	0	3	0	0	otherwise	0	0	0	0	2
+female	0.22	0.65	1	0	0	3	0	3	not limited	0	0	1	1	1
+female	0.22	0.65	1	0	0	3	0	1	limited	0	0	0	0	2
+female	0.22	0.65	1	0	0	3	0	0	not limited	0	0	0	0	1
+female	0.22	0.65	1	0	0	3	0	1	otherwise	0	0	0	0	2
+female	0.22	0.65	1	0	0	4	0	6	not limited	0	0	0	0	3
+female	0.22	0.65	1	0	0	4	0	7	otherwise	0	0	0	0	1
+female	0.22	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.75	0	0	0	0	0	2	not limited	0	0	0	0	0
+female	0.22	0.75	0	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.22	0.75	0	0	0	0	0	0	otherwise	0	0	1	1	0
+female	0.22	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.75	0	0	0	1	0	0	otherwise	1	0	0	0	1
+female	0.22	0.75	0	0	0	1	0	0	otherwise	1	0	0	0	1
+female	0.22	0.75	0	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.22	0.75	0	0	0	1	1	2	not limited	0	0	1	11	1
+female	0.22	0.75	0	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.22	0.75	0	0	0	1	0	0	otherwise	0	0	0	0	3
+female	0.22	0.75	0	0	0	1	14	8	not limited	0	0	0	0	8
+female	0.22	0.75	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.22	0.75	0	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.22	0.75	0	0	0	2	0	0	not limited	1	1	1	1	2
+female	0.22	0.75	0	0	0	2	14	0	otherwise	0	3	0	0	2
+female	0.22	0.75	0	0	0	2	0	3	otherwise	0	0	0	0	1
+female	0.22	0.75	0	0	0	3	1	0	not limited	1	0	0	0	1
+female	0.22	0.75	0	0	0	3	14	2	not limited	2	0	0	0	2
+female	0.22	0.75	0	0	0	3	0	2	not limited	0	7	0	0	1
+female	0.22	0.75	0	0	0	3	0	0	limited	0	1	0	0	0
+female	0.22	0.75	0	0	0	3	1	0	not limited	0	0	0	0	0
+female	0.22	0.75	0	0	0	4	0	0	not limited	0	0	0	0	3
+female	0.22	0.75	0	0	0	5	0	3	otherwise	0	0	0	0	3
+female	0.22	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.75	1	0	0	0	0	0	otherwise	0	0	1	5	1
+female	0.22	0.75	1	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.22	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.75	1	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.22	0.75	1	0	0	0	0	0	not limited	0	0	1	11	0
+female	0.22	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.75	1	0	0	0	0	5	otherwise	0	0	0	0	1
+female	0.22	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.75	1	0	0	1	0	1	not limited	1	1	0	0	1
+female	0.22	0.75	1	0	0	1	0	0	otherwise	1	0	0	0	0
+female	0.22	0.75	1	0	0	1	0	4	otherwise	2	0	0	0	0
+female	0.22	0.75	1	0	0	1	0	4	limited	0	0	0	0	1
+female	0.22	0.75	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.22	0.75	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.22	0.75	1	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.22	0.75	1	0	0	1	1	4	limited	0	0	0	0	0
+female	0.22	0.75	1	0	0	1	0	1	not limited	0	0	0	0	0
+female	0.22	0.75	1	0	0	1	1	0	otherwise	0	0	0	0	1
+female	0.22	0.75	1	0	0	1	0	1	not limited	0	0	0	0	0
+female	0.22	0.75	1	0	0	1	2	0	not limited	0	0	0	0	1
+female	0.22	0.75	1	0	0	1	0	1	otherwise	0	0	0	0	0
+female	0.22	0.75	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.22	0.75	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.22	0.75	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.22	0.75	1	0	0	1	0	1	otherwise	0	0	0	0	0
+female	0.22	0.75	1	0	0	2	0	3	otherwise	1	0	0	0	3
+female	0.22	0.75	1	0	0	2	0	2	not limited	2	0	0	0	1
+female	0.22	0.75	1	0	0	2	14	2	not limited	2	0	0	0	6
+female	0.22	0.75	1	0	0	2	3	0	not limited	1	0	1	11	4
+female	0.22	0.75	1	0	0	2	0	3	otherwise	0	0	0	0	1
+female	0.22	0.75	1	0	0	2	0	0	not limited	0	0	0	0	1
+female	0.22	0.75	1	0	0	3	0	0	not limited	0	0	0	0	1
+female	0.22	0.75	1	0	0	3	0	0	not limited	0	0	0	0	1
+female	0.22	0.75	1	0	0	3	0	2	otherwise	0	0	0	0	0
+female	0.22	0.75	1	0	0	4	0	3	otherwise	0	0	0	0	4
+female	0.22	0.75	1	0	0	4	0	3	not limited	0	0	0	0	1
+female	0.22	0.75	1	0	0	4	0	0	otherwise	0	0	0	0	2
+female	0.22	0.75	1	0	0	5	0	5	not limited	0	0	0	0	4
+female	0.22	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.9	0	0	0	0	0	5	otherwise	0	0	0	0	1
+female	0.22	0.9	0	0	0	0	0	1	not limited	0	0	0	0	0
+female	0.22	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.9	0	0	0	1	4	0	otherwise	1	0	0	0	1
+female	0.22	0.9	0	0	0	1	14	0	otherwise	1	1	1	11	0
+female	0.22	0.9	0	0	0	1	6	0	otherwise	1	0	0	0	1
+female	0.22	0.9	0	0	0	1	0	1	otherwise	0	0	0	0	1
+female	0.22	0.9	0	0	0	1	0	2	otherwise	0	0	0	0	2
+female	0.22	0.9	0	0	0	1	0	0	not limited	0	0	2	22	0
+female	0.22	0.9	0	0	0	2	0	0	not limited	1	0	0	0	0
+female	0.22	0.9	0	0	0	2	0	0	otherwise	1	0	0	0	2
+female	0.22	0.9	0	0	0	2	0	6	otherwise	0	0	1	5	2
+female	0.22	0.9	0	0	0	2	0	6	otherwise	0	0	1	3	0
+female	0.22	0.9	0	0	0	2	2	0	otherwise	0	0	0	0	1
+female	0.22	0.9	0	0	0	2	0	0	otherwise	0	0	0	0	2
+female	0.22	0.9	0	0	0	3	0	2	otherwise	0	4	0	0	0
+female	0.22	0.9	0	0	0	3	0	3	limited	0	0	1	3	2
+female	0.22	0.9	0	0	0	3	0	0	otherwise	0	1	0	0	0
+female	0.22	0.9	0	0	0	4	0	2	not limited	0	0	0	0	1
+female	0.22	0.9	0	0	0	4	1	12	not limited	0	0	0	0	1
+female	0.22	0.9	1	0	0	0	0	0	otherwise	1	0	0	0	0
+female	0.22	0.9	1	0	0	0	0	0	otherwise	1	0	0	0	0
+female	0.22	0.9	1	0	0	0	0	0	otherwise	1	0	1	7	2
+female	0.22	0.9	1	0	0	0	0	2	otherwise	1	0	0	0	0
+female	0.22	0.9	1	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.22	0.9	1	0	0	0	0	0	limited	0	1	0	0	0
+female	0.22	0.9	1	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.22	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.9	1	0	0	0	0	0	limited	0	1	0	0	2
+female	0.22	0.9	1	0	0	0	0	2	otherwise	0	0	0	0	1
+female	0.22	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	2
+female	0.22	0.9	1	0	0	0	0	0	otherwise	0	0	1	4	1
+female	0.22	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.22	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	0.9	1	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.22	0.9	1	0	0	1	0	1	not limited	1	0	0	0	0
+female	0.22	0.9	1	0	0	1	2	0	not limited	1	1	0	0	1
+female	0.22	0.9	1	0	0	1	0	0	not limited	0	0	0	0	3
+female	0.22	0.9	1	0	0	1	0	0	otherwise	0	0	1	3	0
+female	0.22	0.9	1	0	0	1	0	4	otherwise	0	0	1	2	0
+female	0.22	0.9	1	0	0	1	0	2	not limited	0	0	0	0	0
+female	0.22	0.9	1	0	0	1	0	5	otherwise	0	0	0	0	0
+female	0.22	0.9	1	0	0	1	1	0	not limited	0	0	0	0	2
+female	0.22	0.9	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.22	0.9	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.22	0.9	1	0	0	1	0	4	not limited	0	0	0	0	1
+female	0.22	0.9	1	0	0	1	0	2	otherwise	0	0	0	0	0
+female	0.22	0.9	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.22	0.9	1	0	0	2	0	0	not limited	1	0	0	0	2
+female	0.22	0.9	1	0	0	2	0	6	not limited	1	0	1	2	2
+female	0.22	0.9	1	0	0	2	0	3	limited	0	0	0	0	2
+female	0.22	0.9	1	0	0	2	0	0	otherwise	0	0	0	0	1
+female	0.22	0.9	1	0	0	2	0	3	not limited	0	2	1	5	2
+female	0.22	0.9	1	0	0	2	0	2	otherwise	0	0	1	1	2
+female	0.22	0.9	1	0	0	2	0	1	otherwise	0	0	0	0	1
+female	0.22	0.9	1	0	0	2	0	0	limited	0	0	1	5	3
+female	0.22	0.9	1	0	0	2	0	0	not limited	0	0	0	0	1
+female	0.22	0.9	1	0	0	2	1	2	otherwise	0	0	0	0	2
+female	0.22	0.9	1	0	0	2	0	0	not limited	0	0	0	0	1
+female	0.22	0.9	1	0	0	2	0	0	not limited	0	0	0	0	0
+female	0.22	0.9	1	0	0	2	0	0	limited	0	0	1	1	0
+female	0.22	0.9	1	0	0	2	0	1	not limited	0	0	0	0	1
+female	0.22	0.9	1	0	0	3	0	0	otherwise	0	0	0	0	0
+female	0.22	0.9	1	0	0	3	1	2	otherwise	0	0	0	0	2
+female	0.22	0.9	1	0	0	4	1	0	not limited	1	0	0	0	2
+female	0.22	0.9	1	0	0	4	3	2	limited	0	1	0	0	4
+female	0.22	1.1	0	0	0	0	0	0	otherwise	1	0	0	0	0
+female	0.22	1.1	0	0	0	0	0	3	otherwise	0	0	0	0	2
+female	0.22	1.1	0	0	0	0	0	0	not limited	0	0	0	0	4
+female	0.22	1.1	0	0	0	1	5	2	not limited	0	0	0	0	1
+female	0.22	1.1	0	0	0	1	0	0	otherwise	0	0	0	0	2
+female	0.22	1.1	0	0	0	1	14	1	otherwise	0	9	0	0	0
+female	0.22	1.1	0	0	0	1	14	0	not limited	0	0	0	0	1
+female	0.22	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.22	1.1	1	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.22	1.1	1	0	0	0	0	0	limited	0	0	1	6	1
+female	0.22	1.1	1	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.22	1.1	1	0	0	0	0	0	not limited	0	1	0	0	0
+female	0.22	1.1	1	0	0	1	0	0	not limited	1	0	0	0	4
+female	0.22	1.1	1	0	0	1	0	1	otherwise	1	0	0	0	0
+female	0.22	1.1	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.22	1.1	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.22	1.1	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.22	1.1	1	0	0	1	0	0	limited	0	0	0	0	2
+female	0.22	1.1	1	0	0	1	0	2	not limited	0	0	0	0	1
+female	0.22	1.1	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.22	1.1	1	0	0	1	0	3	not limited	0	0	0	0	0
+female	0.22	1.1	1	0	0	2	0	0	otherwise	0	0	0	0	1
+female	0.22	1.1	1	0	0	2	0	6	not limited	0	0	0	0	0
+female	0.22	1.1	1	0	0	2	0	3	otherwise	0	0	0	0	1
+female	0.22	1.1	1	0	0	2	0	3	limited	0	0	0	0	3
+female	0.22	1.1	1	0	0	2	0	0	not limited	0	0	0	0	0
+female	0.22	1.1	1	0	0	2	0	0	otherwise	0	0	0	0	0
+female	0.22	1.1	1	0	0	3	0	4	otherwise	0	0	0	0	2
+female	0.22	1.1	1	0	0	5	0	1	not limited	1	0	0	0	0
+female	0.22	1.3	0	0	0	2	0	2	not limited	0	0	0	0	1
+female	0.27	0	0	0	1	1	0	5	otherwise	3	0	1	1	1
+female	0.27	0	1	0	0	2	0	5	otherwise	2	0	4	7	4
+female	0.27	0.01	0	0	0	1	0	0	otherwise	0	0	0	0	3
+female	0.27	0.01	0	1	0	5	1	2	limited	0	0	0	0	1
+female	0.27	0.01	1	0	0	1	0	0	not limited	0	0	2	22	1
+female	0.27	0.06	0	0	0	1	0	1	limited	0	1	0	0	1
+female	0.27	0.06	0	0	1	0	0	0	otherwise	0	0	0	0	2
+female	0.27	0.06	0	1	0	5	3	12	not limited	1	0	0	0	3
+female	0.27	0.15	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.27	0.15	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.27	0.15	0	0	0	1	0	2	otherwise	0	0	0	0	1
+female	0.27	0.15	0	0	0	3	3	6	otherwise	1	0	1	4	0
+female	0.27	0.15	0	0	1	1	1	3	otherwise	2	1	0	0	1
+female	0.27	0.15	0	1	0	0	0	0	otherwise	0	0	0	0	1
+female	0.27	0.15	1	0	0	3	1	1	not limited	0	0	0	0	0
+female	0.27	0.25	0	0	0	2	14	1	not limited	0	7	0	0	1
+female	0.27	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	0
+female	0.27	0.25	0	0	1	2	0	0	limited	1	0	0	0	1
+female	0.27	0.25	0	0	1	2	0	1	not limited	0	0	0	0	0
+female	0.27	0.25	0	1	0	0	0	3	otherwise	0	0	0	0	0
+female	0.27	0.25	1	0	0	0	0	0	limited	0	0	0	0	0
+female	0.27	0.25	1	0	0	1	4	0	limited	1	0	0	0	2
+female	0.27	0.25	1	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.27	0.25	1	0	0	2	7	6	limited	0	0	2	5	2
+female	0.27	0.35	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.27	0.35	0	0	1	1	0	4	otherwise	1	0	0	0	0
+female	0.27	0.35	0	1	0	2	0	0	not limited	0	0	1	11	1
+female	0.27	0.35	1	0	0	1	14	1	not limited	5	1	1	3	2
+female	0.27	0.35	1	0	0	1	0	0	otherwise	0	1	0	0	1
+female	0.27	0.35	1	0	0	3	0	0	not limited	0	0	0	0	0
+female	0.27	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.27	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.27	0.45	0	0	0	1	0	0	otherwise	0	0	1	1	1
+female	0.27	0.45	0	0	0	2	0	2	limited	1	1	0	0	1
+female	0.27	0.45	0	0	0	2	0	2	otherwise	0	0	0	0	1
+female	0.27	0.45	0	1	0	0	0	0	limited	0	0	1	11	2
+female	0.27	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.27	0.45	1	0	0	2	0	1	not limited	0	0	0	0	0
+female	0.27	0.55	0	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.27	0.55	0	0	0	1	0	3	otherwise	1	0	0	0	2
+female	0.27	0.55	0	0	0	1	2	0	otherwise	0	0	0	0	1
+female	0.27	0.55	0	0	0	2	8	10	not limited	2	0	0	0	2
+female	0.27	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.27	0.55	1	0	0	0	0	2	otherwise	0	0	0	0	1
+female	0.27	0.55	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.27	0.55	1	0	0	3	3	1	not limited	1	6	0	0	2
+female	0.27	0.55	1	0	0	3	0	0	not limited	0	1	0	0	6
+female	0.27	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.27	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.27	0.65	0	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.27	0.65	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.27	0.65	0	0	0	2	5	1	not limited	1	0	0	0	2
+female	0.27	0.65	0	0	0	3	14	10	not limited	1	0	0	0	2
+female	0.27	0.65	0	0	0	4	0	2	otherwise	1	0	0	0	1
+female	0.27	0.65	0	1	0	0	0	1	otherwise	0	0	0	0	0
+female	0.27	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	2
+female	0.27	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	2
+female	0.27	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.27	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.27	0.65	1	0	0	1	4	1	not limited	1	0	0	0	1
+female	0.27	0.65	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.27	0.65	1	0	0	1	1	0	not limited	0	0	0	0	0
+female	0.27	0.65	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.27	0.65	1	0	0	2	3	0	otherwise	1	0	0	0	2
+female	0.27	0.75	0	0	0	0	0	0	otherwise	1	0	0	0	1
+female	0.27	0.75	0	0	0	1	1	0	otherwise	0	0	0	0	0
+female	0.27	0.75	0	0	0	2	8	0	otherwise	7	1	1	11	0
+female	0.27	0.75	0	0	0	2	0	0	not limited	0	0	0	0	4
+female	0.27	0.75	0	0	0	3	0	0	not limited	0	0	0	0	2
+female	0.27	0.75	0	0	0	4	0	0	not limited	0	0	0	0	4
+female	0.27	0.75	1	0	0	0	0	2	otherwise	1	0	0	0	0
+female	0.27	0.75	1	0	0	0	0	0	not limited	0	0	0	0	5
+female	0.27	0.75	1	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.27	0.75	1	0	0	0	0	0	otherwise	0	0	1	11	0
+female	0.27	0.75	1	0	0	0	0	0	not limited	0	0	0	0	4
+female	0.27	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	2
+female	0.27	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.27	0.75	1	0	0	0	0	1	otherwise	0	0	1	6	0
+female	0.27	0.75	1	0	0	1	1	0	otherwise	2	0	0	0	0
+female	0.27	0.75	1	0	0	1	14	0	not limited	1	0	0	0	0
+female	0.27	0.75	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.27	0.75	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.27	0.75	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.27	0.75	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.27	0.75	1	0	0	1	0	1	not limited	0	0	0	0	1
+female	0.27	0.75	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.27	0.75	1	0	0	1	0	2	not limited	0	0	0	0	0
+female	0.27	0.75	1	0	0	1	0	2	otherwise	0	1	0	0	0
+female	0.27	0.75	1	0	0	2	2	2	limited	2	0	0	0	6
+female	0.27	0.75	1	0	0	2	3	1	not limited	1	0	0	0	1
+female	0.27	0.75	1	0	0	2	0	0	otherwise	0	0	0	0	1
+female	0.27	0.75	1	0	0	2	0	2	otherwise	0	0	0	0	1
+female	0.27	0.9	0	0	0	0	0	1	not limited	0	0	0	0	3
+female	0.27	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.27	0.9	0	0	0	1	0	5	not limited	0	0	0	0	1
+female	0.27	0.9	0	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.27	0.9	0	0	0	1	0	6	not limited	0	0	0	0	2
+female	0.27	0.9	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.27	0.9	0	0	0	1	0	0	otherwise	0	0	1	11	0
+female	0.27	0.9	0	0	0	1	0	0	otherwise	0	0	0	0	2
+female	0.27	0.9	0	0	0	1	1	0	otherwise	0	0	0	0	1
+female	0.27	0.9	0	0	0	2	0	8	limited	0	0	0	0	4
+female	0.27	0.9	0	0	0	2	0	1	otherwise	0	0	0	0	1
+female	0.27	0.9	0	0	0	3	0	4	not limited	1	0	0	0	2
+female	0.27	0.9	0	0	0	3	14	4	limited	2	6	4	3	0
+female	0.27	0.9	0	0	0	5	14	8	otherwise	0	0	0	0	3
+female	0.27	0.9	1	0	0	0	0	0	otherwise	1	0	0	0	1
+female	0.27	0.9	1	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.27	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	2
+female	0.27	0.9	1	0	0	0	0	0	not limited	0	0	0	0	4
+female	0.27	0.9	1	0	0	0	0	1	not limited	0	0	0	0	1
+female	0.27	0.9	1	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.27	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.27	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.27	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.27	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.27	0.9	1	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.27	0.9	1	0	0	0	0	0	otherwise	0	0	1	6	1
+female	0.27	0.9	1	0	0	0	0	1	otherwise	0	0	1	11	1
+female	0.27	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.27	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.27	0.9	1	0	0	1	0	3	otherwise	2	0	0	0	2
+female	0.27	0.9	1	0	0	1	0	4	limited	2	0	0	0	2
+female	0.27	0.9	1	0	0	1	0	0	not limited	1	0	1	2	0
+female	0.27	0.9	1	0	0	1	3	0	otherwise	7	0	0	0	3
+female	0.27	0.9	1	0	0	1	0	1	not limited	0	0	0	0	2
+female	0.27	0.9	1	0	0	1	7	0	otherwise	0	0	0	0	4
+female	0.27	0.9	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.27	0.9	1	0	0	1	0	11	not limited	0	0	0	0	2
+female	0.27	0.9	1	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.27	0.9	1	0	0	1	0	5	otherwise	0	0	0	0	0
+female	0.27	0.9	1	0	0	2	0	2	otherwise	1	0	0	0	2
+female	0.27	0.9	1	0	0	2	0	1	otherwise	0	0	0	0	2
+female	0.27	0.9	1	0	0	2	6	0	not limited	0	1	0	0	5
+female	0.27	0.9	1	0	0	2	0	0	not limited	0	0	0	0	1
+female	0.27	0.9	1	0	0	2	0	0	not limited	0	0	0	0	0
+female	0.27	0.9	1	0	0	3	0	5	not limited	0	0	0	0	7
+female	0.27	0.9	1	0	0	3	0	0	otherwise	0	0	0	0	0
+female	0.27	0.9	1	0	0	3	0	4	not limited	0	0	0	0	1
+female	0.27	0.9	1	0	0	3	0	0	otherwise	0	0	0	0	0
+female	0.27	0.9	1	0	0	4	0	0	not limited	2	0	0	0	0
+female	0.27	0.9	1	0	0	5	1	5	not limited	1	1	0	0	1
+female	0.27	0.9	1	0	0	5	1	11	not limited	2	0	0	0	3
+female	0.27	0.9	1	0	0	5	0	1	not limited	0	0	0	0	4
+female	0.27	1.1	0	0	0	0	0	1	not limited	0	0	0	0	0
+female	0.27	1.1	0	0	0	0	0	3	otherwise	0	0	0	0	1
+female	0.27	1.1	0	0	0	0	0	2	otherwise	0	0	0	0	1
+female	0.27	1.1	0	0	0	1	0	5	not limited	0	0	0	0	1
+female	0.27	1.1	0	0	0	1	0	6	otherwise	0	0	0	0	1
+female	0.27	1.1	0	0	0	2	0	1	otherwise	1	6	0	0	0
+female	0.27	1.1	0	0	0	3	3	0	otherwise	1	0	0	0	1
+female	0.27	1.1	0	1	0	0	0	1	otherwise	0	0	0	0	1
+female	0.27	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.27	1.1	1	0	0	1	0	0	otherwise	1	0	0	0	1
+female	0.27	1.1	1	0	0	1	2	2	not limited	1	0	0	0	4
+female	0.27	1.1	1	0	0	1	0	0	otherwise	2	0	0	0	1
+female	0.27	1.1	1	0	0	1	0	2	otherwise	0	0	0	0	1
+female	0.27	1.1	1	0	0	1	0	4	limited	0	0	0	0	3
+female	0.27	1.1	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.27	1.1	1	0	0	1	0	0	not limited	0	0	1	3	0
+female	0.27	1.1	1	0	0	1	0	0	not limited	0	0	0	0	4
+female	0.27	1.1	1	0	0	1	2	1	not limited	0	0	1	2	3
+female	0.27	1.1	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.27	1.1	1	0	0	2	3	8	not limited	1	0	0	0	3
+female	0.27	1.1	1	0	0	2	0	2	not limited	0	0	0	0	1
+female	0.27	1.1	1	0	0	2	1	1	not limited	0	2	0	0	3
+female	0.27	1.1	1	0	0	2	1	0	not limited	0	2	0	0	2
+female	0.27	1.1	1	0	0	3	0	4	otherwise	0	0	0	0	0
+female	0.27	1.3	0	0	0	0	0	0	otherwise	0	1	0	0	1
+female	0.27	1.3	0	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.27	1.3	0	0	0	2	5	0	not limited	1	0	0	0	2
+female	0.27	1.3	0	0	0	3	0	6	not limited	0	0	0	0	6
+female	0.27	1.3	1	0	0	0	0	3	not limited	0	0	0	0	2
+female	0.27	1.3	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.27	1.3	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.27	1.3	1	0	0	1	7	4	not limited	2	0	0	0	0
+female	0.27	1.3	1	0	0	1	7	2	otherwise	2	0	1	2	1
+female	0.27	1.3	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.27	1.3	1	0	0	1	0	1	otherwise	0	0	0	0	0
+female	0.27	1.3	1	0	0	2	1	0	limited	1	2	0	0	3
+female	0.27	1.3	1	0	0	2	0	0	otherwise	0	0	0	0	0
+female	0.27	1.3	1	0	0	2	0	0	otherwise	0	0	0	0	1
+female	0.27	1.5	1	0	0	0	0	0	otherwise	1	0	0	0	0
+female	0.27	1.5	1	0	0	1	0	6	otherwise	1	0	0	0	2
+female	0.27	1.5	1	0	0	2	0	11	otherwise	0	0	0	0	0
+female	0.27	1.5	1	0	0	5	0	2	limited	0	0	0	0	1
+female	0.32	0	0	1	0	3	0	8	not limited	0	0	0	0	4
+female	0.32	0.06	1	0	0	1	0	6	not limited	0	0	0	0	0
+female	0.32	0.15	0	0	0	4	0	6	otherwise	1	0	0	0	3
+female	0.32	0.15	1	0	0	0	0	1	otherwise	2	0	0	0	1
+female	0.32	0.15	1	0	0	4	14	0	limited	7	6	5	22	8
+female	0.32	0.25	0	0	1	2	0	0	limited	1	0	0	0	6
+female	0.32	0.25	0	1	0	4	0	8	not limited	0	0	1	4	1
+female	0.32	0.25	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.32	0.25	1	0	0	1	0	0	otherwise	0	0	2	11	2
+female	0.32	0.25	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.32	0.35	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.32	0.35	0	0	0	1	0	8	otherwise	0	0	0	0	2
+female	0.32	0.35	1	0	0	5	0	0	limited	0	0	0	0	2
+female	0.32	0.45	1	0	0	1	5	7	limited	1	7	3	11	4
+female	0.32	0.45	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.32	0.55	0	0	0	0	0	2	limited	0	0	0	0	1
+female	0.32	0.55	0	0	0	0	0	2	otherwise	0	0	0	0	0
+female	0.32	0.55	0	0	0	0	0	0	limited	0	0	0	0	0
+female	0.32	0.55	0	0	0	1	0	0	not limited	0	1	0	0	1
+female	0.32	0.55	0	0	0	4	14	1	otherwise	0	0	0	0	0
+female	0.32	0.55	0	0	1	1	14	8	limited	4	0	1	22	3
+female	0.32	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.32	0.55	1	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.32	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.32	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.32	0.65	0	0	0	1	8	0	otherwise	1	0	0	0	3
+female	0.32	0.65	0	0	0	1	0	1	otherwise	0	0	1	3	1
+female	0.32	0.65	0	0	0	3	0	1	limited	0	0	1	2	1
+female	0.32	0.65	1	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.32	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.32	0.65	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.32	0.65	1	0	0	2	0	0	otherwise	0	0	1	1	0
+female	0.32	0.65	1	0	0	3	0	0	not limited	0	0	0	0	0
+female	0.32	0.75	0	0	0	0	0	0	otherwise	0	0	1	4	0
+female	0.32	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.32	0.75	0	0	0	2	0	2	otherwise	0	0	0	0	1
+female	0.32	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.32	0.75	1	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.32	0.75	1	0	0	1	0	0	limited	1	0	0	0	0
+female	0.32	0.75	1	0	0	1	0	1	otherwise	1	0	0	0	2
+female	0.32	0.75	1	0	0	1	0	0	otherwise	0	0	0	0	2
+female	0.32	0.75	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.32	0.75	1	0	0	2	0	0	not limited	0	0	0	0	2
+female	0.32	0.75	1	0	0	3	0	2	not limited	0	0	1	3	2
+female	0.32	0.75	1	0	0	5	0	5	not limited	0	4	0	0	4
+female	0.32	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.32	0.9	1	0	0	0	0	0	otherwise	1	0	0	0	2
+female	0.32	0.9	1	0	0	0	0	3	not limited	0	0	0	0	3
+female	0.32	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.32	0.9	1	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.32	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.32	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.32	0.9	1	0	0	1	0	0	otherwise	1	0	0	0	0
+female	0.32	0.9	1	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.32	0.9	1	0	0	1	0	0	otherwise	0	0	1	11	1
+female	0.32	0.9	1	0	0	1	0	7	otherwise	0	0	0	0	0
+female	0.32	0.9	1	0	0	1	0	0	otherwise	0	0	0	0	2
+female	0.32	0.9	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.32	0.9	1	0	0	1	0	4	otherwise	0	0	0	0	0
+female	0.32	0.9	1	0	0	2	0	0	otherwise	0	2	0	0	0
+female	0.32	0.9	1	0	0	2	0	0	otherwise	0	0	0	0	1
+female	0.32	0.9	1	0	0	3	0	3	not limited	3	0	0	0	1
+female	0.32	0.9	1	0	0	4	5	1	not limited	2	0	0	0	2
+female	0.32	0.9	1	0	0	4	0	3	not limited	0	0	0	0	0
+female	0.32	1.1	1	0	0	0	0	1	otherwise	0	0	0	0	1
+female	0.32	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.32	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.32	1.1	1	0	0	0	0	2	otherwise	0	0	0	0	0
+female	0.32	1.1	1	0	0	1	0	0	limited	0	0	0	0	1
+female	0.32	1.1	1	0	0	2	2	0	not limited	1	0	0	0	2
+female	0.32	1.1	1	0	0	2	0	0	not limited	0	0	0	0	2
+female	0.32	1.1	1	0	0	2	0	1	not limited	0	0	0	0	3
+female	0.32	1.1	1	0	0	2	0	1	otherwise	0	0	0	0	2
+female	0.32	1.1	1	0	0	2	0	2	otherwise	0	0	1	1	1
+female	0.32	1.1	1	0	0	3	0	2	not limited	0	0	0	0	0
+female	0.32	1.1	1	0	0	3	0	3	not limited	0	0	0	0	0
+female	0.32	1.1	1	0	0	3	0	2	not limited	0	0	0	0	3
+female	0.32	1.1	1	0	0	4	2	6	not limited	1	0	0	0	4
+female	0.32	1.1	1	0	0	5	1	4	not limited	2	0	0	0	4
+female	0.32	1.1	1	0	0	5	0	0	not limited	0	0	0	0	3
+female	0.32	1.3	0	0	0	0	0	2	otherwise	0	0	0	0	0
+female	0.32	1.3	0	0	0	0	0	1	otherwise	0	0	1	1	1
+female	0.32	1.3	0	0	0	2	0	0	otherwise	0	0	0	0	0
+female	0.32	1.3	0	0	0	3	6	1	not limited	0	0	0	0	1
+female	0.32	1.3	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.32	1.3	1	0	0	1	0	0	otherwise	1	0	0	0	6
+female	0.32	1.3	1	0	0	1	6	4	limited	4	0	0	0	1
+female	0.32	1.3	1	0	0	1	2	2	otherwise	0	0	0	0	1
+female	0.32	1.3	1	0	0	2	0	0	limited	1	0	0	0	2
+female	0.32	1.5	0	0	0	0	0	3	otherwise	0	0	0	0	0
+female	0.32	1.5	0	0	0	4	14	4	not limited	0	0	0	0	2
+female	0.32	1.5	1	0	0	0	0	0	limited	0	0	0	0	0
+female	0.32	1.5	1	0	0	1	5	2	not limited	1	0	0	0	2
+female	0.32	1.5	1	0	0	1	0	0	otherwise	0	1	0	0	3
+female	0.32	1.5	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.32	1.5	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.32	1.5	1	0	0	1	0	2	otherwise	0	0	0	0	0
+female	0.32	1.5	1	0	0	1	0	1	otherwise	0	0	0	0	0
+female	0.32	1.5	1	0	0	1	1	0	not limited	0	0	0	0	2
+female	0.32	1.5	1	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.32	1.5	1	0	0	2	0	0	not limited	0	0	0	0	2
+female	0.32	1.5	1	0	0	2	0	0	not limited	0	0	0	0	1
+female	0.32	1.5	1	0	0	2	0	0	otherwise	0	0	0	0	0
+female	0.32	1.5	1	0	0	3	0	0	not limited	0	0	0	0	1
+female	0.37	0.06	0	1	0	2	0	0	otherwise	1	0	0	0	0
+female	0.37	0.15	1	0	0	2	0	1	not limited	0	2	0	0	1
+female	0.37	0.25	0	0	0	3	0	0	not limited	1	0	0	0	3
+female	0.37	0.25	0	0	1	1	0	0	limited	0	0	0	0	2
+female	0.37	0.25	0	0	1	1	14	4	limited	0	0	0	0	0
+female	0.37	0.25	0	0	1	1	0	5	otherwise	0	0	0	0	1
+female	0.37	0.25	0	0	1	1	0	1	otherwise	0	0	0	0	0
+female	0.37	0.25	0	0	1	2	14	10	limited	0	0	2	7	0
+female	0.37	0.25	0	0	1	4	8	9	limited	0	0	0	0	1
+female	0.37	0.25	1	0	0	3	1	1	limited	1	0	0	0	4
+female	0.37	0.35	1	0	0	1	14	7	limited	3	1	0	0	1
+female	0.37	0.35	1	0	0	2	0	2	limited	0	0	0	0	2
+female	0.37	0.45	0	0	0	1	7	2	not limited	3	0	0	0	1
+female	0.37	0.45	1	0	0	2	14	4	otherwise	1	1	0	0	4
+female	0.37	0.55	0	0	0	0	0	0	otherwise	0	0	1	2	1
+female	0.37	0.55	0	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.37	0.55	0	0	0	1	0	2	otherwise	0	0	0	0	1
+female	0.37	0.55	1	0	0	0	0	0	limited	0	0	1	5	0
+female	0.37	0.55	1	0	0	1	3	0	otherwise	1	0	0	0	2
+female	0.37	0.65	0	0	0	0	0	1	otherwise	0	0	0	0	1
+female	0.37	0.65	1	0	0	0	0	0	otherwise	0	0	1	2	1
+female	0.37	0.65	1	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.37	0.65	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.37	0.65	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.37	0.65	1	0	0	2	1	5	not limited	2	0	0	0	4
+female	0.37	0.65	1	0	0	4	0	0	otherwise	0	0	0	0	4
+female	0.37	0.75	0	0	0	1	7	2	not limited	0	0	0	0	4
+female	0.37	0.75	1	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.37	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.37	0.75	1	0	0	2	0	5	not limited	0	9	0	0	7
+female	0.37	0.75	1	0	0	2	1	0	limited	0	4	1	3	2
+female	0.37	0.9	0	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.37	0.9	0	0	0	1	0	0	not limited	0	0	0	0	3
+female	0.37	0.9	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.37	0.9	0	0	0	1	0	3	not limited	0	0	0	0	0
+female	0.37	0.9	0	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.37	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.37	0.9	1	0	0	1	1	0	not limited	1	0	0	0	3
+female	0.37	0.9	1	0	0	1	0	0	not limited	1	0	0	0	1
+female	0.37	0.9	1	0	0	1	0	1	otherwise	0	0	0	0	0
+female	0.37	0.9	1	0	0	1	0	2	otherwise	0	0	0	0	1
+female	0.37	0.9	1	0	0	1	0	0	limited	0	1	0	0	0
+female	0.37	0.9	1	0	0	2	0	0	not limited	0	0	0	0	4
+female	0.37	0.9	1	0	0	2	0	1	otherwise	0	0	0	0	5
+female	0.37	0.9	1	0	0	3	3	0	limited	2	0	5	11	4
+female	0.37	0.9	1	0	0	5	0	5	otherwise	0	0	0	0	0
+female	0.37	1.1	1	0	0	1	0	0	otherwise	1	0	0	0	1
+female	0.37	1.1	1	0	0	1	0	2	otherwise	0	0	0	0	1
+female	0.37	1.1	1	0	0	1	0	0	limited	0	0	0	0	0
+female	0.37	1.1	1	0	0	2	0	7	otherwise	1	0	0	0	2
+female	0.37	1.1	1	0	0	2	0	6	otherwise	0	0	0	0	0
+female	0.37	1.3	1	0	0	2	2	0	limited	0	0	1	1	4
+female	0.37	1.3	1	0	0	2	0	1	not limited	0	0	1	2	5
+female	0.37	1.3	1	0	0	2	2	1	not limited	0	0	0	0	0
+female	0.37	1.5	1	0	0	1	0	0	not limited	1	0	0	0	0
+female	0.37	1.5	1	0	0	1	0	0	limited	2	0	0	0	3
+female	0.37	1.5	1	0	0	1	0	2	otherwise	0	0	0	0	0
+female	0.37	1.5	1	0	0	1	1	0	limited	0	7	0	0	8
+female	0.37	1.5	1	0	0	1	3	0	otherwise	0	0	0	0	0
+female	0.37	1.5	1	0	0	2	14	7	limited	1	0	3	1	2
+female	0.37	1.5	1	0	0	3	14	0	otherwise	0	0	1	5	2
+female	0.42	0.01	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.42	0.06	0	1	0	0	1	1	otherwise	0	0	0	0	1
+female	0.42	0.06	1	0	0	2	7	1	otherwise	2	0	0	0	2
+female	0.42	0.15	0	0	1	1	4	0	otherwise	0	0	0	0	4
+female	0.42	0.15	0	0	1	4	0	0	not limited	0	0	0	0	4
+female	0.42	0.15	1	0	0	3	7	1	limited	1	0	1	45	7
+female	0.42	0.25	0	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.42	0.25	0	0	1	1	0	2	not limited	0	2	0	0	2
+female	0.42	0.25	0	0	1	2	2	4	limited	0	0	0	0	0
+female	0.42	0.25	0	0	1	3	0	8	not limited	0	0	0	0	5
+female	0.42	0.25	0	0	1	3	0	0	limited	0	0	0	0	0
+female	0.42	0.25	0	1	0	1	0	0	otherwise	0	0	0	0	1
+female	0.42	0.25	1	0	0	1	0	0	otherwise	1	0	0	0	0
+female	0.42	0.35	0	0	1	1	0	4	limited	0	0	1	1	0
+female	0.42	0.45	1	0	0	0	0	0	limited	0	0	0	0	3
+female	0.42	0.45	1	0	0	1	0	7	not limited	0	0	0	0	3
+female	0.42	0.55	0	0	0	1	0	0	not limited	0	0	0	0	4
+female	0.42	0.55	1	0	0	2	0	1	otherwise	0	0	0	0	1
+female	0.42	0.65	0	0	0	1	0	7	otherwise	0	0	0	0	0
+female	0.42	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	3
+female	0.42	0.65	1	0	0	0	0	0	not limited	0	0	2	22	2
+female	0.42	0.65	1	0	0	1	14	6	limited	5	4	2	11	4
+female	0.42	0.65	1	0	0	1	0	1	not limited	0	0	0	0	1
+female	0.42	0.65	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.42	0.75	0	0	0	1	0	0	otherwise	1	0	0	0	2
+female	0.42	0.75	1	0	0	0	0	0	otherwise	0	0	1	3	0
+female	0.42	0.75	1	0	0	1	10	1	otherwise	0	1	0	0	1
+female	0.42	0.75	1	0	0	1	0	1	not limited	0	0	0	0	1
+female	0.42	0.75	1	0	0	2	0	3	otherwise	0	0	0	0	1
+female	0.42	0.9	0	0	0	0	0	1	not limited	0	0	0	0	1
+female	0.42	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.42	0.9	0	0	0	1	0	0	not limited	3	0	0	0	0
+female	0.42	0.9	0	0	0	1	0	2	not limited	0	0	0	0	0
+female	0.42	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.42	0.9	1	0	0	1	0	0	limited	0	0	0	0	0
+female	0.42	0.9	1	0	0	3	0	0	not limited	0	0	0	0	1
+female	0.42	0.9	1	0	0	5	0	0	not limited	0	0	0	0	2
+female	0.42	1.1	1	0	0	1	4	1	not limited	0	0	0	0	3
+female	0.42	1.3	0	0	0	0	0	1	not limited	0	0	0	0	0
+female	0.42	1.3	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.42	1.3	1	0	0	1	0	0	not limited	0	0	0	0	3
+female	0.42	1.3	1	0	0	3	1	3	limited	1	3	0	0	2
+female	0.42	1.5	0	0	0	1	2	0	limited	0	0	0	0	1
+female	0.42	1.5	1	0	0	0	0	0	not limited	1	0	0	0	0
+female	0.42	1.5	1	0	0	3	0	0	not limited	0	0	0	0	2
+female	0.47	0	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.47	0	1	0	0	1	0	0	not limited	0	0	0	0	3
+female	0.47	0.01	0	0	0	1	0	0	not limited	3	0	0	0	1
+female	0.47	0.06	1	0	0	0	0	7	otherwise	0	0	0	0	0
+female	0.47	0.06	1	0	0	0	0	1	not limited	0	0	0	0	0
+female	0.47	0.06	1	0	0	1	1	3	not limited	0	0	0	0	0
+female	0.47	0.15	0	0	1	0	0	0	limited	0	0	0	0	0
+female	0.47	0.15	1	0	0	5	0	11	limited	2	0	0	0	3
+female	0.47	0.25	0	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.47	0.25	0	0	0	2	0	0	otherwise	1	0	0	0	3
+female	0.47	0.25	0	0	1	1	0	0	limited	0	7	1	4	2
+female	0.47	0.25	0	0	1	1	0	1	limited	0	0	0	0	1
+female	0.47	0.25	0	0	1	1	0	0	not limited	0	0	0	0	0
+female	0.47	0.25	0	0	1	2	0	2	limited	1	0	1	1	1
+female	0.47	0.25	0	0	1	2	0	1	limited	0	0	0	0	6
+female	0.47	0.25	0	0	1	3	0	0	limited	0	0	0	0	4
+female	0.47	0.25	0	0	1	3	0	3	otherwise	0	0	0	0	1
+female	0.47	0.25	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.47	0.25	1	0	0	1	0	4	not limited	0	0	2	6	4
+female	0.47	0.25	1	0	0	2	0	0	not limited	2	0	0	0	1
+female	0.47	0.35	0	0	1	5	14	0	limited	0	2	1	11	5
+female	0.47	0.35	1	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.47	0.35	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.47	0.35	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.47	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.47	0.45	0	0	0	1	0	2	not limited	0	0	0	0	0
+female	0.47	0.45	0	0	0	2	0	0	not limited	0	0	0	0	3
+female	0.47	0.45	0	0	1	2	14	4	not limited	6	4	0	0	3
+female	0.47	0.45	1	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.47	0.45	1	0	0	2	0	8	otherwise	0	0	0	0	1
+female	0.47	0.45	1	0	0	2	0	0	otherwise	0	0	1	2	1
+female	0.47	0.55	0	0	0	3	14	9	not limited	0	0	0	0	3
+female	0.47	0.55	0	0	1	5	0	4	limited	0	1	1	4	3
+female	0.47	0.55	0	0	1	5	0	2	not limited	0	0	1	7	4
+female	0.47	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.47	0.55	1	0	0	0	0	0	not limited	0	2	0	0	0
+female	0.47	0.55	1	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.47	0.55	1	0	0	1	0	0	not limited	0	0	1	3	1
+female	0.47	0.55	1	0	0	2	0	3	limited	1	0	0	0	4
+female	0.47	0.55	1	0	0	2	0	1	not limited	0	0	0	0	1
+female	0.47	0.55	1	0	0	2	0	0	not limited	0	0	0	0	0
+female	0.47	0.55	1	0	0	4	0	0	not limited	0	0	0	0	3
+female	0.47	0.55	1	0	0	4	0	2	not limited	0	0	1	4	3
+female	0.47	0.65	0	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.47	0.65	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.47	0.65	0	0	0	2	0	0	limited	0	0	0	0	2
+female	0.47	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	2
+female	0.47	0.65	1	0	0	1	3	1	not limited	1	0	1	11	1
+female	0.47	0.65	1	0	0	1	0	1	not limited	0	0	0	0	2
+female	0.47	0.65	1	0	0	1	5	2	limited	0	0	0	0	0
+female	0.47	0.65	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.47	0.65	1	0	0	2	2	1	not limited	1	2	0	0	5
+female	0.47	0.65	1	0	0	2	0	4	not limited	0	0	0	0	2
+female	0.47	0.65	1	0	0	3	0	0	not limited	0	0	0	0	1
+female	0.47	0.65	1	0	0	3	0	2	not limited	0	0	0	0	1
+female	0.47	0.65	1	0	0	3	0	2	not limited	0	0	0	0	1
+female	0.47	0.65	1	0	0	4	0	0	not limited	0	0	0	0	3
+female	0.47	0.65	1	0	0	4	1	1	limited	0	0	0	0	3
+female	0.47	0.75	0	0	0	1	0	0	otherwise	0	0	0	0	2
+female	0.47	0.75	0	0	0	1	0	0	not limited	0	1	0	0	2
+female	0.47	0.75	0	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.47	0.75	0	0	0	5	3	2	otherwise	7	0	1	2	3
+female	0.47	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.47	0.75	1	0	0	2	0	0	not limited	0	0	0	0	0
+female	0.47	0.75	1	0	0	3	0	0	not limited	0	0	0	0	2
+female	0.47	0.75	1	0	0	5	0	2	limited	2	0	2	1	3
+female	0.47	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.47	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.47	0.9	1	0	0	0	0	0	not limited	0	0	1	1	1
+female	0.47	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.47	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.47	0.9	1	0	0	1	0	1	not limited	1	0	0	0	3
+female	0.47	0.9	1	0	0	1	0	0	otherwise	0	0	0	0	3
+female	0.47	0.9	1	0	0	1	0	1	otherwise	0	0	0	0	0
+female	0.47	0.9	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.47	0.9	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.47	0.9	1	0	0	2	0	6	not limited	1	0	0	0	1
+female	0.47	0.9	1	0	0	2	0	11	otherwise	1	0	0	0	6
+female	0.47	0.9	1	0	0	2	0	0	otherwise	0	0	0	0	1
+female	0.47	0.9	1	0	0	4	0	1	otherwise	0	0	0	0	2
+female	0.47	0.9	1	0	0	5	3	0	not limited	0	1	2	7	2
+female	0.47	1.1	1	0	0	0	0	0	not limited	0	0	0	0	2
+female	0.47	1.1	1	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.47	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.47	1.1	1	0	0	1	0	3	limited	0	0	0	0	2
+female	0.47	1.3	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.47	1.5	0	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.47	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.47	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.47	1.5	1	0	0	1	0	0	not limited	1	0	0	0	1
+female	0.47	1.5	1	0	0	1	0	4	not limited	0	1	0	0	1
+female	0.52	0	0	0	0	3	0	2	limited	0	0	0	0	4
+female	0.52	0	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.52	0	1	0	0	2	0	0	not limited	1	0	0	0	4
+female	0.52	0.06	1	0	0	1	0	1	not limited	0	0	1	5	0
+female	0.52	0.06	1	0	0	1	1	4	not limited	0	0	1	22	3
+female	0.52	0.06	1	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.52	0.15	0	0	1	2	0	0	limited	0	0	0	0	3
+female	0.52	0.15	1	0	0	1	0	1	limited	0	0	0	0	1
+female	0.52	0.15	1	0	0	1	0	4	not limited	0	0	1	7	0
+female	0.52	0.25	0	0	1	0	0	0	not limited	0	0	0	0	1
+female	0.52	0.25	0	0	1	0	0	0	not limited	0	0	0	0	1
+female	0.52	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	1
+female	0.52	0.25	0	0	1	1	0	0	limited	1	0	3	7	2
+female	0.52	0.25	0	0	1	1	0	0	otherwise	2	0	0	0	2
+female	0.52	0.25	0	0	1	1	0	1	not limited	1	0	1	11	0
+female	0.52	0.25	0	0	1	1	0	0	not limited	1	2	0	0	2
+female	0.52	0.25	0	0	1	1	14	5	otherwise	0	0	0	0	2
+female	0.52	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	1
+female	0.52	0.25	0	0	1	1	0	0	not limited	0	0	0	0	1
+female	0.52	0.25	0	0	1	1	8	7	limited	0	0	5	6	5
+female	0.52	0.25	0	0	1	1	0	0	not limited	0	0	0	0	1
+female	0.52	0.25	0	0	1	1	0	0	not limited	0	0	0	0	1
+female	0.52	0.25	0	0	1	2	0	6	not limited	1	0	0	0	3
+female	0.52	0.25	0	0	1	2	0	0	not limited	1	0	0	0	1
+female	0.52	0.25	0	0	1	2	0	1	not limited	0	0	0	0	0
+female	0.52	0.25	0	0	1	2	0	0	not limited	0	0	0	0	4
+female	0.52	0.25	0	0	1	2	7	1	limited	0	0	1	11	4
+female	0.52	0.25	0	0	1	2	0	6	limited	0	0	1	2	1
+female	0.52	0.25	0	0	1	2	0	0	not limited	0	1	0	0	0
+female	0.52	0.25	0	0	1	2	0	0	not limited	0	0	0	0	1
+female	0.52	0.25	0	0	1	3	3	2	limited	3	0	1	2	0
+female	0.52	0.25	0	0	1	3	0	2	not limited	1	0	0	0	4
+female	0.52	0.25	0	0	1	4	14	4	limited	6	0	3	7	8
+female	0.52	0.25	0	0	1	4	3	2	not limited	2	0	1	22	1
+female	0.52	0.25	0	0	1	4	14	4	not limited	2	9	0	0	2
+female	0.52	0.25	0	0	1	4	0	0	limited	0	0	0	0	1
+female	0.52	0.25	0	0	1	5	0	0	not limited	1	0	0	0	6
+female	0.52	0.25	0	0	1	5	14	1	not limited	1	0	0	0	3
+female	0.52	0.25	0	0	1	5	0	7	limited	1	0	0	0	6
+female	0.52	0.25	0	0	1	5	0	2	not limited	0	0	0	0	3
+female	0.52	0.25	0	0	1	5	0	7	limited	0	0	0	0	3
+female	0.52	0.25	0	1	0	4	0	2	not limited	0	0	0	0	2
+female	0.52	0.25	1	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.52	0.25	1	0	0	0	0	0	not limited	0	0	0	0	2
+female	0.52	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.52	0.25	1	0	0	2	0	0	not limited	0	0	0	0	2
+female	0.52	0.25	1	0	0	2	14	4	not limited	0	0	0	0	1
+female	0.52	0.25	1	0	0	3	0	0	not limited	0	0	0	0	2
+female	0.52	0.25	1	0	0	3	0	2	limited	0	0	2	11	6
+female	0.52	0.25	1	0	0	5	0	1	not limited	0	0	0	0	2
+female	0.52	0.35	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.52	0.35	0	0	1	1	14	10	limited	2	0	1	22	0
+female	0.52	0.35	0	0	1	1	3	1	limited	0	0	0	0	1
+female	0.52	0.35	0	0	1	1	0	0	not limited	0	0	0	0	2
+female	0.52	0.35	0	0	1	2	0	1	not limited	0	0	0	0	3
+female	0.52	0.35	0	0	1	3	0	12	limited	1	0	0	0	1
+female	0.52	0.35	0	0	1	3	0	1	not limited	0	0	0	0	1
+female	0.52	0.35	0	0	1	3	0	6	not limited	0	0	0	0	0
+female	0.52	0.35	1	0	0	0	0	0	not limited	0	1	1	11	1
+female	0.52	0.35	1	0	0	0	0	2	otherwise	0	0	0	0	0
+female	0.52	0.35	1	0	0	0	0	0	not limited	0	0	0	0	2
+female	0.52	0.35	1	0	0	1	0	2	otherwise	0	0	0	0	0
+female	0.52	0.35	1	0	0	2	0	0	not limited	0	0	0	0	0
+female	0.52	0.35	1	0	0	3	0	0	not limited	1	0	0	0	2
+female	0.52	0.45	0	0	0	0	0	2	not limited	0	0	0	0	0
+female	0.52	0.45	0	0	0	1	0	1	not limited	1	0	0	0	2
+female	0.52	0.45	0	0	0	5	0	0	not limited	1	0	0	0	2
+female	0.52	0.45	0	0	1	1	14	8	limited	1	0	1	11	4
+female	0.52	0.45	0	0	1	4	0	1	limited	2	0	0	0	6
+female	0.52	0.45	0	0	1	4	0	1	not limited	0	0	0	0	1
+female	0.52	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.52	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.52	0.45	1	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.52	0.45	1	0	0	2	0	1	not limited	0	1	0	0	0
+female	0.52	0.45	1	0	0	3	0	2	not limited	1	0	1	11	4
+female	0.52	0.45	1	0	0	4	3	0	limited	1	0	0	0	2
+female	0.52	0.55	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.52	0.55	0	0	0	3	0	0	not limited	0	0	0	0	1
+female	0.52	0.55	0	0	1	2	14	1	limited	2	0	5	4	5
+female	0.52	0.55	1	0	0	0	0	0	otherwise	0	0	1	1	2
+female	0.52	0.55	1	0	0	3	0	0	not limited	0	0	1	4	3
+female	0.52	0.55	1	0	0	4	0	4	not limited	0	4	0	0	2
+female	0.52	0.55	1	0	0	5	0	0	not limited	0	0	0	0	3
+female	0.52	0.65	0	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.52	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.52	0.65	0	0	0	1	14	1	limited	1	0	1	11	2
+female	0.52	0.65	0	0	0	4	0	3	not limited	0	0	0	0	3
+female	0.52	0.65	0	0	1	1	0	0	not limited	1	2	0	0	3
+female	0.52	0.65	1	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.52	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.52	0.65	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.52	0.65	1	0	0	2	0	3	not limited	1	0	0	0	2
+female	0.52	0.65	1	0	0	3	0	5	otherwise	0	0	0	0	3
+female	0.52	0.75	0	0	0	2	0	0	not limited	0	0	0	0	2
+female	0.52	0.75	0	0	1	1	0	1	not limited	1	0	0	0	1
+female	0.52	0.75	0	0	1	1	0	0	not limited	0	7	0	0	2
+female	0.52	0.75	1	0	0	0	0	0	not limited	1	1	0	0	0
+female	0.52	0.75	1	0	0	1	0	0	not limited	1	0	0	0	5
+female	0.52	0.75	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.52	0.75	1	0	0	1	0	3	not limited	0	0	2	2	1
+female	0.52	0.75	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.52	0.75	1	0	0	2	2	1	otherwise	2	0	0	0	0
+female	0.52	0.75	1	0	0	2	0	2	limited	0	0	0	0	2
+female	0.52	0.75	1	0	0	3	3	0	otherwise	1	0	0	0	1
+female	0.52	0.75	1	0	0	5	0	1	not limited	0	1	0	0	1
+female	0.52	0.9	0	0	0	0	0	2	not limited	0	1	0	0	0
+female	0.52	0.9	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.52	0.9	0	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.52	0.9	0	0	1	2	7	3	not limited	0	7	0	0	1
+female	0.52	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.52	0.9	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.52	0.9	1	0	0	2	14	3	not limited	2	0	0	0	5
+female	0.52	0.9	1	0	0	2	0	0	limited	0	3	0	0	1
+female	0.52	0.9	1	0	0	3	0	5	otherwise	0	0	0	0	2
+female	0.52	1.1	0	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.52	1.1	0	0	1	2	0	1	not limited	0	0	1	2	3
+female	0.52	1.1	1	0	0	0	0	0	limited	0	0	0	0	1
+female	0.52	1.1	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.52	1.1	1	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.52	1.1	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.52	1.1	1	0	0	4	0	0	not limited	0	0	0	0	3
+female	0.52	1.3	1	0	0	1	0	0	otherwise	1	0	0	0	1
+female	0.52	1.3	1	0	0	2	3	0	not limited	5	0	0	0	2
+female	0.52	1.3	1	0	0	2	0	0	not limited	1	0	0	0	3
+female	0.52	1.3	1	0	0	2	0	7	not limited	0	0	0	0	2
+female	0.52	1.3	1	0	0	3	0	9	limited	0	0	0	0	1
+female	0.52	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.52	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.52	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.52	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	3
+female	0.52	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.52	1.5	1	0	0	2	0	1	not limited	0	2	0	0	6
+female	0.52	1.5	1	0	0	3	0	0	otherwise	0	0	0	0	1
+female	0.57	0	0	0	1	2	0	1	not limited	0	0	0	0	4
+female	0.57	0	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.57	0	1	0	0	1	0	1	not limited	2	0	0	0	0
+female	0.57	0	1	0	0	1	0	2	otherwise	0	0	0	0	2
+female	0.57	0	1	0	0	2	0	4	otherwise	0	0	0	0	0
+female	0.57	0	1	0	0	5	2	10	not limited	0	0	0	0	7
+female	0.57	0.01	0	1	0	4	0	1	otherwise	0	0	0	0	1
+female	0.57	0.01	1	0	0	1	0	1	not limited	1	1	1	5	2
+female	0.57	0.06	0	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.57	0.06	1	0	0	2	0	4	not limited	1	0	0	0	4
+female	0.57	0.06	1	0	0	2	0	1	not limited	0	0	0	0	2
+female	0.57	0.15	0	0	1	0	0	0	otherwise	0	0	0	0	1
+female	0.57	0.15	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.57	0.15	0	0	1	2	0	4	otherwise	0	0	0	0	1
+female	0.57	0.15	0	0	1	2	0	0	not limited	0	0	0	0	2
+female	0.57	0.15	0	0	1	4	3	0	not limited	1	0	0	0	5
+female	0.57	0.15	0	0	1	5	0	0	not limited	0	0	0	0	2
+female	0.57	0.15	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.57	0.15	1	0	0	3	7	9	not limited	3	2	1	7	6
+female	0.57	0.15	1	0	0	3	0	3	not limited	0	0	0	0	4
+female	0.57	0.15	1	0	0	3	0	3	limited	0	0	0	0	2
+female	0.57	0.15	1	0	0	5	1	2	limited	1	0	1	11	3
+female	0.57	0.15	1	0	0	5	0	0	not limited	0	0	1	3	6
+female	0.57	0.25	0	0	1	0	0	0	otherwise	1	0	0	0	2
+female	0.57	0.25	0	0	1	0	0	1	otherwise	2	0	0	0	0
+female	0.57	0.25	0	0	1	0	0	0	not limited	0	2	0	0	0
+female	0.57	0.25	0	0	1	0	0	6	not limited	0	0	0	0	0
+female	0.57	0.25	0	0	1	0	0	0	not limited	0	0	1	2	1
+female	0.57	0.25	0	0	1	0	0	0	otherwise	0	1	0	0	0
+female	0.57	0.25	0	0	1	0	0	0	not limited	0	0	0	0	3
+female	0.57	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.57	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	1
+female	0.57	0.25	0	0	1	0	0	0	limited	0	0	0	0	1
+female	0.57	0.25	0	0	1	0	0	0	not limited	0	0	0	0	1
+female	0.57	0.25	0	0	1	0	0	1	not limited	0	0	0	0	0
+female	0.57	0.25	0	0	1	1	0	1	not limited	0	0	0	0	2
+female	0.57	0.25	0	0	1	1	0	3	not limited	0	0	0	0	0
+female	0.57	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	0
+female	0.57	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	1
+female	0.57	0.25	0	0	1	1	0	1	otherwise	0	0	0	0	2
+female	0.57	0.25	0	0	1	2	0	4	not limited	1	0	0	0	1
+female	0.57	0.25	0	0	1	2	0	0	limited	1	0	1	2	3
+female	0.57	0.25	0	0	1	2	0	2	limited	1	0	0	0	3
+female	0.57	0.25	0	0	1	2	0	0	not limited	0	0	0	0	1
+female	0.57	0.25	0	0	1	2	0	1	not limited	0	0	0	0	0
+female	0.57	0.25	0	0	1	2	0	0	not limited	0	0	0	0	3
+female	0.57	0.25	0	0	1	2	0	0	not limited	0	0	0	0	2
+female	0.57	0.25	0	0	1	2	0	0	not limited	0	0	0	0	2
+female	0.57	0.25	0	0	1	2	1	0	otherwise	0	0	0	0	1
+female	0.57	0.25	0	0	1	3	0	0	limited	2	0	0	0	1
+female	0.57	0.25	0	0	1	3	0	0	not limited	0	0	0	0	1
+female	0.57	0.25	0	0	1	3	0	11	not limited	0	0	0	0	2
+female	0.57	0.25	0	0	1	3	0	1	limited	0	0	1	6	1
+female	0.57	0.25	0	0	1	3	0	0	limited	0	0	0	0	1
+female	0.57	0.25	0	0	1	3	0	2	not limited	0	0	0	0	1
+female	0.57	0.25	0	0	1	3	6	1	otherwise	0	0	0	0	0
+female	0.57	0.25	0	0	1	4	0	1	otherwise	1	0	0	0	5
+female	0.57	0.25	0	0	1	4	0	0	not limited	1	0	0	0	1
+female	0.57	0.25	0	0	1	4	0	3	limited	1	1	1	5	2
+female	0.57	0.25	0	0	1	4	14	7	limited	2	0	2	22	5
+female	0.57	0.25	0	0	1	4	0	1	not limited	0	0	0	0	1
+female	0.57	0.25	0	0	1	5	0	2	limited	1	0	1	22	1
+female	0.57	0.25	0	0	1	5	0	0	limited	1	0	0	0	3
+female	0.57	0.25	0	0	1	5	14	7	not limited	2	0	0	0	8
+female	0.57	0.25	0	0	1	5	0	4	otherwise	0	0	0	0	3
+female	0.57	0.25	0	0	1	5	0	0	not limited	0	2	0	0	5
+female	0.57	0.25	0	0	1	5	0	4	limited	0	0	0	0	3
+female	0.57	0.25	0	0	1	5	0	1	limited	0	0	0	0	3
+female	0.57	0.25	0	0	1	5	0	1	otherwise	0	0	0	0	0
+female	0.57	0.25	0	1	0	5	0	1	limited	0	0	0	0	7
+female	0.57	0.25	1	0	0	0	0	0	not limited	1	0	0	0	1
+female	0.57	0.25	1	0	0	0	0	0	otherwise	0	3	0	0	1
+female	0.57	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.57	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.57	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.57	0.25	1	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.57	0.25	1	0	0	1	8	0	not limited	1	0	0	0	0
+female	0.57	0.25	1	0	0	1	14	7	not limited	1	0	0	0	3
+female	0.57	0.25	1	0	0	2	0	0	not limited	0	1	0	0	2
+female	0.57	0.25	1	0	0	2	0	0	not limited	0	0	0	0	0
+female	0.57	0.25	1	0	0	2	0	0	not limited	0	0	0	0	3
+female	0.57	0.25	1	0	0	2	0	2	not limited	0	2	0	0	4
+female	0.57	0.25	1	0	0	3	0	0	not limited	1	0	0	0	2
+female	0.57	0.25	1	0	0	3	7	3	limited	0	0	1	22	3
+female	0.57	0.25	1	0	0	3	4	0	limited	0	1	2	4	8
+female	0.57	0.25	1	0	0	5	0	6	limited	1	0	0	0	3
+female	0.57	0.35	0	0	0	2	2	2	not limited	4	0	0	0	0
+female	0.57	0.35	0	0	1	0	0	0	not limited	1	7	1	11	2
+female	0.57	0.35	0	0	1	0	0	1	otherwise	0	0	0	0	1
+female	0.57	0.35	0	0	1	1	0	0	otherwise	1	0	1	4	1
+female	0.57	0.35	0	0	1	1	0	0	not limited	0	0	0	0	0
+female	0.57	0.35	0	0	1	1	0	1	not limited	0	0	0	0	0
+female	0.57	0.35	0	0	1	1	0	0	otherwise	0	0	0	0	0
+female	0.57	0.35	0	0	1	2	0	0	not limited	1	1	0	0	0
+female	0.57	0.35	0	0	1	2	0	3	limited	0	2	0	0	2
+female	0.57	0.35	0	0	1	2	6	4	limited	0	0	1	7	2
+female	0.57	0.35	0	0	1	2	0	1	not limited	0	0	0	0	2
+female	0.57	0.35	0	0	1	2	0	0	not limited	0	0	0	0	2
+female	0.57	0.35	0	0	1	3	0	0	not limited	1	0	0	0	0
+female	0.57	0.35	0	0	1	3	4	5	limited	3	0	1	2	2
+female	0.57	0.35	0	0	1	3	0	8	not limited	0	0	0	0	3
+female	0.57	0.35	0	0	1	3	14	11	limited	0	1	1	22	6
+female	0.57	0.35	0	0	1	4	0	11	not limited	1	0	0	0	4
+female	0.57	0.35	0	0	1	4	9	1	not limited	0	0	1	5	5
+female	0.57	0.35	0	0	1	5	14	2	limited	1	0	0	0	0
+female	0.57	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.57	0.35	1	0	0	1	10	2	not limited	6	0	1	6	4
+female	0.57	0.35	1	0	0	1	0	0	not limited	0	0	1	1	4
+female	0.57	0.35	1	0	0	2	0	0	otherwise	1	0	0	0	3
+female	0.57	0.35	1	0	0	2	14	6	not limited	4	8	0	0	2
+female	0.57	0.35	1	0	0	2	0	3	not limited	0	0	0	0	2
+female	0.57	0.35	1	0	0	4	0	3	not limited	0	0	0	0	0
+female	0.57	0.45	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.57	0.45	0	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.57	0.45	0	0	0	3	0	0	not limited	0	0	0	0	2
+female	0.57	0.45	0	0	1	0	0	0	limited	0	0	0	0	2
+female	0.57	0.45	0	0	1	1	0	6	limited	1	0	1	7	1
+female	0.57	0.45	0	0	1	2	0	0	limited	0	0	0	0	3
+female	0.57	0.45	0	0	1	3	1	0	limited	1	0	0	0	6
+female	0.57	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.57	0.45	1	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.57	0.45	1	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.57	0.45	1	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.57	0.45	1	0	0	1	0	7	otherwise	0	1	0	0	4
+female	0.57	0.45	1	0	0	2	0	0	not limited	0	0	0	0	3
+female	0.57	0.45	1	0	0	2	0	2	not limited	0	0	0	0	1
+female	0.57	0.45	1	0	0	2	0	0	not limited	0	0	0	0	0
+female	0.57	0.45	1	0	0	3	0	3	not limited	0	0	0	0	1
+female	0.57	0.45	1	0	0	4	4	5	otherwise	2	0	0	0	5
+female	0.57	0.55	0	0	0	3	0	0	not limited	0	0	0	0	2
+female	0.57	0.55	0	0	0	4	2	0	not limited	1	0	0	0	6
+female	0.57	0.55	0	1	0	0	0	6	otherwise	0	0	0	0	1
+female	0.57	0.55	1	0	0	0	0	0	limited	1	0	0	0	0
+female	0.57	0.55	1	0	0	1	8	10	limited	1	0	0	0	2
+female	0.57	0.55	1	0	0	1	0	0	otherwise	1	1	0	0	1
+female	0.57	0.55	1	0	0	1	0	4	not limited	0	0	0	0	2
+female	0.57	0.55	1	0	0	1	0	0	otherwise	0	0	1	4	1
+female	0.57	0.55	1	0	0	1	0	0	not limited	0	0	0	0	3
+female	0.57	0.55	1	0	0	2	0	1	otherwise	2	0	0	0	0
+female	0.57	0.65	0	0	0	0	0	0	not limited	0	0	0	0	2
+female	0.57	0.65	0	0	0	0	0	0	not limited	0	0	0	0	2
+female	0.57	0.65	0	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.57	0.65	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.57	0.65	0	0	0	2	0	6	not limited	0	0	1	7	1
+female	0.57	0.65	0	0	0	5	0	6	not limited	0	0	0	0	0
+female	0.57	0.65	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.57	0.65	1	0	0	0	0	0	otherwise	1	0	0	0	0
+female	0.57	0.65	1	0	0	1	0	2	not limited	0	0	1	3	3
+female	0.57	0.65	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.57	0.65	1	0	0	2	0	7	otherwise	2	0	1	5	2
+female	0.57	0.65	1	0	0	2	0	1	not limited	0	0	0	0	1
+female	0.57	0.65	1	0	0	3	14	1	not limited	1	0	0	0	8
+female	0.57	0.75	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.57	0.75	1	0	0	0	0	0	not limited	0	0	0	0	3
+female	0.57	0.75	1	0	0	1	2	1	not limited	0	0	1	4	3
+female	0.57	0.75	1	0	0	1	0	0	limited	0	0	0	0	3
+female	0.57	0.75	1	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.57	0.75	1	0	0	1	0	1	not limited	0	0	0	0	0
+female	0.57	0.75	1	0	0	2	0	0	limited	0	0	1	7	3
+female	0.57	0.75	1	0	0	2	0	0	not limited	0	0	0	0	1
+female	0.57	0.75	1	0	0	2	0	1	not limited	0	1	0	0	3
+female	0.57	0.75	1	0	0	3	0	0	not limited	1	0	0	0	5
+female	0.57	0.9	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.57	0.9	1	0	0	0	0	1	not limited	0	0	0	0	0
+female	0.57	0.9	1	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.57	0.9	1	0	0	0	0	4	otherwise	0	0	0	0	1
+female	0.57	0.9	1	0	0	1	4	0	not limited	1	1	0	0	2
+female	0.57	0.9	1	0	0	1	1	3	not limited	0	0	1	2	0
+female	0.57	0.9	1	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.57	0.9	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.57	0.9	1	0	0	2	0	4	not limited	1	1	0	0	1
+female	0.57	0.9	1	0	0	2	0	3	not limited	0	1	0	0	0
+female	0.57	0.9	1	0	0	3	0	0	not limited	0	0	0	0	2
+female	0.57	0.9	1	0	0	4	0	0	limited	0	0	0	0	0
+female	0.57	1.1	0	0	1	0	0	0	not limited	0	0	0	0	3
+female	0.57	1.1	0	0	1	2	0	0	not limited	0	0	0	0	3
+female	0.57	1.1	1	0	0	0	0	0	not limited	0	0	0	0	2
+female	0.57	1.1	1	0	0	0	0	0	limited	0	0	0	0	1
+female	0.57	1.1	1	0	0	1	5	1	otherwise	2	0	0	0	3
+female	0.57	1.1	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.57	1.1	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.57	1.3	0	0	1	1	0	0	otherwise	0	0	0	0	3
+female	0.57	1.3	1	0	0	0	0	12	not limited	0	0	1	45	1
+female	0.57	1.3	1	0	0	1	0	2	not limited	0	0	0	0	2
+female	0.57	1.3	1	0	0	2	9	1	not limited	1	0	0	0	3
+female	0.57	1.3	1	0	0	3	0	5	limited	0	0	1	11	2
+female	0.57	1.3	1	0	0	3	0	0	not limited	0	0	0	0	0
+female	0.57	1.5	1	0	0	2	0	1	not limited	0	0	0	0	1
+female	0.57	1.5	1	0	0	2	0	0	not limited	0	0	0	0	1
+female	0.62	0	0	0	0	1	14	2	limited	0	1	0	0	1
+female	0.62	0	0	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.62	0	0	0	0	3	0	6	not limited	1	0	0	0	0
+female	0.62	0	0	0	1	0	14	2	not limited	2	7	0	0	0
+female	0.62	0	0	0	1	1	0	4	otherwise	1	0	0	0	2
+female	0.62	0	1	0	0	0	0	0	not limited	1	0	0	0	2
+female	0.62	0	1	0	0	0	0	5	not limited	0	0	0	0	2
+female	0.62	0	1	0	0	1	0	1	not limited	0	0	0	0	1
+female	0.62	0	1	0	0	2	0	0	otherwise	0	0	0	0	1
+female	0.62	0.01	0	0	0	3	0	0	limited	0	0	0	0	0
+female	0.62	0.01	0	0	1	1	2	0	not limited	4	0	1	2	1
+female	0.62	0.06	1	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.62	0.15	0	0	1	1	0	4	otherwise	1	0	0	0	1
+female	0.62	0.15	0	0	1	1	0	0	not limited	0	0	0	0	4
+female	0.62	0.15	0	0	1	1	0	0	not limited	0	0	0	0	1
+female	0.62	0.15	0	0	1	2	0	0	not limited	0	0	0	0	1
+female	0.62	0.15	0	0	1	3	0	1	limited	0	0	0	0	1
+female	0.62	0.15	0	0	1	3	0	2	limited	0	0	0	0	0
+female	0.62	0.15	0	0	1	5	0	1	not limited	0	0	0	0	6
+female	0.62	0.15	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.62	0.15	1	0	0	3	0	2	not limited	2	0	0	0	8
+female	0.62	0.15	1	0	0	5	3	5	limited	1	0	0	0	6
+female	0.62	0.25	0	0	0	0	0	4	otherwise	1	0	0	0	1
+female	0.62	0.25	0	0	0	1	0	0	not limited	0	0	1	7	0
+female	0.62	0.25	0	0	0	3	5	1	not limited	1	0	0	0	3
+female	0.62	0.25	0	0	1	0	0	0	not limited	1	0	0	0	2
+female	0.62	0.25	0	0	1	0	0	0	not limited	0	0	0	0	2
+female	0.62	0.25	0	0	1	0	0	0	not limited	0	0	0	0	0
+female	0.62	0.25	0	0	1	0	0	0	not limited	0	0	0	0	3
+female	0.62	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.62	0.25	0	0	1	0	0	0	not limited	0	1	0	0	2
+female	0.62	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	1
+female	0.62	0.25	0	0	1	0	0	1	not limited	0	0	0	0	1
+female	0.62	0.25	0	0	1	0	0	0	not limited	0	0	0	0	2
+female	0.62	0.25	0	0	1	0	0	0	not limited	0	0	0	0	1
+female	0.62	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.62	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.62	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.62	0.25	0	0	1	0	0	0	not limited	0	0	0	0	1
+female	0.62	0.25	0	0	1	0	0	0	limited	0	0	0	0	3
+female	0.62	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.62	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.62	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.62	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.62	0.25	0	0	1	1	0	10	otherwise	1	0	1	22	2
+female	0.62	0.25	0	0	1	1	0	0	not limited	1	0	0	0	4
+female	0.62	0.25	0	0	1	1	0	0	not limited	2	0	0	0	0
+female	0.62	0.25	0	0	1	1	14	12	limited	2	0	0	0	7
+female	0.62	0.25	0	0	1	1	0	7	limited	1	0	0	0	1
+female	0.62	0.25	0	0	1	1	14	2	not limited	1	0	0	0	3
+female	0.62	0.25	0	0	1	1	14	5	not limited	1	7	0	0	2
+female	0.62	0.25	0	0	1	1	0	0	not limited	0	0	0	0	2
+female	0.62	0.25	0	0	1	1	0	0	limited	0	0	0	0	2
+female	0.62	0.25	0	0	1	1	0	0	not limited	0	0	0	0	2
+female	0.62	0.25	0	0	1	1	0	0	not limited	0	0	0	0	4
+female	0.62	0.25	0	0	1	1	0	0	not limited	0	0	0	0	2
+female	0.62	0.25	0	0	1	1	0	5	not limited	0	0	1	2	2
+female	0.62	0.25	0	0	1	1	0	2	not limited	0	0	0	0	1
+female	0.62	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	0
+female	0.62	0.25	0	0	1	1	0	0	limited	0	0	0	0	3
+female	0.62	0.25	0	0	1	1	0	0	limited	0	0	0	0	1
+female	0.62	0.25	0	0	1	1	0	0	not limited	0	0	1	11	3
+female	0.62	0.25	0	0	1	1	0	1	not limited	0	0	0	0	2
+female	0.62	0.25	0	0	1	1	0	0	not limited	0	0	0	0	1
+female	0.62	0.25	0	0	1	1	0	2	not limited	0	0	0	0	0
+female	0.62	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	0
+female	0.62	0.25	0	0	1	1	0	0	not limited	0	1	0	0	0
+female	0.62	0.25	0	0	1	1	0	1	not limited	0	0	0	0	0
+female	0.62	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	2
+female	0.62	0.25	0	0	1	2	0	0	not limited	1	0	0	0	2
+female	0.62	0.25	0	0	1	2	0	0	not limited	1	0	0	0	4
+female	0.62	0.25	0	0	1	2	0	1	not limited	1	0	0	0	1
+female	0.62	0.25	0	0	1	2	0	1	not limited	0	0	0	0	2
+female	0.62	0.25	0	0	1	2	0	2	not limited	0	0	0	0	3
+female	0.62	0.25	0	0	1	2	0	0	not limited	0	1	0	0	1
+female	0.62	0.25	0	0	1	2	0	0	not limited	0	1	0	0	7
+female	0.62	0.25	0	0	1	2	0	3	limited	0	0	0	0	5
+female	0.62	0.25	0	0	1	2	0	0	not limited	0	0	0	0	1
+female	0.62	0.25	0	0	1	2	0	1	limited	0	0	0	0	0
+female	0.62	0.25	0	0	1	2	0	0	not limited	0	0	0	0	1
+female	0.62	0.25	0	0	1	2	0	0	otherwise	0	0	0	0	0
+female	0.62	0.25	0	0	1	2	0	0	not limited	0	0	0	0	0
+female	0.62	0.25	0	0	1	2	0	0	not limited	0	0	0	0	0
+female	0.62	0.25	0	0	1	2	0	0	not limited	0	0	0	0	2
+female	0.62	0.25	0	0	1	3	0	8	limited	1	0	0	0	4
+female	0.62	0.25	0	0	1	3	0	0	not limited	1	0	0	0	0
+female	0.62	0.25	0	0	1	3	0	0	not limited	1	0	2	5	3
+female	0.62	0.25	0	0	1	3	0	3	not limited	0	0	0	0	0
+female	0.62	0.25	0	0	1	3	0	1	not limited	0	0	0	0	2
+female	0.62	0.25	0	0	1	3	0	5	not limited	0	0	0	0	4
+female	0.62	0.25	0	0	1	3	0	0	not limited	0	0	0	0	2
+female	0.62	0.25	0	0	1	4	0	1	not limited	2	0	0	0	6
+female	0.62	0.25	0	0	1	4	0	2	not limited	0	0	1	7	6
+female	0.62	0.25	0	0	1	5	0	7	not limited	1	0	1	5	4
+female	0.62	0.25	0	0	1	5	3	1	limited	1	0	0	0	7
+female	0.62	0.25	0	0	1	5	0	7	limited	1	0	0	0	5
+female	0.62	0.25	0	0	1	5	6	4	limited	1	0	1	22	5
+female	0.62	0.25	0	0	1	5	14	5	limited	1	4	1	11	5
+female	0.62	0.25	0	0	1	5	0	1	limited	0	0	0	0	3
+female	0.62	0.25	0	0	1	5	0	0	not limited	0	1	0	0	6
+female	0.62	0.25	0	0	1	5	0	3	not limited	0	0	0	0	1
+female	0.62	0.25	0	0	1	5	14	6	limited	0	0	0	0	3
+female	0.62	0.25	0	1	0	0	0	0	not limited	0	1	0	0	0
+female	0.62	0.25	1	0	0	0	0	0	not limited	1	0	0	0	0
+female	0.62	0.25	1	0	0	0	0	0	not limited	0	0	0	0	3
+female	0.62	0.25	1	0	0	0	0	0	limited	0	1	0	0	4
+female	0.62	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.62	0.25	1	0	0	0	0	0	not limited	0	0	0	0	2
+female	0.62	0.25	1	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.62	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.62	0.25	1	0	0	1	0	0	not limited	2	0	0	0	4
+female	0.62	0.25	1	0	0	1	0	3	not limited	1	0	0	0	0
+female	0.62	0.25	1	0	0	1	0	0	not limited	1	0	0	0	3
+female	0.62	0.25	1	0	0	1	0	0	otherwise	1	0	2	22	1
+female	0.62	0.25	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.62	0.25	1	0	0	1	14	8	not limited	0	0	3	2	3
+female	0.62	0.25	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.62	0.25	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.62	0.25	1	0	0	1	0	0	not limited	0	1	0	0	1
+female	0.62	0.25	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.62	0.25	1	0	0	2	0	1	otherwise	0	0	0	0	1
+female	0.62	0.25	1	0	0	2	0	0	not limited	0	2	0	0	3
+female	0.62	0.25	1	0	0	2	0	0	otherwise	0	0	0	0	1
+female	0.62	0.25	1	0	0	3	0	0	limited	1	2	0	0	3
+female	0.62	0.25	1	0	0	3	0	9	limited	1	0	0	0	6
+female	0.62	0.25	1	0	0	3	0	0	not limited	0	0	0	0	3
+female	0.62	0.25	1	0	0	3	0	3	not limited	0	0	3	6	3
+female	0.62	0.25	1	0	0	3	0	0	not limited	0	0	0	0	3
+female	0.62	0.25	1	0	0	3	0	7	not limited	0	0	0	0	0
+female	0.62	0.25	1	0	0	4	0	2	limited	1	0	0	0	1
+female	0.62	0.25	1	0	0	5	2	2	not limited	1	0	0	0	4
+female	0.62	0.25	1	0	0	5	0	3	not limited	0	0	0	0	4
+female	0.62	0.35	0	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.62	0.35	0	0	0	2	0	0	limited	0	0	0	0	4
+female	0.62	0.35	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.62	0.35	0	0	1	1	0	0	limited	0	2	1	22	1
+female	0.62	0.35	0	0	1	1	0	0	otherwise	0	0	2	5	1
+female	0.62	0.35	0	0	1	1	0	3	limited	0	7	0	0	1
+female	0.62	0.35	0	0	1	1	0	0	not limited	0	0	0	0	0
+female	0.62	0.35	0	0	1	2	0	8	limited	2	1	0	0	6
+female	0.62	0.35	0	0	1	2	0	0	not limited	0	0	1	1	3
+female	0.62	0.35	0	0	1	2	14	9	not limited	0	7	2	45	4
+female	0.62	0.35	0	0	1	2	0	1	not limited	0	0	1	4	1
+female	0.62	0.35	0	0	1	4	0	2	not limited	1	0	0	0	7
+female	0.62	0.35	0	0	1	5	0	1	not limited	0	1	1	5	5
+female	0.62	0.35	1	0	0	0	0	0	limited	0	0	0	0	2
+female	0.62	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.62	0.35	1	0	0	0	0	0	not limited	0	0	1	3	0
+female	0.62	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.62	0.35	1	0	0	1	0	0	not limited	1	0	0	0	1
+female	0.62	0.35	1	0	0	1	1	1	not limited	0	0	0	0	0
+female	0.62	0.35	1	0	0	1	0	4	not limited	0	4	0	0	2
+female	0.62	0.35	1	0	0	1	0	1	not limited	0	0	0	0	3
+female	0.62	0.35	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.62	0.35	1	0	0	1	0	2	otherwise	0	0	0	0	0
+female	0.62	0.35	1	0	0	2	0	0	not limited	1	0	0	0	0
+female	0.62	0.35	1	0	0	2	5	0	limited	1	0	0	0	1
+female	0.62	0.35	1	0	0	2	0	0	not limited	0	0	0	0	4
+female	0.62	0.35	1	0	0	2	0	1	not limited	0	0	0	0	1
+female	0.62	0.35	1	0	0	3	0	0	not limited	0	0	1	6	3
+female	0.62	0.45	0	0	0	1	0	2	not limited	0	0	0	0	0
+female	0.62	0.45	0	0	1	2	0	0	not limited	3	6	1	5	2
+female	0.62	0.45	0	0	1	2	2	3	not limited	0	0	0	0	2
+female	0.62	0.45	0	0	1	5	0	0	limited	1	0	0	0	8
+female	0.62	0.45	1	0	0	1	0	1	not limited	1	0	0	0	4
+female	0.62	0.45	1	0	0	1	0	2	not limited	0	1	0	0	4
+female	0.62	0.45	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.62	0.45	1	0	0	2	0	0	not limited	1	0	0	0	1
+female	0.62	0.45	1	0	0	2	0	0	not limited	0	0	0	0	1
+female	0.62	0.45	1	0	0	2	0	3	otherwise	0	1	0	0	0
+female	0.62	0.55	0	0	1	0	0	0	not limited	1	0	0	0	1
+female	0.62	0.55	1	0	0	2	0	0	not limited	0	0	0	0	2
+female	0.62	0.55	1	0	0	2	0	2	not limited	0	0	0	0	1
+female	0.62	0.55	1	0	0	2	0	0	limited	0	0	0	0	2
+female	0.62	0.55	1	0	0	3	0	6	not limited	0	1	0	0	0
+female	0.62	0.55	1	0	0	3	0	0	not limited	0	0	0	0	3
+female	0.62	0.65	0	0	1	0	0	0	not limited	0	0	0	0	0
+female	0.62	0.65	0	0	1	1	0	0	not limited	0	0	0	0	1
+female	0.62	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.62	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.62	0.65	1	0	0	2	0	0	not limited	0	0	0	0	1
+female	0.62	0.65	1	0	0	2	0	0	not limited	0	1	0	0	0
+female	0.62	0.65	1	0	0	4	0	5	not limited	0	0	0	0	2
+female	0.62	0.75	0	0	0	0	0	1	not limited	0	0	0	0	0
+female	0.62	0.75	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.62	0.75	0	0	0	2	0	0	not limited	0	0	0	0	3
+female	0.62	0.75	1	0	0	0	0	0	limited	0	0	0	0	3
+female	0.62	0.75	1	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.62	0.75	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.62	0.75	1	0	0	3	0	0	not limited	1	2	0	0	3
+female	0.62	0.9	0	0	0	2	1	1	limited	0	0	0	0	0
+female	0.62	0.9	0	0	0	3	0	3	limited	0	0	0	0	2
+female	0.62	0.9	0	0	1	3	0	0	not limited	0	0	0	0	8
+female	0.62	0.9	1	0	0	2	0	0	not limited	1	0	0	0	5
+female	0.62	0.9	1	0	0	2	0	1	not limited	0	0	0	0	2
+female	0.62	0.9	1	0	0	3	0	0	not limited	1	1	0	0	3
+female	0.62	1.1	0	0	1	4	0	2	not limited	0	0	0	0	1
+female	0.62	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.62	1.1	1	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.62	1.1	1	0	0	1	4	0	otherwise	1	0	0	0	3
+female	0.62	1.1	1	0	0	1	0	0	limited	0	0	0	0	1
+female	0.62	1.1	1	0	0	2	0	0	not limited	0	0	0	0	3
+female	0.62	1.1	1	0	0	2	0	0	not limited	0	0	0	0	0
+female	0.62	1.1	1	0	0	3	0	0	not limited	0	0	0	0	5
+female	0.62	1.3	1	0	0	1	4	1	otherwise	1	0	1	11	3
+female	0.62	1.3	1	0	0	1	0	0	not limited	0	1	0	0	2
+female	0.62	1.3	1	0	0	2	0	0	not limited	0	0	0	0	4
+female	0.62	1.5	1	0	0	0	0	0	not limited	0	0	0	0	3
+female	0.62	1.5	1	0	0	1	14	1	otherwise	8	0	1	7	3
+female	0.62	1.5	1	0	0	5	0	1	not limited	1	1	0	0	8
+female	0.67	0.06	1	0	0	0	0	3	not limited	0	0	0	0	0
+female	0.67	0.06	1	0	0	0	0	1	otherwise	0	0	0	0	1
+female	0.67	0.15	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.67	0.15	0	0	1	0	0	0	otherwise	0	0	0	0	1
+female	0.67	0.15	0	0	1	0	0	5	not limited	0	0	0	0	1
+female	0.67	0.15	0	0	1	0	0	5	otherwise	0	0	0	0	0
+female	0.67	0.15	0	0	1	2	0	4	limited	7	7	0	0	2
+female	0.67	0.15	0	0	1	2	0	1	not limited	1	0	0	0	2
+female	0.67	0.15	0	0	1	2	0	0	not limited	0	0	0	0	3
+female	0.67	0.15	0	0	1	4	0	2	limited	2	0	2	2	5
+female	0.67	0.15	1	0	0	0	0	0	limited	0	0	0	0	3
+female	0.67	0.15	1	0	0	1	0	0	not limited	2	0	2	3	2
+female	0.67	0.15	1	0	0	1	0	0	not limited	0	0	0	0	4
+female	0.67	0.15	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.67	0.15	1	0	0	1	0	0	not limited	0	0	0	0	3
+female	0.67	0.15	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.67	0.15	1	0	0	2	0	1	not limited	2	0	0	0	0
+female	0.67	0.15	1	0	0	2	0	4	not limited	0	0	0	0	4
+female	0.67	0.15	1	0	0	2	0	2	not limited	0	0	0	0	3
+female	0.67	0.15	1	0	0	3	0	0	not limited	0	0	0	0	1
+female	0.67	0.25	0	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.67	0.25	0	0	1	0	0	0	not limited	1	0	0	0	2
+female	0.67	0.25	0	0	1	0	0	0	not limited	1	0	0	0	1
+female	0.67	0.25	0	0	1	0	0	0	not limited	1	0	0	0	2
+female	0.67	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	2
+female	0.67	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.67	0.25	0	0	1	0	0	0	not limited	0	0	0	0	1
+female	0.67	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.67	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	3
+female	0.67	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	2
+female	0.67	0.25	0	0	1	0	0	0	not limited	0	1	1	4	0
+female	0.67	0.25	0	0	1	0	0	2	otherwise	0	0	0	0	1
+female	0.67	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	3
+female	0.67	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.67	0.25	0	0	1	0	0	2	limited	0	0	0	0	3
+female	0.67	0.25	0	0	1	0	0	0	not limited	0	0	0	0	1
+female	0.67	0.25	0	0	1	0	0	1	not limited	0	0	0	0	2
+female	0.67	0.25	0	0	1	0	0	0	not limited	0	0	0	0	0
+female	0.67	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.67	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.67	0.25	0	0	1	0	0	4	otherwise	0	0	0	0	0
+female	0.67	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.67	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.67	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.67	0.25	0	0	1	1	0	0	not limited	1	0	0	0	4
+female	0.67	0.25	0	0	1	1	0	4	otherwise	1	0	0	0	3
+female	0.67	0.25	0	0	1	1	0	0	limited	1	7	2	45	2
+female	0.67	0.25	0	0	1	1	0	0	not limited	1	0	1	11	2
+female	0.67	0.25	0	0	1	1	0	0	otherwise	0	0	1	1	0
+female	0.67	0.25	0	0	1	1	0	3	otherwise	0	0	0	0	5
+female	0.67	0.25	0	0	1	1	0	0	not limited	0	0	0	0	0
+female	0.67	0.25	0	0	1	1	0	0	not limited	0	0	0	0	1
+female	0.67	0.25	0	0	1	1	0	0	not limited	0	0	0	0	4
+female	0.67	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	0
+female	0.67	0.25	0	0	1	1	0	1	not limited	0	0	0	0	3
+female	0.67	0.25	0	0	1	1	0	1	not limited	0	0	0	0	0
+female	0.67	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	0
+female	0.67	0.25	0	0	1	1	0	0	not limited	0	2	0	0	1
+female	0.67	0.25	0	0	1	1	0	0	not limited	0	0	0	0	2
+female	0.67	0.25	0	0	1	1	0	2	not limited	0	0	0	0	2
+female	0.67	0.25	0	0	1	1	0	0	not limited	0	0	0	0	2
+female	0.67	0.25	0	0	1	1	0	2	not limited	0	1	0	0	0
+female	0.67	0.25	0	0	1	1	0	0	not limited	0	0	0	0	2
+female	0.67	0.25	0	0	1	1	0	0	not limited	0	0	0	0	3
+female	0.67	0.25	0	0	1	1	0	0	not limited	0	0	0	0	3
+female	0.67	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	1
+female	0.67	0.25	0	0	1	1	0	0	not limited	0	0	0	0	0
+female	0.67	0.25	0	0	1	1	0	0	not limited	0	0	0	0	1
+female	0.67	0.25	0	0	1	1	0	0	not limited	0	0	0	0	2
+female	0.67	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	1
+female	0.67	0.25	0	0	1	1	0	1	otherwise	0	0	0	0	0
+female	0.67	0.25	0	0	1	1	0	0	not limited	0	0	0	0	0
+female	0.67	0.25	0	0	1	1	0	0	otherwise	0	2	0	0	1
+female	0.67	0.25	0	0	1	2	0	0	not limited	1	0	0	0	2
+female	0.67	0.25	0	0	1	2	0	6	otherwise	2	0	0	0	2
+female	0.67	0.25	0	0	1	2	0	0	not limited	2	0	0	0	1
+female	0.67	0.25	0	0	1	2	0	1	not limited	1	0	0	0	4
+female	0.67	0.25	0	0	1	2	0	2	not limited	2	0	1	11	3
+female	0.67	0.25	0	0	1	2	0	1	limited	1	0	0	0	5
+female	0.67	0.25	0	0	1	2	0	0	not limited	1	0	0	0	3
+female	0.67	0.25	0	0	1	2	0	2	limited	1	0	2	7	3
+female	0.67	0.25	0	0	1	2	0	1	not limited	0	0	0	0	0
+female	0.67	0.25	0	0	1	2	0	0	not limited	0	0	2	11	4
+female	0.67	0.25	0	0	1	2	0	0	not limited	0	0	0	0	3
+female	0.67	0.25	0	0	1	2	0	1	otherwise	0	0	0	0	2
+female	0.67	0.25	0	0	1	2	0	2	not limited	0	0	0	0	2
+female	0.67	0.25	0	0	1	2	0	0	not limited	0	0	0	0	2
+female	0.67	0.25	0	0	1	2	0	1	otherwise	0	0	0	0	3
+female	0.67	0.25	0	0	1	2	0	0	not limited	0	0	0	0	7
+female	0.67	0.25	0	0	1	2	0	1	otherwise	0	0	0	0	1
+female	0.67	0.25	0	0	1	2	0	0	otherwise	0	0	0	0	1
+female	0.67	0.25	0	0	1	2	0	0	otherwise	0	0	0	0	1
+female	0.67	0.25	0	0	1	2	1	0	not limited	0	1	0	0	4
+female	0.67	0.25	0	0	1	2	0	0	not limited	0	0	0	0	1
+female	0.67	0.25	0	0	1	2	2	0	not limited	0	0	0	0	0
+female	0.67	0.25	0	0	1	2	0	0	not limited	0	0	0	0	0
+female	0.67	0.25	0	0	1	2	0	3	not limited	0	0	0	0	1
+female	0.67	0.25	0	0	1	2	0	0	otherwise	0	0	0	0	0
+female	0.67	0.25	0	0	1	3	0	4	not limited	1	0	0	0	2
+female	0.67	0.25	0	0	1	3	0	4	otherwise	1	0	0	0	1
+female	0.67	0.25	0	0	1	3	0	1	not limited	2	0	0	0	3
+female	0.67	0.25	0	0	1	3	0	9	not limited	2	0	0	0	2
+female	0.67	0.25	0	0	1	3	14	3	not limited	2	2	0	0	4
+female	0.67	0.25	0	0	1	3	0	1	limited	1	0	0	0	4
+female	0.67	0.25	0	0	1	3	0	2	not limited	0	0	0	0	4
+female	0.67	0.25	0	0	1	3	0	1	not limited	0	0	0	0	4
+female	0.67	0.25	0	0	1	3	2	0	not limited	0	4	1	45	5
+female	0.67	0.25	0	0	1	3	1	2	not limited	0	2	0	0	2
+female	0.67	0.25	0	0	1	3	0	0	not limited	0	0	0	0	4
+female	0.67	0.25	0	0	1	3	0	0	not limited	0	0	0	0	1
+female	0.67	0.25	0	0	1	3	0	0	not limited	0	0	0	0	0
+female	0.67	0.25	0	0	1	3	0	1	not limited	0	1	0	0	3
+female	0.67	0.25	0	0	1	3	0	3	not limited	0	0	0	0	0
+female	0.67	0.25	0	0	1	4	14	0	not limited	2	0	0	0	4
+female	0.67	0.25	0	0	1	4	0	11	not limited	1	0	0	0	8
+female	0.67	0.25	0	0	1	4	4	0	not limited	0	0	0	0	4
+female	0.67	0.25	0	0	1	4	0	0	not limited	0	0	0	0	2
+female	0.67	0.25	0	0	1	5	14	7	not limited	1	1	1	22	3
+female	0.67	0.25	0	0	1	5	0	10	not limited	0	0	2	11	6
+female	0.67	0.25	0	0	1	5	0	0	not limited	0	0	0	0	3
+female	0.67	0.25	0	0	1	5	0	1	not limited	0	0	0	0	1
+female	0.67	0.25	0	0	1	5	0	4	not limited	0	0	0	0	2
+female	0.67	0.25	1	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.67	0.25	1	0	0	0	0	1	otherwise	0	2	0	0	0
+female	0.67	0.25	1	0	0	0	0	0	not limited	0	1	1	11	0
+female	0.67	0.25	1	0	0	0	0	0	not limited	0	0	0	0	4
+female	0.67	0.25	1	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.67	0.25	1	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.67	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.67	0.25	1	0	0	1	0	1	otherwise	1	1	1	22	0
+female	0.67	0.25	1	0	0	1	0	0	otherwise	1	0	0	0	1
+female	0.67	0.25	1	0	0	1	4	1	otherwise	0	0	0	0	1
+female	0.67	0.25	1	0	0	1	0	1	not limited	0	1	1	1	4
+female	0.67	0.25	1	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.67	0.25	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.67	0.25	1	0	0	2	0	1	not limited	1	0	0	0	2
+female	0.67	0.25	1	0	0	2	0	2	not limited	2	0	1	2	4
+female	0.67	0.25	1	0	0	2	0	1	not limited	1	2	0	0	4
+female	0.67	0.25	1	0	0	2	0	0	not limited	0	0	0	0	4
+female	0.67	0.25	1	0	0	2	0	0	not limited	0	0	0	0	0
+female	0.67	0.25	1	0	0	2	0	1	not limited	0	0	0	0	1
+female	0.67	0.25	1	0	0	2	0	2	otherwise	0	0	0	0	1
+female	0.67	0.25	1	0	0	2	0	0	not limited	0	0	0	0	1
+female	0.67	0.25	1	0	0	2	0	0	not limited	0	0	0	0	2
+female	0.67	0.25	1	0	0	3	1	0	not limited	0	0	0	0	4
+female	0.67	0.25	1	0	0	3	0	0	not limited	0	0	0	0	5
+female	0.67	0.25	1	0	0	5	0	9	not limited	2	0	0	0	6
+female	0.67	0.25	1	0	0	5	7	2	limited	0	0	1	7	4
+female	0.67	0.35	0	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.67	0.35	0	0	0	0	0	0	not limited	0	0	0	0	2
+female	0.67	0.35	0	0	1	0	0	1	otherwise	1	0	0	0	0
+female	0.67	0.35	0	0	1	0	0	0	not limited	0	0	0	0	0
+female	0.67	0.35	0	0	1	0	0	0	not limited	0	1	0	0	2
+female	0.67	0.35	0	0	1	0	0	2	not limited	0	0	0	0	2
+female	0.67	0.35	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.67	0.35	0	0	1	0	0	0	not limited	0	0	0	0	0
+female	0.67	0.35	0	0	1	0	0	7	otherwise	0	0	0	0	0
+female	0.67	0.35	0	0	1	0	0	0	not limited	0	0	0	0	0
+female	0.67	0.35	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.67	0.35	0	0	1	1	14	12	limited	6	0	5	22	3
+female	0.67	0.35	0	0	1	1	6	0	not limited	1	0	0	0	2
+female	0.67	0.35	0	0	1	1	0	0	not limited	0	1	0	0	2
+female	0.67	0.35	0	0	1	1	0	4	otherwise	0	0	0	0	2
+female	0.67	0.35	0	0	1	2	0	1	not limited	1	0	0	0	2
+female	0.67	0.35	0	0	1	2	0	0	not limited	1	0	0	0	3
+female	0.67	0.35	0	0	1	3	0	1	not limited	0	0	0	0	3
+female	0.67	0.35	0	0	1	3	0	4	not limited	0	1	0	0	2
+female	0.67	0.35	0	0	1	3	0	1	not limited	0	0	0	0	3
+female	0.67	0.35	0	0	1	4	0	0	not limited	0	0	1	22	2
+female	0.67	0.35	0	0	1	4	0	0	not limited	0	0	0	0	1
+female	0.67	0.35	0	0	1	5	14	9	limited	2	7	1	11	5
+female	0.67	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.67	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.67	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.67	0.35	1	0	0	1	14	3	otherwise	7	7	1	22	2
+female	0.67	0.35	1	0	0	1	3	0	otherwise	0	0	0	0	1
+female	0.67	0.35	1	0	0	1	0	1	not limited	0	0	0	0	1
+female	0.67	0.35	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.67	0.35	1	0	0	1	0	0	limited	0	0	0	0	4
+female	0.67	0.35	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.67	0.35	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.67	0.35	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.67	0.35	1	0	0	2	0	4	not limited	1	0	0	0	0
+female	0.67	0.35	1	0	0	2	0	0	not limited	0	0	0	0	1
+female	0.67	0.35	1	0	0	2	0	1	not limited	0	0	0	0	2
+female	0.67	0.35	1	0	0	3	14	2	not limited	1	0	0	0	2
+female	0.67	0.35	1	0	0	3	0	5	otherwise	0	1	0	0	1
+female	0.67	0.35	1	0	0	3	0	1	not limited	0	0	0	0	1
+female	0.67	0.35	1	0	0	3	0	0	not limited	0	0	0	0	8
+female	0.67	0.35	1	0	0	3	0	0	not limited	0	0	0	0	1
+female	0.67	0.35	1	0	0	3	0	1	not limited	0	0	0	0	2
+female	0.67	0.35	1	0	0	5	14	10	not limited	7	10	3	7	6
+female	0.67	0.45	0	0	0	0	0	0	otherwise	0	0	2	22	0
+female	0.67	0.45	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.67	0.45	0	0	1	1	1	0	not limited	1	0	0	0	2
+female	0.67	0.45	0	0	1	1	0	0	not limited	1	0	0	0	5
+female	0.67	0.45	0	0	1	1	0	0	otherwise	0	0	0	0	0
+female	0.67	0.45	0	0	1	1	2	1	otherwise	0	0	0	0	1
+female	0.67	0.45	0	0	1	1	0	0	not limited	0	0	0	0	1
+female	0.67	0.45	0	0	1	2	0	0	not limited	0	0	0	0	2
+female	0.67	0.45	0	0	1	2	0	1	not limited	0	0	0	0	2
+female	0.67	0.45	0	0	1	3	0	3	not limited	1	0	0	0	4
+female	0.67	0.45	0	0	1	5	0	6	not limited	1	0	0	0	4
+female	0.67	0.45	0	0	1	5	0	0	not limited	1	2	0	0	4
+female	0.67	0.45	0	0	1	5	8	2	not limited	0	0	0	0	3
+female	0.67	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.67	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.67	0.55	0	0	0	1	0	0	otherwise	0	2	2	11	0
+female	0.67	0.55	1	0	0	0	0	0	not limited	0	0	0	0	4
+female	0.67	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.67	0.55	1	0	0	1	0	1	not limited	0	0	0	0	2
+female	0.67	0.55	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.67	0.55	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.67	0.55	1	0	0	1	1	0	otherwise	0	2	0	0	0
+female	0.67	0.55	1	0	0	5	3	2	not limited	1	0	2	11	8
+female	0.67	0.65	0	0	1	3	0	2	limited	0	0	1	11	3
+female	0.67	0.65	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.67	0.65	1	0	0	1	0	2	otherwise	0	0	0	0	0
+female	0.67	0.65	1	0	0	2	0	0	not limited	0	0	0	0	3
+female	0.67	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.67	0.75	1	0	0	4	0	0	limited	1	0	1	4	6
+female	0.67	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.67	0.9	1	0	0	1	0	3	otherwise	0	0	0	0	0
+female	0.67	1.5	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.67	1.5	1	0	0	2	0	0	otherwise	0	0	0	0	2
+female	0.72	0.06	0	0	1	1	0	0	not limited	0	0	0	0	1
+female	0.72	0.06	0	0	1	1	0	0	otherwise	0	0	0	0	0
+female	0.72	0.06	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.72	0.15	0	0	1	0	0	0	not limited	2	0	2	11	6
+female	0.72	0.15	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.15	0	0	1	0	0	0	not limited	0	4	0	0	2
+female	0.72	0.15	0	0	1	0	0	0	not limited	0	0	0	0	0
+female	0.72	0.15	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.15	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.15	0	0	1	1	0	8	limited	1	2	1	22	3
+female	0.72	0.15	0	0	1	1	0	0	otherwise	0	0	1	11	2
+female	0.72	0.15	0	0	1	1	0	0	otherwise	0	0	0	0	2
+female	0.72	0.15	0	0	1	1	0	0	otherwise	0	1	0	0	0
+female	0.72	0.15	0	0	1	1	0	0	not limited	0	0	0	0	0
+female	0.72	0.15	0	0	1	1	0	0	not limited	0	0	0	0	2
+female	0.72	0.15	0	0	1	2	0	0	not limited	0	0	0	0	0
+female	0.72	0.15	0	0	1	2	0	0	not limited	0	0	0	0	2
+female	0.72	0.15	0	0	1	2	0	1	not limited	0	0	0	0	0
+female	0.72	0.15	0	0	1	3	0	2	not limited	1	0	0	0	3
+female	0.72	0.15	0	0	1	3	0	0	limited	1	0	0	0	3
+female	0.72	0.15	0	0	1	4	0	3	not limited	1	0	1	80	5
+female	0.72	0.15	0	0	1	4	0	7	not limited	0	1	1	2	3
+female	0.72	0.15	0	0	1	4	0	0	limited	0	8	0	0	2
+female	0.72	0.15	1	0	0	0	0	0	otherwise	1	0	1	2	0
+female	0.72	0.15	1	0	0	0	0	1	otherwise	0	1	0	0	1
+female	0.72	0.15	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.15	1	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.72	0.15	1	0	0	2	0	0	not limited	0	0	0	0	2
+female	0.72	0.15	1	0	0	3	0	2	not limited	0	0	0	0	3
+female	0.72	0.25	0	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.72	0.25	0	0	0	1	0	0	not limited	1	0	1	11	5
+female	0.72	0.25	0	0	0	2	0	0	not limited	0	0	1	22	2
+female	0.72	0.25	0	0	0	5	0	7	not limited	0	1	0	0	2
+female	0.72	0.25	0	0	1	0	0	0	otherwise	1	0	0	0	0
+female	0.72	0.25	0	0	1	0	0	0	not limited	1	0	1	2	0
+female	0.72	0.25	0	0	1	0	0	0	otherwise	1	0	0	0	1
+female	0.72	0.25	0	0	1	0	0	0	limited	2	0	0	0	1
+female	0.72	0.25	0	0	1	0	0	3	not limited	1	0	0	0	1
+female	0.72	0.25	0	0	1	0	0	0	otherwise	1	2	0	0	0
+female	0.72	0.25	0	0	1	0	0	0	otherwise	1	0	0	0	2
+female	0.72	0.25	0	0	1	0	0	0	otherwise	2	0	0	0	0
+female	0.72	0.25	0	0	1	0	0	0	limited	1	0	0	0	1
+female	0.72	0.25	0	0	1	0	0	2	otherwise	1	0	0	0	0
+female	0.72	0.25	0	0	1	0	0	0	not limited	0	0	0	0	0
+female	0.72	0.25	0	0	1	0	0	0	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	1
+female	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.25	0	0	1	0	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.25	0	0	1	0	0	0	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	2
+female	0.72	0.25	0	0	1	0	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.25	0	0	1	0	0	4	limited	0	0	0	0	3
+female	0.72	0.25	0	0	1	0	0	2	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	0	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	0	0	0	limited	0	7	1	11	2
+female	0.72	0.25	0	0	1	0	0	1	otherwise	0	0	0	0	0
+female	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	1
+female	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.25	0	0	1	0	0	0	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	0	0	0	not limited	0	0	1	3	1
+female	0.72	0.25	0	0	1	0	0	1	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	0	0	1	otherwise	0	0	0	0	1
+female	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.25	0	0	1	0	0	1	otherwise	0	0	0	0	0
+female	0.72	0.25	0	0	1	0	0	2	otherwise	0	0	0	0	0
+female	0.72	0.25	0	0	1	0	0	1	not limited	0	0	0	0	0
+female	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	1
+female	0.72	0.25	0	0	1	0	0	0	otherwise	0	1	0	0	1
+female	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.25	0	0	1	0	0	1	otherwise	0	0	0	0	0
+female	0.72	0.25	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.25	0	0	1	1	14	3	limited	1	0	0	0	7
+female	0.72	0.25	0	0	1	1	0	0	otherwise	1	0	0	0	3
+female	0.72	0.25	0	0	1	1	0	0	otherwise	1	1	0	0	2
+female	0.72	0.25	0	0	1	1	14	2	limited	1	0	3	5	1
+female	0.72	0.25	0	0	1	1	0	2	not limited	1	1	0	0	3
+female	0.72	0.25	0	0	1	1	0	0	not limited	1	0	0	0	1
+female	0.72	0.25	0	0	1	1	0	1	not limited	1	0	0	0	2
+female	0.72	0.25	0	0	1	1	4	0	not limited	1	0	0	0	4
+female	0.72	0.25	0	0	1	1	14	11	limited	1	1	1	11	4
+female	0.72	0.25	0	0	1	1	0	1	not limited	1	0	1	1	4
+female	0.72	0.25	0	0	1	1	3	2	not limited	1	0	0	0	4
+female	0.72	0.25	0	0	1	1	2	0	not limited	2	0	0	0	3
+female	0.72	0.25	0	0	1	1	0	0	otherwise	1	0	0	0	2
+female	0.72	0.25	0	0	1	1	0	2	otherwise	2	0	0	0	3
+female	0.72	0.25	0	0	1	1	0	0	not limited	2	1	0	0	2
+female	0.72	0.25	0	0	1	1	0	1	limited	1	0	0	0	2
+female	0.72	0.25	0	0	1	1	0	0	not limited	1	0	0	0	0
+female	0.72	0.25	0	0	1	1	14	0	limited	1	0	0	0	4
+female	0.72	0.25	0	0	1	1	14	1	limited	1	7	0	0	1
+female	0.72	0.25	0	0	1	1	0	1	limited	2	4	0	0	4
+female	0.72	0.25	0	0	1	1	5	2	not limited	6	0	1	6	0
+female	0.72	0.25	0	0	1	1	0	0	not limited	1	0	0	0	1
+female	0.72	0.25	0	0	1	1	0	0	not limited	2	0	0	0	0
+female	0.72	0.25	0	0	1	1	0	1	not limited	1	4	2	70	4
+female	0.72	0.25	0	0	1	1	14	2	limited	1	8	2	5	2
+female	0.72	0.25	0	0	1	1	3	0	otherwise	0	0	0	0	1
+female	0.72	0.25	0	0	1	1	4	2	otherwise	0	0	0	0	1
+female	0.72	0.25	0	0	1	1	0	3	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	1	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	1	0	0	not limited	0	0	0	0	7
+female	0.72	0.25	0	0	1	1	0	0	not limited	0	0	2	3	2
+female	0.72	0.25	0	0	1	1	0	1	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	1	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	1	0	0	not limited	0	4	0	0	2
+female	0.72	0.25	0	0	1	1	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	1	0	1	not limited	0	0	0	0	6
+female	0.72	0.25	0	0	1	1	0	0	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	1	0	2	otherwise	0	0	0	0	1
+female	0.72	0.25	0	0	1	1	0	1	not limited	0	0	1	22	1
+female	0.72	0.25	0	0	1	1	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	1
+female	0.72	0.25	0	0	1	1	1	1	not limited	0	0	0	0	3
+female	0.72	0.25	0	0	1	1	0	1	not limited	0	0	0	0	0
+female	0.72	0.25	0	0	1	1	0	0	otherwise	0	6	0	0	0
+female	0.72	0.25	0	0	1	1	0	0	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	1	0	0	limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	1	0	0	not limited	0	0	0	0	3
+female	0.72	0.25	0	0	1	1	14	0	limited	0	0	0	0	4
+female	0.72	0.25	0	0	1	1	0	0	not limited	0	1	0	0	3
+female	0.72	0.25	0	0	1	1	0	0	otherwise	0	1	0	0	3
+female	0.72	0.25	0	0	1	1	14	7	limited	0	6	2	22	2
+female	0.72	0.25	0	0	1	1	0	1	not limited	0	0	0	0	3
+female	0.72	0.25	0	0	1	1	0	2	otherwise	0	0	0	0	2
+female	0.72	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	2
+female	0.72	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	1
+female	0.72	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	0
+female	0.72	0.25	0	0	1	1	0	0	not limited	0	0	0	0	0
+female	0.72	0.25	0	0	1	1	14	0	otherwise	0	0	0	0	1
+female	0.72	0.25	0	0	1	1	0	0	not limited	0	0	0	0	0
+female	0.72	0.25	0	0	1	1	0	0	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	1
+female	0.72	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	1
+female	0.72	0.25	0	0	1	1	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	1	0	0	otherwise	0	1	0	0	0
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+female	0.72	0.25	0	0	1	1	0	0	limited	0	0	0	0	0
+female	0.72	0.25	0	0	1	1	0	2	not limited	0	0	0	0	0
+female	0.72	0.25	0	0	1	1	0	0	otherwise	0	0	0	0	1
+female	0.72	0.25	0	0	1	1	0	0	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	1	0	0	not limited	0	0	0	0	0
+female	0.72	0.25	0	0	1	1	0	0	limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	2	10	1	not limited	1	0	1	22	0
+female	0.72	0.25	0	0	1	2	0	0	not limited	1	0	0	0	1
+female	0.72	0.25	0	0	1	2	0	11	limited	2	0	1	11	3
+female	0.72	0.25	0	0	1	2	0	5	not limited	1	0	0	0	3
+female	0.72	0.25	0	0	1	2	0	5	otherwise	1	0	0	0	1
+female	0.72	0.25	0	0	1	2	0	3	not limited	1	0	0	0	5
+female	0.72	0.25	0	0	1	2	0	3	not limited	1	0	0	0	2
+female	0.72	0.25	0	0	1	2	0	0	not limited	1	0	0	0	4
+female	0.72	0.25	0	0	1	2	0	0	not limited	2	0	0	0	2
+female	0.72	0.25	0	0	1	2	0	0	otherwise	1	1	0	0	4
+female	0.72	0.25	0	0	1	2	14	1	limited	7	0	1	11	5
+female	0.72	0.25	0	0	1	2	0	0	otherwise	1	0	0	0	8
+female	0.72	0.25	0	0	1	2	0	1	not limited	2	0	2	1	2
+female	0.72	0.25	0	0	1	2	0	1	not limited	1	0	2	11	4
+female	0.72	0.25	0	0	1	2	14	0	limited	2	1	2	45	1
+female	0.72	0.25	0	0	1	2	0	0	not limited	1	0	1	22	5
+female	0.72	0.25	0	0	1	2	0	1	not limited	1	0	0	0	2
+female	0.72	0.25	0	0	1	2	0	0	not limited	1	0	0	0	2
+female	0.72	0.25	0	0	1	2	0	4	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	2	0	8	not limited	0	1	0	0	3
+female	0.72	0.25	0	0	1	2	0	1	limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	2	0	1	otherwise	0	0	0	0	2
+female	0.72	0.25	0	0	1	2	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	2	0	6	limited	0	4	1	11	3
+female	0.72	0.25	0	0	1	2	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	2	0	2	not limited	0	0	0	0	3
+female	0.72	0.25	0	0	1	2	0	1	not limited	0	0	0	0	5
+female	0.72	0.25	0	0	1	2	0	2	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	2	0	2	limited	0	1	0	0	3
+female	0.72	0.25	0	0	1	2	0	4	limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	2	0	2	not limited	0	0	0	0	0
+female	0.72	0.25	0	0	1	2	1	1	not limited	0	0	0	0	3
+female	0.72	0.25	0	0	1	2	8	0	limited	0	0	0	0	4
+female	0.72	0.25	0	0	1	2	0	0	otherwise	0	0	0	0	1
+female	0.72	0.25	0	0	1	2	0	2	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	2	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	2	0	2	not limited	0	1	0	0	8
+female	0.72	0.25	0	0	1	2	0	0	not limited	0	7	0	0	3
+female	0.72	0.25	0	0	1	2	14	1	not limited	0	1	0	0	5
+female	0.72	0.25	0	0	1	2	0	1	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	2	0	1	not limited	0	0	0	0	0
+female	0.72	0.25	0	0	1	2	14	5	limited	0	0	2	45	1
+female	0.72	0.25	0	0	1	2	0	2	not limited	0	0	0	0	0
+female	0.72	0.25	0	0	1	2	0	0	not limited	0	0	0	0	3
+female	0.72	0.25	0	0	1	2	2	5	not limited	0	9	0	0	4
+female	0.72	0.25	0	0	1	2	0	0	not limited	0	1	1	7	3
+female	0.72	0.25	0	0	1	2	0	0	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	2	0	0	not limited	0	1	0	0	4
+female	0.72	0.25	0	0	1	2	0	0	not limited	0	1	0	0	2
+female	0.72	0.25	0	0	1	2	0	5	limited	0	7	1	1	1
+female	0.72	0.25	0	0	1	2	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	2	0	0	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	2	0	0	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	2	0	4	not limited	0	0	0	0	3
+female	0.72	0.25	0	0	1	2	0	1	not limited	0	0	0	0	0
+female	0.72	0.25	0	0	1	2	0	3	otherwise	0	0	0	0	0
+female	0.72	0.25	0	0	1	2	0	0	not limited	0	0	0	0	0
+female	0.72	0.25	0	0	1	2	0	0	limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	3	0	3	not limited	1	1	0	0	3
+female	0.72	0.25	0	0	1	3	0	3	not limited	1	0	0	0	1
+female	0.72	0.25	0	0	1	3	4	6	not limited	2	1	0	0	4
+female	0.72	0.25	0	0	1	3	3	1	not limited	1	0	0	0	3
+female	0.72	0.25	0	0	1	3	0	1	not limited	1	3	0	0	1
+female	0.72	0.25	0	0	1	3	0	0	not limited	1	0	0	0	4
+female	0.72	0.25	0	0	1	3	14	1	not limited	1	1	1	11	2
+female	0.72	0.25	0	0	1	3	0	0	not limited	1	0	0	0	1
+female	0.72	0.25	0	0	1	3	7	3	not limited	1	0	0	0	2
+female	0.72	0.25	0	0	1	3	14	2	not limited	6	0	2	11	7
+female	0.72	0.25	0	0	1	3	0	4	not limited	1	0	0	0	3
+female	0.72	0.25	0	0	1	3	0	1	not limited	1	0	1	7	2
+female	0.72	0.25	0	0	1	3	0	0	not limited	1	4	0	0	8
+female	0.72	0.25	0	0	1	3	0	0	not limited	4	0	0	0	2
+female	0.72	0.25	0	0	1	3	0	0	not limited	1	0	0	0	1
+female	0.72	0.25	0	0	1	3	0	1	otherwise	1	2	0	0	2
+female	0.72	0.25	0	0	1	3	0	0	limited	1	0	0	0	2
+female	0.72	0.25	0	0	1	3	0	3	not limited	2	0	1	5	2
+female	0.72	0.25	0	0	1	3	0	0	not limited	1	0	0	0	3
+female	0.72	0.25	0	0	1	3	0	2	not limited	1	0	0	0	8
+female	0.72	0.25	0	0	1	3	0	0	not limited	1	0	0	0	3
+female	0.72	0.25	0	0	1	3	0	0	not limited	1	0	0	0	1
+female	0.72	0.25	0	0	1	3	14	6	not limited	1	0	0	0	2
+female	0.72	0.25	0	0	1	3	0	1	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	3	0	2	not limited	0	0	0	0	3
+female	0.72	0.25	0	0	1	3	0	1	otherwise	0	2	0	0	0
+female	0.72	0.25	0	0	1	3	0	1	not limited	0	1	0	0	4
+female	0.72	0.25	0	0	1	3	0	0	limited	0	0	1	11	5
+female	0.72	0.25	0	0	1	3	0	0	not limited	0	0	0	0	3
+female	0.72	0.25	0	0	1	3	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	3	0	4	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	3	0	3	limited	0	0	0	0	5
+female	0.72	0.25	0	0	1	3	0	0	not limited	0	0	0	0	5
+female	0.72	0.25	0	0	1	3	0	1	not limited	0	0	1	70	1
+female	0.72	0.25	0	0	1	3	14	7	limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	3	0	2	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	3	0	0	limited	0	0	2	22	4
+female	0.72	0.25	0	0	1	3	0	0	otherwise	0	0	0	0	2
+female	0.72	0.25	0	0	1	3	0	0	not limited	0	1	0	0	4
+female	0.72	0.25	0	0	1	3	0	0	otherwise	0	0	0	0	4
+female	0.72	0.25	0	0	1	3	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	3	0	0	limited	0	0	0	0	7
+female	0.72	0.25	0	0	1	3	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	3	0	0	limited	0	3	2	45	4
+female	0.72	0.25	0	0	1	3	0	0	not limited	0	4	0	0	6
+female	0.72	0.25	0	0	1	3	0	0	not limited	0	0	1	45	0
+female	0.72	0.25	0	0	1	3	0	0	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	3	0	0	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	3	0	0	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	3	0	0	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	3	0	3	limited	0	0	0	0	0
+female	0.72	0.25	0	0	1	3	0	0	not limited	0	0	0	0	0
+female	0.72	0.25	0	0	1	4	7	3	not limited	1	8	0	0	1
+female	0.72	0.25	0	0	1	4	0	1	limited	1	0	1	6	8
+female	0.72	0.25	0	0	1	4	0	3	limited	1	0	1	11	4
+female	0.72	0.25	0	0	1	4	1	3	not limited	2	0	0	0	3
+female	0.72	0.25	0	0	1	4	0	2	not limited	1	0	0	0	1
+female	0.72	0.25	0	0	1	4	0	1	not limited	2	5	0	0	4
+female	0.72	0.25	0	0	1	4	5	0	not limited	4	0	0	0	4
+female	0.72	0.25	0	0	1	4	0	5	limited	1	0	0	0	3
+female	0.72	0.25	0	0	1	4	0	0	not limited	1	0	0	0	3
+female	0.72	0.25	0	0	1	4	10	0	not limited	2	0	0	0	4
+female	0.72	0.25	0	0	1	4	0	10	not limited	1	0	0	0	3
+female	0.72	0.25	0	0	1	4	0	2	limited	1	0	0	0	5
+female	0.72	0.25	0	0	1	4	0	0	not limited	1	0	0	0	4
+female	0.72	0.25	0	0	1	4	14	12	limited	1	1	3	22	5
+female	0.72	0.25	0	0	1	4	2	0	not limited	1	1	1	7	6
+female	0.72	0.25	0	0	1	4	14	9	limited	1	1	2	22	4
+female	0.72	0.25	0	0	1	4	0	0	not limited	0	0	0	0	0
+female	0.72	0.25	0	0	1	4	0	5	not limited	0	0	0	0	3
+female	0.72	0.25	0	0	1	4	0	0	not limited	0	0	0	0	4
+female	0.72	0.25	0	0	1	4	0	3	limited	0	0	0	0	8
+female	0.72	0.25	0	0	1	4	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	4	0	0	not limited	0	0	0	0	4
+female	0.72	0.25	0	0	1	4	0	2	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	4	0	3	limited	0	0	0	0	3
+female	0.72	0.25	0	0	1	4	5	1	not limited	0	0	0	0	5
+female	0.72	0.25	0	0	1	4	0	0	limited	0	0	0	0	3
+female	0.72	0.25	0	0	1	4	0	2	otherwise	0	0	0	0	1
+female	0.72	0.25	0	0	1	4	0	2	not limited	0	1	0	0	3
+female	0.72	0.25	0	0	1	4	0	1	not limited	0	0	1	1	2
+female	0.72	0.25	0	0	1	4	0	1	not limited	0	0	2	22	2
+female	0.72	0.25	0	0	1	4	0	2	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	4	3	5	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	4	0	0	not limited	0	1	0	0	3
+female	0.72	0.25	0	0	1	4	0	1	not limited	0	0	1	22	0
+female	0.72	0.25	0	0	1	4	0	0	not limited	0	0	0	0	1
+female	0.72	0.25	0	0	1	5	14	7	not limited	6	0	1	2	3
+female	0.72	0.25	0	0	1	5	0	6	not limited	1	1	0	0	2
+female	0.72	0.25	0	0	1	5	0	1	limited	2	11	0	0	4
+female	0.72	0.25	0	0	1	5	0	2	limited	2	0	0	0	1
+female	0.72	0.25	0	0	1	5	0	2	not limited	2	2	2	11	5
+female	0.72	0.25	0	0	1	5	14	0	not limited	7	0	0	0	3
+female	0.72	0.25	0	0	1	5	14	0	not limited	2	0	1	22	7
+female	0.72	0.25	0	0	1	5	0	0	not limited	1	0	0	0	4
+female	0.72	0.25	0	0	1	5	0	0	limited	1	0	0	0	8
+female	0.72	0.25	0	0	1	5	0	1	not limited	1	0	0	0	5
+female	0.72	0.25	0	0	1	5	7	1	limited	1	0	1	22	4
+female	0.72	0.25	0	0	1	5	0	5	not limited	1	0	0	0	8
+female	0.72	0.25	0	0	1	5	0	8	not limited	1	0	1	11	7
+female	0.72	0.25	0	0	1	5	0	3	not limited	1	0	0	0	7
+female	0.72	0.25	0	0	1	5	0	2	not limited	0	4	0	0	3
+female	0.72	0.25	0	0	1	5	0	2	not limited	0	0	0	0	0
+female	0.72	0.25	0	0	1	5	0	3	not limited	0	0	0	0	3
+female	0.72	0.25	0	0	1	5	3	2	not limited	0	1	0	0	7
+female	0.72	0.25	0	0	1	5	0	0	not limited	0	0	0	0	4
+female	0.72	0.25	0	0	1	5	0	0	not limited	0	0	0	0	0
+female	0.72	0.25	0	0	1	5	14	6	not limited	0	0	3	1	8
+female	0.72	0.25	0	0	1	5	1	5	not limited	0	0	1	2	8
+female	0.72	0.25	0	0	1	5	0	3	limited	0	0	0	0	5
+female	0.72	0.25	0	0	1	5	0	0	not limited	0	0	0	0	4
+female	0.72	0.25	0	0	1	5	1	0	limited	0	0	3	45	5
+female	0.72	0.25	0	0	1	5	0	1	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	5	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	5	2	7	limited	0	0	0	0	8
+female	0.72	0.25	0	0	1	5	0	6	not limited	0	0	0	0	2
+female	0.72	0.25	0	0	1	5	14	1	not limited	0	0	0	0	8
+female	0.72	0.25	0	0	1	5	0	6	limited	0	9	3	7	3
+female	0.72	0.25	0	0	1	5	0	1	not limited	0	0	0	0	5
+female	0.72	0.25	0	0	1	5	0	3	not limited	0	0	0	0	7
+female	0.72	0.25	0	0	1	5	14	9	not limited	0	0	1	80	3
+female	0.72	0.25	0	0	1	5	0	6	limited	0	7	0	0	4
+female	0.72	0.25	0	0	1	5	0	0	not limited	0	0	1	4	4
+female	0.72	0.25	0	0	1	5	0	3	not limited	0	0	0	0	1
+female	0.72	0.25	0	1	0	3	0	0	not limited	0	0	0	0	3
+female	0.72	0.25	1	0	0	0	0	0	not limited	1	0	0	0	2
+female	0.72	0.25	1	0	0	0	0	1	not limited	1	0	0	0	1
+female	0.72	0.25	1	0	0	0	0	0	not limited	1	0	1	2	2
+female	0.72	0.25	1	0	0	0	0	0	limited	1	1	0	0	4
+female	0.72	0.25	1	0	0	0	0	0	not limited	1	1	0	0	2
+female	0.72	0.25	1	0	0	0	0	0	otherwise	1	0	0	0	0
+female	0.72	0.25	1	0	0	0	0	0	not limited	0	0	0	0	3
+female	0.72	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.25	1	0	0	0	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.72	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.25	1	0	0	0	0	3	otherwise	0	0	0	0	1
+female	0.72	0.25	1	0	0	0	0	0	not limited	0	0	2	22	4
+female	0.72	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	4
+female	0.72	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.25	1	0	0	0	0	0	otherwise	0	1	0	0	1
+female	0.72	0.25	1	0	0	0	0	0	limited	0	1	0	0	0
+female	0.72	0.25	1	0	0	0	0	1	otherwise	0	0	0	0	1
+female	0.72	0.25	1	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.72	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.25	1	0	0	0	0	0	otherwise	0	1	0	0	0
+female	0.72	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.25	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.25	1	0	0	1	0	8	not limited	2	0	1	7	3
+female	0.72	0.25	1	0	0	1	0	0	not limited	1	0	0	0	2
+female	0.72	0.25	1	0	0	1	0	1	not limited	1	0	0	0	3
+female	0.72	0.25	1	0	0	1	1	0	not limited	1	0	0	0	0
+female	0.72	0.25	1	0	0	1	0	0	not limited	1	0	1	22	2
+female	0.72	0.25	1	0	0	1	14	2	not limited	1	0	0	0	2
+female	0.72	0.25	1	0	0	1	0	0	not limited	1	0	0	0	3
+female	0.72	0.25	1	0	0	1	0	0	not limited	1	0	0	0	2
+female	0.72	0.25	1	0	0	1	0	2	not limited	1	7	1	3	3
+female	0.72	0.25	1	0	0	1	0	2	not limited	1	1	1	2	2
+female	0.72	0.25	1	0	0	1	0	2	not limited	0	0	0	0	1
+female	0.72	0.25	1	0	0	1	0	0	not limited	0	2	0	0	3
+female	0.72	0.25	1	0	0	1	0	0	otherwise	0	1	0	0	0
+female	0.72	0.25	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.72	0.25	1	0	0	1	0	0	not limited	0	0	0	0	3
+female	0.72	0.25	1	0	0	1	0	1	not limited	0	1	0	0	3
+female	0.72	0.25	1	0	0	1	0	2	limited	0	5	0	0	4
+female	0.72	0.25	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	1	0	0	1	0	0	not limited	0	0	2	11	3
+female	0.72	0.25	1	0	0	1	0	0	not limited	0	0	1	22	3
+female	0.72	0.25	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.72	0.25	1	0	0	1	0	0	not limited	0	0	2	45	3
+female	0.72	0.25	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.72	0.25	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.72	0.25	1	0	0	2	0	0	otherwise	1	0	1	5	6
+female	0.72	0.25	1	0	0	2	12	6	not limited	4	2	2	45	4
+female	0.72	0.25	1	0	0	2	0	2	not limited	1	0	0	0	6
+female	0.72	0.25	1	0	0	2	0	0	not limited	1	0	0	0	2
+female	0.72	0.25	1	0	0	2	4	0	limited	2	1	0	0	2
+female	0.72	0.25	1	0	0	2	0	0	not limited	1	0	0	0	5
+female	0.72	0.25	1	0	0	2	0	3	not limited	0	0	0	0	2
+female	0.72	0.25	1	0	0	2	0	0	not limited	0	0	0	0	4
+female	0.72	0.25	1	0	0	2	0	0	not limited	0	1	1	4	1
+female	0.72	0.25	1	0	0	2	0	1	otherwise	0	0	0	0	5
+female	0.72	0.25	1	0	0	2	0	1	not limited	0	0	0	0	2
+female	0.72	0.25	1	0	0	2	0	0	not limited	0	0	0	0	3
+female	0.72	0.25	1	0	0	2	0	4	limited	0	0	1	1	2
+female	0.72	0.25	1	0	0	2	0	0	not limited	0	0	0	0	3
+female	0.72	0.25	1	0	0	2	0	0	not limited	0	0	0	0	4
+female	0.72	0.25	1	0	0	2	0	1	limited	0	0	0	0	2
+female	0.72	0.25	1	0	0	2	0	2	not limited	0	0	0	0	1
+female	0.72	0.25	1	0	0	2	0	1	limited	0	0	1	22	4
+female	0.72	0.25	1	0	0	2	0	0	limited	0	7	1	6	1
+female	0.72	0.25	1	0	0	3	0	3	not limited	1	0	0	0	2
+female	0.72	0.25	1	0	0	3	0	0	otherwise	1	0	0	0	1
+female	0.72	0.25	1	0	0	3	0	0	not limited	2	0	1	80	4
+female	0.72	0.25	1	0	0	3	0	2	not limited	1	0	0	0	3
+female	0.72	0.25	1	0	0	3	0	1	limited	2	0	0	0	2
+female	0.72	0.25	1	0	0	3	0	0	not limited	1	1	0	0	3
+female	0.72	0.25	1	0	0	3	2	1	not limited	1	1	1	7	4
+female	0.72	0.25	1	0	0	3	2	0	not limited	1	2	0	0	5
+female	0.72	0.25	1	0	0	3	0	5	not limited	1	0	3	6	3
+female	0.72	0.25	1	0	0	3	0	2	not limited	0	0	0	0	1
+female	0.72	0.25	1	0	0	3	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	1	0	0	3	0	2	limited	0	0	0	0	5
+female	0.72	0.25	1	0	0	3	0	6	not limited	0	5	0	0	6
+female	0.72	0.25	1	0	0	3	0	0	not limited	0	0	0	0	1
+female	0.72	0.25	1	0	0	3	0	0	limited	0	0	0	0	1
+female	0.72	0.25	1	0	0	4	0	3	otherwise	1	0	0	0	3
+female	0.72	0.25	1	0	0	4	3	0	not limited	1	1	0	0	1
+female	0.72	0.25	1	0	0	4	0	1	not limited	1	2	1	11	5
+female	0.72	0.25	1	0	0	4	0	2	not limited	2	3	3	11	6
+female	0.72	0.25	1	0	0	4	0	11	limited	0	0	0	0	5
+female	0.72	0.25	1	0	0	4	0	0	not limited	0	1	1	22	8
+female	0.72	0.25	1	0	0	4	0	2	not limited	0	0	0	0	0
+female	0.72	0.25	1	0	0	4	0	1	not limited	0	0	0	0	2
+female	0.72	0.25	1	0	0	4	0	3	limited	0	1	1	11	3
+female	0.72	0.25	1	0	0	4	14	0	limited	0	0	0	0	3
+female	0.72	0.25	1	0	0	4	0	0	not limited	0	0	0	0	1
+female	0.72	0.25	1	0	0	5	13	3	limited	2	0	0	0	5
+female	0.72	0.25	1	0	0	5	14	5	not limited	7	0	1	7	8
+female	0.72	0.25	1	0	0	5	14	3	limited	6	0	2	22	5
+female	0.72	0.25	1	0	0	5	0	0	not limited	2	1	0	0	2
+female	0.72	0.25	1	0	0	5	0	0	not limited	0	0	0	0	7
+female	0.72	0.25	1	0	0	5	14	9	limited	0	0	0	0	4
+female	0.72	0.25	1	0	0	5	0	0	not limited	0	0	0	0	2
+female	0.72	0.25	1	0	0	5	12	5	limited	0	2	5	11	7
+female	0.72	0.25	1	0	0	5	0	0	not limited	0	0	0	0	4
+female	0.72	0.25	1	0	0	5	1	0	limited	0	0	0	0	8
+female	0.72	0.25	1	0	0	5	0	2	not limited	0	1	0	0	5
+female	0.72	0.25	1	0	0	5	14	2	limited	0	0	0	0	5
+female	0.72	0.25	1	0	0	5	0	5	not limited	0	0	2	11	7
+female	0.72	0.25	1	0	0	5	4	3	not limited	0	0	0	0	3
+female	0.72	0.35	0	0	0	0	0	0	otherwise	0	1	0	0	1
+female	0.72	0.35	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.35	0	0	1	0	0	1	otherwise	2	0	0	0	2
+female	0.72	0.35	0	0	1	0	0	0	not limited	1	0	0	0	1
+female	0.72	0.35	0	0	1	0	0	5	not limited	1	0	0	0	2
+female	0.72	0.35	0	0	1	0	0	0	otherwise	0	0	0	0	1
+female	0.72	0.35	0	0	1	0	0	2	not limited	0	0	0	0	0
+female	0.72	0.35	0	0	1	0	0	0	otherwise	0	0	1	5	4
+female	0.72	0.35	0	0	1	0	0	0	otherwise	0	0	1	45	1
+female	0.72	0.35	0	0	1	0	0	0	limited	0	0	0	0	2
+female	0.72	0.35	0	0	1	0	0	0	otherwise	0	0	0	0	2
+female	0.72	0.35	0	0	1	0	0	0	not limited	0	0	0	0	1
+female	0.72	0.35	0	0	1	0	0	0	not limited	0	0	1	11	3
+female	0.72	0.35	0	0	1	0	0	0	otherwise	0	0	1	2	0
+female	0.72	0.35	0	0	1	0	0	1	otherwise	0	0	0	0	0
+female	0.72	0.35	0	0	1	0	0	0	not limited	0	0	0	0	0
+female	0.72	0.35	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.35	0	0	1	0	0	4	otherwise	0	0	0	0	0
+female	0.72	0.35	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.35	0	0	1	1	0	0	limited	0	0	0	0	4
+female	0.72	0.35	0	0	1	1	0	0	not limited	0	0	0	0	5
+female	0.72	0.35	0	0	1	1	0	0	not limited	0	0	0	0	2
+female	0.72	0.35	0	0	1	1	0	0	not limited	0	0	1	5	4
+female	0.72	0.35	0	0	1	1	0	0	not limited	0	0	1	3	2
+female	0.72	0.35	0	0	1	1	0	1	not limited	0	0	0	0	2
+female	0.72	0.35	0	0	1	1	14	0	limited	0	1	0	0	2
+female	0.72	0.35	0	0	1	1	0	0	not limited	0	1	0	0	2
+female	0.72	0.35	0	0	1	2	0	1	otherwise	1	0	0	0	2
+female	0.72	0.35	0	0	1	2	0	0	not limited	1	0	1	11	3
+female	0.72	0.35	0	0	1	2	0	2	otherwise	1	0	0	0	0
+female	0.72	0.35	0	0	1	2	0	2	limited	0	7	0	0	3
+female	0.72	0.35	0	0	1	2	0	1	limited	0	0	0	0	2
+female	0.72	0.35	0	0	1	2	0	0	otherwise	0	0	0	0	2
+female	0.72	0.35	0	0	1	2	0	5	not limited	0	0	0	0	6
+female	0.72	0.35	0	0	1	2	0	0	not limited	0	0	0	0	0
+female	0.72	0.35	0	0	1	2	0	2	not limited	0	0	0	0	2
+female	0.72	0.35	0	0	1	3	0	9	limited	1	0	0	0	1
+female	0.72	0.35	0	0	1	3	0	1	not limited	1	1	0	0	4
+female	0.72	0.35	0	0	1	3	0	4	not limited	1	0	0	0	1
+female	0.72	0.35	0	0	1	3	0	0	not limited	0	0	0	0	2
+female	0.72	0.35	0	0	1	3	4	2	not limited	0	0	0	0	7
+female	0.72	0.35	0	0	1	3	0	0	not limited	0	0	0	0	0
+female	0.72	0.35	0	0	1	3	8	2	limited	0	0	0	0	1
+female	0.72	0.35	0	0	1	3	0	1	not limited	0	0	0	0	0
+female	0.72	0.35	0	0	1	4	0	1	not limited	1	1	0	0	3
+female	0.72	0.35	0	0	1	4	0	2	not limited	0	0	0	0	5
+female	0.72	0.35	0	0	1	4	0	0	not limited	0	2	2	45	4
+female	0.72	0.35	0	0	1	5	0	4	not limited	1	0	0	0	3
+female	0.72	0.35	0	0	1	5	0	0	not limited	1	1	0	0	5
+female	0.72	0.35	0	0	1	5	0	2	not limited	2	1	0	0	4
+female	0.72	0.35	1	0	0	0	0	0	not limited	1	0	0	0	1
+female	0.72	0.35	1	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.72	0.35	1	0	0	0	0	0	not limited	0	1	0	0	3
+female	0.72	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.72	0.35	1	0	0	0	0	0	not limited	0	0	0	0	6
+female	0.72	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.72	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.72	0.35	1	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.72	0.35	1	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.72	0.35	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.35	1	0	0	1	14	4	not limited	0	1	0	0	4
+female	0.72	0.35	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.72	0.35	1	0	0	1	0	0	not limited	0	1	0	0	1
+female	0.72	0.35	1	0	0	1	0	0	not limited	0	0	0	0	0
+female	0.72	0.35	1	0	0	1	0	2	not limited	0	0	0	0	0
+female	0.72	0.35	1	0	0	2	0	1	not limited	1	0	2	11	1
+female	0.72	0.35	1	0	0	2	0	0	not limited	1	0	0	0	4
+female	0.72	0.35	1	0	0	3	0	3	not limited	1	0	0	0	1
+female	0.72	0.35	1	0	0	3	0	0	not limited	0	0	0	0	2
+female	0.72	0.35	1	0	0	3	0	0	not limited	0	0	0	0	6
+female	0.72	0.35	1	0	0	4	0	0	not limited	4	0	3	7	3
+female	0.72	0.35	1	0	0	4	14	6	limited	1	11	1	80	4
+female	0.72	0.35	1	0	0	4	13	4	limited	6	2	1	5	6
+female	0.72	0.35	1	0	0	4	0	1	not limited	0	0	0	0	4
+female	0.72	0.35	1	0	0	4	0	7	not limited	0	0	0	0	5
+female	0.72	0.35	1	0	0	4	0	10	not limited	0	0	0	0	1
+female	0.72	0.35	1	0	0	5	7	1	not limited	2	1	0	0	2
+female	0.72	0.35	1	0	0	5	0	2	limited	1	0	0	0	5
+female	0.72	0.45	0	0	0	1	0	3	not limited	0	0	0	0	1
+female	0.72	0.45	0	0	1	0	0	0	not limited	0	1	0	0	2
+female	0.72	0.45	0	0	1	0	0	0	not limited	0	0	0	0	1
+female	0.72	0.45	0	0	1	0	0	0	not limited	0	0	0	0	4
+female	0.72	0.45	0	0	1	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.45	0	0	1	1	14	1	otherwise	1	0	0	0	5
+female	0.72	0.45	0	0	1	1	0	0	otherwise	0	0	0	0	0
+female	0.72	0.45	0	0	1	2	0	0	not limited	1	0	1	45	2
+female	0.72	0.45	0	0	1	4	0	2	not limited	0	0	0	0	2
+female	0.72	0.45	0	0	1	5	6	1	not limited	1	0	0	0	4
+female	0.72	0.45	1	0	0	0	0	0	not limited	0	0	0	0	0
+female	0.72	0.45	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.72	0.45	1	0	0	0	0	0	not limited	0	0	0	0	2
+female	0.72	0.45	1	0	0	1	0	0	not limited	1	0	0	0	1
+female	0.72	0.45	1	0	0	1	0	0	otherwise	0	0	0	0	0
+female	0.72	0.45	1	0	0	1	0	0	limited	0	0	0	0	2
+female	0.72	0.45	1	0	0	1	0	2	not limited	0	1	0	0	0
+female	0.72	0.45	1	0	0	1	14	4	limited	0	1	1	22	6
+female	0.72	0.45	1	0	0	1	0	0	not limited	0	0	0	0	1
+female	0.72	0.45	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.72	0.45	1	0	0	2	0	0	limited	0	0	0	0	3
+female	0.72	0.45	1	0	0	2	0	0	not limited	0	0	0	0	1
+female	0.72	0.45	1	0	0	2	0	0	otherwise	0	0	0	0	1
+female	0.72	0.45	1	0	0	3	0	0	not limited	1	0	0	0	2
+female	0.72	0.45	1	0	0	3	0	1	not limited	1	0	0	0	2
+female	0.72	0.45	1	0	0	3	14	4	not limited	0	2	0	0	0
+female	0.72	0.45	1	0	0	3	2	1	not limited	0	0	0	0	3
+female	0.72	0.45	1	0	0	4	14	2	limited	1	0	0	0	4
+female	0.72	0.45	1	0	0	5	7	2	not limited	3	0	0	0	6
+female	0.72	0.45	1	0	0	5	0	2	not limited	0	1	5	11	8
+female	0.72	0.45	1	0	0	5	0	3	not limited	0	1	0	0	2
+female	0.72	0.55	0	0	0	0	14	3	limited	0	4	0	0	2
+female	0.72	0.55	0	0	1	0	0	0	not limited	0	0	0	0	0
+female	0.72	0.55	0	0	1	1	0	4	not limited	1	1	0	0	2
+female	0.72	0.55	0	0	1	1	0	1	limited	2	7	1	80	2
+female	0.72	0.55	0	0	1	1	0	2	otherwise	0	0	0	0	1
+female	0.72	0.55	0	0	1	1	14	1	not limited	0	0	0	0	0
+female	0.72	0.55	0	0	1	2	0	0	not limited	0	0	1	22	4
+female	0.72	0.55	0	0	1	2	0	3	limited	0	1	0	0	6
+female	0.72	0.55	0	0	1	3	0	0	not limited	1	0	0	0	6
+female	0.72	0.55	0	0	1	3	7	0	not limited	0	0	0	0	3
+female	0.72	0.55	0	0	1	3	0	1	limited	0	7	0	0	4
+female	0.72	0.55	0	0	1	3	14	10	not limited	0	0	0	0	0
+female	0.72	0.55	0	0	1	4	0	1	not limited	1	1	0	0	1
+female	0.72	0.55	0	0	1	4	0	1	not limited	0	1	0	0	4
+female	0.72	0.55	0	0	1	4	0	0	not limited	0	0	0	0	3
+female	0.72	0.55	0	0	1	5	10	7	limited	2	1	0	0	3
+female	0.72	0.55	0	0	1	5	0	2	not limited	0	0	0	0	7
+female	0.72	0.55	1	0	0	0	0	0	not limited	1	0	0	0	4
+female	0.72	0.55	1	0	0	0	0	0	not limited	0	0	0	0	4
+female	0.72	0.55	1	0	0	0	0	0	otherwise	0	0	1	4	0
+female	0.72	0.55	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.55	1	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.72	0.55	1	0	0	1	0	4	otherwise	1	0	0	0	2
+female	0.72	0.55	1	0	0	1	0	2	not limited	0	0	0	0	1
+female	0.72	0.55	1	0	0	1	0	0	otherwise	0	0	1	1	1
+female	0.72	0.55	1	0	0	1	0	1	not limited	0	0	1	1	4
+female	0.72	0.55	1	0	0	1	0	0	otherwise	0	0	0	0	1
+female	0.72	0.55	1	0	0	2	0	6	not limited	0	0	0	0	1
+female	0.72	0.55	1	0	0	2	0	0	not limited	0	1	0	0	1
+female	0.72	0.55	1	0	0	2	0	4	not limited	0	0	0	0	0
+female	0.72	0.55	1	0	0	2	0	0	not limited	0	0	0	0	1
+female	0.72	0.55	1	0	0	3	0	3	not limited	0	0	1	22	4
+female	0.72	0.55	1	0	0	3	14	2	not limited	0	1	0	0	4
+female	0.72	0.55	1	0	0	4	0	2	limited	1	0	0	0	5
+female	0.72	0.55	1	0	0	4	0	0	not limited	1	0	0	0	3
+female	0.72	0.55	1	0	0	4	0	0	not limited	0	0	0	0	1
+female	0.72	0.65	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.65	0	0	0	1	14	0	otherwise	1	1	0	0	2
+female	0.72	0.65	0	0	1	3	0	10	not limited	1	0	2	11	4
+female	0.72	0.65	0	0	1	4	14	5	not limited	1	0	0	0	8
+female	0.72	0.65	0	0	1	5	0	3	limited	1	0	1	22	4
+female	0.72	0.65	1	0	0	1	2	0	not limited	0	0	0	0	3
+female	0.72	0.65	1	0	0	1	14	0	not limited	0	0	1	22	1
+female	0.72	0.65	1	0	0	1	0	0	not limited	0	1	0	0	2
+female	0.72	0.65	1	0	0	2	0	3	otherwise	0	1	0	0	3
+female	0.72	0.65	1	0	0	2	0	2	otherwise	0	0	0	0	1
+female	0.72	0.65	1	0	0	3	0	0	not limited	1	1	0	0	3
+female	0.72	0.65	1	0	0	4	0	9	not limited	0	0	1	11	5
+female	0.72	0.65	1	0	0	5	0	3	not limited	0	0	0	0	3
+female	0.72	0.75	0	0	1	1	0	1	not limited	0	0	0	0	1
+female	0.72	0.75	1	0	0	0	0	0	otherwise	1	0	0	0	2
+female	0.72	0.75	1	0	0	0	0	0	not limited	0	0	0	0	2
+female	0.72	0.75	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.72	0.75	1	0	0	1	0	1	not limited	1	0	0	0	2
+female	0.72	0.75	1	0	0	1	0	0	not limited	0	1	0	0	4
+female	0.72	0.75	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.72	0.75	1	0	0	1	0	0	not limited	0	0	1	22	0
+female	0.72	0.75	1	0	0	1	0	0	not limited	0	0	0	0	2
+female	0.72	0.75	1	0	0	1	0	1	not limited	0	1	0	0	0
+female	0.72	0.75	1	0	0	4	0	1	not limited	0	0	0	0	4
+female	0.72	0.9	1	0	0	0	0	1	otherwise	0	0	0	0	0
+female	0.72	0.9	1	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.72	0.9	1	0	0	1	0	0	not limited	0	2	0	0	1
+female	0.72	0.9	1	0	0	2	14	4	limited	1	9	2	7	3
+female	0.72	0.9	1	0	0	2	0	1	limited	0	1	0	0	3
+female	0.72	0.9	1	0	0	3	0	0	not limited	1	1	0	0	3
+female	0.72	0.9	1	0	0	3	0	0	otherwise	0	0	0	0	1
+female	0.72	0.9	1	0	0	5	0	0	not limited	1	0	0	0	3
+female	0.72	1.1	1	0	0	0	0	0	not limited	0	0	0	0	3
+female	0.72	1.1	1	0	0	0	0	0	not limited	0	0	0	0	1
+female	0.72	1.1	1	0	0	0	0	0	otherwise	0	0	0	0	1
+female	0.72	1.1	1	0	0	2	0	0	not limited	0	0	0	0	0
+female	0.72	1.3	0	0	0	0	0	0	otherwise	0	0	0	0	0
+female	0.72	1.3	0	0	1	2	0	0	not limited	0	0	0	0	0
+female	0.72	1.3	1	0	0	0	0	0	otherwise	0	1	0	0	1
+female	0.72	1.5	1	0	0	2	0	0	not limited	0	0	1	11	1
diff --git a/man/mc_influence.Rd b/man/mc_influence.Rd
deleted file mode 100644
index 2886d739c81d4688bc55658ce53b94a54d5689e3..0000000000000000000000000000000000000000
--- a/man/mc_influence.Rd
+++ /dev/null
@@ -1,23 +0,0 @@
-% Generated by roxygen2 (4.1.1): do not edit by hand
-% Please edit documentation in R/mc_influence.R
-\name{mc_influence}
-\alias{mc_influence}
-\title{Influence measures}
-\usage{
-mc_influence(object, id)
-}
-\arguments{
-\item{object}{An object of mcglm class.}
-
-\item{id}{a vector which identifies the clusters. The length and order of id should be the
-same as the number of observations. Data are assumed to be sorted so that observations on a cluster
-are contiguous rows for all entities in the formula.}
-}
-\value{
-A matrix. Note that the function assumes that the data are in the correct order.
-}
-\description{
-Compute influence measures for multivariate covariance generalized linear models.
-Leverage, DFBETA and Cook's distance for unit sample and observations.
-}
-
diff --git a/man/mc_qll.Rd b/man/mc_qll.Rd
deleted file mode 100644
index 7de1f9f44baf290c559806a32271f49e2ab9befc..0000000000000000000000000000000000000000
--- a/man/mc_qll.Rd
+++ /dev/null
@@ -1,24 +0,0 @@
-% Generated by roxygen2 (4.1.1): do not edit by hand
-% Please edit documentation in R/mc_qll.R
-\name{mc_qll}
-\alias{mc_qll}
-\title{Compute quasi-likelihood function.}
-\usage{
-mc_qll(y, mu, variance, power)
-}
-\arguments{
-\item{y}{A vector of observed values.}
-
-\item{mu}{A vector of fitted values.}
-
-\item{variance}{Variance function (constant, tweedie, poisson_tweedie, binomial).}
-
-\item{power}{Power parameter value.}
-}
-\value{
-The quasi-likelihood values.
-}
-\description{
-Given a variance function mc_qll function computes the quasi-likelihood values.
-}
-
diff --git a/man/mc_rw1.Rd b/man/mc_rw1.Rd
deleted file mode 100644
index 4c7a136378607fbee49b92e3977198fcbc826240..0000000000000000000000000000000000000000
--- a/man/mc_rw1.Rd
+++ /dev/null
@@ -1,20 +0,0 @@
-% Generated by roxygen2 (4.1.1): do not edit by hand
-% Please edit documentation in R/mc_rw1.R
-\name{mc_rw1}
-\alias{mc_rw1}
-\title{Random walk first order model}
-\usage{
-mc_rw1(n_time, intrinsic = TRUE)
-}
-\arguments{
-\item{n_time}{Number observations time.}
-
-\item{intrinsic}{Logical indicating if the models is intrinsic (rho = 1) or not.}
-}
-\value{
-A matrix. Note that the function assumes that the data are in the correct order.
-}
-\description{
-Builds a random walk first order model matrix.
-}
-
diff --git a/man/mc_rw2.Rd b/man/mc_rw2.Rd
deleted file mode 100644
index b8c7ee1500e87c1ae627518c1296f2619a871872..0000000000000000000000000000000000000000
--- a/man/mc_rw2.Rd
+++ /dev/null
@@ -1,20 +0,0 @@
-% Generated by roxygen2 (4.1.1): do not edit by hand
-% Please edit documentation in R/mc_rw2.R
-\name{mc_rw2}
-\alias{mc_rw2}
-\title{Random walk second order model}
-\usage{
-mc_rw2(n_time, intrinsic = TRUE)
-}
-\arguments{
-\item{n_time}{Number observations time.}
-
-\item{intrinsic}{Logical indicating if the models is intrinsic (rho = 1) or not.}
-}
-\value{
-A matrix. Note that the function assumes that the data are in the correct order.
-}
-\description{
-Builds a random walk second order model matrix.
-}
-
diff --git a/man/mc_sic.Rd b/man/mc_sic.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..e88fc662558ee3878db5ef2caf0fbc8831cacb60
--- /dev/null
+++ b/man/mc_sic.Rd
@@ -0,0 +1,28 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/mc_sic.R
+\name{mc_sic}
+\alias{mc_sic}
+\title{Compute the score information criterion (SIC) for multivariate
+covariance generalized linear models.}
+\usage{
+mc_sic(object, scope, data, response, penalty = 2)
+}
+\arguments{
+\item{object}{an object representing a model of \code{mcglm} class.}
+
+\item{scope}{a vector containing all covariate names to be tested.}
+
+\item{data}{data frame containing the all variables envolved}
+
+\item{response}{Indicate for which response variable SIC is computed.}
+
+\item{penalty}{penalty term (default = 2).}
+}
+\value{
+A data frame with SIC values for each covariate in the scope
+argument.
+}
+\description{
+Compute the SIC for McGLMS.
+}
+
diff --git a/man/mc_sic_covariance.Rd b/man/mc_sic_covariance.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..01fab7dcea81b0e6fc4d091c1cdf151d63396ac5
--- /dev/null
+++ b/man/mc_sic_covariance.Rd
@@ -0,0 +1,31 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/mc_sic_covariance.R
+\name{mc_sic_covariance}
+\alias{mc_sic_covariance}
+\title{Compute the score information criterion (SIC) for multivariate
+covariance generalized linear models.}
+\usage{
+mc_sic_covariance(object, scope, idx, data, penalty = 2, response)
+}
+\arguments{
+\item{object}{an object representing a model of \code{mcglm} class.}
+
+\item{scope}{a list of matrices to be tested in the matrix linear
+predictor.}
+
+\item{idx}{Indicator of matrices belong to the same effect.}
+
+\item{data}{data frame containing all variables envolved in the model.}
+
+\item{penalty}{penalty term (default = 2).}
+
+\item{response}{Indicate for which response variable SIC is computed.}
+}
+\value{
+A data frame with SIC values for each matrix in the scope
+argument.
+}
+\description{
+Compute SIC for covariance parameters in McGLMS.
+}
+
diff --git a/man/mc_unstructured.Rd b/man/mc_unstructured.Rd
deleted file mode 100644
index 52e12987bc6a47f591ad7751770d43f3465a34e4..0000000000000000000000000000000000000000
--- a/man/mc_unstructured.Rd
+++ /dev/null
@@ -1,18 +0,0 @@
-% Generated by roxygen2 (4.1.1): do not edit by hand
-% Please edit documentation in R/mc_unstructured.R
-\name{mc_unstructured}
-\alias{mc_unstructured}
-\title{Unstructured model}
-\usage{
-mc_unstructured(n_time)
-}
-\arguments{
-\item{n_time}{Number of observations per unit sample.}
-}
-\value{
-A matrix. Note that the function assumes that the data are in the correct order.
-}
-\description{
-Builds a unstructured model matrix.
-}
-
diff --git a/man/qic.mcglm.Rd b/man/qic.mcglm.Rd
deleted file mode 100644
index 02235c1afd4b475b93b3badb8ff5f0aed4dd594f..0000000000000000000000000000000000000000
--- a/man/qic.mcglm.Rd
+++ /dev/null
@@ -1,25 +0,0 @@
-% Generated by roxygen2 (4.1.1): do not edit by hand
-% Please edit documentation in R/mc_qic.R
-\name{qic.mcglm}
-\alias{qic.mcglm}
-\title{Compute Quasi Information Criterion (QIC) for McGLMs.}
-\usage{
-qic.mcglm(object, object.iid)
-}
-\arguments{
-\item{object}{An object of \code{mcglm} class.}
-
-\item{object.iid}{An object of \code{mcglm} class contained the model
-    fitted using independent covariance structure.}
-}
-\value{
-The QIC value.
-}
-\description{
-\code{qic.mcglm} is a function which computes the QIC
-    for McGLMs.
-}
-\author{
-Wagner Hugo Bonat, \email{wbonat@ufpr.br}
-}
-