diff --git a/inst/doc/v07_excesso-zeros.R b/inst/doc/v07_excesso-zeros.R
index 4960463149e40824826ed80d92a02526c0388cd4..65e881606f751005ba1d9d453f6b75e1de07b47d 100644
--- a/inst/doc/v07_excesso-zeros.R
+++ b/inst/doc/v07_excesso-zeros.R
@@ -93,7 +93,6 @@ m1ZBN <- zeroinfl(f1, data = peixe, dist = "negbin")
m2ZBN <- zeroinfl(f2, data = peixe, dist = "negbin")
-
## ------------------------------------------------------------------------
## Via log-verossimilhança
@@ -144,14 +143,13 @@ lmtest::lrtest(m1HBN, m3HBN)
vuong(m1BN, m3HBN)
-## ----diag, cache = TRUE--------------------------------------------------
+## ----diag, cache = TRUE, fig.height = 4----------------------------------
## Uma pequena avaliação dos resÃduos
p1 <- xyplot(residuals(m3HBN) ~ fitted(m3HBN),
type = c("p", "g", "spline"))
p2 <- qqmath(~residuals(m3HBN, type = "pearson"),
- aspect = "xy",
type = c("p", "g"),
panel = function(x, ...) {
panel.qqmathline(x, ...)
@@ -285,14 +283,14 @@ cbind("vglm_posnegbin" = exp(coef(mcount)[2]),
## ------------------------------------------------------------------------
-sinistros <- read.table("base.txt", header = TRUE)
-str(sinistros)
-## help(sinistros)
+data(seguros)
+str(seguros)
+## help(seguros)
## ------------------------------------------------------------------------
-summary(sinistros)
+summary(seguros)
## ------------------------------------------------------------------------
@@ -300,40 +298,40 @@ summary(sinistros)
library(pscl)
## Preditores
-f0 <- nsinist ~ 1 + offset(log(expos))
-f1 <- nsinist ~ sexo + valor + offset(log(expos))
-f2 <- nsinist ~ sexo * valor + offset(log(expos))
+f0 <- nsinist ~ 1
+f1 <- nsinist ~ sexo + valor + log(expos)
+f2 <- nsinist ~ sexo * valor + log(expos)
## Poisson
-m0P <- glm(f0, data = sinistros, family = poisson)
-m1P <- glm(f1, data = sinistros, family = poisson)
-m2P <- glm(f2, data = sinistros, family = poisson)
+m0P <- glm(f0, data = seguros, family = poisson)
+m1P <- glm(f1, data = seguros, family = poisson)
+m2P <- glm(f2, data = seguros, family = poisson)
## Hurdle Poisson
-m0HP <- hurdle(f0, data = sinistros, dist = "poisson")
-m1HP <- hurdle(f1, data = sinistros, dist = "poisson")
-m2HP <- hurdle(f2, data = sinistros, dist = "poisson")
+m0HP <- hurdle(f0, data = seguros, dist = "poisson")
+m1HP <- hurdle(f1, data = seguros, dist = "poisson")
+m2HP <- hurdle(f2, data = seguros, dist = "poisson")
## Zero Inflated Poisson
-m0ZP <- zeroinfl(f0, data = sinistros, dist = "poisson")
-m1ZP <- zeroinfl(f1, data = sinistros, dist = "poisson")
-m2ZP <- zeroinfl(f2, data = sinistros, dist = "poisson")
+m0ZP <- zeroinfl(f0, data = seguros, dist = "poisson")
+m1ZP <- zeroinfl(f1, data = seguros, dist = "poisson")
+m2ZP <- zeroinfl(f2, data = seguros, dist = "poisson")
## Binomial Negativa
library(MASS)
-m0BN <- glm.nb(f0, data = sinistros)
-m1BN <- glm.nb(f1, data = sinistros)
-m2BN <- glm.nb(f2, data = sinistros)
+m0BN <- glm.nb(f0, data = seguros)
+m1BN <- glm.nb(f1, data = seguros)
+m2BN <- glm.nb(f2, data = seguros)
## Hurdle Binomial Negativa
-m0HBN <- hurdle(f0, data = sinistros, dist = "negbin")
-m1HBN <- hurdle(f1, data = sinistros, dist = "negbin")
-m2HBN <- hurdle(f2, data = sinistros, dist = "negbin")
+m0HBN <- hurdle(f0, data = seguros, dist = "negbin")
+m1HBN <- hurdle(f1, data = seguros, dist = "negbin")
+m2HBN <- hurdle(f2, data = seguros, dist = "negbin")
## Zero Inflated Poisson
-m0ZBN <- zeroinfl(f0, data = sinistros, dist = "negbin")
-m1ZBN <- zeroinfl(f1, data = sinistros, dist = "negbin")
-m2ZBN <- zeroinfl(f2, data = sinistros, dist = "negbin")
+m0ZBN <- zeroinfl(f0, data = seguros, dist = "negbin")
+m1ZBN <- zeroinfl(f1, data = seguros, dist = "negbin")
+m2ZBN <- zeroinfl(f2, data = seguros, dist = "negbin")
## ------------------------------------------------------------------------
@@ -351,92 +349,68 @@ cbind("Poisson" = sapply(list(m0P, m1P, m2P), logLik),
## ------------------------------------------------------------------------
## Testes de razão de verossimilhanças
-lmtest::lrtest(m0ZP, m1ZP, m2ZP)
+lmtest::lrtest(m0HP, m1HP, m2HP)
-lmtest::lrtest(m0ZBN, m1ZBN, m2ZBN)
+lmtest::lrtest(m0HBN, m1HBN, m2HBN)
## ------------------------------------------------------------------------
## Para a componente de contagens não negativas é necessário um modelo
## Binomial Negativa, considerando superdispersão dos dados?
-vuong(m1ZP, m1ZBN)
+vuong(m1HP, m1HBN)
## ------------------------------------------------------------------------
## Estimativas do modelo proposto
-summary(m1ZP)
+summary(m1HP)
## Retira efeito de sexo na componente das contagens nulas
-m3ZP <- zeroinfl(nsinist ~ sexo + valor + offset(log(expos)) |
- offset(log(expos)) + valor,
- data = sinistros)
+m3HP <- hurdle(nsinist ~ 1 | sexo + valor + log(expos),
+ data = seguros)
+
+lmtest::lrtest(m1HP, m3HP)
-lmtest::lrtest(m1ZP, m3ZP)
+summary(m3HP)
## ------------------------------------------------------------------------
## Avaliação de diferentes especificações para a componente de contagens
## nulas
-m3ZP.probi <- zeroinfl(nsinist ~ sexo + valor + offset(log(expos)) |
- offset(log(expos)) + valor, link = "probit",
- data = sinistros)
+m3HP.pois <- hurdle(nsinist ~ 1 | sexo + valor + log(expos),
+ data = seguros, zero.dist = "poisson")
-m3ZP.clogl <- zeroinfl(nsinist ~ sexo + valor + offset(log(expos)) |
- offset(log(expos)) + valor, link = "cloglog",
- data = sinistros)
-
-## Comparação das funções de ligação
-sapply(list("logit" = m3ZP, "probit" = m3ZP.probi,
- "cloglog" = m3ZP.clogl), logLik)
+m3HP.negb <- hurdle(nsinist ~ 1 | sexo + valor + log(expos),
+ data = seguros, zero.dist = "negbin")
+m3HP.geom <- hurdle(nsinist ~ 1 | sexo + valor + log(expos),
+ data = seguros, zero.dist = "geometric")
-## ----diag2, cache = TRUE-------------------------------------------------
-
-##======================================================================
-## Uma pequena avaliação dos resÃduos
-p1 <- xyplot(residuals(m3ZP) ~ fitted(m3ZP),
- ## aspect = "xy",
- type = c("p", "g", "spline"))
-
-p2 <- qqmath(~residuals(m3ZP, type = "pearson"),
- ## aspect = "xy",
- type = c("p", "g"),
- panel = function(x, ...) {
- panel.qqmathline(x, ...)
- panel.qqmath(x, ...)
- })
-
-qqsimul <- hnp::hnp(m3ZP, plot = FALSE, sim = 50)
-
-p3 <- with(qqsimul,
- xyplot(residuals ~ x, pch = 20) +
- as.layer(
- xyplot(median + lower + upper ~ x,
- type = "l", col = 1, lty = c(2, 1, 1)))
- )
-
-print(p1, split = c(1, 1, 3, 1), more = TRUE)
-print(p2, split = c(2, 1, 3, 1), more = TRUE)
-print(p3, split = c(3, 1, 3, 1), more = FALSE)
+## Comparação das funções de ligação
+sapply(list("binomial" = m3HP, "poisson" = m3HP.pois,
+ "negbin" = m3HP.negb, "geometric" = m3HP.geom), logLik)
## ------------------------------------------------------------------------
## Região para predição
pred <- expand.grid(sexo = c("M", "F"),
- valor = 1:200, expos = 1)
+ valor = 1:200,
+ expos = c(0.1, 0.5, 1))
##-------------------------------------------
## Estimando as médias
-aux <- predict(m3ZP, newdata = pred, type = "response")
+aux <- predict(m3HP, newdata = pred, type = "response")
predmu <- cbind(pred, fit = aux)
-xyplot(fit ~ valor,
+xyplot(fit ~ valor | factor(expos),
+ layout = c(NA, 1),
groups = sexo,
data = predmu,
type = c("l", "g"),
+ strip = strip.custom(strip.names = TRUE,
+ var.name = "Exposição"),
auto.key = list(
columns = 2, cex.title = 1,
lines = TRUE, points = FALSE,
@@ -448,9 +422,10 @@ xyplot(fit ~ valor,
##-------------------------------------------
## Estimando probabilidades
pred <- expand.grid(sexo = c("M", "F"),
- valor = c(1, 50), expos = 1)
+ valor = c(1, 50),
+ expos = 0.5)
-py <- c(t(predict(m3ZP, type = "prob", at = 0:5, newdata = pred)))
+py <- c(t(predict(m3HP, type = "prob", at = 0:5, newdata = pred)))
carac <- with(pred, paste("Sexo:", sexo, "Valor:", valor))
da <- data.frame(y = 0:5, py = py,
sexo = rep(c("M", "F"), each = 6),
diff --git a/inst/doc/v07_excesso-zeros.html b/inst/doc/v07_excesso-zeros.html
index 1a458986e89a67b531a482846821c0b44069bf45..4b80c4c1771d143e2a8b70333a284c9de09c0b37 100644
--- a/inst/doc/v07_excesso-zeros.html
+++ b/inst/doc/v07_excesso-zeros.html
@@ -456,7 +456,6 @@ p1 <- xyplot(residuals(m3HBN) ~ fitted(m3HBN),
type = c("p", "g", "spline"))
p2 <- qqmath(~residuals(m3HBN, type = "pearson"),
- aspect = "xy",
type = c("p", "g"),
panel = function(x, ...) {
panel.qqmathline(x, ...)
@@ -475,7 +474,7 @@ qqsimul <- hnp::hnp(m3HBN, plot = FALSE)</code></pre>
print(p1, split = c(1, 1, 3, 1), more = TRUE)
print(p2, split = c(2, 1, 3, 1), more = TRUE)
print(p3, split = c(3, 1, 3, 1), more = FALSE)</code></pre>
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" 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" title alt style="display: block; margin: auto;" /></p>
<pre class="r"><code>## Estimativas dos parâmetros e testes de Wald
summary(m3HBN)</code></pre>
<pre><code>##
@@ -623,8 +622,8 @@ cbind("vglm_posnegbin" = coef(mcount)[-2],
<h1>Número de sinistros em uma seguradora de veÃculos</h1>
<div id="analise-exploratoria-1" class="section level2">
<h2>Análise exploratória</h2>
-<pre class="r"><code>sinistros <- read.table("base.txt", header = TRUE)
-str(sinistros)</code></pre>
+<pre class="r"><code>data(seguros)
+str(seguros)</code></pre>
<pre><code>## 'data.frame': 16483 obs. of 7 variables:
## $ tipo : Factor w/ 6 levels "hatch","mono",..: 1 2 5 4 2 1 1 4 5 5 ...
## $ idade : int 59 45 42 63 36 33 35 63 54 32 ...
@@ -633,17 +632,18 @@ str(sinistros)</code></pre>
## $ valor : num 24.6 23.4 86.6 77.5 25.9 ...
## $ expos : num 0.5 0.7 0.79 0.01 0.51 0.79 0.81 0.01 0.76 0.79 ...
## $ nsinist: int 1 0 0 0 0 0 0 0 0 0 ...</code></pre>
-<pre class="r"><code>## help(sinistros)</code></pre>
+<pre class="r"><code>## help(seguros)</code></pre>
<p>Dados referentes ao acompanhamento de clientes de uma seguradora de automóveis ao longo de um ano. Foram registrados as variáveis descritas abaixo para 16483 clientes.</p>
<ul>
<li><code>tipo</code>: Tipo de veÃculo segurado. Fator com seis nÃveis (<em>hatch</em>, <em>mono</em>, <em>picape</em>, <em>sedan</em>, <em>wagon</em> e <em>suv</em>).</li>
+<li><code>idade</code>: Idade do cliente, em anos.</li>
<li><code>sexo</code>: Sexo do cliente. Fator com dois nÃveis, <em>M</em> para clientes do sexo masculino e <em>F</em> para feminino.</li>
<li><code>civil</code>: Estado civil do cliente. Fator com quatro nÃveis (<em>Casado</em>, <em>Divorciado</em>, <em>Solteiro</em> e <em>Viuvo</em>).</li>
<li><code>valor</code>: Valor do veÃculo segurado, em 1000 reais.</li>
<li><code>expos</code>: PerÃodo de cobertura do cliente durante o ano sob análise.</li>
<li><code>nsinist</code>: Número de sinistros registrados no perÃodo.</li>
</ul>
-<pre class="r"><code>summary(sinistros)</code></pre>
+<pre class="r"><code>summary(seguros)</code></pre>
<pre><code>## tipo idade sexo civil
## hatch :6080 Min. :18.00 F:6694 Casado :13327
## mono :1546 1st Qu.:41.00 M:9789 Divorciado: 729
@@ -664,40 +664,40 @@ str(sinistros)</code></pre>
<pre class="r"><code>library(pscl)
## Preditores
-f0 <- nsinist ~ 1 + offset(log(expos))
-f1 <- nsinist ~ sexo + valor + offset(log(expos))
-f2 <- nsinist ~ sexo * valor + offset(log(expos))
+f0 <- nsinist ~ 1
+f1 <- nsinist ~ sexo + valor + log(expos)
+f2 <- nsinist ~ sexo * valor + log(expos)
## Poisson
-m0P <- glm(f0, data = sinistros, family = poisson)
-m1P <- glm(f1, data = sinistros, family = poisson)
-m2P <- glm(f2, data = sinistros, family = poisson)
+m0P <- glm(f0, data = seguros, family = poisson)
+m1P <- glm(f1, data = seguros, family = poisson)
+m2P <- glm(f2, data = seguros, family = poisson)
## Hurdle Poisson
-m0HP <- hurdle(f0, data = sinistros, dist = "poisson")
-m1HP <- hurdle(f1, data = sinistros, dist = "poisson")
-m2HP <- hurdle(f2, data = sinistros, dist = "poisson")
+m0HP <- hurdle(f0, data = seguros, dist = "poisson")
+m1HP <- hurdle(f1, data = seguros, dist = "poisson")
+m2HP <- hurdle(f2, data = seguros, dist = "poisson")
## Zero Inflated Poisson
-m0ZP <- zeroinfl(f0, data = sinistros, dist = "poisson")
-m1ZP <- zeroinfl(f1, data = sinistros, dist = "poisson")
-m2ZP <- zeroinfl(f2, data = sinistros, dist = "poisson")
+m0ZP <- zeroinfl(f0, data = seguros, dist = "poisson")
+m1ZP <- zeroinfl(f1, data = seguros, dist = "poisson")
+m2ZP <- zeroinfl(f2, data = seguros, dist = "poisson")
## Binomial Negativa
library(MASS)
-m0BN <- glm.nb(f0, data = sinistros)
-m1BN <- glm.nb(f1, data = sinistros)
-m2BN <- glm.nb(f2, data = sinistros)
+m0BN <- glm.nb(f0, data = seguros)
+m1BN <- glm.nb(f1, data = seguros)
+m2BN <- glm.nb(f2, data = seguros)
## Hurdle Binomial Negativa
-m0HBN <- hurdle(f0, data = sinistros, dist = "negbin")
-m1HBN <- hurdle(f1, data = sinistros, dist = "negbin")
-m2HBN <- hurdle(f2, data = sinistros, dist = "negbin")
+m0HBN <- hurdle(f0, data = seguros, dist = "negbin")
+m1HBN <- hurdle(f1, data = seguros, dist = "negbin")
+m2HBN <- hurdle(f2, data = seguros, dist = "negbin")
## Zero Inflated Poisson
-m0ZBN <- zeroinfl(f0, data = sinistros, dist = "negbin")
-m1ZBN <- zeroinfl(f1, data = sinistros, dist = "negbin")
-m2ZBN <- zeroinfl(f2, data = sinistros, dist = "negbin")</code></pre>
+m0ZBN <- zeroinfl(f0, data = seguros, dist = "negbin")
+m1ZBN <- zeroinfl(f1, data = seguros, dist = "negbin")
+m2ZBN <- zeroinfl(f2, data = seguros, dist = "negbin")</code></pre>
</div>
<div id="comparacao-dos-ajustes-1" class="section level2">
<h2>Comparação dos ajustes</h2>
@@ -710,156 +710,158 @@ cbind("Poisson" = sapply(list(m0P, m1P, m2P), logLik),
"ZIBinNeg" = sapply(list(m0ZBN, m1ZBN, m2ZBN), logLik)
)</code></pre>
<pre><code>## Poisson HUPoisson ZIPoisson BinNeg HUBinNeg ZIBinNeg
-## [1,] -4123.756 -3822.441 -3723.772 -3808.049 -3822.030 -3723.759
-## [2,] -4113.112 -3813.065 -3714.354 -3802.173 -3812.650 -3714.290
-## [3,] -4113.102 -3811.952 -3713.520 -3802.157 -3811.594 -3713.485</code></pre>
+## [1,] -4018.628 -3712.755 -3712.755 -3723.977 -3712.756 -3712.756
+## [2,] -3998.623 -3696.327 -3696.251 -3710.146 -3696.327 -3696.251
+## [3,] -3998.609 -3695.106 -3694.896 -3710.140 -3695.106 -3694.896</code></pre>
<pre class="r"><code>## Testes de razão de verossimilhanças
-lmtest::lrtest(m0ZP, m1ZP, m2ZP)</code></pre>
+lmtest::lrtest(m0HP, m1HP, m2HP)</code></pre>
<pre><code>## Likelihood ratio test
##
-## Model 1: nsinist ~ 1 + offset(log(expos))
-## Model 2: nsinist ~ sexo + valor + offset(log(expos))
-## Model 3: nsinist ~ sexo * valor + offset(log(expos))
+## Model 1: nsinist ~ 1
+## Model 2: nsinist ~ sexo + valor + log(expos)
+## Model 3: nsinist ~ sexo * valor + log(expos)
## #Df LogLik Df Chisq Pr(>Chisq)
-## 1 2 -3723.8
-## 2 6 -3714.4 4 18.8370 0.0008461 ***
-## 3 8 -3713.5 2 1.6681 0.4342916
+## 1 2 -3712.8
+## 2 8 -3696.3 6 32.8576 1.117e-05 ***
+## 3 10 -3695.1 2 2.4414 0.295
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
-<pre class="r"><code>lmtest::lrtest(m0ZBN, m1ZBN, m2ZBN)</code></pre>
+<pre class="r"><code>lmtest::lrtest(m0HBN, m1HBN, m2HBN)</code></pre>
<pre><code>## Likelihood ratio test
##
-## Model 1: nsinist ~ 1 + offset(log(expos))
-## Model 2: nsinist ~ sexo + valor + offset(log(expos))
-## Model 3: nsinist ~ sexo * valor + offset(log(expos))
+## Model 1: nsinist ~ 1
+## Model 2: nsinist ~ sexo + valor + log(expos)
+## Model 3: nsinist ~ sexo * valor + log(expos)
## #Df LogLik Df Chisq Pr(>Chisq)
-## 1 3 -3723.8
-## 2 7 -3714.3 4 18.9367 0.0008088 ***
-## 3 9 -3713.5 2 1.6097 0.4471484
+## 1 3 -3712.8
+## 2 9 -3696.3 6 32.8579 1.117e-05 ***
+## 3 11 -3695.1 2 2.4406 0.2951
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<pre class="r"><code>## Para a componente de contagens não negativas é necessário um modelo
## Binomial Negativa, considerando superdispersão dos dados?
-vuong(m1ZP, m1ZBN)</code></pre>
+vuong(m1HP, m1HBN)</code></pre>
<pre><code>## Vuong Non-Nested Hypothesis Test-Statistic:
## (test-statistic is asymptotically distributed N(0,1) under the
## null that the models are indistinguishible)
## -------------------------------------------------------------
## Vuong z-statistic H_A p-value
-## Raw -0.1757064 model2 > model1 0.43026
-## AIC-corrected -0.1757064 model2 > model1 0.43026
-## BIC-corrected -0.1757064 model2 > model1 0.43026</code></pre>
+## Raw 0.2024475 model1 > model2 0.41978
+## AIC-corrected 0.2024475 model1 > model2 0.41978
+## BIC-corrected 0.2024475 model1 > model2 0.41978</code></pre>
</div>
<div id="avaliacao-do-modelo-proposto-1" class="section level2">
<h2>Avaliação do modelo proposto</h2>
<pre class="r"><code>## Estimativas do modelo proposto
-summary(m1ZP)</code></pre>
+summary(m1HP)</code></pre>
<pre><code>##
## Call:
-## zeroinfl(formula = f1, data = sinistros, dist = "poisson")
+## hurdle(formula = f1, data = seguros, dist = "poisson")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
-## -0.3036 -0.2174 -0.2045 -0.1932 17.6045
+## -0.2981 -0.2157 -0.2059 -0.1962 13.9964
##
-## Count model coefficients (poisson with log link):
-## Estimate Std. Error z value Pr(>|z|)
-## (Intercept) 0.082267 0.146473 0.562 0.574
-## sexoM -0.144597 0.120124 -1.204 0.229
-## valor -0.006457 0.003420 -1.888 0.059 .
-##
-## Zero-inflation model coefficients (binomial with logit link):
+## Count model coefficients (truncated poisson with log link):
+## Estimate Std. Error z value Pr(>|z|)
+## (Intercept) -0.463668 0.161143 -2.877 0.00401 **
+## sexoM -0.207454 0.129131 -1.607 0.10815
+## valor -0.001857 0.003340 -0.556 0.57822
+## log(expos) 0.030484 0.097958 0.311 0.75565
+## Zero hurdle model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
-## (Intercept) 2.764149 0.163096 16.948 < 2e-16 ***
-## sexoM 0.038435 0.132175 0.291 0.77121
-## valor -0.012444 0.003989 -3.120 0.00181 **
+## (Intercept) -2.969721 0.086905 -34.172 < 2e-16 ***
+## sexoM -0.148773 0.073399 -2.027 0.04267 *
+## valor 0.005012 0.001721 2.913 0.00358 **
+## log(expos) 0.186904 0.046857 3.989 6.64e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
-## Number of iterations in BFGS optimization: 22
-## Log-likelihood: -3714 on 6 Df</code></pre>
+## Number of iterations in BFGS optimization: 17
+## Log-likelihood: -3696 on 8 Df</code></pre>
<pre class="r"><code>## Retira efeito de sexo na componente das contagens nulas
-m3ZP <- zeroinfl(nsinist ~ sexo + valor + offset(log(expos)) |
- offset(log(expos)) + valor,
- data = sinistros)
+m3HP <- hurdle(nsinist ~ 1 | sexo + valor + log(expos),
+ data = seguros)
-lmtest::lrtest(m1ZP, m3ZP)</code></pre>
+lmtest::lrtest(m1HP, m3HP)</code></pre>
<pre><code>## Likelihood ratio test
##
-## Model 1: nsinist ~ sexo + valor + offset(log(expos))
-## Model 2: nsinist ~ sexo + valor + offset(log(expos)) | offset(log(expos)) +
-## valor
-## #Df LogLik Df Chisq Pr(>Chisq)
-## 1 6 -3714.4
-## 2 5 -3714.4 -1 0.0846 0.7712</code></pre>
+## Model 1: nsinist ~ sexo + valor + log(expos)
+## Model 2: nsinist ~ 1 | sexo + valor + log(expos)
+## #Df LogLik Df Chisq Pr(>Chisq)
+## 1 8 -3696.3
+## 2 5 -3697.9 -3 3.095 0.3772</code></pre>
+<pre class="r"><code>summary(m3HP)</code></pre>
+<pre><code>##
+## Call:
+## hurdle(formula = nsinist ~ 1 | sexo + valor + log(expos),
+## data = seguros)
+##
+## Pearson residuals:
+## Min 1Q Median 3Q Max
+## -0.2965 -0.2163 -0.2055 -0.1958 14.2575
+##
+## Count model coefficients (truncated poisson with log link):
+## Estimate Std. Error z value Pr(>|z|)
+## (Intercept) -0.65911 0.06449 -10.22 <2e-16 ***
+## Zero hurdle model coefficients (binomial with logit link):
+## Estimate Std. Error z value Pr(>|z|)
+## (Intercept) -2.969721 0.086905 -34.172 < 2e-16 ***
+## sexoM -0.148773 0.073399 -2.027 0.04267 *
+## valor 0.005012 0.001721 2.913 0.00358 **
+## log(expos) 0.186904 0.046857 3.989 6.64e-05 ***
+## ---
+## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+##
+## Number of iterations in BFGS optimization: 10
+## Log-likelihood: -3698 on 5 Df</code></pre>
<pre class="r"><code>## Avaliação de diferentes especificações para a componente de contagens
## nulas
-m3ZP.probi <- zeroinfl(nsinist ~ sexo + valor + offset(log(expos)) |
- offset(log(expos)) + valor, link = "probit",
- data = sinistros)
-
-m3ZP.clogl <- zeroinfl(nsinist ~ sexo + valor + offset(log(expos)) |
- offset(log(expos)) + valor, link = "cloglog",
- data = sinistros)
-
-## Comparação das funções de ligação
-sapply(list("logit" = m3ZP, "probit" = m3ZP.probi,
- "cloglog" = m3ZP.clogl), logLik)</code></pre>
-<pre><code>## logit probit cloglog
-## -3714.396 -3716.296 -3717.328</code></pre>
-<pre class="r"><code>##======================================================================
-## Uma pequena avaliação dos resÃduos
-p1 <- xyplot(residuals(m3ZP) ~ fitted(m3ZP),
- ## aspect = "xy",
- type = c("p", "g", "spline"))
+m3HP.pois <- hurdle(nsinist ~ 1 | sexo + valor + log(expos),
+ data = seguros, zero.dist = "poisson")
-p2 <- qqmath(~residuals(m3ZP, type = "pearson"),
- ## aspect = "xy",
- type = c("p", "g"),
- panel = function(x, ...) {
- panel.qqmathline(x, ...)
- panel.qqmath(x, ...)
- })
+m3HP.negb <- hurdle(nsinist ~ 1 | sexo + valor + log(expos),
+ data = seguros, zero.dist = "negbin")
-qqsimul <- hnp::hnp(m3ZP, plot = FALSE, sim = 50)</code></pre>
-<pre><code>## Zero-inflated Poisson model</code></pre>
-<pre class="r"><code>p3 <- with(qqsimul,
- xyplot(residuals ~ x, pch = 20) +
- as.layer(
- xyplot(median + lower + upper ~ x,
- type = "l", col = 1, lty = c(2, 1, 1)))
- )
+m3HP.geom <- hurdle(nsinist ~ 1 | sexo + valor + log(expos),
+ data = seguros, zero.dist = "geometric")
-print(p1, split = c(1, 1, 3, 1), more = TRUE)
-print(p2, split = c(2, 1, 3, 1), more = TRUE)
-print(p3, split = c(3, 1, 3, 1), more = FALSE)</code></pre>
-<p><img 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gF5ZOxW9s05i1MqC5ADjYk/Hda6MyAnqmObNYV9+9/3ymOGuzIgK3vgu7pCRt5fbfmIdeValrARm5QFyLFqxJ/GP7ozILuaA3AK92jfoapRuCsDci/4KrZdXdaolW5LQIpqK0/mPGs+KQuQzOifse3RsIfuDMjqngDMwAGJ6G8U7sqAgDVBY5b2CjN2am9LQN7mCduwSlmAgCNBg5YODdrr1o30L0cAsAoHpJ7xMoouDQj4e+rQeS8tzEwCINdvmq1JaZkUBgh4NHvIzGT3fg8ydjoAGVUwQI4ah7s2IGIykwBIEVX5SS7wiEXJrQEZsATbZG9ekmQSDgGhJAGQNIltdAiIQUoBpN0W8u/zfsbhEBBK1o3FEvUiHQJikFIAqaN31PBLA+NwCAglqYBkXT916tSRhjphS4MgILSUAkhp/fKi84YZh0NAKEkE5PdwckFYeAeRJqUAQnkb7bPMOBwCQkkiIBVr7Lt2HRO8g0iTQgDJV+kngdQ8bhwBAaEkERBPKau0QkBoKQSQF176nasm77YgIJQkAlKX7Bc07R3kFwSElkIAuRLHEQEBoSQRkAcTiBG9H4tKBAGhpRBANnbkiICAUJIISLWAoGLFikWhohJBQGgpBJCJieTfXy+aREBAKEkEpGOTcRMnTvy0iKhEEBBaCgGk7Vbyb5e1JhEQEEoSAVnzN/FniqhEEBBaCgGEWl6q5CWTCAgIJakvCgv2zfj2qsg0EBBaygAkXb8weoY20yQGAkJJIiAZjREEQSeJeg0CATFIGYCcqUj+vVTKNMa5AEk/8jPTwY4SAJlQatOjnNvDt4tKpCxA0n8nS9WNAVndg/z7Yo9pjFMB8ktUjcYR/Q1zMJQASMOn+FbnxG2Qg9FYqfbLcWtARn3BFaMwQFJfssRQdfph+CGsRjWfTEcoAZCh5J9PRCVSEiCPwjdmgfQWk9wakKY/ccUoCpAz73l5Vz5mFkPV6dkJ+DbZj/a1owRARhKtj3s1RCVSECC69l4BXt0e3/fLc2dAIm4Tf7bdNYtREiC3wrdfPrY+wsxHKlWnh35F/PGis1ACIKv7nXv418Loz0QlUhAg34R0/ydrUvVs75duDMgznwJwsEHlGeE3zKKUBEifYWVKNgx8v71pDFWn5wzGt3cC6SquBEB0Y1AEQVplCFsypBxA/tP4RkU3fNDkq4B8NwZkXR3wJz5nobB5lJIAqRK6FoCXtYJNY6g6/ShiGwBP602lI5QACAC3N604I66XVzmA6Op4J0duHlP708KJbtwGuRl2YWktHBCWGqAkQMrUwf/+oDKd6U3X6ROxZeoHDDOMR1YGIADsWShuMK9yALkc4511MraMt6ZOnhsDUnNeA3LW23vmcUoCpHUUVq2z6/g9MIkx1Ons0z8/ZEQoAZCE3smHkNJlxL1LVwwgP8e3nwyyz1Txw1un7gpIclBHkg9kh3mkkgBZVLr4qE9L9vQxfduv6DfpLfNBv+J5qUNEJVIMIBdLpFRtPO4jzTT8wF0BWeFJ4jHsNEukkgB5FrVg7pdHPxhpGqNoQIYAXZGxTvseRNdgQs7uxHaVicdWdwWkOclHc9ZIJQECjsbU7BDZyfQGomxA+uSfRo4AUFtUIsUAAh7UjOtYpir5FsBdAYnF8VAnLGV9WagoQMDbI5/2Hms2HEbRgGyLC6iSc29lfVGJlANIypxOXX/Uv3l1U0CyPKIwQCaDxjvZYpUFSHad2gu+LNXdpNNU0YCAaztTwR/LfhaVRjGAnA0ZsHRkkL5t6qaAnCmdNKTdivwsH7ZhTgoDJPEDrPpmVV1vHKNoQK4lSXBgrRRA8kpuw7Znw18QR+4JyOaKpLPRk5VZo5UFSNnz+N7mtsYxigYkoOwz8YmUAsjF0sSfNqRfWvcEpPZisq7MH8EarSxACv+L7+mXBKOlaED6krP9l4pKpBRAjtUk/vReTfxxT0CijnaIa3UDgMdPWaOVBUjrhfje0FHGMYoG5GbP5Tfv3Pm7nahESgHkmS8+U+pNDLm6olsCkqUuhzXRi3H66VcWIFeCV6e//CLqiXGMogEprn8JKyqRUgABU6ueyb0e35M8cEtA7pC+lc9zxSsLEHCmnpd/G1NnnooGZPDEQ4cPH97fSFQixQCSv7i4KnqKvrDcEpDfqxOA/PvzMWbo7VRqT2GAsErRgJx6RPz5QVQixQBiJLcEZN2H/TE+PtA1+5EZWv0AtQcBoWTdAjoLRVlDQGjJDcjMMQWbJq3NzfFhdkb+ZlgZGQJCSSIg6Qt6xDdr1thsEguvICC05AZk0CLizx9lmYEtl9O7EBBKEgHp4tWpypDe5buJSgQBoSU3IC12EX9mMntOr4QZqg8EhJLUF4Wncccm8/4TtmQIAkJLbkBK/EX8ufWIETYw0bAPAaEkEZCSACRkgJRWohJBQGjJDMjtCPP68CSQ0R6RDZD0qyZVQ1SdfjG314RLigBk8COwZVz6Ch9RiRQDyJtpHT89Rh24IyDf9jIPm/IR40AmQJ5294v17Z/KjBZTp/8KaDxofNQCJQCS3Dw5tzKCmLli4ZVSAFmt1voGhFEui9wRkPbriD8rGE4FMkL/ZhjIA4iuxeB3IL1nV2a0iDqdExjcp33Q3PArCgAE1+s16zg/j1UKAWQnGjawamx4hH7dSncEpBAx+k8Xfc0QtMzocVkeQM7E4t7mMyNuMqLpOv17Tc/AzvcYMWZ1+hMtVq2uhnwwTwmAPNlwAIDH4tIoA5B3Poinf/uRJYqMJo/dEJBsNTEI60ohwxSkglKHmRbyALK5ExHSlDnNiKrTp0I3ZzydWJzx+GVWp0Oi8e3UqjMVAMgfAUhnAFZx+nZllTIA+RZVpb2YXijadxh57IaA3I3MP/LxoJPLexqCfqpqZCEPIL8Sw6x1JZlDxKg6/f46fNtjmiHGtE7rVIG4e9IVfj8rAJAGq1ImYIm7ClsypAxAygdF7gPg43CvNeSxGwJyrGwEPhKr5VeGoPobjCzkASSr1Aqsns+uks+Ipuq033N8q7/HEDKr07GTi+x4dr5wmUw2QArWfdT3m6eO84tFOHjPihGVSBmAhIR4e7S+MAOJIVdXckdARpBDseun0CFnC+caWcjUi3U5tvbAauX+ZUZTgJCOtpf2McSY1en1ZZbU0Ib7JrH1YhW0rLFiTfNyz7nOwtaAfALAAOy+XFJUImUAEqYp7OmFIFrqadb9AHmoJQGJNwR1+5+xiVzvQbJ2f/2zMaoUIMP7YQ2m9JIMJ3fmdfp/AeViypxifQ+yoi7u46nLKPMYrsz0kgjI/Jl3u6Vt8UsQlUgRgDz0btTWr7BWRU+GdD9Amugn8xieqpICU41NlPcm/U3lhgtnFR3MiGGp02+v3c1jz6w1MWz5eBzXx9gakNwWeAmXfS0qkSIAWdexZp1pw4v7/UkFuB0gOeQNJGTqX3TQZ6ZuC5UHCMhdPXL8IWaMqKEmTfbh24ucbQKbd/Pm70mcsiVbXBpFALK8X966kWP3VT5DBbgdICAQ5+MT0GMFFZAWdM/ERIGAmMlyQB4cu/LZQHxnKucccdu/KHx1gytLTikCkHOF7p86m37Tl8bC7QBZQzTQ80DJi1TIyramNq4ESFY/n9jw2Mixd+7PCzZbpkowM4mAZH+MIGEz84QNmVIEIKC6KqKIf8Ri+tjtAClNtj8e+tKuzeqZrVbsSoAM9Ks2uotP8IehAW3POKybdxo6dP+eob2ccJ30BeqiQajG5xQd4HaAEP4avgVrW1MB94LMvJtIA+TJlnmfLz8kbg6E3QHJUCdg1fS0xxxpmUkEJHY8vt34o5CdkZQASK433hGyJaQLHeJ2gHTDAbkFzp2gAr7sb2YjBZCzTREkoJA/grQ5KObp296AXECJpnLl5tIykwiIN/ELnF9XVCIlAPKH9h/8T3nDXFO3A2R10dK1DjIDKh0ysxEPSP6Q4nPO41Ux69xXVZqIGKVnb0D+RInDsm2kZSZ1yu1uIrE/vr3Jb2qQEgDZF0iMhKtSiw5xN0AyK6j8PmcGXIvMNzMSDUh2nyWGXHRHmyVbfD72BiRT2wrLYpvHd9Iyk+q0YSDu+fnb6vi+xctMKQGQOwFx2DPyZfV3dIibAZJHtNF/AGfpJf8mma3dJAGQR9eMgt+cs/iE7N5InxYU3qGaf+kcaZlJBKSsnzY0NFTliW18Le7IUAIgoH/pwJYNtYyxq24GyFriLWECaLRbH6AretbcSkIb5EDChGNSTsjugORP9QzVtrgvMTOJgHRrM+MLvRJLWZpIEYC8GaIJ03RnjKxwM0DGEIDMfu2Vpg84xfb9iQdkE57tOAkn5IAXhXkP3kjOTOoKUwxHqfMsTaQIQLBLvmfUq+lmgBB3EN9Xe+j+lWGJLFbiAak87N7bkzXNm/uCsiUgt/q932Jpntxz0q9c43QKzi9FAJI5u2ndoUxvN24GSF53BCl6C4ykZh7lhv3DYiUekAb4O7HnEkrQhoCcD5r+x+7a3eUGpKi28mRxk9H1UgIguXVa/XL803DGyyw3AwSAEruxyrCOegRYVInNRjwg5PzMsfjmrqjTsSEglddhm3dldssMyFuRI0xoKQGQxQ3xkphueE/ofoCE3Tfsv+gcd5HNRjwgZImexDdDRZ2OOEDuDHn/w73krlmdTvcgeppnjJIZkOs3JaxPiEsJgHRd9Gm9DzY9CDKEuBsgOg19weBV9CD2b0I8IKFD9EroHsSXwEwmgDxblfgjc8qUcZ0+GT73180VJxL7ZnX6tQfx0z1TbkCKqMpPctpHrBZ+7XqOrN3FjQE5qcYaHdnktNZdzTiMxAOCICo1IZXIZZWMAfk5rPOYOmUfGKKN6nRB8f3Y9lXMBfyA8xFru8yApElsoysCkCh1+zGNSoQYXqS7GyCfIQiyHOxtQRyMmM1hJR6Q9vTkuTcdRJ2QESCPI/A3jP9raIg2qtN3Iok/Q+fiW65Gehe5G+mSpQBAniDlsEZ6E9UAQ5B7AXKb6OXN+Zqcu1qJiwPxgOw0hIlbVskIkNXEegH5wYZbiFGdvk14vgIjOABRSDevZCkAkE1o+hdN6w4N6G0Ici9ADhPvCR+O+BI/eOHH1ZoUD0hiBvk3d/DQMaLOyAiQeeSq1KUv09FGdTq/EO7eLq3oafxA0d7dJUkBgKwKWY9ts3xmGoLcC5ADKMZHrK7NNvxgd1MuMwltkAr69ykFl9SizsgIkL3V8Yr6yNfwtRnX6UPB837oGtqUaMVDQOxgcqKoX/0ek2pqDB6h3AyQovgNZCEYTAzB/nQml5l4QIJUvpv1u5GizsgIkPyG3ZpFFY6cYIg2qdN/VVEX7tiwPO7fSuGALAdgqbCVkRQAiC5KrVZrvJnukdwKkAMICQipqie47MQD0vJMLJKQSeyyvnrklHEv1hEPrX9A7TKG+mFSpy9G41NNJnUGigckEABfkUkUAMg0dQNPf633WEaQWwEymgBE79f+pQ+nWxoJgIA3vZCqxGXVFnVKxoA0XJbznw50NfSumdTpz4l3IG88siEgdgEkYAjI/S+/dhgjyK0AmYvzURLoiIf432tx2kkBBOi+8/LH3T9YA4jfU3y7yeCC16ROjyQ6sHReqRAQewCSjRKuK6d7McLcCpCXRTBAToM1xNIPGztx2okHhPBjeq0sMjLbKkDCiTV9lhtWwDKp06vwqeXgZBEAAbEHIPkqov+/U2FGmFsBcqk8Gr0P+x0mvon53IOmxANSg9hm9ENq3rMGkI/xQY+ZFbfR0SZ1Orva2NS8EyXwty4QEDuYtA5Y/C57s3oZI8idACHcVkdlgpaEI6yxMzgNxQMSpi/G9T5B4aLOyRiQV3EfrFtSnrFwiWmdftrfH/Eu1OFPCIhdAHkU6IGiWqNHC3cCpDfRRr8AShGeiXuv5DQUD0iPgQvInb9Lso7F4nShZjJYMWvFgBH7GdFmdVoX3/zEjXlB5wUBKTiz44yJiSIA4XYmpwBA5hZv/l79GKM5kO4ECOnXPRmEE/Ntm+3hNLTGs2JaX/OwVwtK4CNtL8+c80GySZTY+SBbq+eerO6p8TkpAMjd6qU6lqqRZBSjAEB4SkIBgDzxUqtUmoZBTxhh7gTIVJyPWlgVIo4qsHhr0EsqID/2Hsnm/ja5/R4EqxaZtfLA2niTOLGAjJj3d/CG3Ex10HFeQPJqJBaAgik1jCYvORIQ7Ndnt3koX0koAJDJyJS8nEXaGOZPpzsBktkJQZrQsy3CkjkNxQOS0rrQoIztiE+MhvXB7SFeLX4YCMBjxORbEwvIqC+6fYkl0MxozwvI6TIF/w2p0yTsOG9mtBw31IS7JBQASCG/3rVbrv4E3ccIswUgexYmCRsZnYhMvVi6CYPo/VwV97wF8YD09Jk8vUf9OXngRV+2GxNRLaaNx5favG0cIxaQvSUrnANgYb3zZXkB2dnmTujYEwfCyjFvIYoBhLUkFACINzLszI5qtRHGpFOrAUnonXwIKV3mKksUd3NMJkB2hSLBp7E/G/GDhzwz/yTMKFwDwD+x+BVnD2axI6rFUNyjY8ANMiRnVAKhD+Jev3796tlrDj1/aRrSzWfotsHB57fVSuNIQmT2e/Gm016/flm0wteMmGdmmVF6wpnZmWDyPBPWWFgkAjIvCXBZ/wn1xtBX/YLrRGm9sIeJukjozB2TURWzNF48p/bG1tOf6GW+CzRVy3zQr3heqrl/Sb7mmDyAvPDHmiAxBWAY0UtxsSy3pYTBivgsv3jid/gDFjuiWkzCx8B5viRD8teuIjSh9Lt3714/f8ehF2mmIRl9guNH3n1ZY14GRxIis4xG2tvv0j5p/G033swopXBlln42hDzPVUcsLBIBmZcEuKf/hNbjqE99lcp1orRevbK9SZKnNrFP0w7qcszAVNpkfGv9iZqut8SrIUBXZCyxrKmxeJtj8gDSWt+J1W4rfrSvEbelhKEm+PCpjXjvWEZ1FjuiWizviN3To0weZsR7NdF1L/FRFe9iO/l7sR6rIxpFNX28mvFOxZGPWE82HMDOgSWCuyTkf8R6HFhRpfVBQ41cpVn7iNUn/zRyhHUAEk9zTBZA/iP48MjJCyM+/ZFT7z4AACAASURBVLvu3KbiAfmnxJdk7XvbrQuL3QMkB6uFkW/A7kkmMVLc/kz3bjpnUeRClhhgyKzd6CP/gPy6y4UyI2RrQP4IQDoDsOon8xjukpAfEBAZOLB1Db/w35hh1gKyLS6gSs69lfXNY3iaY7IA8jsByHvgGDke/Quey5XQzatLImpf2vAVLDfgvR8ho49jLef+i6eZTmKUAEhm6DFsezoomT2JPrN7eCM9voE8jfQGq1ImYIm7mkXwlIT8gDzy8599Zkdpj3RmoNW9WNd2poI/lv1sHsHSHBseS0rzl7m53TWbAGQveEN2uZHTbtkVWog8zwqZAnmyvTX/XdRZSQDkNEF4SocN5lGAkdl/CXWa/u/tpsT51/kyI2VrQBII92BZnKvqskp2QLa33lUtpvLKmkbNLRu9B2HxSsvSHHt8iVSEHHeQ9jgfBlfVH37PbVpxLXmepnc+M5kAkp6SknL/Q1FnJQGQk3gSkNKV5QLyvimtLfeNobY/L19vxvBg6mHMcYBgTdIBAPxUUlQi2QHZUtLD0yukdO1fmYFWAPLiDq2/WbpueJpjsjxiDcQBGU4f1v+F21Tam/Q/KxD3KETU+scSAEkPwlcjuRp2yzzq05p9OnYtO+PHwb3nEnWs8zAdAHdDL3JnRsrWgMyfebdb2ha/BFGJZAdkDRJTuGxHL63Rt2EFIDMQhsyjeZpj8rRB1AgSfBs8nU8ekiMW2SUNkGplluz+CZO9AQGri2y+tbfsBPOIW4GBE36YEKAOCtKGRr4AIMeDcNc1fjJPZoRsDUhuC7xOlH0tbMmQ7IAURVDUF9WgTNeW1gBytO2vh/Xa19Aslq85JgcgumrYVxaXA7a1JA6z/HgWEpQGiJeUq5LkvHpvk5L1FrL4iF7viZV41iSkcdLzPX7NsNSeRPCC4XyZ4bJ5N2/+nsQpWzinNLNLbkByEd8v3y/khaK2aqS/22vYF+ctTQ5Akokb3SXQfRF+pOvVlsdWGiAtya4HTlcQrLLpAjqzAvDM3lfNAU/fblaB0x01NXdkZYGWK6RkRkjyUJNXN0TdSHHJDchqJKZm2Y+OoiojExs10rk65dklByBPCUBupvgTPo++rsRXetIAef7RvbS0tKc871dYZFNAtnhcwDIrpBpR3M+rEbI27JtZgVoEjYzNkpIZIYmAZH+MIGEzRa6DIDcgw4n3ZF5IsJGJtYCkL+gR36xZ42BhS4bkAOQC7jOuakEfwi/I+RDeVTykAXKzDFdzjEc2BeShd8iIpR9r0ULnQUE3JOgCuOkbiHpUC74rJTNCEgGZhg7dv2doL+7ReGySG5D3kOBAjQpBPjYysRaQLl6dqgzpXb6bqERyAFIIr7v1wCu8KlwpsYzXVhog9Uot2r5jx2Zr5qSbSWSdTizdq3OfokiTmw+XeYeEA9B1Pui2AszsIykzXBIBiR2Pbzf+KCqR3IBoUFXrzg1QxLiUrAUk4DT+Smjef8KWDMkAyDLixz2O2J8asoTfWBogPuRrk7Wizsu2gBSsqhxScZ7KJ8g/PHq0PwDlsTbXanC6qqTMcEkExPsUvs2vK2RnJJkByUfifTxQlcqkm95aQEoCkJABUlqJSiQDID4EIJ3wRZjyfIRWSZMGSHNyNqG4UQL2WOU2fNvovovfzfb+A9Q++CjsX7C3keTMJALShZhNmO2Pb29amkjuO4iq2NWJveZU9TQ2sRaQwY/AlnHpK3xEJXI8IA/I5kEofqf7q4SQtTRAkgf+9/bt25e9uIxZZQ9ARnfHfgeeFJ4VvighLm46eFd9qeTMJAKSPvAFtv2WGNhsPhOCQ3IDUk/bYNcvQ5CPjE2sBSS5eXJuZQRpLyqR4wHZSvARMgXfXynYYJIGSAznO1Me2QOQrOZlR/QPXQAuDXy/cPSkicV76iRnJhGQsn7a0NBQlSe28bW4POQGJC1ME+iLVjYxsUU37+s168QtSudwQPI98Zqr9iG6eD9eIGQuDZC+CZu3bt26vqaoM7MHIAD8OmuRfiDKoZmzDluRmURAurWZ8YVeiaWEzUnJDQjIn9205UZTExu9B9ksbMKQwwG5Rfy0+xAdTHlFzguZSwPkQDLx5ztRZ2YfQGyWmURA1jPGic3jNjOW7ICwykpAXn9HaKnpjYlfDgfkJQHI+8T4xB/eEzQXD4hJj7/lLwDsCkjWlsTVz63KTCIgHwubmEt2QLK3Ja5KMTWxEpBsHykP3o5vg3TGTtGb8Bf3d8QBQWvRgNzrz6zi+fMtfwFgT0BuFmkzoU+IwcmT4wBB4o+Ke0mIS25A7hZvOb5fyHYTE2sfsVrdyM3LuzrimahEMnTzjogYS1aPseOFjcXfQS5VW0YNXX236f3lHOYssiMguhrfYNuTEfQ9xHGAjL47ve1ScWN55QekHr62xLkwk0Gs1gJCDho/OVpUIhkAmTMS3CR8E1WxwD2HhDbInQ99Go6auWD6iAY+dXgG0pvJjoD8E0X8irdfb0VmEgHJAKDgt0GjRPnHkRuQ+8H4SzLQ1cTzn20a6Y/DhG0YkgGQLivISba/+1jQ4SapkX55fPUwbdF6Y6+JOi+rAMme26xOwgOTCDqzs+Tk+8GLLcuMVRIBwUdHpkwPQ5pt5PbOZyaZAblSct8H73X6nVyjyCArASl4i+nNw1GholI5HhCddwpohA/PL7HVAmtrnFeLlDWAFDRrtv/EuFATt5Z0Zi998fWq0uLWU6NqHQfIlLebW6u9eh/NP9x5tcXD3mUG5I1HzPyNX0cVM3GvYCUgL1GyjT5K2JQhxwPyKOBmfiT2qUkhlnxf0gER7YWVHRD68Z23Tq+rjVf9uW2MIwyZTa51HUzW+hYqe8qCzNglERAPFVJtaSq+p5s/wNJEMgPyyMs7uIJPrOalsYm1j1jVZm7ZunXXeXF9Fg4HRNcWQeK8sFrwPZvfQzNJA4THCyu3WADJmRrg5z2C/F5463Q/ojPghZdxhCGzvNmBnmi3V2Bj2APhzNglERDtELodttXP0kQyA7LeKxRB0MDo34xNrAVkufjuPBkA2YXf5fAhWD2/tcRc4oxCLi+sfGIBZFz9W+BhB3I8DG+d7kMA8tLL+CtgZlYQQ7h3/+Rz4czYJREQ0p9jFn5iSev5TQ2SGZD+qnnvkveEqfYZm9joTbrCp9zOwgHBvuq8qOvCxlIB4fLCyitzQB4H4v2ymUXP4Qe8dXoV+YjV3DjCCBCUaCSv6QEEM2OXREAiie1vI0UlkhmQ99R4rZyHmHS9WQHIrcO09jcSldLhgKzHAWkBwEpz5xJskgYItxdWHpkDcoScRdGDcKfOW6fzGzfFGukh/xpHMDN74xvfc0M+mPIZEMyMXRIAyb3y+F1EFqaMjeJWKZQZkErFgms07Rlm6ifXCkDm8rv94ZHDARmlQhAtVuvr7bTIXBog3F5YeWQOyHlydF88caoWdPPu7FKz3S5GBCOz50UqV1xZr/lfYWTXs2MAyYqkK4VTTZjqpAry9fbz9TYxsQKQM32TkpP79U9Ovv1Tz2RRKR0OSAT+dfUH6V7CJY9LYi8WpxdWHpkDkheHz0k8FE6ECdfpbRFLzq6Pm2WIYGTWb3RuWx8PRKXvY3TQI9aJH3aU2oTrJ+EaypTMgLREUNRDg5qO1LMCkBzcwU11ojNv3zRRKR0OCNEZ3R3cKGKZuVXvQVi8sPKIpZF+KbJ1s+Jeq4l9wTr9Lgxvq/wXapgkycgs8nZmpc4LB1eLOsjMLH/NwH5LjX2V2boNslfYxFwyA+KlVXsHeqvjTEysbaQ3IbYZ/qISORqQ48QNfzo4zrZ2B4tEAyLghZVHbO9BjvhW+nBQ0CZ8VxCQ0+Q4aoanYUZmIQ+WxuvA1vY/FrtfQGeW27j2yu+aVDPK2HFLsPFIXkCyETSuXBjqE2BiYi0go4ghsruiRCVyNCAD9Quk72hpmb1oQAS8sPKIBZD82NUjq9YcHPoQWADIH5XfbUhcm97N4CqCkVnrr4csBKBNfV807L2rVGbz6iU2aTG/wziWzMzlPoAcQFQoqqqJmI4JsRaQe83+eHV7gR/L4uA8cjQghGN3VS5Y3scye9GA8Hth5RMLIFeKxnx69OTAwFXAAkAy/KI6TuwW6W/oymJk9m/I+0PO9YqoeVObs7BYqj6z+qFD9v/cuWR5lszM5T6AdEKQ6MoqBKlkYmL1e5B1Hljla67s4e4d1Ng59gBg5hjL7EUDIt0LKwsgZ4KJ93rFceeogoDkFQr49uz66EBDlWJmdruJOu5Tv7tjOmAPYUv1mUV2xLeNQ1kyM5f7AOKBeFeKijAfNGX9i8L0wxsvi/TD6mhAovA7yAQAhvEsmsOUVY10cV5YWQB5rcIXqMgIxye3CwJyruxJvJu33n46wjizyVGf+bSKvTmySnj5e2RmgcQCJt0iWTIzlzWA5M53Ju/uCKLVegapkdMmJvZbQIdHjgaE6MSqD0Bt4cmEhKQBIskLK1sjPaTkyVfXWtZvASwA5CD5RNfF4GzAJLMT4zTzX5YZcKFDk/AbRGbFIibdTZ4TYPSOwj53kEoBi8X8csoMCOrZqmkQipg6FLYKkKTXOkldNw4G5DJCvkh/4s05rNxY0gCR5IWVDZCPm1XWxs1ohXs7EATkXjD+BWYVvURHmA0NHtRjaldwNPxhYmcis36DexQp1LGpUce8TQE5kDDhGLHz1xfvN35oeTpZAdGhaF0PxAv1MDWxCpDIQQVhTvAm/X3iDLeC7iMsTCANEEleWNkAeRU7ZP/ejnXwVxXC7/Y+afv03eRQ/670vCkzQN609H6/adTP4FY0kdnTQglHf+1c0W7dvJvwwtb3kekOWzL9Ri9ZAXnsg6JaHxXSz9TEKkCO3wPdpx/Cx2KJ67pxLCB5xBMWqjsU9tTCFNIAkeSFlXU+SPrUJi0WEq/yhAHJmuyrRr2qhqqpVgjL7Ku6ww9iVe56ETKz56Oq1kw0roK2BKTysHtvT9YU98xNSFZAnnqq8WpSxuyNt7VtkN9eEH+UPJq3HXEDiQTjp1iaQhogkryw2mBO+qLwnlkAtPfVf5Msmc1vghsP6eOQoSYN8EHuzyVUJlkBSUeb6J6m9UXNHLzZopEuehqdYwEJJQCZDNpZTLFE37xSvLDaAJA2nnh9uuexjjxkySy3buNtu7sXT3IIIMOI7Vh8I+Qk3FiyAvKtR9CYA6tLa6abmlgLiKRpdI4FpAQByEtQ0mK/69K7eUV7YbUBIE0i8G2y92zykC2znEWd2k1Pd8xgxS7E9iS+GSoqoayATA1IHtGqx/YIs7nj1gIiaRqdDID0BhkeFi8qadV7EHFeWG0AyEw1/r5hSuQm8lBu16OhQ/RK6B4kKqGsgPyiPoNtk1VmY7GtBUTSNDqHApJL3EA2gD9LW5xEEiDSvLDaAJCcsKhdx0f6FeVug4jIzEQSAEEQlZqQSmTfpqyA5Mb6LD67JiDWzMRaQCRNo3PsHYRYluAS2N7a4hSSAJHmhdUWjuOeV9L4+pejnnLlBqQ9/f78TQdRCeV9UZhc2cvHo5r5kClrAZE0jc6hgDzAu+80BWC+5U/E0h6xJHlhtY1nxbs/Xcx/fjwpvcAWmTElARDGpE1xfZsyzwcp2DuN7Wu3uhdLyjQ6hwIykfhZTwKdBJcFoSUNEEleWG3lejRzcEiMCvHo+0x2QBIzyL+5g4daODZUL3kBORyMIqp482aq1YA82XAAgMfCdkw5FJCpBCDvkoLShW31sqaRLtILq60AGdxlTuWL/yvft1GB3IAgFf4hdwouqUUllBWQ5x7N0/L2ebc1M7EWkD8CkM4ArPpJVCKHAvIPzkdDsKaL5UkkASLNC6uNAHnh98wfK9T626vtkRuQIJUv1ZEXyWtoKlkB6ROOb3ejZr+i1gLSYFXKBKwcu4pK5FBAdB5VYgZmgM9mWJ5EEiDSvLDaCJBL5W4UalemZu0vR8+WG5CWZ2KRhExi13TyEb9kBeS9+p9WLdf7NnLR1MTqF4XE66CsGFGJHArIE2K+/NvY45YnkfaIJckLq40AeRS4GV1082ho9b5LZAcEvOmFVCW+YXF9m7IC0tSj7/kricHIc1MTawH5BIABAPxUUlQihwJyhTi3ea1EVFxpgEjywmqrNkiHwFKzwdaiJfzuyQ8I0H3n5Y+v1OREgPRBv8OuNsx8IJ21gMyfebdb2ha/BFGJHArIierbe/0KWlu+KpqVb9KtnnLLlMV1+rYq2sfPv6JHS9l7seLxzbWyyMhsZwKkbQuVX5TW17wBaS0guS3w5+6y4iZXOhSQr1UI4ns99KaIJKIBke6F1VaApHmemRYYXauI/zm5AalBbDP6ITXvOREg7ZuGxUb79TG/Wqu7efP3JE7ZYvEoJ1IOBYTwqljvPTHPP6IBke6F1WZLsFVfVmkJSCs2KTZVZkDC9DVqvU9QuKiEsgLS0zcVgNRg8xfe1gJyLSlH2MhUDgVEQ3h2F9XNJhoQ6V5YbQbIFf+g/WvKDQK19sgMSI+B+jeyf5d0orFY9So03Lq7V1gDMxNrAQkoK25oBSGHAhKMA1LV0tm2hEQDIt0Lq+0W8VxfuHX3FYOKe1U4AtL3b/jbuswoWeW0IU2ctzRZASm3PlbrWXlbFTMTawHpm0j8WSoqkcMfsdQjRVVbaY10SV5YbQfISm1YybAB14uODl4UXad9eC/zO7sjAfmx98grIpPICkh9lVrrpQ3oZGZiLSA3ey6/eefO3+1EJXIkII+JdkHnucKWBkkDRJIXVh5Acjclzj9jaZ3WDffwCmkaXr5MRLeGGuz0MzqYD4RyDCAprQsNytiO+MRoVgobMyUrIAGIVu0divQyM7EWkOJK92ryhji9ThYtvUZJGiCSvLByA/KsXL0Zw4LmcSU0qdOfq2f/FY+gCBruXQxZhgU88ss3TeIYQHr6TJ7eo/6cPPCi71lRCeUE5B2CoCiCqMzd/1sLyOCJhxS+wlQIxkeFnt+JSSLxPYgUL6zcgHQcoQPgr5BLLFG4jOv03QC8B98X8YqrWdbDM/gWduCZaprEQTMK1wDwTyzeaZg9WFRCOQGZiHiPGV7OEzGfA2ktIKceEX+U69XkPH4DCe0oaiKs1BeFErywcgKS5YF/M2mffs6R0LhOb2vhnw/2op7aOQW9vcIGLAbgVqhZx7aDBitewDbxRIbi3AnKCUg4grU+8lsjEWYmNnI9Kk6OBGQn8YgVv1tMGnkX0CH1iljdOW0OV/ebcZ3e3rLWctDPPwCZtK456tdmNnha1/zhzEFDTXDnERvx9liGhcux6CUnIN6ItsX3S8MQ87a0ywPyH85H9QYHxaSRAIhUL6ycgOgK4W6U05pxtXSN6/TjwPVekSEq1KPngIXINK+okp6xA/80TeIYQP4p8SX5OW+7iZhhAOQFJAprgqAqFDlnZuLygPyCAzKs6gkxaSQAItULK3cbZHOJk7r04RWyOBKa1OkZqgaNghHPSaV2N/fsGD8lZPYvs0O2mSRxUDevjvS/lTZ8xT1R6eQEZBiChEWoEa25icsDEk60QQIsW71TLwmASPXCytPN+10hH882/3AlNKnTTT7t37xvjH+AJ4r4j/on6AYWtFgbV2cZsyvL0S8Kwe+irOUEpArugwVBK5ibuDogd4gfdc8WohJJa4NI8sLK+6LwbZ6ldTpPjdepO2VUqoo/vAI/tsEOtkYV33CgxjCGkSMBSU9JSbn/oagkcgLi6VVCpQoKY1nY3dUBKUUAEi3u5YT0RrpoL6w2epOeqyIMFw7KxDPb3AGf23Cu7m8gtRCjm9hxgPxZgXzaFNWlJycgqu4gJ6uglNniB64PiJb4onqJGoolERBJXlhtNdSkLr6KZ17tTXhmyS1QVVCHuH/8XwLw8WIJmRkkEZBqZZbs/gmT0wCCRoWgnvERLK4gXR0QX2Lpg0lcbxPYJQ0QSV5YbQXItfD5Z39v26oAFKwor9KERLfTqIsuwsL7M0bJOQ4QLylfsJyAaBG1lz+KdDQ3cXVAPsQBSRwhaiiWREAkeWG12WDFuwMq1F+YC8CMcj3L1anRvEEEUhscGj/AjzFk0HGAtCT9hIvqOpQTkALUM0it9sId9JjK1QGpit9ACnquFpVIGiCSvLDabjQvAPfXLvtjn/fFDmV+eRZyM1WjrhbRLqhYZUM1cRwgzz+6l5aW9rS7qEQyAnIEQSMqlFOjLG82XRyQf4kmyOHaR0SlkgaIJC+sNgRkYUinBuoA/8CqxQ+DjpvvBUQENut9UNdvuKTM9JIIyM0yEl4JyQjIQkSt8US9VGXNTVwckH3E97SqCOfrBFZJ7MWS4oXVdoD8XuTRqbCTVWMuh/n0As12DawQiw/qBbcN814dB0i9Uou279ix2WnmpI9FygHwsgjSxtzExQEhVpREUnzM3B3xSiIgUrywCgKie/iSNcasTvddArp9A05oftkUoSnvXSU0ojUxQPOFwZON4wDxuU38WSsqkYyA1ENVFUb2UCPzzU1cHJDvcD68slDT1a/5JQ0QSV5YhQBZHhrpX51tyLtZnW61C9Q5Ah77BvYO7aT2LTTg4dSeePC69w2ZOQyQ5mTXgMVLehGSEZBaahWKohpkjbmJiwOyFAekzhNx02AlAiLJC6sAIFsKnwMFy6JZ5piY1ekRk/OqBmi0/hfbhI0Y1azZl5mpxSY9y9gUeoq2cBwgyQP/e/v27Uvz+Xl8khGQ/qgaQbQahGVVdxcHhHgNUvQsyxgbPkl8USjFC6sAIOWJYfoJLOvzmtXppIg2WgT7ntWFNlZov2NH24oZ9zoFetZkjIdyHCAxUsZtygjIJ+TpalhMXBuQjcR1B2wRt/KsREAkeWEVAMTjCb7dyDJu3LxOb0OJaaP42EzcYUOPiaYGjgOkb8LmrVu3rq8pKpGMgKj0QGeam7g2IK2IClP+y5HikkkDRJIXVgFAIojH+LksebIAotIGUs7rqj4AR98zNXAcIAeSiT+i5jnLCMhbqthYvAu6NCD3CKdxyNJ+X4lLJw0QSV5YBQAZ0RK7GSRHsHimN6/Tu5Fghn/HkO+qmRo4BhAd7yGP5AME5XGK6cqAFNSvRVz2sSCRnyexm1eKF1YBQFLiS40YwOraxLxOv0JQhKmAIyausRwCyL3+zIvJn2+503DZAOmFuCkgB8ssJC57bymRCaUBIskLq+B7kCMLV7C+5WSp00URY6li1n5UodInTw2ZcX2MLe8gl6oto26i7za9v9zyhLIBQheYucsGlwZEV2PFFfw3tXKFVSJTSgNEkhdWW47FemgCCOKFeFdbNLQolbuD2iB3PvRpOGrmgukjGvjUMZsXzyO5ADHcd1l6eV0ZkIvRefXxy65SXGxKaYBI8sJqS0BALAMO8mtHUbR0L2pROIc10i+Prx6mLVpv7DVRqWQC5D5dZCzTpVwakGXd8ojr9mkkNqU0QCR5YbUEkHOdy8RvNY1hq9OflTK9hyBalWcQ1Z3l8Dnp4iQTIFq6rFj7E1wXkPwqa8lfUfUssUmlAVKcu6XHLQsA2V3o+ws/Vx9nEsNWp1OLBZkCoiqqVVHegh0LiOjZxzIBQhdVN1YT1wXkYjH9hatFdixJBUSSF1ZhQLLCzmM7r6JNBjax1umng7xNAPEopdb6GTLjkK0BkTT7WB5AMumfEnYT1wWkH3Xl4qbb4pIGiCQvrMKA/EW+mh9g4n6bq07fKmwESIDKA/WZNueMPjMO2XxGoZTZx/IAsoYqKY6fUZcFpAL9y2DmxFlQVrkeFSdhQK6WIPb6mnSYctbprLunPjEA4on4+Pomjo38DGvKTJu8kyORrQGRNPtYHkC26QsK5TBxVUBoPvxLiE+sKEDyon/Ddh5GmKxCyg0IltkjT+pRS6VSe2BX86rE7gkRwyZVrZvBmsbWgEiafSwPIDn6gmrGYeKigCRRfITOFOe9jJCiAAG/Bk39ZUnhL0xieAEB+8KH+eP3jwAE0XyPhy5vWOR52rv8Huz+j2wNiKTZx/IAckk/UJFr3JiLAkK9/Ak7EH1DfGplAQJufPR+19UbfrhrFMMPCPh3VOuPuzeKrTD8fBA+x/Gvrn59cTeN/0ayprF5L5aU2cfyALLC6xstomoUxfU46JqATNXzkQgGL5CQXGGA4AushVSJ9TL6ggQAMajBJgBmR1YMrVj1/jvw0os1jZ3eg4hbCUIeQH5UqXzCfTSeiRwmrgmI/rb5GNwKvighueIAWR4X3G/pR6rRjBhLANFtHdCg04jQn48Vnhk9buDI7u/ANuMpGq92rz2h48tMPCAv7tASuRKEPIBcQdCgoHAVwvWVuyYg5BNWO3Ai4GspyRUHSI3ojdh2qpYxWEgYkLujQv3VHr5RPoXUmobXXhVO9Eo9EHmUaXogvPmAuIavbQrIDGYns6iU8gAyQX+qyzhMXBMQT7LjrqDuN5KSKw6QqBgAkteMClxniBEE5GB4fQ+yAxMN++A5ADfbIerqRq2CJxG/AZDbt59NATna9tfDeu0TtxKEPIBU0gMyiMPENQHpjF/yLjCvFMscSgukOECqFwVfBVeJUNU0VGQhQLKiVhL3UX13RdT5rIOlTD5mcT98m+6dbktA3u017It7ZyoPIGo9IFyTTl0TkAD8kkd3L3FZWnLFAbIHnREZ3NDbW2NY7kMIkNMV/BjPOhgmqHocnln694kb9G/UJ5MN06gkezXSF4qylgcQqoS4ztUlAdERjXSPiZz1TkCKAwR8iKrCI6v1nK2hR4sIAXK0mAoxFooGBNSaHdFmTHxR0m3Vqtb49oFvls0BSV/QI75Zs8bBohLJAsg6qnRuc5i4JiDEw7fZnGyLpTxACgprVJETM/7T/EbFCAGS6oEwH7HoG4mqzFmwqUwubvK6xNx88KjeJNt383bx6lRlSO/y7ONjuSQLIHzT0Qm5JCCAAORbYTsOKQ8Q0DIO2+iWBe6gYgQb6R0QjcktxBPxSVCpAkInuvV4BgAAIABJREFUNvPu8AA3+btWWCWfz/JsD0jAadxF2Lz/RCWSBRDBDjeXBOQ+ccn/k5xegYD8qtoGfolRoTH79QGCgDxUMwnBfylViNZDhXig4furdwohHikK7l9J481MIiAlAUjIACmtRCWSA5BRVPkU5TJxSUC+Iq7ZdPSS5VIgIKC9b7AqKLpEm8AL5LHwe5A1Ro9XKH7kqUYxTFRXQ5P+15yZxtaADH4EtoxLX+EjbMmQHIBEC91AXBMQohMLkfIOnZQSAXk3Tq1FVUVDVbHksQVv0teqURNPQEhVFC0ZhYYmgOdeAPw3qnX3rTrezKT65m2enFsZQcQ5tJQDEF/q54PTxBUBSSEvOl3YkkNKBASAKjExjwH4HCG97Voy1OTZ59W0TERQNBDxxbaqR+CFt+568Gf715QdypuZFd28r9es45ygxSo5APHUl0xlThNXBAQVuGsKSpmANCLuiT/5NiGOLB2smJP8fVAgQQlZGbSeRUJDz4H/tQW1V+IfUOw4X2bWvQfZLMpaDkCo14RTOU1cEBC98xu19ByUCcjXyEvsd7liVdJVvcWjeQG48lmrRjMulG2xe5wKjZh2txgasGVs6L00L2LRlMmT+DKTBsjr7wgt5f5dZpMcgHggah+VB4pw++9yPUB0+h+FW9KzUCYg+WhQv37Rw2LIJ3sRgJB60sa/ogeiLe3vXSygTsID8NyHaH7MGMeXmTRAsn2Emr5skgOQKH0HH7dfD9cDJJT8btjWerBUdgIkJ1+Xb+qdVIzjuM5eny4/3UFznTgQDQgA1w49nqjxiCxdmvTHE7cL22RX2MmXmcRHrFY3cvPyro7g9DTJVhKyADIFVXtpPNBAbhPXA0TfAhHvy8QgOwGCT4KtYhImBpC8hmpE7bWBPJAACJ7Zi1WJW/VV80jwtzePNm1j4/kghMgnlpOjueLZSkIWQFJL4T19mrPcJq4HCMmHH/vSl5bJToAMePHihamLFXGuR29t+y25qgpRt8+UCghT59pEVZ5DPFzYp5H+OIwrhq0k5HmTfsILa61O4jFxUUDQxtZkYSdA2EZUi/XNmxek6r2yCVLZFoAYZGNACt5ievNwVCiXAevYcjkAuYX6TVsYyzlbCrggIK/JFsgBa/KwEyAN44Nr090lF8hJRYsrZGdnv3mRzaFX6SYBnyO/Zj9fWgPZnJLJkYQ7s5emmdFKyeKIeHs+XD/76YGIC32pf8wdxWVgVBKZR8hP6DKN+tTUNK4TpZVmG5NSnr9OHv9TGS2PSSq1N70LeZ5HskR961JkT0B+Jvt4xS2MbiI7AfJVfmbP4noPyZm1axAqE4s9bTxLecGhp89MAip6vTjph/feD33OkeTZU4szo/WEK+L5mWDyPGuMEVWAM7ds3brrPOfqUsySAL/oP+G9McInasFVGi7XAhOvQHyAcxGEqzCZHzT2Pf2JHhT1rUuRPQH5kwCkpVV52K+b9x5i8gAk9hGrhkdBDDooCY1EdnEkUcAjFlguvPCaWUnI8oilQT589rw3X3+0vR+xWDv07AlIHt7Ni0pY64kh+wCCf1/PTUeNigVkETIW7QI6IX/7sHu4UgYgenFNuWUtCVkAUSH4o4ZGRkBYO/Ts2kj/Mcirksh10U1lH0CmZQCweL9JoFhAdLEIUisa6QbKcr3okRmQW4dpcTq6Zy0JeQBRqxq09uEZq2h3QFg79OwKyMZOcTO7W5eFfQA5+8HoL0+YBopeYaqgO6IOWZI3A0WKrGdNIjMgc5mDhzlsWEtCnqEmlf0QxKsez7AkewPC2qFnV0CmBPiW6GddFsocakIpSn0OlCfqX1e2JDIDcqZvUnJyv/7Jybd/6pksKqUcgDQmQa7HbWJvQIw69LJTSUXbEZA0vItR3EwdM1U5SJ6nYAnLAkiyJ6JCEG1UJIp8yFKxZQYkB781VCceGvZNE5VSDkD0K3KZNQIMsjcgRh16PfR3XtVfvGmsEjmJ8pFVeQTqzzNEyK2WLICArM81SMVGrStUQlTVzHsjlNBIJ0fkZ/iLSiQHIIiqVEy0LypjGwSwdejZ8xGrL1G17wob8kjZj1gAd0pReFjlkh4IGjjdLE4JgIwiZrrvihKyM5IsgMT4BfrWQOXrxWLv0LMnIJ9ZOVkKl+IBCUE8i6vqVkQ8PMPN3jgoAZB7zf54dXuBX19RiWQA5BmCVmrV0k/G9yDsHXr2BKScOwAyRoWgJT5EEI0GqWT6NKkEQMA63PFSc87h7qySAZAUBFGHhaoQb24TOwPC3qFnT0BqEo0c6/JQPCDvypBDMvGJtEVN7iGKAASkH954mStTDskAyA19Y7Mwt4nLDVb8Hb9gq8byOgEgILcB7jjRdxyq9kDbG9soAxBCil9A5yyC4mOxkCLcJi4HyG0ckN+E7fikfEDA0gbBnoh/XTSogndEOaP6JDsgSa91zrKAznZEE6r29kJ4LtLlAAkj+mety8MJAMko2QNFmiGqKr5IeEejkbayAxI5qCDMSeakr0RQlQfWXCrFbeJygJAP59bl4QSAgIvtUQT1Dvk4RqPyjmYO7pcdkOP3QPfph/CxWIpfQGcdotKoUA3CsxwvBIRFzgBISsHYchgh+DM0ivoxVlaTHRBMv70g/ih+AZ3F+jtdR24T1wSkmHV5OAcgOfGofuYeGhpkqFJKAATXnoVJ4hLIAAi1QGEHbhNXA0RH1BmuRa8tlHMAAvL9PDFEGkWqES9VranUnFglAJLQO/kQUrrMVVGJZACkAYKEhYYgSFNuE1cD5C3xizBM2JBPTgII+FqrQtQhXiXwNz9+gfpp+EoApGU+6Fc8L3WIqEQyABJEdPOiyPvcJq4GyDMCkFnWZeIsgOjKIdQSUliLPYD8KpUAyBCgKzIWAHE3chkA8dI/YtXlNnE1QA4QFyxu9UgzOQsg4CgORjlqehI5gUoJgPTJP40cAaC2qEQyAEKtMMSz1pKrAUI6NfndukycBhDwHqJvqOMrdpYdvuqdMgDZFhdQJefeSp7eUxbJAAj103KU28TVANlFXPB96zJxHkCy1fSgLEwfdip8SxGAgGs7U8Efy34WlUZGQLiWuAWuB8h04oLNZsGLk/MAAmID9c66CUZUwaopb5UAyJMNBwB4LC6NjIDwmLgaIOSkRXFLG5nJiQD5Sh1FLQKDSR3nVXWCAgD5IwDpDMCqn0Qlcjwg+W4ISKT4MUDmciJAdO1VDD48ynrW8OV0Euo4QBqsSpmAJWZ1KsEpxwPyuxsConIzQLCHShUDEexRS73RisxMJfVFIcDXSc+KEZXI8YB8TJUZj42rAYK6HSA/1kA8DXxoNIg3V/VyHCCfADAAgJ9KikrkeEBK6guNb36dqwFC1hIrM3EqQO4Fo4iW4sPLC9Gqe0vPzFQSAZk/8263tC1+CaISOR6QEH2p8XmJcjFArpLVxMpcnAoQME2FoCrEg7h3BuGuXpFqyZIzM5FEQHJb4KdR9rWoRI4HhHo2jeWxcTFAKhEX7GtlLs4FiK4z3c2LosSKYqh6pdTMTCS1mzd/T+KULdwrY7LK8YBQS8jX4bFxMUDIwTV8S2pZIucCBOTHUN803kZHkOBaYf4npWZmLImAXEuS4F7f8YBQhfYtj42LASLYa2eRnAwQsKQojkYsoytLxTY+1XGABJQV5/GHkHyA8K1mAgFhkbMBklFKZbiH6PWV+bfuOED6JhJ/lopKJB8gfDauBUiOYLe2RXI2QMARb/x1OuOVOoqoyqabJnEcIDd7Lr95587f7UQlgoDQshsg5BzjIGuzcTpAXvts8EB8VUb3EXXAY5C5bf4Gw7g0xwFS3IKaZyaHA/LK/QBpT1zvAGuzcTpAQEJrD0RF9GSR/z0QJEbT6lbx+mPaRtDtdccBMnjiIb4VptjlcEBquB8g5EPGNWuzcT5A3vVWYYB4VySG9qoQRIMgof4qH41n1eM7i7xNfVggJjOGJAJyinQYrHCvJl7uB4ht2uhOCAgA3RGtT43+jEcsVQwSnJO7OeRmzVK+0UFLRWVGyzrXo+LkcECo+YS8npwhICxyRkAO+BqNWSRa6qpPFvStWdW7Wz74M3YLBMRUVKdGDT4jFwSEZ0VGC+WMgIB4Uz4QVOPpX7uxWoNPKD1eAlycMHIbBwjuCQjVpTGbz8ilALlGXG+g1fk4IyCZXmaA4D8Wi1oinvjjVR46OXrotCa12CuNWwIyjSokHpcNLgYIufxaNavzcUZAPlGbvSvE5YEG+OOvI/7zLpGGZTaIfYCtOwISTpcRr51LAUJWEXHvb9nkjIC8p2UFBFFVRIJ2pOd2iSkWUWsTSA62KDNargvIFUMR8ebiUoCQ15tldT7OCEhD1kcsfWMEVfn5Dr/1fYl5bzSs447cEBDGzwlvLq4ESL4F12uRnBGQjSj7HYS+k9S+/fxm4Pfsz5/uB0gzRsnw5uJKgDSz4HotkjMCAlry8oFLM6tC4LbLR1icOrgfIJbeQFwJkC3BxPW2sD4npwQEfB8gAAhWKXzx5y3v5qazRdwPEEOhCOTiOoCcDiSmmyIXrc/KOQEB4P43Gk40MHlqsccwNaJC1V59CgQzI+SqgNy07PkKuBAg10J3kJecYn1ezgoIrqtj4rgaI3h4VXxGLlpE3bDAksxcFpBgy56vgOsA8iIStfSSheXMgOCZPR7mwUGICi1SSq3x0TYuvcyizFwVENTtADH8atogM2cHBNNyjpsI9oxVzsu7eOUO76VakpmrAmJpC8RlADHwYf1ILJcA5GOO1gj54B2FhgZELLEgMxcFBLW8trgGIBZ32lkmFwBkAh8ghMK0NU4JZuaagGTRhfCxYC4uAYiv4REi0xb5uQAgxwN56fBCPNBeEdpKg+atus2XmWsCYpgYIJyLSwBCX+922+TnAoCAMWo2MOiHLO8ITw+faT7enm1DByxOnLiJ4xtxTUAMT1juAUgD/eXW/c9GGboCIGCbHzceqKZVRW9V03Wa69O7dNXEeHqUDprJmplrAkI/b1iQiysAov9B2JxvqwxdAhAArgyPZgdEgwQ3roaq9jXwfXjfu+ZAv+c34tYU3c+WmWsDMtY9ANGRV9vIdjm6CCC4Xi6KL2Y2FxdBfVAVUgdEFwcP1b9ViAMp69qtZ3Vi5ZqA0K/M3AMQ/cO2DXN0IUD0ujb9/SDmC3Zsv2jVsdpJYLrPjdBWIOVwneM12TJzTUA83AoQG75Bp+R6gBBK+mvpJ3UrxJUvGert32BJ96CiicHdwuuN9ZgFUmb0/7o7WxLXBKQcqsEbYlq3AIR6frDFC0JKLgqISWaZX7xf3D8yVJ0QeXZl8NqQ82xJXBOQE6iPVuuhGucOgJygHhqEy9RyuQcguHR3knbF+KPa6EK7WJO4JiDgGw+VGu1jUS7ODgj1gBVisxyBOwGCq+D2rRM3ONbzcFFAQO6JfW8ty8XZAdHzEW+zDHG5FyB8mbkqIJabuAYgtuUDAkILAuIagLA2MKULAkIJAuISgITZLDtSEBBKEBBnB8QTB6SzzbIjBQGhBAFxdkDG42PvWBzZWCUICCUIiLMDAhZqQkbbLjdSEBBKEBCnB+RieO3ptsuNFASEEgTE6QE5G1l7hu1yIwUBoQQBcX5ASvfiXd9BiiAglCAgTg/ImSqDvrFdbqQgIJQgIC4AyEDrFwQxEQSEEgTE6QE5Xa4LBIQpCIhNTZwfkMjKEBCmICA2NXF6QP6IgoAYCQJiUxOnB+QUBMRYEBCbmjg9IMeiISBGgoDY1MTpAdlbqREEhCkIiE1NnB6Qbe1gN6+RICA2NXF6QDZ2HrBM2EqcICCUICBOD8i6rv0hIExBQGxq4vSAfF+5BgSEKQiITU2cH5DyVSAgTEFAbGoCAWERBIQSBAQCwiIICCUIiPMDUqEqBIQpCIhNTZwfkK79ICBMQUBsauL8gHSH70GMBAGxqYkLANJ/ue1yIwUBoQQBcX5A6jWFgDAFAbGpidMDsrZ8FQgIUxAQm5o4PyDlICBGgoDY1AQCwiIICCUICASERRAQShAQ5wekVQcICFMQEJuaOD8gPWA3r5EgIDY1gYCwCAJCCQLi/IC07bTCdrmRgoBQgoA4PSBryleBgDAFAbGpCQSERRAQShAQCAiLICCUICAQEBZBQChBQJwfkDqNICBMQUBsauL8gPTsBwFhCgJiUxMICIsgIJQgIM4PSI++EBCmICA2NXF+QCrVWGm73EhBQChBQJwekO/KVYGAMAUBsakJBIRFdgLk8sw5HySbhLknIGwlAQExyD0ByayVB9bGmwS6JSCsJQEBMcimgFSo5iSA/DAQgMeIyVdCAaLbHK9BUVTlzYxMT/nrjxO/bN++ccVcTJMn6vX53LkLVy3YuOvw+etJqTmmH+MEgLCWhAMBuaFBEUT1zk0A6dHbSQCZNh4Aneq2caAeEN0Hvoi5/KOKl6hYO75z124JCcMxMibjnEzCdoYlJLRvHR9fq0IJX0QTHFuvee/Rs5fvOPXXo1ynAIS1JBwFyA2UKt7/3AGQglhE87nNctPLPoAMxc8z4AZ5kFk+llChuGfPnj1NWRjFwodBKH5zUWnKPnlG68lT8u+9GxeO/LzmqxlDujSqWTRIow0qWqZaw4bNP/jgg059CfX8gFCrhgy1/RCLGDhy5LjEWV999dXqzT/u+OX4hX//vfvMVE/PBJPnGTtcXBFaXhLggP4Tqo2jPjXlqdmJmJ1YiqBJCpuJN8ooVnEfNL6a/kQP2LAo2GUlINFeHhq1SoXXGvJCr9jovCjZB5BJo7CN50v90d+XCK2tlJubm/GyUyFeQLixoWUSYWyiUofP2rr1+w1b9x08uBXXuuXLF8wYP37koEGD+nWJr1u5eqkSJYI9sXtWcHCJCtWrN26flUso40IEeZ6XXogqQTElkan/hD7Tc/VKe50rpNfsJtE+nho1VjdUHCVjLK5cjD4ojdqb3pM8zyvZNiwKdpkB4uepvy4V13fOKw8bn599AFneEbvtR5k8zOgfsdqFSABEukhqfN//ej/5MEM/YqWnJCVdO3f48An9sX0esVhLwsJHrKnVvdRUNbG2GJzhEUtlk+/bxudnH0BSIt+A3ZNMAvWAzOJ/xHKEPBvM+n3H2tVLT+UxTs8+gLCWhDEguptHTh5eP/OT5nGBWutJ4BATkNepqUlJdy9dOn/48MHt2zevWvXt3LnTJ04cuXlXr65dW8XH16wR08wORcEuAyC2ufQgG5+fnd6D7Oy/eJppp5MekIziNikI64SivirEQ1Xo0lfF0JjpWfjp2ek9CFtJ0IDsrawN7lwrOhpV2wsM6nrrfbW0bGxsRHCwGkECsYfL2LgaNWrGx7fq2rVXQsLIiROnz5377bFTP2zffvDw4QuXhg+zR1GwigbERiVg6/OT4U3643jbFIVUEXfyMFXk7UpfBPhWPZ97tfEA/PRkeJP+c9Tupzciw8e+H+Gr1djt5oEJvfT3g/8ep6Zm8p6VvR+xWN+ZUoDY5OpRtc1PWp6hJn+o7VkdBBSAl6Qn6vnml1qL0a+xs3kdeRk4FpChwy5dwJ5vIidtPrU4Kk7TuGgAar+nKxStCZTwHoT9nakekDBJF6bvl1Gp1J417HDGuGQei5UTrlbZoAkqSgQgalVE8q0iyzTT8LPosgE4FpDmMfjzTXP0w167PinZQFWvQiEPjcYupYCO0H+k/ICwvzPVA0I10A11Hqv1Go2np69vrh3OxXIpa7BiUjGjPm37yAPBsvdFfbO3N5qhWYV/dLPdQI5HrDxtGkjfWyVR+0WzIuogle0vmvnEIT8gxu9Mbx8mFUYGFCbPmP8ZUA6VW0yepyAnso3mzRlSR622UWenQf6qkpeLfR+qwu8dJyPTgCxtkG5jQPrLMgG9mkaEaD2lIYAyf3KJ31wfH9aPlB8Q43emE2qQ8rhMHhPfrTdXWvkUXoY8zyZC7CpxuPurA79sXjy+TZGYSoW02J0H/4dDZAFHKtQHRf3VwcuCP146PGgPnpkMgDyLbTV3YlhEmRisbvMBgEVrND4+3C8wnWKwovE7U0pUI70gVFXYDh9qtZT1iMUu60fzvn19+Nf7t89dzMYze/r05q/HUv48sGjKjkzwePaQmUmEjRzzQTKXDB5zMu/0ji3bly75dtGSfXcf5Zkbu8poXvZ3prYcrGgPuQcglmQGJ0zZGRD2d6YQEFoQEFpuCQj7O1MICC0ICC33BIRVEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaEBBaf5S5dOnS6d8ucejISa6YXy9wRJw+LD6zQ1yZnVnnQECGU5969DjXidI6ekzQ5JgFJkcFTY7TJiMcCMhP1IeeEDxBx1icvMg8KuM4QG7F4St5RsRyKCKKKyaqOEeEjTNrbvUlWqpFhhON5DpRWpEOMzEU5yKHFUVz6iOLqwRPUFVCyEJbVMjCM0bIwseo7sTdsvBCbLR0z+6WXDG9vuOK8UjjiNhrttoEpX4ruGI4PdweaMyVxJ4a84WgyYTpgiZTpgiaTJ8gaPLFGEETO+qVl6CJ9rWQRYzpgttmEn5karlb8ETYBAGxiyAglCAghCAgxoKAUIKAEIKAGAsCQgkCQggCYiwICCUICCEIiLEgIJQgIIQgIMaCgFCCgBC6Oocr5ttTXDF9uNY5vT6LK8my41wxfbM5Im7O4EpiT23eK2iyVfgL27FD0GT3VkGTvZsFTeyonL6CJr0FF7wdkipkMf6+kMWcq4InwiYbAQIF5ZqCgEBB8QgCAgXFIwgIFBSPICBQUDyCgEBB8UjRgOhO/C3C2H7nAWVXKfqbswqQyzPnfJDM3KMDTGPeJoSH9H3DmgaAR9PZI673+yWfNWbvuHndLxvHvFpQIs8oQG6RF8UtQ4lwSvhiLMjEglOxp4SvQf/NcUv4Igtmlo7okSFwJhJLwRpAMmvlgbXxjD06wCxm+7Q3lwpNYIvBfkC6RrBGXG/1lv1jHtbRgXMtjGKS2+9B8pgBcou8KB7RJcIpCy5GOBNLTsWOEr4G/TfHI+GLvDw552XZufxnIrUUrAHkh4EAPEbeGPboALOYQ7kAfN6BLQ1WAIsKs0UUvHeR42POhb4Cx0YYxQDwEC9mZoC8Ii+KR3SJcMqCixHOxJJTsaMs+UIeCgAifJF/ZgIwcin/mUgtBWsAmTYeA1N127BHB5jF4OYffs2WBrxccjiWLeJk1NqWDU+xxeQ1KLJkYqZx5mQxMwNklf6iBESUCKcsvBj+TCw9FTvJkmsQAgSX0EWC9H7pvPGSS8EaQIZ+jm0Cbhj26ACzGGxzr3k2WxowN/twabaI2SGXdV+GvWNLcj6+eOljRmn0xcwMkFX6i+IXWSKcsuxiBDKx8FTsJUuuwQJAhC6y4NMPO3CO+SMkuRSsAWTSKGzj+dKwRweYxWBNrY4prGmOngKHy7BFjGkPQLb6MkvM5XZ5uWOMP0ZfzMwAuTQmNDT2gP6ieEwyqRLhlEUXI5QJOMp/KnaWJdcgDIjgRWLa6MXnaUl6KVgDyPKOALyJKjDs0QFmMSB35H32NE3KlCmsLfGbecTXrbC7s+8TliQTlwOQ4/eIGaMvZmaAnKIuik9UiXDKkosRzMSiU7GfLLkGQUAELxKf6pCMcHpTA9aUgjWApES+AbsnAbD5pX6PCjCPyR9+9sWLF7ksMZh+LcWW5F7EO/BXSx1LzMpPAUhqbpQGgAdIjiFAASIuikd0iXDKgosRzsSSU7GjLPlCiG+OR8IXOfIpAN+NFzgViaVg1XuQnf0XT8sB2cV+1e9Rf8xjRiG4zrKlAd+308y5xxKx+6Olk1+wZZY/9vOVX7wyjtn7ETL6OOME5Jb+orhlKBFOCV+MBZlYcCr2lPA16L85bglf5M8tJi8TmjojtRQU/SYdCkpuQUCgoHgEAYGC4hEEBAqKRxAQKCgeQUCgoHgEAYGC4hEEBAqKRxAQKCgeQUCgoHgEAYGC4hEEBAqKRxAQKCgeQUCgoHgEAYGC4pEiAMk5O49ctlrcgP1Hipj5AeXScgwgum/CB5J7WbnUhqETfZHD+N9L/c2c7N1qFRzeKwWAHqHV41u2bBmEJO5sj4R2Hlq/2g/gWt98u585lJvLMYB8Mebus4JyizEUIq/oN0ZKIgC5W81sWrGu+BfnV3u3BmAg4dXljKpCBniNDMEihiK/gCWfOOLkodxZDgHkufoEth3/OwA/II/1GyMlE4B8aO77K+lLbDO5EAAL8KM3sb43sT+qIXgMkgDyiwvMNoWCslIOAeQsch2A3It3ANiBvNNvjEQActnHfDHHd7jl9EHkga4XQiy3p8YBeYuMB2BOU/ueOJQi9I7Po4+d5QhArtdFQiK/boSsAHtLIxGRk/DNmdwFnyQ0Po3FJo9qXL0fDsj4htjRL/1ikpv71L/5opV3jb/I5P9Qfom/w24auAhATiJHATiPPHHA+TtATxMaFm/f49zaTtUe1PWqjN1qge67UUMbTnwHTiUUv9k34DpLubiNNnrVygQFLUbI8uEOuYOcwu8grzFAwCYki9zkt5wJwMqAJ2B9s1Twqj8OSHXcyVhyAjLgr39LlvnozztlKuJpNzaPbbCdyOWaV9VMYgcH5EXNsdheNrLFEedvd51o8wzk9Eey/m4SMP7uP9XDsUIa208HnpZsUvBoItIjsWySWbm4k86oPgNLZVmt2JGA6IwA2YPcBSAF2ZDugS9wTTxihc3EbTcjDwBYhCQBsATBG+ZpBbrZyI/YTkZZf72PV/UlX3mzAAADKUlEQVSQk2NHHSR2A+Y54vztraywk9h2gRcA44LyAdiHFddt9BzAS2o/OIQsB89yzMrFrTRBta2fTO4AHQcIMAIkEek+cOj4TvsPEw7xCEC0REN8K4I9cG5H0gDYjegdTub59Ma2/ZHt+uyIRyxShT53xPnbW6eJQljgC8DEYtjONeQUVkYPsb1HyAxwGME7JtjKxX2UWcb//+3dTWgTQRgG4K/+FIkRFVQqoZciKII/FSVW0WIRRFGEUgyK8SIRqkZBoZeKghchBCkSDPhHqY0oCvEQaSGKeikFg7Wol1atCopCoRIbY9tkP2c2rTQVRnPIztq8z2HZZJfd2dl52cwy2dX1yH5dAWmWp1poI3lZ6JMBqTwnvzAbwl25NCoaQm4ll5+5lfzm/GheQOZdtqL8xXaD+nlSQF6KgIRJ9jRG6JIIiFw4pV5KjOGdE9S0a8sDMpyb3BrvPfSQvG5EZUA2m72w/IZwXN6/GJz1gl851psP+M76mGccmdhwmjqtKH+xPaEWMb0wOSBddFHM9ZX1IiBC4FqwvEfPri0JSJyeycbcIk9uWzwtJ99WOO+nf175lN3ujKYe7Be/tfn0FrluKw3KjsgXue57Du8b5hFvmIeWLcgNQ0k0cpI8Exvupo9WlL/YMmvnx1LxahGQkxUsA/KQjR1LB9g41sQcI/NFjfn1Ulo6642Me+UPLfu2IiD36mhnLHGIVoc4WePwG+ZkyFflaogyfz24xHl2gGoj3OtIMV/dQAeeX3eT56loCA2P3tVX7/F1MXtpsVtYVTHzVGQvLTz6Jrfl4C4Lim+Bz55FVTcDc7m5suzwWxGQrR38/cS6xjO3DY7U0u7QH/Wiu8CWand9YH5dvknLA4ZtMVhx3Mb2vI/Zv9+3WNNRrLJoIPsgJiOjPPR/qJfpaGxMy8g7OwUkUVNgFTzeNp3ayu+AgI3YKSAcChS0erJu6piu/xoCYke2CgjfKeid8t3a3npRFOdnF3T0YAl7BaSEjTYtJ9/UvwGAdggIgAICAqCAgAAoICAACggIgAICAqCAgAAoICAACggIgAICAqCAgAAoICAACr8A+mLCGCr9IfgAAAAASUVORK5CYII=" title alt style="display: block; margin: auto;" /></p>
+## Comparação das funções de ligação
+sapply(list("binomial" = m3HP, "poisson" = m3HP.pois,
+ "negbin" = m3HP.negb, "geometric" = m3HP.geom), logLik)</code></pre>
+<pre><code>## binomial poisson negbin geometric
+## -3697.874 -3697.899 -3696.887 -3697.874</code></pre>
</div>
<div id="predicao-1" class="section level2">
<h2>Predição</h2>
<pre class="r"><code>## Região para predição
pred <- expand.grid(sexo = c("M", "F"),
- valor = 1:200, expos = 1)
+ valor = 1:200,
+ expos = c(0.1, 0.5, 1))
##-------------------------------------------
## Estimando as médias
-aux <- predict(m3ZP, newdata = pred, type = "response")
+aux <- predict(m3HP, newdata = pred, type = "response")
predmu <- cbind(pred, fit = aux)
-xyplot(fit ~ valor,
+xyplot(fit ~ valor | factor(expos),
+ layout = c(NA, 1),
groups = sexo,
data = predmu,
type = c("l", "g"),
+ strip = strip.custom(strip.names = TRUE,
+ var.name = "Exposição"),
auto.key = list(
columns = 2, cex.title = 1,
lines = TRUE, points = FALSE,
title = "Número de crianças"))</code></pre>
-<p><img 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" 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" title alt style="display: block; margin: auto;" /></p>
<pre class="r"><code>##-------------------------------------------
## Estimando probabilidades
pred <- expand.grid(sexo = c("M", "F"),
- valor = c(1, 50), expos = 1)
+ valor = c(1, 50),
+ expos = 0.5)
-py <- c(t(predict(m3ZP, type = "prob", at = 0:5, newdata = pred)))
+py <- c(t(predict(m3HP, type = "prob", at = 0:5, newdata = pred)))
carac <- with(pred, paste("Sexo:", sexo, "Valor:", valor))
da <- data.frame(y = 0:5, py = py,
sexo = rep(c("M", "F"), each = 6),
@@ -874,7 +876,7 @@ useOuterStrips(
strip = strip.custom(strip.names = TRUE, var.name = "Sexo"),
strip.left = strip.custom(strip.names = TRUE, var.name = "Valor")
)</code></pre>
-<p><img 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UseJ7yYQFQjEFG6AjmndUEgShGIKF2BjExcziwlENUIRJSuQMYNu3PW9NFdzycQ1QhElLaneS+IP4t1cg2BqEYgovS9H6TyurF3cBxEPQIRpfkNU7cRiGoEIkpDIHMrHd8aRCCqEYio/AJ50d7iHogHcpnFKdg0IhBR+QUywH7YPRAPpOKU+At5E27m1bzKeQLJ2EcRiFJRA6kt7jfw1EGdT+nkHo4HUjOp9TEIr+ZVzhNIxj6KQJSKGsgnE5esXbu235qx7mHvg/TRBKKaE4jPPopAlIp8F6t51RNN9iD7KfeoE0j1FUMHDhjQ/wgCUc0JxGcfRSBK5fEYZMcjrw/yjjmBDD7stJIRZ/QaQiCqtd7FansfRSBK5fUg/aVR3pHWj/2ZGn+hYvksAlEt/TFIW/soAlEqz+MgDWvc3zuB9KirG1FTd8/JBKKa+0F65j6KQJTKM5AVZcnl3yZPTBhV2hLI2bPrbvhq9djOBKKa9ziIs49atyyp+/vpPyaQ/OR7JL0puWhevSrhwT6xIq6Kv9B94MyFJZbF52Ip5w0ktY+yH03uoyYe+U76jwkkP9EDaXx/046D3sHEXazOl9yTfCZr/ARezatcxpH0Js/33MVSKWogW8qtom5dSufvcw8nAim+sOfg22qdYyEEolRaIG3uowhEqaiBjFu+rdm2d65e5B5OBDK6rm7K8JLL7yEQHZxAfPZRBKJU1EAWtyyXuIdbj6TXXHfykFsWEohyTiA++ygCUSpqIIv2JxaNFe5h10tNKvsedS6BqOYE4rOPIhClogayYeSUuTVVE0qecQ+nBTL9vCOtI4YTiGpOID77KAJRKvKzWLtX1s6oXrrdM5oI5Oa6ujmji62iwTcv4C6Wck4gPvsoAlFKx1tuh359UJHVozx1iikCUar1Way291EEopSOQCyr0xkVrQ9FCEQp3lEoSkcgR46Zl/6eEAJRikBE6Qik3P2mKQJRikBEcZ500xCIKAIxDYGIIhDTEIgoAjENgYgiENMQiCgCMQ2BiCIQ0xCIKAIxDYGIIhDTEIgoAjENgYgiENMQiCgCMQ2BiCIQ0xCIKAIxDYGIIhDTEIgoAjENgYgiENMQiCgCMQ2BiCIQ0xCIKAIxDYGIIhDTEIgoAjENgYjKM5B9b7q/JxDtvIF4NwGBKBU1kJ3zJjzeZNsNnt8mEO2cQHw2AYEoFTWQW/vW3HjFpwQizwnEZxMQiFJRAzlug21vvvZDAhHnBOKzCQhEqaiB9NwVu9hV9RaBSHMC8dkEBKJU1ECm1cVPjbfvLgKR5gTiswkIRKmogexa9UJ8caDKPUwg2jmB+GwCAlEqz6d5G9a4vycQ7bxP83o3AYEolWcgK8qSy93HWkklBKKZN5DUJrBnD0zqvDH9xwSSn3yPpDe1LHd+kfCv3ILolnEkPbUJPqpP6vVB+k8JJD/RA2l8f9OOg95B7mJplxZIm5uAu1hKRQ1kS7lV1K1L6fx97mEC0c4JxGcTEIhSUQMZt3xbc+yO1epF7mEC0c4JxGcTEIhSUQNZ3LJc4h4mEO2cQHw2AYEoFTWQRfsTi8YK9zCBaOcE4rMJCESpqIFsGDllbk3VhJJn3MMEop0TiM8mIBClIj+LtXtl7Yzqpds9owSiXeuzWG1vAgJRincUmoZ3FIoiENMQiCgCMQ2BiCIQ0xCIKAIxDYGIIhDTEIgoAjENgYgiENMQiCgCMQ2BiCIQ0xCIKAIxDYGIIhDTEIgoAjENgYgiENMQiCgCMQ2BiCIQ0xCIKAIxDYGIIhDTEIgoAjENgYgiENMQiCgCMQ2BiCIQ0xCIKAIxDYGIIhDTEIgoAjENgYgiENMQiChOoGMaTqAjihPomIYT6IjiBDqm4QQ6okw5gc5NA8MN+iTLP9poIifQebp7Fp5W/8d1PKacQGfQqndDnfhO1n+2wUROoLPgzvDVfecCLX9fB2PKCXQGvRY+66SMQKaOCHf+Z1n9wR2GyAl0FswJnzUnI5APlmVhi7pVIcGUE+hEC+Sk7z8XquQ976wP68O9m91a0kDkBDrRAqkeNiXUGd6HTB1cnsdBGta0fPHa2oQlfTtWIG+GzyrLCOTkfv1DHbbZO2vZveFqm7yzXlsV7vnm9Bne4yDOJvjPll8//oP0H4sGUh0+6+4676zvZfN45ynvrPceCfdoxkbamsXqXvXX9Bl5BrKiLLncM6Y84ZzeBRBI2frwWf3+yzur3zdmhTr6z95Zfc4fFerwj9JneANJbQJ7YXITlHfZmP7jjh/I3Nn/N9S0B7yzZp0dfmt1+re9s2YODF/dfb+bPiPfI+meXWJHu4slGciG8FmlGYH0fjd8Vq+t6TMyjqR7b5Xa8S5WtEDmh8+qzAxkYfisGYu9syrvD5817bH0GaYcSSeQFJEj6QSSYsqRdAJJETmSTiApphxJJ5AUkSPpBJJiypF0AkkROZJOICnGHEknkBYyR9IJpEVhH0kv6EA63pF0AkljxpH0Qg6kAx5JJ5AwBBJIy3EQLwJJEQjkb96fhiCQQARSaIHc4f1pCAIJRCCFFohV/lKz9xeCEEggAim0QGb9d93YJ/+/91f8EUggAim0QL607YMv3lm10ftLfuqtNhS3NYioHgzZBgPb+woWljMDA4k/SP+0rqd1+U+zfLj+179k+viLdNu2hNv6hVc2sz5s11mfaJv1kXvK/pBN8KXrtz/L4h/Y8pn3aknO+qu+WQ0KZu0ODOS+XT+7qtMRt750YO31P8x83Shw6HEFcniRNfzJL+JfNT/2jfa5PkCH4gqk83Tn4fLPu8pfF6DDcQXyffvzTS13rTb/uD2uDdDBuAJpusOyet4f9rAQOHS4Aqn9yozfPDvjlpwOFgKFzBXIwLnxy5/+on2uCtDxuAI58rX45YGvZj37w82Z/k8bY4gs47O1PBra+woWls8DA7lhdfyy6Zj45X9m0Ud90VEZunTL5pPBkKUjQo+kH9veV7GQdAs+kt44pSF2+f2z419PD9oq25O5rQr/ZEXkJ8fXYiE/Ia/FOq1r5+OPP76oS+zi6KB3Uu0pTfZ2TAmBaEYgokICuenqRQ+2WHBKFv+5LF7Ni/z4BvIPE5OObL8P1i5AIYH8S9qruh/N4j9HINr5BvJ2y2cv98h4JT6ik39POvLDXSxRBGIaAhFFIKYhEFEEYhoCEUUgpiEQUQRiGgIRRSCmIRBRBGIaAhFFIKYhEFHygcwcEe7iv/r895FrIP87i9U9YrXG62u4dvh098efC9Uv45MVkZLrJyteH766v57xyYpIMeX0B0gR+ehRpBCIaQhEFIGYhkBEEYhpCEQUgZiGQEQRiGkIRBSBmIZARBGIaQhEFIGYhkBEEYhpCEQUgZiGQEQRiGkIRBSBmIZARBGIaQhEFIGYhkBEEYhpCEQUgZiGQEQRiGkIRBSBmIZARBGIaQhEFIGYhkBE5RXIjm3eEQLRjkBERQ1k4xWVe17u0vVKz0lyCUQ7AhEVNZCb3nr53lNn793sOScxgWjnDiTzRpxAlIoayKO2/UT/fba9zD1MINo5gfjciBOIUlEDuaf58xPK99t2jXuYQLRzAvG5EScQpaIG8uwFx1/89jVLZ85xDxOIdk4gPjfiBKJU5GexPn5rv73h3mUH3KMEop0TiM+NOIEoledxkIY17u8JRDsnEJ8bcQJRKs9AVpQll3urpiWMLyUQzVqfxWr7RpxAlMr3SHpTcnFg+bKEeX0IRDPvcRDnRvy3jyQd+6f0HxNIfqIH0vj+ph0HvYPcxdLOG0jqRtz+UfJGfFrXd9N/TCD5iRrIlnKrqFuX0vn73MMEol3GkfQmz/fcxVIpaiDjlm9rtu2dqxe5hwlEu7RA2rwRJxClogayuGW5xD1MINo5gfjciBOIUlEDWbQ/sWiscA8TiHZOID434gSiVNRANoycMremakLJM+5hAtHOCcTnRpxAlIr8LNbulbUzqpdu94wSiHZOID434gSiFO8oNI0TiM+NOIEoRSCmaX0Wq+0bcQJRikBMwzsKRRGIaQhEFIGYhkBEEYhpCEQUgZiGQEQRiGkIRBSBmIZARBGIaQhEFIGYhkBEEYhpCEQUgZiGQEQRiGkIRBSBmIZARBGIaQhEFIGYhkBEEYhpCEQUgZiGQEQRiGkIRBSBmIZARBGIaQhEFIGYhkBEEYhpCEQUgZiGQEQRiGkIRBSBmIZAROUTyO6Nf2n2DBGIdgQiKmIg7x207Vd7djp6zA73OIFo5wqkjX0UgSgVMZBXXrDti77d1LzBc5JuAtEuFYjfPopAlIoYSPPtX9qj4/uuJ9zjyUBqbjj39IFDRo6ZTSDqpQLx20cRiFJRH4P8ccz65e/EbuKnuocTgdx1tNVrwGlDTu7bedhCAlEtFYjfPopAlIr8IP2PI3r3GDem+Nfu0UQgA75WnbxztWD8hQSimvMYxGcfRSBKRX8W6+CrD81+fKtnMBHI6a2PP84jENVaH6S3vY8iEKXyPA7SsKbli+2bE1b1jRVxvtNH7ckEolras1ht7qMIRKk8A1lRllzuLumecExJrIixI74Zf+xRN2dSf25BlPMeB3H2UR+sTer5QfqPCSQ/+R5Jb3J/m3wW6+pjrMOPPuIrVqezFhCIat5AUvsoe96IpMM3pv+YQPITPZDG9zftOOgdbDkOUvuNa68Ydc2ke3maV72MI+mefRR3sZSKGsiWcquoW5fS+fvcw/FAaiffHcui8pIR4+YSiHppgbS5jyIQpaIGMm75tmbb3rl6kXs4HkiNNbqubsph1nHF3SsIRDknEJ99FIEoFTWQxS3LJe7heCAL4oH0O2ZaXd29w+4mENWcQHz2UQSiVNRAFu1PLBor3MOJu1jxQDpfF7/xqD6XQFRzAvHZRxGIUlED2TByytyaqgklz7iH44EsjAfSc2ri8ccgAlHNCcRnH0UgSkV+Fmv3ytoZ1Uu3e0bjgdxnXTpvwZiLE4H0IhDVnEB89lEEopSGdxQuOG3w6YNOGTgndlty6akEolrrs1ht76MIRCmtb7ktv24+gajGOwpF8Z500xCIKAIxDYGIIhDTEIgoAjENgYgiENMQiChdgQw4KX55ygACUY1AROkKZNjw+OWIYQSiGoGI4i6WaQhEFIGYhkBEEYhpCEQUgZiGQEQRiGkIRJSuQGZULSAQLQhElK5AuvScQyBaEIgoXYGceUlicTWBqEYgonQFctfQsXdVVn6LN0wpRyCidAVynJVEIKoRiChdgZx94W2TJ0++pYxAVCMQUboCmZI8u9T1BKIagYjSdhyk9ubLrvo7nsVSj0BE6Qqkpn/8EchFBKIcgYjSFciFPa6bvaDy3IkEohqBiNIVSFnyOOHFBKIagYjSFcg5rQsCUYpAROUXyIv2FveAE8jIxOXMUgJRjUBE5RfIAPth94ATyLhhd86aPrrr+QSimieQjH0UgSgVNZDa4n4DTx3U+ZRO7uHWp3kviD+LdXINgajmCSRjH0UgSkUN5JOJS9auXdtvzVj3cNr7QSqvG3sHx0HUcwLx2UcRiFKR72I1r3qiyR5kP+Ue9b5h6jYCUc0JxGcfRSBK5fEYZMcjrw/yjsUDmVvp+BYn0FGu9S5W2/soAlEqrwfpL43yjsQDucxKQyCqpT8GaWsfRSBK5XkcpGGN+/t4IBWnxF/Im3Azr+ZVzv0gPXMfRSBK5RnIirLkcm/VtITxpXV1NZNaH4Pwal7lvMdBnH3U/i+SSglEoXyPpDclFweWL0uY18f9IH00gajmDSS1j7IntdyrLXKtPALJT/RAGt/ftOOgd9B5Fqv6iqEDBwzofwSBqJZxJL3J8z13sVSKGsiWcquoW5fS+fvcw04ggw87rWTEGb2GEIhqaYG0uY8iEKWiBjJu+bZm2965epF7uPVjf6bGX6hYPotAVHMC8dlHEYhSUQNZ3LJc4h52AulRVzeipu6ekwlENScQn30UgSgVNZBF+xOLxgr3cOuHNsyuu+Gr1WM7E4hqTiA++ygCUSpqIBtGTplbUzWh5Bn3cCKQq+IvdB84c2GJZfG5WMo5gfjsowhEqcjPYu1eWTujeul2z2gikM6X3JN8Jmv8BF7Nq5wTiM8+ikCU0vGOwuILew6+rdY5FkIgSrU+i9X2PopAlNIRyOi6uinDSy6/h0B04B2ForR9LlbNdScPuWUhgShHIKJ0nkCnsu9R5xKIagQiSl8g08870jpiOIGoRiCidARyc13dnNHFVtHgmxdwF0s5AhGlI5ChXx9UZPUoT51iikCUIhBROgKxrE5nVLQ+FCEQpQhElI5AjhwzL/09IQSiFIGI0hFIuftNUwSiFIGI4jzppiEQUQRiGgIRRSCmIRBRBGIaAhFFIKYhEFEEYhoCEUUgpiEQUQRiGgIRRSCmIRBRBGIaAhFFIKYhEFEEYhoCEUUgpiEQUQRiGgIRRSCmIRBRBGIaAhFFIKYhEFF5BrLvTff3BKKdNxDvJiAQpaIGsnPehMebbLvB89sEop0TiM8mIBClogZya9+aG6/4lEDkOYH4bAICUSpqIMdtsO3N135IIOKcQHw2AYEoFTWQnrtiF7uq3iIQaU4gPpuAQJSKGsi0uvi5I/fdRSDSnEB8NgGBKBU1kF2rXogvDlS5hwlEOycQn01AIErl+TRvw5qWLz7ZnLCqL4Fo5n2a19kEKQSiUp6BrChLLvf07Z5wTAmBaOYNJLUJ7JkDkzq7Vh6B5CffI+lN7m+5i6VdxpH01Cb4uD6p+IP0nxJIfqIH0vj+ph0HvYMEol1aIG1uAu5iKRU1kC3lVlG3LqXz97mHCUQ7JxCfTUAgSkUNZNzybc22vXP1IvcwgWjnBOKzCQhEqaiBLG5ZLnEPE4h2TiA+m4BAlIoayKL9iUVjhXuYQLRzAvHZBASiVNRANoycMremakLJM+5hAtHOCcRnExCIUpGfxdq9snZG9dLtnlEC0a71Way2NwGBKMU7Ck3DOwpFEYhpCEQUgZiGQEQRiGkIRBSBmIZARBGIaQhEFIGYhkBEEYhpCEQUgZiGQEQRiGkIRBSBmIZARBGIaQhEFIGYhkBEEYhpCEQUgZiGQEQRiGkIRESKY5wAAAZkSURBVBSBmIZARBGIaQhEFIGYhkBEEYhpCEQUgZiGQEQRiGkIRBSBmIZARBGIaQhEFIGYhkBEEYhpCESUKSfQuX9iuFs+z/avNpnICXQa1mZhh44/r6Mx5QQ6gx74Saje7+Tyl5tK5AQ6tSeOCnVira4/sSMx5QQ6g14Ln3XSoRWIzhPoLJgTPmvOAl1/Ykdiygl0CCRF5AQ6BJJiygl0CCRF5AQ6BJJiygl0ogVy68BwZ3yW1R/cYYicQCdaIDvrs7BL3aqQYMoJdKIFctLTr4YqfS+7P7ijEDmBTrRAFvY+K1TJQ6pWhIw8j4M0rHF/39ECeTN8VllGIL94JNzf781lNankPQ7i3QTtGEh1dfisu+u8sxqzud35MsqqUiHPQFaUJZd7q6YljC8tgEDKKmaF6vpf3lm3jQj31S+8s6qzOL4zeXf6DG8gqU1g/1PLrx/1bvqPO34gNaXhtzvF3/bO+kV5Fl70zno2i9U98fX0GfkeSW9KLg4sX5Ywr08hBLI+fFa/jED6/dNzoXr82Turd+33QnXdmj4j40h6yyaw/y25CZYd96f0n3b8QObOD59V+YB31qzbwlf3uMXeWZXjw1f3xY+lzzDlSLoBgWwIn1WaGci74bN6bU2fIXIk3YBAFobPmpEZyP3hs6YpCUT8SDqBtBA5kk4gKYV9JL2gA+l4R9IJpJUZR9ILOpCOdySdQFqZcSS9oAPpeEfSCaSVGUfSCzqQjncknUDSmHEkvZAD6XhH0gkkFIEEUvw0b9sIJIVA3AgkgUBSCMSNQBIIJIVA3AgkgUBSCMSNQBIIJKUjBtJzcoYxvVyfhVE2/YFQvZZ6P0HjhBnhs45f5p3V567wWd1/5J1VUhk+69invLN6zQyfdcxPvLN6zAqfdfTP0mfcEhqI6x+ZfFH4P3DZJO/VuvWS8FmX3OqdNemy8FkXTfbOumlU+KwLK7yzbrg8fNb5U72zrr0yfNbIb6bP+IHaQD4cMSTD4DNdL0E+sUcWLva+cPmELCYdf4l3Vr9Is/pmM+tSNbP6ZDPrMteUX4Zsgztdv31mFv9AjzO8VyurWWd6Z50RadbQbGYN884aks2ssyLNGu6aUqk0EKDQEQgQgECAAAQCBCAQIACBAAFMCaS5va+AFnsPNB9ot08UClSQ6zvK6pYOZOP9D4/fmvOsz78zYH/Ok3ZN69Vj8s5cZx28/9TiSVE+l+mTutznHGNZ1rAI/1b25NZ3ga5u4UD2jNxvLy/PddbWcc9auQeyqnZnfZ95uc7aOH/vjtMeyfkfs5snFof/ktc3GhoaMj48SyXB9V2gq1s4kJVTbHublfN+xv44QiAv7LPtmgm5zvrDHtuufDLnf8xe9Q/9cp+UcdxWNcH1XaCrWziQ2rmx+Is+yHlelEDibnw8wqTG2xtznrPjibUDc/+XLinvfp7W888Jr+8CXN3CgcyoiV1025TzvIgbbMsVTeG/5HFw5o0TXst51iNNazNe5xbuuwf23Nxf5+Nh2fVdiKtbOJDqqthFl9xPdxdtg+269tMIs2z7p0fk+qn9L71mrx0U6d/aYuk816Lo+i7I1S0cyNJrbXtn78wP0AwTaYPtq/ww90kNsf9tzfn/tKMGDerXeUDGpyeHiD82+OzEHCflRHJ9F+bqFg7k05Kd9uoIpyH+yMr9cMGBu95saGjwflRnmMrttv2juTn/Y7a95pScp9R+adtLfhPh38qa4Pou0NUtfRzkVxVLanP///pzU61Zr+Q6qcqKezPHWc9fOf8fw96D0Zanrjns4S05znlz/KzF/xHh38qB3Pou0NVtypF0oF0QCBCAQIAABAIEIBAgAIEAAQgECEAgQAACAQIQCBCAQIAABAIEIBAgAIEAAQgECEAgQAACAQIQCBCAQIAABAIEIBCDfK/z4TV/+3KWdcff2vuaHDoIxCTTi3bZ9n/n/GHUiI5ATPIH62e2XfdSe1+NQwmBGOWsq+yDVxfkyW06KgIxypOdPn0xwskCEBmBGOWLLnV35n6yAERHIGa5tWdVe1+FQwuBmOUla2N7X4VDC4GY5cDl7X0NDjEEYpa1P23va3CIIRCjNN/AQXRZBGKQzT//zg/b+zocagjEID8+uiL3s6khLwQCBCAQIACBAAEIBAhAIEAAAgECEAgQgECAAAQCBCAQIACBAAEIBAjwP2UJ8Tj+CGcjAAAAAElFTkSuQmCC" 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" title alt style="display: block; margin: auto;" /></p>
</div>
</div>
diff --git a/vignettes/v07_excesso-zeros.Rmd b/vignettes/v07_excesso-zeros.Rmd
index e8d956b93ba32e61d13e30a6f71e3757183b0773..28b43a6c4c7fb23760e2a90687110c0851174b22 100644
--- a/vignettes/v07_excesso-zeros.Rmd
+++ b/vignettes/v07_excesso-zeros.Rmd
@@ -128,7 +128,6 @@ m2HBN <- hurdle(f2, data = peixe, dist = "negbin")
m1ZBN <- zeroinfl(f1, data = peixe, dist = "negbin")
m2ZBN <- zeroinfl(f2, data = peixe, dist = "negbin")
-
```
## Comparação dos ajustes ##
@@ -190,14 +189,13 @@ vuong(m1BN, m3HBN)
## Avaliação do modelo proposto ##
-```{r diag, cache = TRUE}
+```{r diag, cache = TRUE, fig.height = 4}
## Uma pequena avaliação dos resÃduos
p1 <- xyplot(residuals(m3HBN) ~ fitted(m3HBN),
type = c("p", "g", "spline"))
p2 <- qqmath(~residuals(m3HBN, type = "pearson"),
- aspect = "xy",
type = c("p", "g"),
panel = function(x, ...) {
panel.qqmathline(x, ...)
@@ -344,9 +342,9 @@ cbind("vglm_posnegbin" = exp(coef(mcount)[2]),
```{r}
-sinistros <- read.table("base.txt", header = TRUE)
-str(sinistros)
-## help(sinistros)
+data(seguros)
+str(seguros)
+## help(seguros)
```
@@ -356,6 +354,7 @@ abaixo para 16483 clientes.
* `tipo`: Tipo de veÃculo segurado. Fator com seis nÃveis (_hatch_,
_mono_, _picape_, _sedan_, _wagon_ e _suv_).
+* `idade`: Idade do cliente, em anos.
* `sexo`: Sexo do cliente. Fator com dois nÃveis, _M_ para clientes do
sexo masculino e _F_ para feminino.
* `civil`: Estado civil do cliente. Fator com quatro nÃveis (_Casado_,
@@ -366,7 +365,7 @@ abaixo para 16483 clientes.
```{r}
-summary(sinistros)
+summary(seguros)
```
@@ -377,40 +376,40 @@ summary(sinistros)
library(pscl)
## Preditores
-f0 <- nsinist ~ 1 + offset(log(expos))
-f1 <- nsinist ~ sexo + valor + offset(log(expos))
-f2 <- nsinist ~ sexo * valor + offset(log(expos))
+f0 <- nsinist ~ 1
+f1 <- nsinist ~ sexo + valor + log(expos)
+f2 <- nsinist ~ sexo * valor + log(expos)
## Poisson
-m0P <- glm(f0, data = sinistros, family = poisson)
-m1P <- glm(f1, data = sinistros, family = poisson)
-m2P <- glm(f2, data = sinistros, family = poisson)
+m0P <- glm(f0, data = seguros, family = poisson)
+m1P <- glm(f1, data = seguros, family = poisson)
+m2P <- glm(f2, data = seguros, family = poisson)
## Hurdle Poisson
-m0HP <- hurdle(f0, data = sinistros, dist = "poisson")
-m1HP <- hurdle(f1, data = sinistros, dist = "poisson")
-m2HP <- hurdle(f2, data = sinistros, dist = "poisson")
+m0HP <- hurdle(f0, data = seguros, dist = "poisson")
+m1HP <- hurdle(f1, data = seguros, dist = "poisson")
+m2HP <- hurdle(f2, data = seguros, dist = "poisson")
## Zero Inflated Poisson
-m0ZP <- zeroinfl(f0, data = sinistros, dist = "poisson")
-m1ZP <- zeroinfl(f1, data = sinistros, dist = "poisson")
-m2ZP <- zeroinfl(f2, data = sinistros, dist = "poisson")
+m0ZP <- zeroinfl(f0, data = seguros, dist = "poisson")
+m1ZP <- zeroinfl(f1, data = seguros, dist = "poisson")
+m2ZP <- zeroinfl(f2, data = seguros, dist = "poisson")
## Binomial Negativa
library(MASS)
-m0BN <- glm.nb(f0, data = sinistros)
-m1BN <- glm.nb(f1, data = sinistros)
-m2BN <- glm.nb(f2, data = sinistros)
+m0BN <- glm.nb(f0, data = seguros)
+m1BN <- glm.nb(f1, data = seguros)
+m2BN <- glm.nb(f2, data = seguros)
## Hurdle Binomial Negativa
-m0HBN <- hurdle(f0, data = sinistros, dist = "negbin")
-m1HBN <- hurdle(f1, data = sinistros, dist = "negbin")
-m2HBN <- hurdle(f2, data = sinistros, dist = "negbin")
+m0HBN <- hurdle(f0, data = seguros, dist = "negbin")
+m1HBN <- hurdle(f1, data = seguros, dist = "negbin")
+m2HBN <- hurdle(f2, data = seguros, dist = "negbin")
## Zero Inflated Poisson
-m0ZBN <- zeroinfl(f0, data = sinistros, dist = "negbin")
-m1ZBN <- zeroinfl(f1, data = sinistros, dist = "negbin")
-m2ZBN <- zeroinfl(f2, data = sinistros, dist = "negbin")
+m0ZBN <- zeroinfl(f0, data = seguros, dist = "negbin")
+m1ZBN <- zeroinfl(f1, data = seguros, dist = "negbin")
+m2ZBN <- zeroinfl(f2, data = seguros, dist = "negbin")
```
@@ -432,9 +431,9 @@ cbind("Poisson" = sapply(list(m0P, m1P, m2P), logLik),
```{r}
## Testes de razão de verossimilhanças
-lmtest::lrtest(m0ZP, m1ZP, m2ZP)
+lmtest::lrtest(m0HP, m1HP, m2HP)
-lmtest::lrtest(m0ZBN, m1ZBN, m2ZBN)
+lmtest::lrtest(m0HBN, m1HBN, m2HBN)
```
@@ -442,7 +441,7 @@ lmtest::lrtest(m0ZBN, m1ZBN, m2ZBN)
## Para a componente de contagens não negativas é necessário um modelo
## Binomial Negativa, considerando superdispersão dos dados?
-vuong(m1ZP, m1ZBN)
+vuong(m1HP, m1HBN)
```
@@ -451,14 +450,15 @@ vuong(m1ZP, m1ZBN)
```{r}
## Estimativas do modelo proposto
-summary(m1ZP)
+summary(m1HP)
## Retira efeito de sexo na componente das contagens nulas
-m3ZP <- zeroinfl(nsinist ~ sexo + valor + offset(log(expos)) |
- offset(log(expos)) + valor,
- data = sinistros)
+m3HP <- hurdle(nsinist ~ 1 | sexo + valor + log(expos),
+ data = seguros)
+
+lmtest::lrtest(m1HP, m3HP)
-lmtest::lrtest(m1ZP, m3ZP)
+summary(m3HP)
```
@@ -466,48 +466,18 @@ lmtest::lrtest(m1ZP, m3ZP)
## Avaliação de diferentes especificações para a componente de contagens
## nulas
-m3ZP.probi <- zeroinfl(nsinist ~ sexo + valor + offset(log(expos)) |
- offset(log(expos)) + valor, link = "probit",
- data = sinistros)
-
-m3ZP.clogl <- zeroinfl(nsinist ~ sexo + valor + offset(log(expos)) |
- offset(log(expos)) + valor, link = "cloglog",
- data = sinistros)
-
-## Comparação das funções de ligação
-sapply(list("logit" = m3ZP, "probit" = m3ZP.probi,
- "cloglog" = m3ZP.clogl), logLik)
-
-```
-
-```{r diag2, cache = TRUE}
-
-##======================================================================
-## Uma pequena avaliação dos resÃduos
-p1 <- xyplot(residuals(m3ZP) ~ fitted(m3ZP),
- ## aspect = "xy",
- type = c("p", "g", "spline"))
-
-p2 <- qqmath(~residuals(m3ZP, type = "pearson"),
- ## aspect = "xy",
- type = c("p", "g"),
- panel = function(x, ...) {
- panel.qqmathline(x, ...)
- panel.qqmath(x, ...)
- })
+m3HP.pois <- hurdle(nsinist ~ 1 | sexo + valor + log(expos),
+ data = seguros, zero.dist = "poisson")
-qqsimul <- hnp::hnp(m3ZP, plot = FALSE, sim = 50)
+m3HP.negb <- hurdle(nsinist ~ 1 | sexo + valor + log(expos),
+ data = seguros, zero.dist = "negbin")
-p3 <- with(qqsimul,
- xyplot(residuals ~ x, pch = 20) +
- as.layer(
- xyplot(median + lower + upper ~ x,
- type = "l", col = 1, lty = c(2, 1, 1)))
- )
+m3HP.geom <- hurdle(nsinist ~ 1 | sexo + valor + log(expos),
+ data = seguros, zero.dist = "geometric")
-print(p1, split = c(1, 1, 3, 1), more = TRUE)
-print(p2, split = c(2, 1, 3, 1), more = TRUE)
-print(p3, split = c(3, 1, 3, 1), more = FALSE)
+## Comparação das funções de ligação
+sapply(list("binomial" = m3HP, "poisson" = m3HP.pois,
+ "negbin" = m3HP.negb, "geometric" = m3HP.geom), logLik)
```
@@ -517,16 +487,20 @@ print(p3, split = c(3, 1, 3, 1), more = FALSE)
## Região para predição
pred <- expand.grid(sexo = c("M", "F"),
- valor = 1:200, expos = 1)
+ valor = 1:200,
+ expos = c(0.1, 0.5, 1))
##-------------------------------------------
## Estimando as médias
-aux <- predict(m3ZP, newdata = pred, type = "response")
+aux <- predict(m3HP, newdata = pred, type = "response")
predmu <- cbind(pred, fit = aux)
-xyplot(fit ~ valor,
+xyplot(fit ~ valor | factor(expos),
+ layout = c(NA, 1),
groups = sexo,
data = predmu,
type = c("l", "g"),
+ strip = strip.custom(strip.names = TRUE,
+ var.name = "Exposição"),
auto.key = list(
columns = 2, cex.title = 1,
lines = TRUE, points = FALSE,
@@ -539,9 +513,10 @@ xyplot(fit ~ valor,
##-------------------------------------------
## Estimando probabilidades
pred <- expand.grid(sexo = c("M", "F"),
- valor = c(1, 50), expos = 1)
+ valor = c(1, 50),
+ expos = 0.5)
-py <- c(t(predict(m3ZP, type = "prob", at = 0:5, newdata = pred)))
+py <- c(t(predict(m3HP, type = "prob", at = 0:5, newdata = pred)))
carac <- with(pred, paste("Sexo:", sexo, "Valor:", valor))
da <- data.frame(y = 0:5, py = py,
sexo = rep(c("M", "F"), each = 6),