Aletarcoes gerais

R/mc_plot.mcglm.R

+#' Default Multivariate Covariance Generalized Linear models plotting
+#'
+#' @description takes a fitted mcglm object by \code{mcglm()} and plots, residuals,
+#' influence diagnostic measures and algorithm check.
+#'
+#' @param object a fitted mcglm object as produced by \code{mcglm()}.
+#' @param type Specify which graphical analysis will be performed, options are: residuals, influence
+#' and algorithm.
+#' @exportMethod
+plot.mcglm <- function(object, type = "residuals") {
+  n_resp <- length(object$beta_names)
+  if(type == "residuals") {
+    par(mar=c(2.6, 2.5, 0.1, 0.1), mgp = c(1.6, 0.6, 0), mfrow = c(2,n_resp))
+    for(i in 1:n_resp){
+      plot(residuals(object, type = "pearson")[,i] ~ fitted(object)[,i],
+           ylab = "Pearson residuals", xlab = "Fitted values")
+      temp <- loess.smooth(fitted(object)[,i],residuals(object, type = "pearson")[,i])
+      lines(temp$x,temp$y)
+    }
+    for(i in 1:n_resp) {
+      plot(residuals(object, type = "pearson")[,i] ~ object$observed[,i],
+           ylab = "Pearson residuals", xlab = "Observed values")
+      temp <- loess.smooth(object$observed[,i],residuals(object, type = "pearson")[,i])
+      lines(temp$x,temp$y)
+    }
+  }
+  if(type == "algorithm") {
+    n_iter <- length(na.exclude(object$IterationCovariance[,1]))
+    par(mar=c(2.6, 2.5, 0.1, 0.1), mgp = c(1.6, 0.6, 0), mfrow = c(2,2))
+    matplot(object$IterationRegression[1:c(n_iter+5),], type = "l", lty = 2, ylab = "Regression",
+            xlab = "Iterations")
+    matplot(object$IterationCovariance[1:c(n_iter+5),], type = "l", lty = 2, ylab = "Covariance",
+            xlab = "Iterations")
+    matplot(object$ScoreRegression[1:c(n_iter+5),], type = "l", lty = 2, ylab = "Quasi-score Regression",
+            xlab = "Iterations")
+    matplot(object$ScoreCovariance[1:c(n_iter+5),], type = "l", lty = 2, ylab = "Quasi-score Covariance",
+            xlab = "Iterations")
+    }
+}

R/mc_summary.mcglm.R

+#' Summarizing Multivariate Covariance Generalized Linear Models fits.
+#'
+#' @description Summary for mcglm objects.
+#'
+#' @param object an object of class mcglm, usually, a result of a call to \code{mcglm}.
+#' @exportMethod
+
+summary.mcglm <- function(object) {
+  n_resp <- length(object$beta_names)
+  output <- list()
+  for(i in 1:n_resp) {
+    cat("Call: ")
+    print(object$linear_pred[[i]])
+    cat("\n")
+    cat("Link function:", object$link[[i]])
+    cat("\n")
+    cat("Variance function:", object$variance[[i]])
+    cat("\n")
+    cat("Covariance function:", object$covariance[[i]])
+    cat("\n")
+    cat("Regression:\n")
+    tab_beta <- coef(object, std.error = TRUE, response = i, type = "beta")[,1:2]
+    tab_beta$"z value" <- tab_beta[,1]/tab_beta[,2]
+    rownames(tab_beta) <- object$beta_names[[i]]
+    output[i][[1]]$Regression <- tab_beta
+    print(tab_beta)
+    cat("\n")
+    cat("Power:\n")
+    tab_power <- coef(object, std.error = TRUE, response = i, type = "power")[,1:2]
+    tab_power$'z value' <- tab_power[,1]/tab_power[,2]
+    rownames(tab_power) <- NULL
+    output[i][[1]]$Power <- tab_power
+    print(tab_power)
+    cat("\n")
+    cat("tau:\n")
+    tab_tau <- coef(object, std.error = TRUE, response = i, type = "tau")[,1:2]
+    tab_tau$'z value' <- tab_tau[,1]/tab_tau[,2]
+    rownames(tab_tau) <- NULL
+    output[i][[1]]$tau <- tab_tau
+    print(tab_tau)
+    cat("\n")
+  }
+  cat("Correlation matrix:\n")
+  tab_rho <- coef(object, std.error = TRUE, response = NA, type = "correlation")[,c(3,1,2)]
+  tab_rho$'z value' <- tab_rho[,2]/tab_rho[,3]
+  print(tab_rho)
+  cat("\n")
+  cat("Algorithm:")
+  print(object$con$method)
+  cat("Correction:")
+  print(object$con$correct)
+  cat("Number iterations:")
+  iteration_cov <- length(na.exclude(object$IterationCovariance[,1]))
+  print(iteration_cov)
+}
+

man/confint.mcglm.Rd

+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/mc_confint.mcglm.R
+\name{confint.mcglm}
+\alias{confint.mcglm}
+\title{Confidence Intervals for mcglm}
+\usage{
+\method{confint}{mcglm}(object, level = 0.95)
+}
+\arguments{
+\item{object}{a fitted mcglm object.}
+
+\item{level}{the confidence level required.}
+}
+\value{
+A data.frame with confidence intervals, parameters names,
+response number and parameters type.
+}
+\description{
+Computes confidence intervals for parameters in a fitted mcglm.
+}
+

man/mc_updateBeta.Rd

+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/mc_updatedBeta.R
+\name{mc_updateBeta}
+\alias{mc_updateBeta}
+\title{Updated regression parameters}
+\usage{
+mc_updateBeta(list_initial, betas, information, n_resp)
+}
+\arguments{
+\item{list_initial}{A list of initial values.}
+
+\item{betas}{A vector with actual regression parameters values.}
+
+\item{information}{A list with information about the number of parameters in the model. In general
+the output from \code{[mcglm]{mc_getInformation}}.}
+
+\item{n_resp}{A numeric specyfing the number of response variables.}
+}
+\value{
+A list with updated values of the regression parameters.
+}
+\description{
+This function update a list of regression parameters. It will be useful only inside the
+fitting algorithm.
+}
+

man/mc_updateCov.Rd

+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/mc_updatedCov.R
+\name{mc_updateCov}
+\alias{mc_updateCov}
+\title{Updated covariance parameters}
+\usage{
+mc_updateCov(list_initial, covariance, list_power_fixed, information, n_resp)
+}
+\arguments{
+\item{list_initial}{A list of initial values.}
+
+\item{covariance}{A vector with actual covariance parameters values.}
+
+\item{list_power_fixed}{A list of logicals indicating if the power parameter should be
+estimated or not.}
+
+\item{information}{A list with information about the number of parameters in the model. In general
+the output from \code{[mcglm]{mc_getInformation}}.}
+
+\item{n_resp}{A numeric specyfing the number of response variables.}
+}
+\value{
+A list with updated values of the covariance parameters.
+}
+\description{
+This function update a list of covariance parameters. It will be useful only inside
+the fitting algorithm.
+}
+

man/plot.mcglm.Rd

+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/mc_plot.mcglm.R
+\name{plot.mcglm}
+\alias{plot.mcglm}
+\title{Default Multivariate Covariance Generalized Linear models plotting}
+\usage{
+\method{plot}{mcglm}(object, type = "residuals")
+}
+\arguments{
+\item{object}{a fitted mcglm object as produced by \code{mcglm()}.}
+
+\item{type}{Specify which graphical analysis will be performed, options are: residuals, influence
+and algorithm.}
+}
+\description{
+takes a fitted mcglm object by \code{mcglm()} and plots, residuals,
+influence diagnostic measures and algorithm check.
+}
+

man/print.mcglm.Rd

+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/mc_print.mcglm.R
+\name{print.mcglm}
+\alias{print}
+\alias{print.mcglm}
+\title{Print a Multivariate Covariance Generalized Linear Model}
+\usage{
+print.mcglm(object)
+}
+\arguments{
+\item{object}{fitted model objects of class mcglm as produced by mcglm().}
+}
+\description{
+The default print method for a mcglm object.
+}
+

man/summary.mcglm.Rd

+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/mc_summary.mcglm.R
+\name{summary.mcglm}
+\alias{summary.mcglm}
+\title{Summarizing Multivariate Covariance Generalized Linear Models fits.}
+\usage{
+summary.mcglm(object)
+}
+\arguments{
+\item{object}{an object of class mcglm, usually, a result of a call to \code{mcglm}.}
+}
+\description{
+Summary for mcglm objects.
+}
+