R/PrintZOIPM.R

In ZOIP: ZOIP Distribution, ZOIP Regression, ZOIP Mixed Regression

#' print.ZOIPM
#'
#' print a ZOIP model mixed.
#'
#' @param x An object of class \code{ZOIPM}.
#' @param ... other arguments.
#'
#' @examples
#'
#' library(ZOIP)
#'
#' N<-2
#' ni<-10
#' set.seed(12345)
#' Ciudad <- rep(1:N, each=ni)
#' Total_mora<-rexp(N*ni,rate=1)
#' set.seed(12345)
#' b0i <- rep(rnorm(n=N,sd=0.5), each=ni)
#' set.seed(12345)
#' b1i <- rep(rnorm(n=N,sd=0.4), each=ni)
#'
#' neta <- (-1.13+b0i)+0.33*Total_mora
#' neta2<-(0.33+b1i)+0.14*Total_mora
#'
#' mu <- 1 / (1 + exp(-neta))
#' sigma <- 1 / (1 + exp(-neta2))
#'
#' p0 <- 0.05
#' p1 <- 0.05
#'
#' mu[mu==1] <- 0.999
#' mu[mu==0] <- 0.001
#'
#' sigma[sigma==1] <- 0.999
#' sigma[sigma==0] <- 0.001
#' family<-'R-S'
#' set.seed(12345)
#' Y <- rZOIP(n=length(mu), mu = mu, sigma = sigma ,p0=p0,p1=p1,family=family)
#'
#' data_sim<-data.frame(Y,Total_mora,Ciudad)
#'
#' n.points <- 3
#' pruning <- TRUE
#'
#' formula.mu=Y~Total_mora
#' formula.sigma=~Total_mora
#' formula.p0=~1
#' formula.p1=~1
#' formula.random= ~ 1 | Ciudad
#' link=c('logit','logit','identity','identity')
#' optimizer<-'nlminb'
#' \donttest{
#' mod<-RMM.ZOIP(formula.mu=formula.mu,formula.sigma=formula.sigma,formula.p0=formula.p0,
#'               formula.p1=formula.p1,data=data_sim,formula.random=formula.random,link=link,
#'               family=family,optimizer=optimizer,n.points=n.points,pruning=pruning)
#' mod
#' }
#'
#' @export

print.ZOIPM<-function(x, ...){

  cat("Call:\n")
  print(x$call)
  cat("\n Results: \n")
  cat("\n Estimated fixed coefficients for h(mu): \n")
  print(x$Fixed_Parameters.mu)
  cat("\n Estimated fixed coefficients for h(sigma): \n")
  print(x$Fixed_Parameters.sigma)
  cat("\n Estimated fixed coefficients for h(p0): \n")
  print(x$Fixed_Parameters.p0)
  cat("\n Estimated fixed coefficients for h(p1): \n")
  print(x$Fixed_Parameters.p1)
  cat("\n Estimated random coefficients for h(mu) and h(sigma) \n")
  print(x$Parameters.randoms)
  cat("\n message \n")
  print(x$message)
  cat("\n time \n")
  print(x$Time)
  cat("\n iterations \n")
  print(x$num.iter)
  cat("\n Log-likelihood \n")
  print(x$logverosimilitud)

}