# R/SummaryZOIPM.R In jucdiaz/ZOIP: ZOIP Distribution, ZOIP Regression, ZOIP Mixed Regression

#### Documented in summary.ZOIPM

```#' summary.ZOIPM
#'
#' Summarize a ZOIP model mixed.
#'
#' @param object An object of class \code{ZOIPM}.
#' @param ... other arguments.
#'
#' @examples
#'
#' library(ZOIP)
#'
#' N<-2
#' ni<-10
#' set.seed(12345)
#' 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)
#'
#'
#' n.points <- 3
#' pruning <- TRUE
#'
#' formula.mu=Y~Total_mora
#' formula.sigma=~Total_mora
#' formula.p0=~1
#' formula.p1=~1
#' formula.random= ~ 1 | Ciudad
#' optimizer<-'nlminb'
#' \donttest{
#' mod<-RMM.ZOIP(formula.mu=formula.mu,formula.sigma=formula.sigma,formula.p0=formula.p0,
#'               family=family,optimizer=optimizer,n.points=n.points,pruning=pruning)
#' summary(mod)
#' }
#'
#' @export

summary.ZOIPM<-function(object, ...){

estimate <- c(object\$Fixed_Parameters.mu,object\$Fixed_Parameters.sigma
,object\$Fixed_Parameters.p0,object\$Fixed_Parameters.p1,object\$Parameters.randoms[,1])
se       <- sqrt(diag(solve(object\$HM)))
zvalue   <- estimate / se
pvalue   <- 2 * stats::pnorm(abs(zvalue), lower.tail=F)
res      <- cbind(estimate=estimate, se=se, zvalue=zvalue, pvalue=pvalue)
colnames(res) <- c('Estimate', 'Std. Error', 'z value', 'Pr(>|z|)')
res      <- as.data.frame(res)

a <- 1:length(object\$Fixed_Parameters.mu)
b <-
(length(object\$Fixed_Parameters.mu) + 1):(length(object\$Fixed_Parameters.mu) +
length(object\$Fixed_Parameters.sigma))
c <-
(length(object\$Fixed_Parameters.mu) + length(object\$Fixed_Parameters.sigma) +
1):(
length(object\$Fixed_Parameters.mu) + length(object\$Fixed_Parameters.sigma) + length(object\$Fixed_Parameters.p0)
)
d <-
(
length(object\$Fixed_Parameters.mu) + length(object\$Fixed_Parameters.sigma) + length(object\$Fixed_Parameters.p0) +
1
):(
length(object\$Fixed_Parameters.mu) + length(object\$Fixed_Parameters.sigma) + length(object\$Fixed_Parameters.p0) +
length(object\$Fixed_Parameters.p1)
)
e <-
(
length(object\$Fixed_Parameters.mu) + length(object\$Fixed_Parameters.sigma) + length(object\$Fixed_Parameters.p0) +
length(object\$Fixed_Parameters.p1) + 1
):(
length(object\$Fixed_Parameters.mu) + length(object\$Fixed_Parameters.sigma) + length(object\$Fixed_Parameters.p0) +
length(object\$Fixed_Parameters.p1) + length(object\$Parameters.randoms[, 1])
)
cat("---------------------------------------------------------------\n")
cat(paste("Fixed effects for ",
cat("---------------------------------------------------------------\n")
stats::printCoefmat(res[a,], P.value=TRUE, has.Pvalue=TRUE)
cat("---------------------------------------------------------------\n")
cat(paste("Fixed effects for ",
cat("---------------------------------------------------------------\n")
stats::printCoefmat(res[b,], P.value=TRUE, has.Pvalue=TRUE)
cat("---------------------------------------------------------------\n")
cat(paste("Fixed effects for ",
cat("---------------------------------------------------------------\n")
stats::printCoefmat(res[c,], P.value=TRUE, has.Pvalue=TRUE)
cat("---------------------------------------------------------------\n")
cat(paste("Fixed effects for ",