R/post.check.R

In SemiParBIVProbit: Semiparametric Copula Regression Models

post.check <- function(x, main = "Histogram and Density Estimate of Residuals", 
                          main2 = "Histogram and Density Estimate of Residuals",
                          xlab = "Quantile Residuals", xlab2 = "Quantile Residuals", 
                          intervals = FALSE, n.sim = 100, prob.lev = 0.05, ...){

if(x$triv == TRUE ) stop("This function is not suitable for trivariate probit models.")
y1m <- y2m <- NA
qr <- qr1 <- qr2 <- NULL


if(x$univar.gamlss == FALSE){###


if(x$surv.flex == TRUE){ ###

par(mfrow = c(2, 2))

qr1 <- -log(x$fit$p1)
H1  <- -log(survfit(Surv(qr1, x$cens1) ~ 1,  type = "kaplan-meier", conf.type = "none")$surv)

hist(qr1, freq = FALSE, main = main, xlab = "Cox-Snell residuals", ylab = "Density", ...)
lines(density(qr1, adjust = 2), lwd = 2)

qqplot(qr1, H1, xlab = "Cox-Snell residuals", ylab = "Cumulative Hazards of residuals")
abline(0, 1, col = "red")

qr2 <- -log(x$fit$p2)
H2  <- -log(survfit(Surv(qr2, x$cens2) ~ 1,  type = "kaplan-meier", conf.type = "none")$surv)

hist(qr2, freq = FALSE, main = main, xlab = "Cox-Snell residuals", ylab = "Density", ...)
lines(density(qr2, adjust = 2), lwd = 2)

qqplot(qr2, H2, xlab = "Cox-Snell residuals", ylab = "Cumulative Hazards of residuals")
abline(0, 1, col = "red")



}###




if(x$surv.flex == FALSE){##


if(x$Cont == "NO"){


y2     <- x$y2
eta2   <- x$eta2
sigma2 <- x$sigma2
nu     <- x$nu



if(x$VC$margins[2] %in% c("ZTP")){
    ly2 <- length(y2)
    y2m <- list()
    my2 <- max(y2)
    for(i in 1:ly2){ y2m[[i]] <- seq(0, y2[i]); length(y2m[[i]]) <- my2+1} 
    y2m <- do.call(rbind, y2m)     
                                 }





if(x$VC$ccss == "yes"){
   eta2 <- eta2[x$inde]
   if(!is.null(x$X3) && !(x$VC$margins[2] %in% x$VC$m1d) ){
   sigma2 <- sigma2[x$inde]
   nu     <- nu[x$inde]
                                                          }
                      }
                      
                      




if(x$VC$margins[2] %in% c(x$VC$m2,x$VC$m3)){p <- distrHsAT(y2, eta2, sigma2, nu, x$margins[2])$p2
                                            qr <- qnorm(p)
                                            } 

if(x$VC$margins[2] %in% c(x$VC$m1d,x$VC$m2d,x$VC$m3d)){ 
                                            pd <- distrHsATDiscr(y2, eta2, sigma2, nu, x$margins[2], y2m = y2m) 
                                            p <- pd$p2
                                            d <- pd$pdf2  
                                            if(intervals == TRUE) set.seed(100)
                                            qr <- qnorm( runif(y2, p - d, p) )                                           
                                            } 
                                                    
if(x$VC$ccss == "yes") qr <- qr - mean(qr) 



par(mfrow = c(1, 2))

hist(qr, freq = FALSE, main = main, xlab = xlab, ylab = "Density", ...)
lines(density(qr, adjust = 2), lwd = 2)

if(intervals == FALSE){qqnorm(qr); abline(0, 1, col = "red")}
if(intervals == TRUE) int.postcheck(x, x$VC$margins[2], n.rep = n.sim, prob.lev = prob.lev, y2m = y2m)

                    




}




if(x$Cont == "YES"){

y1 <- x$y1
y2 <- x$y2


if(x$VC$margins[1] %in% c("ZTP")){
     
    ly1 <- length(y1)
    y1m <- list()
    my1 <- max(y1)
    for(i in 1:ly1){ y1m[[i]] <- seq(0, y1[i]); length(y1m[[i]]) <- my1+1} 
    y1m <- do.call(rbind, y1m)     
     
}


if(x$VC$margins[2] %in% c("ZTP")){
     
    ly2 <- length(y2)
    y2m <- list()
    my2 <- max(y2)
    for(i in 1:ly2){ y2m[[i]] <- seq(0, y2[i]); length(y2m[[i]]) <- my2+1} 
    y2m <- do.call(rbind, y2m)     
     
}









if(x$VC$margins[1] %in% c(x$VC$m2,x$VC$m3))   p1 <- distrHsAT(x$y1, x$eta1, x$sigma21, x$nu1, x$margins[1])$p2 
if(x$VC$margins[1] %in% c(x$VC$m1d,x$VC$m2d,x$VC$m3d)){

pd <- distrHsATDiscr(x$y1, x$eta1, x$sigma21, x$nu1, x$margins[1], y2m = y1m) 
p <- pd$p2
d <- pd$pdf2   

set.seed(100)
p1 <- runif(y1, p - d, p) 

}



if(x$VC$margins[2] %in% c(x$VC$m2,x$VC$m3))   p2 <-      distrHsAT(x$y2, x$eta2, x$sigma22, x$nu2, x$margins[2])$p2 
if(x$VC$margins[2] %in% c(x$VC$m1d,x$VC$m2d,x$VC$m3d)){

pd <- distrHsATDiscr(x$y2, x$eta2, x$sigma22, x$nu2, x$margins[2], y2m = y2m)
p <- pd$p2
d <- pd$pdf2   

set.seed(100)
p2 <- runif(y2, p - d, p) 

}


par(mfrow = c(2, 2))

qr1 <- qnorm(p1)
hist(qr1, freq = FALSE, main = main, xlab = xlab, ylab = "Density", ...)
lines(density(qr1, adjust = 2),lwd=2)


if(intervals == FALSE){qqnorm(qr1); abline(0, 1, col = "red")}
if(intervals == TRUE) int.postcheck(x, x$VC$margins[1], n.rep = n.sim, prob.lev = prob.lev, y2m = y1m, eq = 1)


qr2 <- qnorm(p2)
hist(qr2, freq = FALSE, main = main2, xlab = xlab2, ylab = "Density", ...)
lines(density(qr2, adjust = 2),lwd=2)

if(intervals == FALSE){qqnorm(qr2); abline(0, 1, col = "red")}
if(intervals == TRUE) int.postcheck(x, x$VC$margins[2], n.rep = n.sim, prob.lev = prob.lev, y2m = y2m, eq = 2)

}


}##

}###




if(x$univar.gamlss == TRUE){

if(x$surv.flex == FALSE){ ###

if(x$VC$margins[1] %in% c("GEVlink") ) stop("It does not make much sense to check the residuals for a binary response model.")

y1 <- x$y1

if(x$VC$margins[1] %in% c("ZTP")){    
    ly1 <- length(y1)
    y1m <- list()
    my1 <- max(y1)
    for(i in 1:ly1){ y1m[[i]] <- seq(0, y1[i]); length(y1m[[i]]) <- my1+1} 
    y1m <- do.call(rbind, y1m)         
                                 }


if(x$VC$margins[1] %in% c(x$VC$m2,x$VC$m3)) p1 <- distrHsAT(x$y1, x$eta1, x$sigma2, x$nu, x$margins[1])$p2 
if(x$VC$margins[1] %in% c(x$VC$m1d,x$VC$m2d,x$VC$m3d)){
      pd <- distrHsATDiscr(x$y1, x$eta1, x$sigma2, x$nu, x$margins[1], y2m = y1m) 
      p <- pd$p2
      d <- pd$pdf2   
      p1 <- runif(y1, p - d, p) 
                                                      }

par(mfrow = c(1, 2))

qr <- qnorm(p1)
hist(qr, freq = FALSE, main = main, xlab = xlab, ylab = "Density", ...)
lines(density(qr, adjust = 2), lwd = 2)

if(intervals == FALSE){qqnorm(qr); abline(0, 1, col = "red")}
if(intervals == TRUE) int.postcheck(x, x$VC$margins[1], n.rep = n.sim, prob.lev = prob.lev, y2m = y1m)

}###


if(x$surv.flex == TRUE){ ###

par(mfrow = c(1, 2))

qr <- -log(x$fit$p1)
H  <- -log(survfit(Surv(qr, x$cens) ~ 1,  type = "kaplan-meier", conf.type = "none")$surv)

hist(qr, freq = FALSE, main = main, xlab = "Cox-Snell residuals", ylab = "Density", ...)
lines(density(qr, adjust = 2), lwd = 2)

qqplot(qr, H, xlab = "Cox-Snell residuals", ylab = "Cumulative Hazards of residuals")
abline(0, 1, col = "red")

}###


}


L <- list(qr = qr, qr1 = qr1, qr2 = as.numeric(qr2))

invisible(L)


}