Nothing
nongauss_y<- function( y_type = "continuous", y_nonneg = FALSE, tr_num = 0){
nongauss <-list( y_type = y_type, tr_num = tr_num, y_nonneg = y_nonneg, call = match.call() )
class( nongauss ) <- "nongauss_y"
return( nongauss=nongauss )
}
print.nongauss_y <- function(x, ...)
{
if(x$y_type=="continuous"){
if( (x$tr_num == 0) & (x$y_nonneg == FALSE) ){
cat("y ~ N( xb, sig )\n")
cat(" \n")
cat(" - N(): Normal distribution\n")
cat(" - xb : Regression term with fixed and random coefficients in b\n")
cat(" which is specified by resf or resf_vc function\n")
cat(" - sig: Variance parameter\n")
} else if( (x$tr_num == 0) & (x$y_nonneg == TRUE) ){
cat("Box-cox transformation is applied to y to estimate\n")
cat("y ~ P( xb, par ) (or f(y, par)~N(xb, sig) where f() denotes transformation)\n")
cat(" \n")
cat(" - P(): Distribution optimized through the transformation\n")
cat(" - xb : Regression term with fixed and random coefficients in b\n")
cat(" which is specified by resf or resf_vc function\n")
cat(" - par: Parameter estimating data distribution\n")
} else if( x$tr_num > 0 ){
if( x$y_nonneg == FALSE ){
mess0<-ifelse(x$tr_num==1,"1 SAL transformation is",
paste(x$tr_num," SAL transformation functions are",sep=""))
} else {
mess0<-paste("Box-Cox and ",x$tr_num," SAL transformations are",sep="")
}
mess <-paste(mess0, " applied to y to estimate",sep="")
cat(paste(mess,"\n",sep=""))
cat("y ~ P( xb, par ) (or f(y,par)~N(xb, sig), where f() denotes transformation)\n")
cat(" \n")
cat(" - P(): Distribution optimized through the transformations\n")
cat(" - xb : Regression term with fixed and random coefficients in b\n")
cat(" which is specified by resf or resf_vc function\n")
cat(" - par: Parameters estimating data distribution\n")
}
} else if( x$y_type == "count" ){
if( x$tr_num == 0 ){
cat("Log-Gaussian approximation estimating\n")
cat("y ~ oPois( mu, sig ), mu = exp( xb )\n")
cat(" \n")
cat(" - oPois(): Overdispersed Poisson distribution\n")
cat(" - xb : Regression term with fixed and random coefficients in b\n")
cat(" which is specified by resf or resf_vc function\n")
cat(" - sig : Dispersion parameter (overdispersion if sig > 1)\n")
} else {
mess0<-paste("Log-Gaussian and ",x$tr_num," SAL transformations are applied to y to estimate",sep="")
cat(paste(mess0,"\n",sep=""))
cat(" y ~ P( mu, par ), mu = exp( xb )\n")
cat(" \n")
cat(" - P(): Distribution optimized through the transformations\n")
cat(" - xb : Regression term with fixed and random coefficients in b\n")
cat(" which is specified by resf or resf_vc function\n")
cat(" - par: Parameters estimating data distribution\n")
}
}
invisible(x)
}
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