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R/display.R
In arm: Data Analysis Using Regression and Multilevel/Hierarchical Models
setMethod("display", signature(object = "lm"),
function(object, digits=2, detail=FALSE)
{
out <- NULL
out$call <- object$call
summ <- summary (object)
out$sigma.hat <- summ$sigma
out$r.squared <- summ$r.squared
if(detail){
coef <- summ$coef[,,drop=FALSE]
}
else{
coef <- summ$coef[,1:2,drop=FALSE]
}
dimnames(coef)[[2]][1:2] <- c("coef.est","coef.se")
out$coef <- coef[,"coef.est"]#,drop=FALSE]
out$se <- coef[,"coef.se"]#,drop=FALSE]
out$t.value <- summ$coef[,3]
out$p.value <- summ$coef[,4]
out$n <- summ$df[1] + summ$df[2]
out$k <- summ$df[1]
print (out$call)
pfround (coef, digits)
cat("---\n")
cat (paste ("n = ", out$n, ", k = ", out$k,
"\nresidual sd = ", fround (out$sigma.hat, digits),
", R-Squared = ", fround (out$r.squared, 2), "\n", sep=""))
return(invisible(out))
}
)
setMethod("display", signature(object = "bayesglm"),
function(object, digits=2, detail=FALSE)
{
out <- NULL
out$call <- object$call
summ <- summary(object, dispersion = object$dispersion)
if(detail){
coef <- summ$coefficients
coef[ rownames( coef ) %in% rownames( summ$coef[, , drop = FALSE]) , ] <- summ$coef[ , , drop = FALSE ]
out$z.value <- coef[,3]#,drop=FALSE]
out$p.value <- coef[,4]#,drop=FALSE]
}
else{
coef <- matrix( NA, length( object$coefficients ),2 )
rownames(coef) <- names( object$coefficients ) ## M
coef[ rownames( coef ) %in% rownames( summ$coef[, 1:2, drop = FALSE]) , ] <- summ$coef[ , 1:2, drop = FALSE ] ## M
}
dimnames(coef)[[2]][1:2] <- c( "coef.est", "coef.se")
out$coef <- coef[,"coef.est"]#,drop=FALSE]
out$se <- coef[,"coef.se"]#,drop=FALSE]
out$n <- summ$df[1] + summ$df[2]
out$k <- summ$df[1]
out$deviance <- summ$deviance
out$null.deviance <- summ$null.deviance
print(out$call)
pfround(coef, digits)
cat("---\n")
cat(paste("n = ", out$n, ", k = ", out$k, "\nresidual deviance = ",
fround(out$deviance, 1), ", null deviance = ", fround(out$null.deviance, 1), " (difference = ", fround(out$null.deviance - out$deviance, 1), ")", "\n", sep = ""))
out$dispersion <- if (is.null(object$dispersion)){
summ$dispersion
} else {
object$dispersion
}
if (out$dispersion != 1) {
out$overdispersion.parameter <- out$dispersion
cat(paste("overdispersion parameter = ", fround(out$dispersion, 1), "\n", sep = ""))
if (family(object)$family == "gaussian") {
out$sigma.hat <- sqrt(out$dispersion)
cat(paste("residual sd is sqrt(overdispersion) = ", fround(out$sigma.hat, digits), "\n", sep = ""))
}
}
return(invisible(out))
}
)
#setMethod("display", signature(object = "bayesglm.h"),
# function (object, digits = 2, detail = FALSE)
# {
# call <- object$call
# summ <- summary(object, dispersion = object$dispersion)
# if(detail){
# coef <- summ$coefficients
# coef[ rownames( coef ) %in% rownames( summ$coef[, , drop = FALSE]) , ] <- summ$coef[ , , drop = FALSE ]
# }
# else{
# coef <- matrix( NA, length( object$coefficients ),2 )
# rownames(coef) <- names( object$coefficients ) ## M
# coef[ rownames( coef ) %in% rownames( summ$coef[, 1:2, drop = FALSE]) , ] <- summ$coef[ , 1:2, drop = FALSE ] ## M
# }
# dimnames(coef)[[2]][1:2] <- c( "coef.est", "coef.se")
# #n <- summ$df[1] + summ$df[2]
# n <- summ$df.residual
# k <- summ$df[1]
# print(call)
# if(max(object$batch)>0){
# nn<- strsplit( rownames( coef )[seq( from= length( object$batch ) + 1 ,to = nrow( coef ))], "." , fixed=TRUE)
# bb<- c( object$batch,unlist( lapply (nn , function( lst ) { lst[[3]] } ) ) )
# }
# else {bb<- c( object$batch)}
# cc<- cbind( fround( coef, digits ), bb )
# dimnames(cc)[[2]][3]<-"batch"
# print( cc , quote = FALSE )
# cat("---\n")
# cat(paste("n = ", n, ", k = ", k, "\nresidual deviance = ",
# fround(summ$deviance, 1), ", null deviance = ", fround(summ$null.deviance,
# 1), " (difference = ", fround(summ$null.deviance -
# summ$deviance, 1), ")", "\n", sep = ""))
# dispersion <- if (is.null(object$dispersion))
# summ$dispersion
# else object$dispersion
# if (dispersion != 1) {
# cat(paste("overdispersion parameter = ", fround(dispersion,
# 1), "\n", sep = ""))
# if (family(object)$family == "gaussian") {
# cat(paste("residual sd is sqrt(overdispersion) = ",
# fround(sqrt(dispersion), digits), "\n", sep = ""))
# cat(paste("group sd is sigma.batch = ",
# fround(object$sigma.batch, digits), "\n", sep = ""))
# }
# }
# }
#)
setMethod("display", signature(object = "glm"),
function(object, digits=2, detail=FALSE)
{
out <- NULL
out$call <- object$call
summ <- summary(object, dispersion = object$dispersion)
if(detail){
coef <- summ$coef[, , drop = FALSE]
out$z.value <- coef[,3]#,drop=FALSE]
out$p.value <- coef[,4]#,drop=FALSE]
}
else{
coef <- summ$coef[, 1:2, drop = FALSE]
}
dimnames(coef)[[2]][1:2] <- c("coef.est", "coef.se")
out$n <- summ$df[1] + summ$df[2]
out$k <- summ$df[1]
out$coef <- coef[,"coef.est"]
out$se <- coef[,"coef.se"]
print(out$call)
pfround(coef, digits)
out$deviance <- summ$deviance
out$null.deviance <- summ$null.deviance
cat("---\n")
cat(paste(" n = ", out$n, ", k = ", out$k, "\n residual deviance = ",
fround(out$deviance, 1), ", null deviance = ", fround(out$null.deviance, 1), " (difference = ", fround(summ$null.deviance - summ$deviance, 1), ")", "\n", sep = ""))
out$dispersion <- if (is.null(object$dispersion)){
summ$dispersion
} else {
object$dispersion
}
if (out$dispersion != 1) {
cat(paste(" overdispersion parameter = ", fround(out$dispersion, 1), "\n", sep = ""))
if (family(object)$family=="gaussian") {
out$sigma.hat <- sqrt(out$dispersion)
cat(paste(" residual sd is sqrt(overdispersion) = ",
fround(out$sigma.hat, digits), "\n", sep = ""))
}
}
return(invisible(out))
}
)
#setMethod("display", signature(object = "mer"),
# function(object, digits=2)
# {
# call <- object@call
# print (call)
# #object <- summary(object)
# fcoef <- fixef(object)
# useScale <- attr( VarCorr(object), "sc")
# corF <- vcov(object)@factors$correlation
# coefs <- cbind(fcoef, corF@sd)
# if (length (fcoef) > 0){
# dimnames(coefs) <- list(names(fcoef), c("coef.est", "coef.se"))
# pfround (coefs, digits)
# }
# cat("\nError terms:\n")
# vc <- as.matrix.VarCorr (VarCorr (object), useScale=useScale, digits)
# print (vc[,c(1:2,4:ncol(vc))], quote=FALSE)
# ngrps <- lapply(object@flist, function(x) length(levels(x)))
# REML <- object@status["REML"]
# llik <- logLik(object, REML)
# AIC <- AIC(llik)
# dev <- object@deviance["ML"] # Dbar
# n <- object@devComp["n"]
# Dhat <- -2*(llik) # Dhat
# pD <- dev - Dhat # pD
# DIC <- dev + pD # DIC=Dbar+pD=Dhat+2pD
# cat("---\n")
# cat(sprintf("number of obs: %d, groups: ", n))
# cat(paste(paste(names(ngrps), ngrps, sep = ", "), collapse = "; "))
# cat(sprintf("\nAIC = %g, DIC = ", fround(AIC, 1)))
# cat(fround(DIC, 1))
# cat("\ndeviance =", fround (dev, 1), "\n")
# if (useScale < 0){
# cat("overdispersion parameter =", fround (.Call("mer_sigma",
# object, FALSE, PACKAGE = "lme4"), 1), "\n")
# }
# }
#)
setMethod("display", signature(object = "merMod"),
function(object, digits=2, detail=FALSE)
{
out <- NULL
out$call <- object@call
print (out$call)
#object <- summary(object)
#summ <- summary(object)
fcoef <- fixef(object)
#coefs <- attr(summ, "coefs")
#useScale <- attr (VarCorr (object), "sc")
useScale <- getME(object, "devcomp")$dims["useSc"]
corF <- vcov(object)@factors$correlation
coefs <- cbind(fcoef, corF@sd)
if (length (fcoef) > 0){
if (!useScale) {
coefs <- coefs[, 1:2, drop = FALSE]
out$z.value <- coefs[, 1]/coefs[, 2]
out$p.value <- 2 * pnorm(abs(out$z.value), lower.tail = FALSE)
coefs <- cbind(coefs, `z value` = out$z.value, `Pr(>|z|)` = out$p.value)
}
else {
out$t.value <- coefs[, 1]/coefs[, 2]
coefs <- cbind(coefs, `t value` = out$t.value)
}
dimnames(coefs)[[2]][1:2] <- c("coef.est", "coef.se")
if(detail){
pfround (coefs, digits)
}
else{
pfround(coefs[,1:2], digits)
}
}
out$coef <- coefs[,"coef.est"]
out$se <- coefs[,"coef.se"]
cat("\nError terms:\n")
vc <- as.matrix.VarCorr (VarCorr (object), useScale=useScale, digits=digits)
print (vc[,c(1:2,4:ncol(vc))], quote=FALSE)
out$ngrps <- lapply(object@flist, function(x) length(levels(x)))
is_REML <- isREML(object)
llik <- logLik(object, REML=is_REML)
out$AIC <- AIC(llik)
out$deviance <- deviance(refitML(object)) # Dbar
out$n <- getME(object, "devcomp")$dims["n"]
Dhat <- -2*(llik) # Dhat
pD <- out$deviance - Dhat # pD
out$DIC <- out$deviance + pD # DIC=Dbar+pD=Dhat+2pD
cat("---\n")
cat(sprintf("number of obs: %d, groups: ", out$n))
cat(paste(paste(names(out$ngrps), out$ngrps, sep = ", "), collapse = "; "))
cat(sprintf("\nAIC = %g, DIC = ", round(out$AIC,1)))
cat(round(out$DIC, 1))
cat("\ndeviance =", fround (out$deviance, 1), "\n")
if (useScale < 0){
out$sigma.hat <- .Call("mer_sigma", object, FALSE, PACKAGE = "lme4")
cat("overdispersion parameter =", fround (out$sigma.hat, 1), "\n")
}
return(invisible(out))
}
)
setMethod("display", signature(object = "polr"),
function(object, digits=2, detail=FALSE)
{
out <- NULL
out$call <- object$call
summ <- summary(object)
if(detail){
coef <- summ$coef[, , drop = FALSE]
out$t.value <- coef[,"t value"]
}
else{
coef <- summ$coef[, 1:2, drop = FALSE]
}
dimnames(coef)[[2]][1:2] <- c("coef.est", "coef.se")
out$coef <- coef[,"coef.est"]
out$se <- coef[,"coef.se"]
out$n <- summ$n
out$k <- nrow (coef)
out$k.intercepts <- length (summ$zeta)
print(out$call)
pfround(coef, digits)
cat("---\n")
cat(paste("n = ", out$n, ", k = ", out$k, " (including ", out$k.intercepts,
" intercepts)\nresidual deviance = ",
fround(deviance(object), 1),
", null deviance is not computed by polr",
"\n", sep = ""))
#cat("AIC:", fround(AIC(object), 1), "\n")
return(invisible(out))
}
)
setMethod("display", signature(object = "svyglm"),
function(object, digits=2, detail=FALSE)
{
out <- NULL
out$call <- object$call
out$survey.design <- object$survey.design
summ <- summary(object)
if(detail){
coef <- summ$coef[, , drop = FALSE]
out$z.value <- coef[,3]#,drop=FALSE]
out$p.value <- coef[,4]#,drop=FALSE]
}
else{
coef <- summ$coef[, 1:2, drop = FALSE]
}
dimnames(coef)[[2]][1:2] <- c("coef.est", "coef.se")
out$n <- summ$df[1] + summ$df[2]
out$k <- summ$df[1]
out$coef <- coef[,"coef.est"]
out$se <- coef[,"coef.se"]
print(out$call)
cat("\n")
print(out$survey.design)
cat("\n")
pfround(coef, digits)
out$deviance <- summ$deviance
out$null.deviance <- summ$null.deviance
cat("---\n")
cat(paste(" n = ", out$n, ", k = ", out$k, "\n residual deviance = ",
fround(out$deviance, 1), ", null deviance = ", fround(out$null.deviance, 1), " (difference = ", fround(summ$null.deviance - summ$deviance, 1), ")", "\n", sep = ""))
out$dispersion <- summ$dispersion[1]
if (out$dispersion != 1) {
cat(paste(" overdispersion parameter = ", fround(out$dispersion, 1), "\n", sep = ""))
if (family(object)$family=="gaussian") {
out$sigma.hat <- sqrt(out$dispersion)
cat(paste(" residual sd is sqrt(overdispersion) = ",
fround(out$sigma.hat, digits), "\n", sep = ""))
}
}
return(invisible(out))
}
)
#setMethod("display", signature(object = "bayespolr"),
# function(object, digits=2)
# {
# call <- object$call
# summ <- summary(object)
# coef <- summ$coef[, 1:2, drop = FALSE]
# dimnames(coef)[[2]] <- c("coef.est", "coef.se")
# n <- summ$n # or maybe should be "nobs", I don't know for sure
# k <- nrow (coef)
# k.intercepts <- length (summ$zeta)
# print(call)
# pfround(coef, digits)
# cat("---\n")
# cat(paste("n = ", n, ", k = ", k, " (including ", k.intercepts,
# " intercepts)\nresidual deviance = ",
# fround(summ$deviance, 1),
# ", null deviance is not computed by bayespolr",
# "\n", sep = ""))
# }
#)
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setMethod("display", signature(object = "lm"),
function(object, digits=2, detail=FALSE)
{
out <- NULL
out$call <- object$call
summ <- summary (object)
out$sigma.hat <- summ$sigma
out$r.squared <- summ$r.squared
if(detail){
coef <- summ$coef[,,drop=FALSE]
}
else{
coef <- summ$coef[,1:2,drop=FALSE]
}
dimnames(coef)[[2]][1:2] <- c("coef.est","coef.se")
out$coef <- coef[,"coef.est"]#,drop=FALSE]
out$se <- coef[,"coef.se"]#,drop=FALSE]
out$t.value <- summ$coef[,3]
out$p.value <- summ$coef[,4]
out$n <- summ$df[1] + summ$df[2]
out$k <- summ$df[1]
print (out$call)
pfround (coef, digits)
cat("---\n")
cat (paste ("n = ", out$n, ", k = ", out$k,
"\nresidual sd = ", fround (out$sigma.hat, digits),
", R-Squared = ", fround (out$r.squared, 2), "\n", sep=""))
return(invisible(out))
}
)
setMethod("display", signature(object = "bayesglm"),
function(object, digits=2, detail=FALSE)
{
out <- NULL
out$call <- object$call
summ <- summary(object, dispersion = object$dispersion)
if(detail){
coef <- summ$coefficients
coef[ rownames( coef ) %in% rownames( summ$coef[, , drop = FALSE]) , ] <- summ$coef[ , , drop = FALSE ]
out$z.value <- coef[,3]#,drop=FALSE]
out$p.value <- coef[,4]#,drop=FALSE]
}
else{
coef <- matrix( NA, length( object$coefficients ),2 )
rownames(coef) <- names( object$coefficients ) ## M
coef[ rownames( coef ) %in% rownames( summ$coef[, 1:2, drop = FALSE]) , ] <- summ$coef[ , 1:2, drop = FALSE ] ## M
}
dimnames(coef)[[2]][1:2] <- c( "coef.est", "coef.se")
out$coef <- coef[,"coef.est"]#,drop=FALSE]
out$se <- coef[,"coef.se"]#,drop=FALSE]
out$n <- summ$df[1] + summ$df[2]
out$k <- summ$df[1]
out$deviance <- summ$deviance
out$null.deviance <- summ$null.deviance
print(out$call)
pfround(coef, digits)
cat("---\n")
cat(paste("n = ", out$n, ", k = ", out$k, "\nresidual deviance = ",
fround(out$deviance, 1), ", null deviance = ", fround(out$null.deviance, 1), " (difference = ", fround(out$null.deviance - out$deviance, 1), ")", "\n", sep = ""))
out$dispersion <- if (is.null(object$dispersion)){
summ$dispersion
} else {
object$dispersion
}
if (out$dispersion != 1) {
out$overdispersion.parameter <- out$dispersion
cat(paste("overdispersion parameter = ", fround(out$dispersion, 1), "\n", sep = ""))
if (family(object)$family == "gaussian") {
out$sigma.hat <- sqrt(out$dispersion)
cat(paste("residual sd is sqrt(overdispersion) = ", fround(out$sigma.hat, digits), "\n", sep = ""))
}
}
return(invisible(out))
}
)
#setMethod("display", signature(object = "bayesglm.h"),
# function (object, digits = 2, detail = FALSE)
# {
# call <- object$call
# summ <- summary(object, dispersion = object$dispersion)
# if(detail){
# coef <- summ$coefficients
# coef[ rownames( coef ) %in% rownames( summ$coef[, , drop = FALSE]) , ] <- summ$coef[ , , drop = FALSE ]
# }
# else{
# coef <- matrix( NA, length( object$coefficients ),2 )
# rownames(coef) <- names( object$coefficients ) ## M
# coef[ rownames( coef ) %in% rownames( summ$coef[, 1:2, drop = FALSE]) , ] <- summ$coef[ , 1:2, drop = FALSE ] ## M
# }
# dimnames(coef)[[2]][1:2] <- c( "coef.est", "coef.se")
# #n <- summ$df[1] + summ$df[2]
# n <- summ$df.residual
# k <- summ$df[1]
# print(call)
# if(max(object$batch)>0){
# nn<- strsplit( rownames( coef )[seq( from= length( object$batch ) + 1 ,to = nrow( coef ))], "." , fixed=TRUE)
# bb<- c( object$batch,unlist( lapply (nn , function( lst ) { lst[[3]] } ) ) )
# }
# else {bb<- c( object$batch)}
# cc<- cbind( fround( coef, digits ), bb )
# dimnames(cc)[[2]][3]<-"batch"
# print( cc , quote = FALSE )
# cat("---\n")
# cat(paste("n = ", n, ", k = ", k, "\nresidual deviance = ",
# fround(summ$deviance, 1), ", null deviance = ", fround(summ$null.deviance,
# 1), " (difference = ", fround(summ$null.deviance -
# summ$deviance, 1), ")", "\n", sep = ""))
# dispersion <- if (is.null(object$dispersion))
# summ$dispersion
# else object$dispersion
# if (dispersion != 1) {
# cat(paste("overdispersion parameter = ", fround(dispersion,
# 1), "\n", sep = ""))
# if (family(object)$family == "gaussian") {
# cat(paste("residual sd is sqrt(overdispersion) = ",
# fround(sqrt(dispersion), digits), "\n", sep = ""))
# cat(paste("group sd is sigma.batch = ",
# fround(object$sigma.batch, digits), "\n", sep = ""))
# }
# }
# }
#)
setMethod("display", signature(object = "glm"),
function(object, digits=2, detail=FALSE)
{
out <- NULL
out$call <- object$call
summ <- summary(object, dispersion = object$dispersion)
if(detail){
coef <- summ$coef[, , drop = FALSE]
out$z.value <- coef[,3]#,drop=FALSE]
out$p.value <- coef[,4]#,drop=FALSE]
}
else{
coef <- summ$coef[, 1:2, drop = FALSE]
}
dimnames(coef)[[2]][1:2] <- c("coef.est", "coef.se")
out$n <- summ$df[1] + summ$df[2]
out$k <- summ$df[1]
out$coef <- coef[,"coef.est"]
out$se <- coef[,"coef.se"]
print(out$call)
pfround(coef, digits)
out$deviance <- summ$deviance
out$null.deviance <- summ$null.deviance
cat("---\n")
cat(paste(" n = ", out$n, ", k = ", out$k, "\n residual deviance = ",
fround(out$deviance, 1), ", null deviance = ", fround(out$null.deviance, 1), " (difference = ", fround(summ$null.deviance - summ$deviance, 1), ")", "\n", sep = ""))
out$dispersion <- if (is.null(object$dispersion)){
summ$dispersion
} else {
object$dispersion
}
if (out$dispersion != 1) {
cat(paste(" overdispersion parameter = ", fround(out$dispersion, 1), "\n", sep = ""))
if (family(object)$family=="gaussian") {
out$sigma.hat <- sqrt(out$dispersion)
cat(paste(" residual sd is sqrt(overdispersion) = ",
fround(out$sigma.hat, digits), "\n", sep = ""))
}
}
return(invisible(out))
}
)
#setMethod("display", signature(object = "mer"),
# function(object, digits=2)
# {
# call <- object@call
# print (call)
# #object <- summary(object)
# fcoef <- fixef(object)
# useScale <- attr( VarCorr(object), "sc")
# corF <- vcov(object)@factors$correlation
# coefs <- cbind(fcoef, corF@sd)
# if (length (fcoef) > 0){
# dimnames(coefs) <- list(names(fcoef), c("coef.est", "coef.se"))
# pfround (coefs, digits)
# }
# cat("\nError terms:\n")
# vc <- as.matrix.VarCorr (VarCorr (object), useScale=useScale, digits)
# print (vc[,c(1:2,4:ncol(vc))], quote=FALSE)
# ngrps <- lapply(object@flist, function(x) length(levels(x)))
# REML <- object@status["REML"]
# llik <- logLik(object, REML)
# AIC <- AIC(llik)
# dev <- object@deviance["ML"] # Dbar
# n <- object@devComp["n"]
# Dhat <- -2*(llik) # Dhat
# pD <- dev - Dhat # pD
# DIC <- dev + pD # DIC=Dbar+pD=Dhat+2pD
# cat("---\n")
# cat(sprintf("number of obs: %d, groups: ", n))
# cat(paste(paste(names(ngrps), ngrps, sep = ", "), collapse = "; "))
# cat(sprintf("\nAIC = %g, DIC = ", fround(AIC, 1)))
# cat(fround(DIC, 1))
# cat("\ndeviance =", fround (dev, 1), "\n")
# if (useScale < 0){
# cat("overdispersion parameter =", fround (.Call("mer_sigma",
# object, FALSE, PACKAGE = "lme4"), 1), "\n")
# }
# }
#)
setMethod("display", signature(object = "merMod"),
function(object, digits=2, detail=FALSE)
{
out <- NULL
out$call <- object@call
print (out$call)
#object <- summary(object)
#summ <- summary(object)
fcoef <- fixef(object)
#coefs <- attr(summ, "coefs")
#useScale <- attr (VarCorr (object), "sc")
useScale <- getME(object, "devcomp")$dims["useSc"]
corF <- vcov(object)@factors$correlation
coefs <- cbind(fcoef, corF@sd)
if (length (fcoef) > 0){
if (!useScale) {
coefs <- coefs[, 1:2, drop = FALSE]
out$z.value <- coefs[, 1]/coefs[, 2]
out$p.value <- 2 * pnorm(abs(out$z.value), lower.tail = FALSE)
coefs <- cbind(coefs, `z value` = out$z.value, `Pr(>|z|)` = out$p.value)
}
else {
out$t.value <- coefs[, 1]/coefs[, 2]
coefs <- cbind(coefs, `t value` = out$t.value)
}
dimnames(coefs)[[2]][1:2] <- c("coef.est", "coef.se")
if(detail){
pfround (coefs, digits)
}
else{
pfround(coefs[,1:2], digits)
}
}
out$coef <- coefs[,"coef.est"]
out$se <- coefs[,"coef.se"]
cat("\nError terms:\n")
vc <- as.matrix.VarCorr (VarCorr (object), useScale=useScale, digits=digits)
print (vc[,c(1:2,4:ncol(vc))], quote=FALSE)
out$ngrps <- lapply(object@flist, function(x) length(levels(x)))
is_REML <- isREML(object)
llik <- logLik(object, REML=is_REML)
out$AIC <- AIC(llik)
out$deviance <- deviance(refitML(object)) # Dbar
out$n <- getME(object, "devcomp")$dims["n"]
Dhat <- -2*(llik) # Dhat
pD <- out$deviance - Dhat # pD
out$DIC <- out$deviance + pD # DIC=Dbar+pD=Dhat+2pD
cat("---\n")
cat(sprintf("number of obs: %d, groups: ", out$n))
cat(paste(paste(names(out$ngrps), out$ngrps, sep = ", "), collapse = "; "))
cat(sprintf("\nAIC = %g, DIC = ", round(out$AIC,1)))
cat(round(out$DIC, 1))
cat("\ndeviance =", fround (out$deviance, 1), "\n")
if (useScale < 0){
out$sigma.hat <- .Call("mer_sigma", object, FALSE, PACKAGE = "lme4")
cat("overdispersion parameter =", fround (out$sigma.hat, 1), "\n")
}
return(invisible(out))
}
)
setMethod("display", signature(object = "polr"),
function(object, digits=2, detail=FALSE)
{
out <- NULL
out$call <- object$call
summ <- summary(object)
if(detail){
coef <- summ$coef[, , drop = FALSE]
out$t.value <- coef[,"t value"]
}
else{
coef <- summ$coef[, 1:2, drop = FALSE]
}
dimnames(coef)[[2]][1:2] <- c("coef.est", "coef.se")
out$coef <- coef[,"coef.est"]
out$se <- coef[,"coef.se"]
out$n <- summ$n
out$k <- nrow (coef)
out$k.intercepts <- length (summ$zeta)
print(out$call)
pfround(coef, digits)
cat("---\n")
cat(paste("n = ", out$n, ", k = ", out$k, " (including ", out$k.intercepts,
" intercepts)\nresidual deviance = ",
fround(deviance(object), 1),
", null deviance is not computed by polr",
"\n", sep = ""))
#cat("AIC:", fround(AIC(object), 1), "\n")
return(invisible(out))
}
)
setMethod("display", signature(object = "svyglm"),
function(object, digits=2, detail=FALSE)
{
out <- NULL
out$call <- object$call
out$survey.design <- object$survey.design
summ <- summary(object)
if(detail){
coef <- summ$coef[, , drop = FALSE]
out$z.value <- coef[,3]#,drop=FALSE]
out$p.value <- coef[,4]#,drop=FALSE]
}
else{
coef <- summ$coef[, 1:2, drop = FALSE]
}
dimnames(coef)[[2]][1:2] <- c("coef.est", "coef.se")
out$n <- summ$df[1] + summ$df[2]
out$k <- summ$df[1]
out$coef <- coef[,"coef.est"]
out$se <- coef[,"coef.se"]
print(out$call)
cat("\n")
print(out$survey.design)
cat("\n")
pfround(coef, digits)
out$deviance <- summ$deviance
out$null.deviance <- summ$null.deviance
cat("---\n")
cat(paste(" n = ", out$n, ", k = ", out$k, "\n residual deviance = ",
fround(out$deviance, 1), ", null deviance = ", fround(out$null.deviance, 1), " (difference = ", fround(summ$null.deviance - summ$deviance, 1), ")", "\n", sep = ""))
out$dispersion <- summ$dispersion[1]
if (out$dispersion != 1) {
cat(paste(" overdispersion parameter = ", fround(out$dispersion, 1), "\n", sep = ""))
if (family(object)$family=="gaussian") {
out$sigma.hat <- sqrt(out$dispersion)
cat(paste(" residual sd is sqrt(overdispersion) = ",
fround(out$sigma.hat, digits), "\n", sep = ""))
}
}
return(invisible(out))
}
)
#setMethod("display", signature(object = "bayespolr"),
# function(object, digits=2)
# {
# call <- object$call
# summ <- summary(object)
# coef <- summ$coef[, 1:2, drop = FALSE]
# dimnames(coef)[[2]] <- c("coef.est", "coef.se")
# n <- summ$n # or maybe should be "nobs", I don't know for sure
# k <- nrow (coef)
# k.intercepts <- length (summ$zeta)
# print(call)
# pfround(coef, digits)
# cat("---\n")
# cat(paste("n = ", n, ", k = ", k, " (including ", k.intercepts,
# " intercepts)\nresidual deviance = ",
# fround(summ$deviance, 1),
# ", null deviance is not computed by bayespolr",
# "\n", sep = ""))
# }
#)
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