Nothing
summary.glmmlasso <-
function(object,...)
{
## 1. part
table1 <- round(c(object$aic,object$bic,object$logLik,object$deviance,
object$objective),1)
names(table1) <- c("AIC","BIC","logLik","deviance","objective")
cat("Generalized linear mixed model fit by the Laplace approximation","\n")
cat("family =",object$family,"; ntot =",object$ntot,"; N =",object$N,
"; p =",object$p,"; lambda =",round(object$lambda,3),"\n")
print(table1)
## 2a. part
cat("","\n")
cat("Random effects:\n")
if(object$covStruct == "Sym"){
m <- fill.mat(object$theta, object$stot)
##
cov <- tcrossprod(m)
sd <- sqrt(diag(cov)) ## standard deviations
corr <- cov2cor(cov) ## correlation matrix
tableCov <- round(cov,5)
tableSd <- round(sd,5)
matrixCorr <- round(corr,5)
##colnames(matrixVar) <- c("Variance","Std.Dev.")
cat("\nCovariance Matrix:\n")
print(tableCov)
cat("\nCorrelation Matrix:\n")
print(matrixCorr)
cat("\nStandard Deviations:\n")
print(tableSd)
out <- list(cov = tableCov, corr = matrixCorr, sd = tableSd)
}else{
tableVar <- round(c(object$theta^2),5)
tableSd <- round(c(object$theta),5)
matrixVar <- cbind(tableVar,tableSd)
colnames(matrixVar) <- c("Variance","Std.Dev.")
##if (length(object$ranInd)>1) rownames(matrixVar) <-
##c("(Intercept)",paste("X",object$ranInd[-1],sep=""))
##else rownames(matrixVar) <- c("(Intercept)")
print(matrixVar)
out <- list(var = matrixVar)
}
## 3. part
cat("","\n")
cat("Fixed effects:","\n")
penalty <- rep("",length(object$coefficients))
penalty[object$unpenalized] <- "(n)"
table3 <- data.frame(round(object$coefficients,5),penalty)
xnames <- rownames(object$data$X)
if (is.null(xnames)){
rownames(table3) <- c("(Intercept)",paste("X", 2:length(object$fixef),
sep=""))
}else{
rownames(table3) <- c("(Intercept)",rownames(object$data$X)[-1])
}
table3 <- table3[object$coefficients!=0,]
colnames(table3) <- c("Estimate"," ")
if (dim(table3)[1]==1) rownames(table3) <- c("(Intercept)")
cat("|active set|=",sum(object$fixef!=0),"\n")
print(table3)
## 4th part
cat("","\n")
if (object$family=="binomial") cat("Misclassification Error:", object$gof,
"\n")
if (object$family=="poisson") cat("Lack-of-fit:",object$gof,"\n")
cat("Number of iterations:",object$nIter,"\n")
invisible(out)
}
print.glmmlasso <-
function(x,...)
{
## 1. part
table1 <- round(c(x$aic,x$bic,x$logLik,x$deviance,x$objective),1)
names(table1) <- c("AIC","BIC","logLik","deviance","objective")
cat("Generalized linear mixed model fit by the Laplace approximation","\n")
cat("family =",x$family,"; ntot =",x$ntot,"; N =",x$N,"; p =",x$p,
"; lambda =",round(x$lambda,3),"\n")
print(table1)
## 2a. part
cat("","\n")
cat("Random effects:\n")
if(x$covStruct == "Sym"){
m <- fill.mat(x$theta, x$stot)
##
cov <- tcrossprod(m)
sd <- sqrt(diag(cov)) ## standard deviations
corr <- cov2cor(cov) ## correlation matrix
tableCov <- round(cov,5)
tableSd <- round(sd,5)
matrixCorr <- round(corr,5)
##colnames(matrixVar) <- c("Variance","Std.Dev.")
cat("\nCovariance Matrix:\n")
print(tableCov)
cat("\nCorrelation Matrix:\n")
print(matrixCorr)
cat("\nStandard Deviations:\n")
print(tableSd)
}else{
tableVar <- round(c(x$theta^2),5)
tableSd <- round(c(x$theta),5)
matrixVar <- cbind(tableVar,tableSd)
colnames(matrixVar) <- c("Variance","Std.Dev.")
if (length(x$ranInd)>1) rownames(matrixVar) <-
c("(Intercept)",paste("X",x$ranInd[-1],sep=""))
else rownames(matrixVar) <- c("(Intercept)")
print(matrixVar)
}
## 3. part
cat("","\n")
cat("Fixed effects:","\n")
cat("|active set|=",sum(x$fixef!=0),"\n")
}
plot.glmmlasso <-
function(x,...)
{
par(mfrow=c(2,2))
## Tukey-Anscombe plots
plot(x$workResid~x$eta,col=x$data$group,main="Tukey-Anscombe Plot",
xlab="eta",ylab="working residuals")
abline(h=0,col="grey")
if (x$family=="binomial")
{
plot(x$respResid~x$mu,col=x$data$group,main="Tukey-Anscombe Plot",
xlab="mu",ylab="response residuals")
abline(h=0,col="grey")
} else
{
plot(x$data$y~x$fitted,col=x$data$group,main="Tukey-Anscombe Plot",
xlab="Predicted values",ylab="Observed values")
}
## QQ-plot of the random effects
qqnorm(x$ranef,main="QQ-Plot of the random effects")
qqline(x$ranef)
## histogram of the fixed effects
fixEf <- as.vector(x$fixef)
hist(fixEf,main=paste("|active set| = ",sum(fixEf!=0)),xlab="fixed effects")
rug(fixEf,lwd=3)
}
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