Nothing
summary.fitsaemodel <-
function (object, digits=6, ...){
saemodel <- attr(object, "saemodel")
n <- saemodel$n
g <- saemodel$g
p <- saemodel$p
fixeffnames <- attr(saemodel, "xnames")
# retrieve the estimating method
method <- attr(object, "method")
# check whether the model converged
converged <- object$converged
if (converged != 1){
cat("ALGORITHM FAILED TO CONVERGE! use convergence() to learn more \n")
}else{
cat("ESTIMATION SUMMARY \n")
cat("Method: ", method$type, "\n")
# branch: robust vs. non-robust methods
if (length(method) > 1){
tuning <- method$tuning
if (length(tuning) == 1){
cat(paste("Robustness tuning constant: ",names(tuning), " = ", as.numeric(tuning), "\n"))
}else{
for (i in 1:length(tuning)){
cat(tuning[i], "\n")
}
}
}
#----------------------
# fixed effects table
df <- n - g - p + 1
fixed <- object$beta
stdfixed <- sqrt(diag(object$vcovbeta))
tTable <- cbind(fixed, stdfixed, fixed/stdfixed, df, fixed)
colnames(tTable) <- c("Value", "Std.Error", "t-value", "df", "p-value")
rownames(tTable) <- fixeffnames
tTable[, 5] <- 2 * pt(-abs(tTable[, 3]), tTable[, 4])
cat("---\n")
cat("Fixed effects\n")
printCoefmat(tTable, digits=digits, P.values=TRUE, has.Pvalue=TRUE)
#----------------------
# robustness properties
robustness <- attr(object, "robustness")
# branch robust vs non-robust
if (!is.null(robustness)){
wgt <- t(robustness$wgt) / n
colnames(wgt) <- c("fixeff", "residual var", "area raneff var")
rownames(wgt) <- "sum(wgt)/n"
cat("---\n")
cat("Degree of downweighting/winsorization:\n")
cat("\n")
print.default(format(t(wgt), digits = digits), print.gap = 2, quote = FALSE)
}
}
}
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