Nothing
print.unpencoxIC <- function(x, ...) {
xnames <- x$xnames
n <- x$n
ind.normalize <- x$normalize.X
ind.convergence <- x$convergence
ind.trun <- x$ind.trun
pcon <- ifelse(ind.convergence, "converged", "failed to converge")
iteration <- x$iteration
coef <- x$b
nb <- length(coef)
se <- x$se
z <- coef/se
pvalue <- pnorm(abs(z), lower.tail = FALSE)
ci <- cbind(coef - qnorm(0.975) * se, coef + qnorm(0.975) * se)
digit <- paste("%.", max(3, getOption("digits") - 3), "f", sep = "")
pcoef <- sprintf(digit, coef)
pz <- sprintf(digit, z)
if(all(!is.na(x$cov))) {
ppvalue <- fun_less(sprintf(digit, pvalue))
} else {
ppvalue <- rep(NA, length(se))
}
pci <- t(apply(ci, 1, function(x, digit) sprintf(digit, x), digit = digit))
pse <- sprintf(digit, se)
if(is.null(xnames)) {
pxnames <- paste(" X", 1:nb, sep = "")
} else {
pxnames <- paste(" ", xnames, sep = "")
}
results <- cbind(pcoef, pci, pse, pz, ppvalue)
colnames(results) <- c("coef", "lower.CI", "upper.CI", "se", "z", "p")
rownames(results) <- pxnames
presults <- as.data.frame(results)
presults
cat("\n===========================================================\n")
cat(" Unpenalized Nonparametric Maximum Likelihood Estimation\n")
if(ind.trun) {
cat(" (Input: interval censored and left truncated data)")
} else {
cat(" (Input: interval censored data)")
}
cat("\n-----------------------------------------------------------\n")
print(presults)
cat("-----------------------------------------------------------\n")
cat(paste(" * n = ", n, "\n", sep = ""))
cat(paste(" * EM algorithm ", pcon, " after ", iteration, " iterations\n", sep = ""))
if(isTRUE(x$normalize.X)) cat(paste(" * X is normalized", sep = ""))
cat("\n===========================================================\n")
}
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