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#### AUTHOR: Arnost Komarek ####
#### 01/05/2004 ####
#### ####
#### FILE: print.smoothSurvReg.R ####
#### ####
#### FUNCTIONS: print.smoothSurvReg ####
###########################################
### ====================================================================
### print.smoothSurvReg: Print objects of class 'smoothSurvReg'
### ====================================================================
## x .......... object of class 'smoothSurvReg'
## spline ..... T/F, do I want to print an information concerning the fitted spline?
## digits ..... # of printed digits
## ... ........ other arguments passed to 'print' function
print.smoothSurvReg <- function(x, spline, digits = min(options()$digits, 4), ...)
{
if (x$fail >= 99) {
cat("No summary, smoothSurvReg failed.\n")
return(invisible(x))
}
if(missing(digits))
digits <- min(options()$digits, 4)
if(missing(spline)) spline <- (nrow(x$spline) <= 31)
if(!is.null(cl <- x$call)) {
cat("Call:\n")
dput(cl)
}
est.scale <- x$estimated["Scale"]
est.c <- x$estimated["ccoef"]
## Estimates of regres parameters
if (!is.null(x$regres)){
nregres <- dim(x$regres)[1]
cat("\nEstimated Regression Coefficients:\n")
# cat("-----------------------------\n")
scale <- NULL
if ("Scale" %in% rownames(x$regres)){
regres <- x$regres[1:(nregres-1),]
scale <- x$regres[nregres, "Value"]
}
else{
regres <- x$regres
}
Z.P <- regres$Value/regres[["Std.Error"]]
pv.P <- 2 * pnorm(-abs(Z.P))
Z.V <- regres$Value/regres[["Std.Error2"]]
pv.V <- 2 * pnorm(-abs(Z.V))
regres[["Z"]] <- Z.P
regres[["Z2"]] <- Z.V
regres[["p"]] <- pv.P
regres[["p2"]] <- pv.V
print(regres, digits = digits, ...)
if (!is.null(scale)){
cat("\nScale =", format(scale, digits=digits), "\n")
}
}
## Fixed regression components of the model
regres.fixed <- NULL
tfr.names <- character(0)
if (!est.scale){
tfr.names <- c(tfr.names, "Log(scale)", "Scale")
regres.fixed <- c(regres.fixed, x$init.regres["Log(scale)", "Value"], x$init.regres["Scale", "Value"])
}
if (!is.null(regres.fixed)){
regres.fixed <- data.frame(Value = regres.fixed)
rownames(regres.fixed) <- tfr.names
nregres <- dim(regres.fixed)[1]
cat("\nFixed Regression Coefficients:\n")
# cat("-------------------------\n")
scale <- NULL
if ("Scale" %in% rownames(regres.fixed)){
regres <- as.data.frame(regres.fixed[1:(nregres-1),])
colnames(regres) <- "Value"
scale <- regres.fixed[nregres, "Value"]
}
else{
regres <- regres.fixed
}
print(regres, digits = digits, ...)
if (!is.null(scale)){
cat("\nScale:", format(scale, digits=digits), "\n")
}
}
## Possibly adjusted intercept and scale
# cat("\nAdjusted Intercept and Scale:\n")
# cat("-----------------------------\n")
# print(x$adjust, digits = digits, ...)
## Fitted error distribution
# cat("\n(Fitted) Error Distribution:\n")
# cat("----------------------------\n")
# m.err <- x$error.dist$Mean
# sd.err <- x$error.dist$SD
# cat("Mean:", format(m.err, digits=digits), "\n")
# cat("Scale:", format(sd.err, digits=digits), "\n")
if(spline){
Z.P <- x$spline[["c coef."]]/x$spline[["Std.Error.c"]]
pv.P <- 2 * pnorm(-abs(Z.P))
Z.V <- x$spline[["c coef."]]/x$spline[["Std.Error2.c"]]
pv.V <- 2 * pnorm(-abs(Z.V))
spline.print <- x$spline[,1:5] ## do not print columns with a's
spline.print[["Z"]] <- Z.P
spline.print[["Z2"]] <- Z.V
spline.print[["p"]] <- pv.P
spline.print[["p2"]] <- pv.V
cat("\nDetails on (Fitted) Error Distribution:\n")
# cat("---------------------------------------\n")
print(spline.print, digits = digits, ...)
}
## Likelihood and iterations
digits <- digits+3
cat("\nPenalized Loglikelihood and Its Components:\n")
# cat("----------------------------------\n")
pll <- x$loglik[1, "Penalized Log Likelihood"]
ll <- x$loglik[1, "Log Likelihood"]
penalty <- x$loglik[1, "Penalty"]
df <- x$degree.smooth$df
nparam <- x$degree.smooth[["Number of parameters"]]
nU <- x$degree.smooth[["Mean param."]]
nUscale <- x$degree.smooth[["Scale param."]]
nUc <- x$degree.smooth[["Spline param."]]
lambda <- x$degree.smooth$Lambda
loglambda <- x$degree.smooth[, "Log(Lambda)"]
aic <- x$aic
n <- dim(x$y)[1]
nmiss <- length(x$na.action)
cat(" Log-likelihood:", format(ll, digits=digits), "\n")
cat(" Penalty:", format(penalty, digits=digits), "\n")
cat(" Penalized Log-likelihood:", format(pll, digits=digits), "\n\n")
cat("Degree of smoothing:\n")
cat(" Number of parameters:", format(nparam, digits=digits), "\n")
cat(" Mean parameters:", format(nU, digits=digits), "\n")
cat(" Scale parameters:", format(nUscale, digits=digits), "\n")
cat(" Spline parameters:", format(nUc, digits=digits), "\n\n")
cat(" Lambda:", format(lambda, digits=digits), "\n")
cat(" Log(Lambda):", format(loglambda, digits=digits), "\n")
cat(" df:", format(df, digits=digits), "\n\n")
cat("AIC (higher is better): ", format(aic, digits=digits), "\n\n")
cat("Number of Newton-Raphson Iterations: ", x$iter, "\n")
if (nmiss > 0) cat("n = ", n, " (", nmiss, "observations deleted due to missing)", "\n", sep = "")
else cat("n =", n, "\n")
}
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