penAFT (version 0.3.2)

penAFT.trace: Print trace plot for the semiparametric AFT fit using penAFT or penAFT.cv

Description

Print the trace plot for the regularized Gehan estimator.

Usage

penAFT.trace(fit, groupNames=NULL)

Value

No return value; prints a trace plot as described in Details.

Arguments

fit

A fitted model from penAFT or penAFT.cv.

groupNames

A list of group names to be printed when fit used penalty "SG".

Details

The function penAFT.trace returns a trace plot for a fitted model obtained from either penAFT or penAFT.cv. If the model is fit using the sparse group-lasso penalty, you may provide names for the groups (in order of the integer values specifying the groups). This feature may not be desired if there are a large number of groups, however. The vertical blue line indicates the tuning parameter which minimized cross-validated linear predictor scores and the vertical black line indicates the tuning parameter minimizing the cross-validated Gehan loss according to the one-standard error rule.

Examples

# --------------------------------------
# Generate data  
# --------------------------------------
set.seed(1)
genData <- genSurvData(n = 50, p = 50, s = 10, mag = 2, cens.quant = 0.6)
X <- genData$X
logY <- genData$logY
delta <- genData$status


# ------------------------------------------
# Fit elastic net penalized estimator with CV
# -------------------------------------------
fit.cv <- penAFT.cv(X = X, logY = logY, delta = delta,
                   nlambda = 10,
                   penalty = "EN",
                   alpha = 1, nfolds = 5)

# -- print plot
penAFT.trace(fit.cv)

# \donttest{
  # ------------------------------------------
  # Fit sparse group-lasso estimator with CV
  # -------------------------------------------
  groups <- rep(1:5, length=10)
  fit.sg.cv <- penAFT.cv(X = X, logY = logY, delta = delta,
                    nlambda = 50, groups = groups,
                    penalty = "SG", tol.rel= 1e-5,
                    alpha = 0, nfolds = 5)
  
  penAFT.trace(fit.sg.cv, groupNames = paste("Group", 1:5, sep="-"))
# }