prof_lambda: Profiling tuning parameters

In tramnet: Penalized Transformation Models

Profiling tuning parameters

Description

Computes the regularization path of all coefficients for a single tuning parameter, lambda, over a sequence of values.

Usage

prof_lambda(model, min_lambda = 0, max_lambda = 15, nprof = 5,
  as.lm = FALSE)

Arguments

model	model of class "tramnet"
min_lambda	minimal value of lambda (default = 0)
max_lambda	maximal value of lambda (default = 15)
nprof	number of profiling steps (default = 5)
as.lm	return scaled coefficients for class "tramnet_Lm"

Value

Object of class "prof_lambda" which contains the regularization path of all coefficients and the log-likelihood over the penalty parameter lambda

Author(s)

Lucas Kook

Examples

library("tramnet")
if (require("survival") & require("penalized")) {
  data("nki70", package = "penalized")
  nki70$resp <- with(nki70, Surv(time, event))
  x <- scale(model.matrix( ~ 0 + DIAPH3 + NUSAP1 + TSPYL5 + C20orf46, data = nki70))
  y <- Coxph(resp ~ 1, data = nki70, order = 10, log_first = TRUE)
  fit <- tramnet(y, x, lambda = 0, alpha = 1)
  pfl <- prof_lambda(fit)
  plot_path(pfl)
}

tramnet documentation built on April 1, 2023, 12:20 a.m.