hdcox.lasso: Lasso Model Selection for High-Dimensional Cox Models

Description Usage Arguments Examples

View source: R/01-hdnom-models.R

Description

Automatic lasso model selection for high-dimensional Cox models, evaluated by penalized partial-likelihood.

Usage

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hdcox.lasso(x, y, nfolds = 5L, rule = c("lambda.min", "lambda.1se"),
  seed = 1001)

Arguments

x

Data matrix.

y

Response matrix made by Surv.

nfolds

Fold numbers of cross-validation.

rule

Model selection criterion, "lambda.min" or "lambda.1se". See cv.glmnet for details.

seed

A random seed for cross-validation fold division.

Examples

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library("survival")
library("rms")

# Load imputed SMART data
data("smart")
x = as.matrix(smart[, -c(1, 2)])
time = smart$TEVENT
event = smart$EVENT
y = Surv(time, event)

# Fit Cox model with lasso penalty
fit = hdcox.lasso(x, y, nfolds = 5, rule = "lambda.1se", seed = 11)

# Prepare data for hdnom.nomogram
x.df = as.data.frame(x)
dd = datadist(x.df)
options(datadist = "dd")

# Generate hdnom.nomogram objects and plot nomogram
nom = hdnom.nomogram(
  fit$lasso_model, model.type = "lasso",
  x, time, event, x.df, pred.at = 365 * 2,
  funlabel = "2-Year Overall Survival Probability")

plot(nom)

road2stat/hdnom documentation built on July 2, 2018, 9:33 a.m.