hdnom (version 5.0)

hdnom.compare.validate: Compare High-Dimensional Cox Models by Model Validation

Description

Compare High-Dimensional Cox Models by Model Validation

Usage

hdnom.compare.validate(x, time, event, model.type = c("lasso", "alasso",
  "flasso", "enet", "aenet", "mcp", "mnet", "scad", "snet"),
  method = c("bootstrap", "cv", "repeated.cv"), boot.times = NULL,
  nfolds = NULL, rep.times = NULL, tauc.type = c("CD", "SZ", "UNO"),
  tauc.time, seed = 1001, trace = TRUE)

Arguments

x

Matrix of training data used for fitting the model; on which to run the validation.

time

Survival time. Must be of the same length with the number of rows as x.

event

Status indicator, normally 0 = alive, 1 = dead. Must be of the same length with the number of rows as x.

model.type

Model types to compare. Could be at least two of "lasso", "alasso", "flasso", "enet", "aenet", "mcp", "mnet", "scad", or "snet".

method

Validation method. Could be "bootstrap", "cv", or "repeated.cv".

boot.times

Number of repetitions for bootstrap.

nfolds

Number of folds for cross-validation and repeated cross-validation.

rep.times

Number of repeated times for repeated cross-validation.

tauc.type

Type of time-dependent AUC. Including "CD" proposed by Chambless and Diao (2006)., "SZ" proposed by Song and Zhou (2008)., "UNO" proposed by Uno et al. (2007).

tauc.time

Numeric vector. Time points at which to evaluate the time-dependent AUC.

seed

A random seed for cross-validation fold division.

trace

Logical. Output the validation progress or not. Default is TRUE.

References

Chambless, L. E. and G. Diao (2006). Estimation of time-dependent area under the ROC curve for long-term risk prediction. Statistics in Medicine 25, 3474--3486.

Song, X. and X.-H. Zhou (2008). A semiparametric approach for the covariate specific ROC curve with survival outcome. Statistica Sinica 18, 947--965.

Uno, H., T. Cai, L. Tian, and L. J. Wei (2007). Evaluating prediction rules for t-year survivors with censored regression models. Journal of the American Statistical Association 102, 527--537.

Examples

# NOT RUN {
# Load imputed SMART data
data(smart)
x = as.matrix(smart[, -c(1, 2)])[1:1000, ]
time = smart$TEVENT[1:1000]
event = smart$EVENT[1:1000]

# Compare lasso and adaptive lasso by 5-fold cross-validation
cmp.val.cv = hdnom.compare.validate(
  x, time, event, model.type = c("lasso", "alasso"),
  method = "cv", nfolds = 5, tauc.type = "UNO",
  tauc.time = seq(0.25, 2, 0.25) * 365, seed = 1001)

print(cmp.val.cv)
summary(cmp.val.cv)
plot(cmp.val.cv)
plot(cmp.val.cv, interval = TRUE)
# }