boot.glmnet: Bootstrap Validation for Survival Model

In ampel-leipzig/ameld: Data and Model of End-Stage Liver Disease used in the AMPEL Project

Bootstrap Validation for Survival Model

Description

Boostrap validation for survival data as described in Harrell et al. 1996.

Usage

bootstrap(
  x,
  y,
  fun = rcv.glmnet,
  nboot = 200L,
  m = 50,
  times = 90,
  ...,
  s = "lambda.1se",
  verbose = interactive()
)

## S3 method for class 'boot.glmnet'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

x	matrix, data/feature matrix.
y	Surv, survival time and status as Surv object.
fun	model function, e.g. rcv.glmnet().
nboot	integer number of bootstrap samples
m	integer, individuals/observations per interval
times	numeric predict survival at times.
s	character/numeric, value(s) of the penality parameter lambda. See glmnet::predict.cv.glmnet() for details.
verbose	logical, if TRUE a progressbar is shown.
digits	integer(1), number of digits shown in table.
...	further params passed to fun.

Value

A list, with the fitted model fit and the over-optimistic error.

References

Harrell Jr, Frank E., Kerry L. Lee, and Daniel B. Mark. "Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors." Statistics in medicine 15.4 (1996): 361-387. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4")}

Examples

# nboot should usually be higher but to keep the runtime of the example low
# we choose 2 here
data(eldd)
x <- na.omit(eldd)
y <- Surv(x$DaysAtRisk, x$Deceased)
x <- as.matrix(x[,c("Age", "ALB_S", "BILI_S", "CRE_S", "INR_C")])
boot <- bootstrap(
    x, y, rcv.glmnet, family = "cox",
    nboot = 2, nrepcv = 2, nfolds = 3
)
boot

ampel-leipzig/ameld documentation built on Aug. 23, 2024, 7:31 p.m.