varianceControl {fastcmprsk}

Controls for Variance Calculation

Description

Controls for variance calculation for the fastcmprsk package.

Usage

varianceControl(
  B = 100L,
  seed = 1991L,
  useMultipleCores = FALSE,
  extractMatrix = FALSE
)

Arguments

B

Integer: Number of bootstrap samples needed for variance estimation.

seed

Integer: Seed value for bootstrapping. Results may differ if parallelized.

useMultipleCores

Logical: Set to TRUE if parallelizing. (Default is FALSE).

extractMatrix

Logical: Extract matrix of bootstrap estimates (Default is FALSE)

Details

Variance-covariance estimation is done via bootstrap. Independent bootstrap runs can be performed both in serial and parallel. Parallelization is done via the doParallel package.

Value

Returns a list for variance options inputted into fastCrr.

B

same as what is defined in argument.

seed

same as what is defined in argument.

mcores

same as what is defined in argument useMultipleCores.

extract

same as what is defined in argument extractMatrix.

Examples

library(fastcmprsk)
set.seed(10)
ftime <- rexp(200)
fstatus <- sample(0:2, 200, replace = TRUE)
cov <- matrix(runif(1000), nrow = 200)
dimnames(cov)[[2]] <- c('x1','x2','x3','x4','x5')
vc <- varianceControl(B = 100, seed = 2019, useMultipleCores = FALSE)
fit1 <- fastCrr(Crisk(ftime, fstatus) ~ cov, variance = TRUE, var.control = vc)
fit1$var # Estimated covariance matrix via bootstrap