PwePred (version 1.0.0)

cv.pwexpm: Cross Validate a Piecewise Exponential Model

Description

Cross validate an existing piecewise exponential model.

Usage

# S3 method for default
cv.pwexpm(Surv, data, nfold=5, nsim=100, breakpoint=NULL,
             nbreak=0, exclude_int=NULL, min_pt_tail=5, max_set=1000, seed=1818,
             optimizer='mle', tol=1e-4, parallel=FALSE, mc.core=4, ...)
# S3 method for pwexpm
cv.pwexpm(Surv, nfold=5, nsim=100, max_set=1000, seed=1818,
             optimizer='mle', tol=1e-4, parallel=FALSE, mc.core=4, ...)

Value

A object of class "cv.pwexpm" is a numeric vector containing the CV log likelihood in each round of simulation. The plot function can be used to make a boxplot of the CV log likelihoods from pwexpm.

Arguments

Surv

a Surv object indicating event time and status or a pwexpm object.

data

a data frame in which to interpret the variables named in the Surv argument.

nfold

the number of folds used in CV.

nsim

the number of simulations.

breakpoint

pre-specified breakpoints. See pwexpm.

nbreak

total number of breakpoints. See pwexpm.

exclude_int

an interval that excludes any estimated breakpoints. See pwexpm.

min_pt_tail

the minimum number of events used for estimating the tail (the hazard rate of the last piece). See pwexpm.

max_set

maximum estimated combination of breakpoints. See pwexpm.

seed

a random seed. Do not set seed if seed=NULL.

optimizer

one of the optimizers: mle, ols, or hybrid. See pwexpm.

tol

the minimum allowed gap between two breakpoints. The gap is calculated as (max(time)-min(time))*tol. Keep it as default in most cases.

parallel

logical. If TRUE, use doSNOW package to run in parallel.

mc.core

number of processes allowed to be run in parallel.

...

internal function reserved.

Author

Tianchen Xu

Details

Use cross validation obtain the prediction log likelihood.

See Also

cv.pwexpm_fit

Examples

event_dist <- function(n)rpwexpm(n, rate = c(0.1, 0.01, 0.2), breakpoint =  c(5,14))
dat <- simdata(rand_rate = 20, drop_rate = 0.03,  total_sample = 1000,
               advanced_dist = list(event_dist=event_dist),
               add_column = c('censor_reason','event','followT','followT_abs'))

# here nsim=10 is for demo purpose, pls increase it in practice!!
# \donttest{
cv0 <- cv.pwexpm(Surv(followT, event), data=dat, nsim = 10, nbreak = 0)
cv1 <- cv.pwexpm(Surv(followT, event), data=dat, nsim = 10, nbreak = 1)
cv2 <- cv.pwexpm(Surv(followT, event), data=dat, nsim = 10, nbreak = 2)
sapply(list(cv0,cv1,cv2), median)
# }