tranSurv (version 1.2.0)

trReg: Fitting regression model via structural transformation model

Description

trReg fits transformation model under dependent truncation and independent censoring via a structural transformation model.

Usage

trReg(formula, data, subset, tFun = "linear", method = c("kendall",
  "adjust"), B = 0, control = list())

Arguments

formula

a formula expression, of the form response ~ predictors. The response is assumed to be a survival::Surv object with both left truncation and right censoring. See ?survival::Surv for more details.

data

an optional data.frame in which to interpret the variables occurring in the formula.

subset

an optional vector specifying a subset of observations to be used in the fitting process.

tFun

a character string specifying the transformation function or a user specified function indicating the relationship between X, T, and a. When tFun is a character, the following are permitted:

linear

linear transformation structure

log

log-linear transformation structure

exp

exponential transformation structure

method

a character string specifying the underlying model. See Details.

B

a numerical value specifies the bootstrap size. When B = 0, the bootstrap standard errors will not be computed.

control

ca list of control parameters. The following arguments are allowed:

lower

The lower bound to search for the transformation parameter; default at -1.

upper

The upper bound to search for the transformation parameter; default at 20.

tol

The tolerance used in the search for the transformation parameter; default at 0.01.

G

The number of grids used in the search for the transformation parameter; default at 50. A smaller G could results in faster search, but might be inaccurate.

Q

The number of cutpoints for the truncation time used when method = "adjust".

parallel

an logical value indicating whether parallel computation will be applied when B is not 0.

parCl

an integer value specifying the number of CPU cores to be used when parallel = TRUE. The default value is half the CPU cores on the current host.

Examples

# NOT RUN {
library(survival)
data(channing, package = "boot")
chan <- subset(channing, entry < exit)
trReg(Surv(entry, exit, cens) ~ sex, data = chan)
trReg(Surv(entry, exit, cens) ~ sex, data = chan, method = "adjust", control = list(G = 10))

# }