bootwrq: Bootstrap procedure for weighted quantile regressions

In weightQuant: Weights for Incomplete Longitudinal Data and Quantile Regression

Description

A subject-level bootstrap method for weighted quantile regressions is implemented in this function. Quantile regressions are estimated in a generalized estimating equation framework with independent working covariance matrix. Weights are estimated using weightsIMD or weightsMMD functions.

Usage

bootwrq(B, form, tau, data, Y, X1 = NULL, X2 = NULL, subject,
death, time, interval.death = NULL, impute = NULL, weight = NULL,
wcompute = 2, seed = NULL, intermittent, file = NULL,
nproc = 1, MPI = FALSE)

Arguments

B	integer, number of bootstrap samples
form	formula indicating the quantile regression model to be estimated
tau	numeric vector indicating the quantiles to be estimated
data	data frame containing the data
Y	character indicating the name of the response outcome
X1	optional character vector passed to the weight functions
X2	optional character vector passed to the weight functions
subject	character indicating the name of the subject identifier
death	optional character passed to the weight functions
time	optional character passed to the weight functions
interval.death	optional numeric vector passed to the weight function weightsMMD
impute	optional numeric vector passed to the weight function weightsIMD
weight	character indicating the name of the weight variable in data
wcompute	integer indicating if weights should be estimated in each bootstrap sample. If wcompute=0, weights are supposed to be known. If wcompute=1, weights are re-estimated in each bootstrp sample. If wcompute=2, both results are returned.
seed	optional integer vector of length B indicating the seeds.
intermittent	logical indicating if data contains intermittent missing data. If intermittent=TRUE, the weights are estimated using weightsIMD function, if intermittent=FALSE, the weights are estimated using weightsMMD function.
file	optional character indicating the name of the results file. If file=NULL, no results file is created.
nproc	number of processors to be used for parallel computing. Default to 1, sequential computation.
MPI	logical indicating if MPI parallelization should be used. Default to FALSE.

Value

a matrix with B columns containing the results on each bootstrap sample.

Author(s)

Viviane Philipps, Robert Darlin Mba

See Also

summary.bootwrq, test.bootwrq

Examples

## Not run: 
## computation of the weights with intermittent missing data 
w_simdata <- weightsIMD(data=simdata,Y="Y",X1="X",X2=NULL,subject="id",
death="death",time="time",impute=20,name="w_imd")$data

## estimation of the weighted quantile regressions
## for the first quartile and the median
m_simdata <- rq(Y~time*X,data=w_simdata,weights=w_imd,tau=c(0.25,0.5))

## estimation of the standard erros using the bootstrap procedure
boot_simdata <- bootwrq(B=1000, form=Y~time*X, tau=c(0.25,0.5),
data=w_simdata, Y="Y",X1="X",X2=NULL,subject="id",
death="death",time="time",impute=20,wcompute=0,intermittent=TRUE)

## the summary of the results
summary(boot_simdata,m_simdata)

## comparison of the covariate effects
## between the first quartile and the median
test.bootwrq(boot_simdata,m_simdata)

## End(Not run)

weightQuant documentation built on Jan. 5, 2022, 5:08 p.m.