#' Composite Regression Estimation simulated data
#'
#' A simulated datasets containing survey data.
#' The primary key of the data is Identifier and time
#'\itemize{
#' \item Identifier identifier
#' \item Status Employment status
#' \item Sampling.weight Sampling weight
#' \item time date
#' \item Month.in.sample indicator of month in sample rotation group
#' \item Gender Gender
#' \item Income Income
#' \item State State
#' \item Hobby Hobby
#' }
#' @format A data frame
#' @source
# imaginary
if(FALSE){
set.seed(1)
CRE_data<-
plyr::adply(0:10,1,function(i){
rgsize<-100
n=8*rgsize
X<-data.frame(Identifier=i*rgsize+c(sapply(c(0:3,12:15),function(j){(j*rgsize+1):((j+1)*rgsize)})),
Status=sample(as.factor(c("employed","unemployed","not in the labor force")),n,replace=TRUE),
"Sampling.weight"=1,
time=as.Date(i,"2010-01-01"),
"Month in sample"=rep(8:1,each=rgsize),
Gender=sample(as.factor(c("male","female")),n,replace=TRUE),
Income=VGAM::rpareto(n,.5,.5),
State=sample(as.factor(rownames(state.x77)),n,prob=state.x77[,"Population"],replace=TRUE),
Hobby=sample(as.factor(c("Shopping","TV")),n,replace=TRUE))
X$"Sampling.weight"<-sampling::calib(Xs = model.matrix(~X$State+0),d=X$"Sampling.weight",total = state.x77[,"Population"],method="linear")
X
})
save(CRE_data,file="data/CRE_data.rda")
}
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