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\name{rmodel}
\alias{rmodel}
\title{Response probability using logistic regression}
\description{Computes the response probabilities using logistic regression for non-response
adjustment. For stratified sampling, the same logistic model is used for all strata.}
\usage{rmodel(formula,weights,X)}
\arguments{
\item{formula}{regression model formula (y~x).}
\item{weights}{vector of weights; its length is equal to n, the sample size.}
\item{X}{sample data frame.}
}
\value{The function returns the sample data frame with a new column 'prob_resp', which contains
the response probabilities.}
\seealso{
\code{\link{rhg}}
}
\examples{
# Example from An and Watts (New SAS procedures for Analysis of Sample Survey Data)
# generates artificial data (a 235X3 matrix with 3 columns: state, region, income).
# the variable "state" has 2 categories ('nc' and 'sc').
# the variable "region" has 3 categories (1, 2 and 3).
# the sampling frame is stratified by region within state.
# the income variable is randomly generated
data=rbind(matrix(rep("nc",165),165,1,byrow=TRUE),matrix(rep("sc",70),70,1,byrow=TRUE))
data=cbind.data.frame(data,c(rep(1,100), rep(2,50), rep(3,15), rep(1,30),rep(2,40)),
1000*runif(235))
names(data)=c("state","region","income")
# computes the population stratum sizes
table(data$region,data$state)
# not run
# nc sc
# 1 100 30
# 2 50 40
# 3 15 0
# there are 5 cells with non-zero values; one draws 5 samples (1 sample in each stratum)
# the sample stratum sizes are 10,5,10,4,6, respectively
# the method is 'srswor' (equal probability, without replacement)
s=strata(data,c("region","state"),size=c(10,5,10,4,6), method="srswor")
# extracts the observed data
x=getdata(data,s)
# generates randomly the 'status' column (1 - respondent, 0 - nonrespondent)
status=round(runif(nrow(x)))
x=cbind(x,status)
# computes the response probabilities
rmodel(x$status~x$income+x$Stratum,weights=1/x$Prob,x)
# the same example without stratification
rmodel(x$status~x$income,weights=1/x$Prob,x)
}
\keyword{survey}
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