rmodel: Response probability using logistic regression

In sampling: Survey Sampling

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

formula	regression model formula (y~x).
weights	vector of weights; its length is equal to n, the sample size.
X	sample data frame.

Value

The function returns the sample data frame with a new column 'prob_resp', which contains the response probabilities.

See Also

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)

sampling documentation built on Nov. 2, 2023, 6:26 p.m.