# R Dataset / Package robustbase / foodstamp

Documentation

On this R-data statistics page, you will find information about the foodstamp data set which pertains to Food Stamp Program Participation. The foodstamp data set is found in the robustbase R package. You can load the foodstamp data set in R by issuing the following command at the console data("foodstamp"). This will load the data into a variable called foodstamp. If R says the foodstamp data set is not found, you can try installing the package by issuing this command install.packages("robustbase") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the foodstamp R data set. The size of this file is about 1,578 bytes.

## Food Stamp Program Participation

### Description

This data consists of 150 randomly selected persons from a survey with information on over 2000 elderly US citizens, where the response, indicates participation in the U.S. Food Stamp Program.

### Usage

data(foodstamp)

### Format

A data frame with 150 observations on the following 4 variables.

participation

participation in U.S. Food Stamp Program; yes = 1, no = 0

tenancy

tenancy, indicating home ownership; yes = 1, no = 0

suppl.income

supplemental income, indicating whether some form of supplemental security income is received; yes = 1, no = 0

income

monthly income (in US dollars)

### Source

Data description and first analysis: Stefanski et al.(1986) who indicate Rizek(1978) as original source of the larger study.

Electronic version from CRAN package catdata.

### References

Rizek, R. L. (1978) The 1977-78 Nationwide Food Consumption Survey. Family Econ. Rev., Fall, 3–7.

Stefanski, L. A., Carroll, R. J. and Ruppert, D. (1986) Optimally bounded score functions for generalized linear models with applications to logistic regression. Biometrika 73, 413–424.

Künsch, H. R., Stefanski, L. A., Carroll, R. J. (1989) Conditionally unbiased bounded-influence estimation in general regression models, with applications to generalized linear models. J. American Statistical Association 84, 460–466.

### Examples

data(foodstamp)(T123 <- xtabs(~ participation+ tenancy+ suppl.income, data=foodstamp))
summary(T123) ## ==> the binary var's are clearly not independentfoodSt <- within(foodstamp, {
rm(income)
})m1 <- glm(participation ~ ., family=binomial, data=foodSt)
summary(m1)
rm1 <- glmrob(participation ~ ., family=binomial, data=foodSt)
summary(rm1)
## Now use robust weights.on.x :
rm2 <- glmrob(participation ~ ., family=binomial, data=foodSt,
weights.on.x = "robCov")
summary(rm2)## aha, now the weights are different:
which( weights(rm2, type="robust") < 0.5)

--

Dataset imported from https://www.r-project.org.

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