## 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, package="robustbase")`

### 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 independent
foodSt <- within(foodstamp, {
logInc <- log(1 + income)
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)
```