robustbase (version 0.93-6)

foodstamp: 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)

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.

K<U+00FC>nsch, 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

# NOT RUN {
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)
# }