data-india: Childhood Malnutrition in India

In JeffreyRacine/R-Package-ma: Model Averaging

Description

Demographic and Health Survey data on childhood nutrition in India.

Usage

data(india)

Format

A data frame with 37623 observations on the following 21 variables.

cheight

child's height (centimeters); a numeric vector

cage

child's age (months); a numeric vector

breastfeeding

duration of breastfeeding (months); a numeric vector

csex

child's sex; a factor with levels male female

ctwin

whether or not child is a twin; a factor with levels single birth twin

cbirthorder

birth order of the child; a factor with levels 1 2 3 4 5

mbmi

mother's BMI (kilograms per meter squared); a numeric vector

mage

mother's age (years); a numeric vector

medu

mother's years of education; a numeric vector

edupartner

father's years of education; a numeric vector

munemployed

mother's employment status; a factor variable with levels unemployed employed

mreligion

mother's religion; a factor variable with levels christian hindu muslim other sikh

mresidence

mother's residential classification; a factor with levels urban rural

wealth

mother's relative wealth; a factor with levels poorest poorer middle richer richest

electricity

electricity access; a factor with levels no yes

radio

radio ownership; a factor with levels no yes

television

television ownership; a factor with levels no yes

refrigerator

refrigerator ownership; a factor with levels no yes

bicycle

bicycle ownership; a factor with levels no yes

motorcycle

motorcycle ownership; a factor with levels no yes

car

car ownership; a factor with levels no yes

Source

http://www.econ.uiuc.edu/~roger/research/bandaids/india.Rda

References

Koenker, R. (2011), "Additive models for quantile regression: Model selection and confidence bandaids," Brazilian Journal of Probability and Statistics 25(3), pp. 239-262.

Examples

## Not run: 
data(india)
attach(india)

faccsex <- factor(csex)
facctwin <- factor(ctwin)
faccbirthorder <- factor(cbirthorder)
facmunemployed <- factor(munemployed)
facmreligion <- factor(mreligion)
faccar <- factor(car)

## Estimate a semiparametric additive model averaged model

model <- lm.ma(cheight ~ faccsex + facctwin + faccbirthorder +
               facmunemployed + facmreligion + faccar + cage +
               mbmi + medu,
               basis="additive",
               vc=FALSE)

summary(model)

plot(model,plot.data=TRUE)
plot(model,plot.deriv=TRUE)

## End(Not run)

JeffreyRacine/R-Package-ma documentation built on May 7, 2019, 10:35 a.m.