indonRespir {HRW}

Respiratory infection in Indonesian children

Description

Indonesian Children's Health Study of respiratory infections for a cohort of 275 Indonesian children. The data are longitudinal with each child having between 1 and 6 repeated measurements.

Usage

data(indonRespir)

Format

A data frame with 1200 observations on the following 12 variables:

idnum

child identification number.

respirInfec

indicator of presence of resipiratory infection.

age

age of the child in years.

vitAdefic

indicator of Vitamin A deficiency:
1 = the child had Vitamin A deficiency,
0 = the child did not have Vitamin A deficiency.

female

indicator of child being female:
1 = the child is female,
0 = the child is male.

height

height of the child in centimeters.

stunted

indicator of the child being "short for his/her age":
1 = the child is "short for his/her age",
0 = the child is not "short for his/her age"

visit2

indicator that the child had exactly 2 clinical visits:
1 = the exact number of clinical visits was 2,
0 = the exact number of clinical visits was not 2.

visit3

indicator that the child had exactly 3 clinical visits:
1 = the exact number of clinical visits was 3,
0 = the exact number of clinical visits was not 3.

visit4

indicator that the child had exactly 4 clinical visits:
1 = the exact number of clinical visits was 4,
0 = the exact number of clinical visits was not 4.

visit5

indicator that the child had exactly 5 clinical visits:
1 = the exact number of clinical visits was 5,
0 = the exact number of clinical visits was not 5.

visit6

indicator that the child had exactly 6 clinical visits:
1 = the exact number of clinical visits was 6,
0 = the exact number of clinical visits was not 6.

Source

Sommer, A. (1982). Nutritional Blindness. New York: Oxford University Press.

References

Diggle, P., Heagerty, P., Liang, K.-L. and Zeger, S. (2002). Analysis of Longitudinal Data (Second Edition). Oxford: Oxford University Press.

Examples

library(HRW) ; data(indonRespir)
if (require("mgcv"))
{
   fit <- gamm(respirInfec ~ s(age) + vitAdefic + female + height
            + stunted + visit2 + visit3 + visit4  + visit5 + visit6,
            random = list(idnum = ~1),family = binomial,data = indonRespir)
   summary(fit$gam)
}