I have data collected from a survey administered on a subset of the population. I also have the population proportions of variables such as gender, race and housing type. I would like to combine the weights from each separate cross tab (of gender, race and housing type) such that the weighted proportions of my survey data matches that of the population.
I have tried the following:
library(survey) gender.population <- read.table("http://dl.dropbox.com/u/822467/Gender.csv", header = TRUE, sep = ",") housing.population <- read.table("http://dl.dropbox.com/u/822467/Housing.csv", header = TRUE, sep = ",") race.population <- read.table("http://dl.dropbox.com/u/822467/Race.csv", header = TRUE, sep = ",") survey.sample <- read.table("http://dl.dropbox.com/u/822467/survey.sample.csv", header = TRUE, sep = ",") survey.object.sample <- svydesign(id = ~1, data = survey.sample) survey.object.sample.weighted <- rake(survey.object.sample, list(~gender, ~housing, ~race), list(gender.population, housing.population, race.population)) str(survey.object.sample.weighted$postStrata)
I see from survey.object.sample.weighted$postStrata that weights have been assigned separately for each of the variable. My question is: Is it possible to get 1 weight for each subject instead of 3 weights as shown in the package?