For example, let Data = data.frame(sex = ..., age =, ..., ...). Then,
Sel.fem50 <- (Data$sex=="Female") & (Data$age> 50)
fem50Answers <- FUN(... data=Data[Sel.fem50,])If you want to do this to many subgoups, consider "split", "by", "lapply", "sapply", "tapply", "aggregate", etc. See Venables and Ripley, Modern Applied Statistics with S, 4th ed.
Hope this helps. Spencer Graves
[EMAIL PROTECTED] wrote:
In a message dated 2/23/03 1:19:39 PM Eastern Standard Time, [EMAIL PROTECTED] writes:
On Wed, 19 Feb 2003 [EMAIL PROTECTED] wrote:
Hi,
is there a way to analyze subpopulations (e.g. women over 50, those who
answered "yes" to a particular question) in a survey using Survey
package?
Other packages (e.g. Stata, SUDAAN) do this with a subpopulation option
to
identify the subpopulation for which the analysis shoud be done. I did
not
see this option in the Survey package. Is there another way to do this?
Not directly.
This only really matters for svymean. For the regression models it's just a convenience as you can specify a model that has an interaction with the subpopulation indicator to get estimates and standard errors in the subpopulation.
For svymean you can use a regression model too: Instead of a hypothetical svymean(~x, design=d, subpop=race==2) do svyglm(x~I(race==2)+0, design=d)
I need to work out if there's a general way to handle subpopulations or whether it needs to be coded on a case by case basis.
-thomas
Thanks a lot for the answer. Is there a way to get quantiles and cross-tabulations in subpopulations? If I think of proportion as mean of binary indicator (y: "got milk?"=1), can I use the solution above for proportions in subpopulations?
svyglm(y~I(race==2)+0, design=d)
If I create a binary indicator for a subpopulation (men over 50), can I use it with svytable?
svytable( ~y+z,design=d)
--anupam.
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Prediction is very difficult, especially about the future. -- Niels Bohr
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