Dear useRs,
What is an efficient way to randomly sample from clustered data such
that I get equal representation from each cluster? For example, let's
say I want to randomly sample two cases from each cluster created by the
"id" variable in the following data frame:
> id<-c(rep("100", 4),rep("101", 3), rep("102", 6), rep("103", 7))
> sex<-sample(c("m","f"), 20, replace=TRUE)
> weight<-rnorm(n=20, mean=150, sd=3)
> attitude<-sample(1:7, 20, replace=TRUE)
> Dataf<-data.frame(id,sex,weight,attitude)
> Dataf
id sex weight attitude
1 100 m 146.5064 6
2 100 f 150.2317 4
3 100 f 149.3686 5
4 100 m 144.7218 7
5 101 m 147.9071 4
6 101 m 148.3802 6
7 101 m 154.4634 1
8 102 m 153.2719 5
9 102 m 148.9821 5
10 102 f 148.0656 1
11 102 f 148.8949 6
12 102 m 146.9963 4
13 102 m 153.0542 4
14 103 m 148.1558 1
15 103 f 148.0482 4
16 103 m 151.8044 2
17 103 f 155.4976 4
18 103 m 150.0423 1
19 103 f 146.0487 5
20 103 m 154.6651 7
>
Here's the R code I wrote that obviously does not work:
sapply(split(Dataf, Dataf$id), sample, size=2)
I would prefer a data frame (i.e., Dataf2) as the final output and it
should look something like this:
> Dataf2
id sex weight attitude
1 100 m 146.5064 6
2 100 m 144.7218 7
3 101 m 147.9071 4
4 101 m 154.4634 1
5 102 m 153.2719 5
6 102 m 148.9821 5
7 103 f 155.4976 4
8 103 f 146.0487 5
>
Thanks in advance in your assistance.
Tony
------------------------------------------------------------------
Tony N. Brown, Ph.D.
Associate Professor of Sociology
Faculty Head of Hank Ingram House, The Commons
Research Fellow, Vanderbilt Center for Nashville Studies
Vanderbilt University
(615) 322-7518
(615) 322-7505 fax
[EMAIL PROTECTED] <mailto:[EMAIL PROTECTED]>
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