Hello,
First, you can try to split your data frame in this way:
list.year<-split(pca, unique(pca$year))
And then apply the principal component analysis over the list "list.year".
Regards,
Carlos Ortega
www.qualityexcellence.es
On Tue, Mar 22, 2011 at 12:35 PM, mathijsdevaan <mathijsdev...@gmail.com>wrote:
> Hi,
>
> I am trying to calculate Principal Component Scores per id per year using
> the psych package. The following lines provide the scores per obeservation
>
> pca = data.frame(read.table(textConnection(" id year A B C D
> 1001 1972 64 56 14 23
> 1003 1972 60 55 62 111
> 1005 1972 57 51 10 47
> 1007 1972 59 49 7 10
> 1009 1972 65 50 9 32
> 1011 1972 52 58 3 5
> 1013 1972 63 52 9 27
> 1001 1973 65 56 14 25
> 1003 1973 61 55 62 7
> 1005 1973 58 51 10 47
> 1007 1973 60 49 7 19
> 1009 1973 66 50 9 32
> 1011 1973 53 58 3 5
> 1013 1973 64 52 9 28
> 1001 1974 66 56 14 23
> 1003 1974 62 55 62 11
> 1005 1974 59 51 10 46
> 1007 1974 61 49 7 10
> 1009 1974 67 50 9 38
> 1011 1974 54 58 3 5
> 1013 1974 65 52 9 24
> 1001 1975 67 56 1 23
> 1003 1975 63 55 6 1
> 1005 1975 60 51 1 47
> 1007 1975 62 49 7 10
> 1009 1975 68 50 9 32
> 1011 1975 55 58 3 5
> 1013 1975 66 52 9 27"),head=TRUE))
>
> library(psych)
> pcascores<-principal(pca[,-2], nfactors=1, score=TRUE)
>
> However, the scores should be based on the observations per year rather
> than
> the full set of observations. The final output should be a data frame with
> 7
> rows (one for each id) and 4 columns (one for each year) filled with the
> Principal Component Scores per id per year. Any ideas on how to do this?
>
> Thanks!
>
> --
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