Christian,
One of the arguments to prcomp() is retx, with a default value of
TRUE. As explained in the help file, if retx is TRUE the prcomp object
reurned by the function contains the projection of the original data
along the principal components (which many of us call "scores"). Thus
> z <- prcomp(cbind(runif(20),runif(20)))
> z$x
PC1 PC2
[1,] -0.176564769 -0.175010202
[2,] 0.286746995 -0.464200150
[3,] -0.010385993 0.415020704
[4,] 0.002108131 -0.150929991
[5,] -0.030974478 0.005799164
[6,] 0.516400555 0.051731728
[7,] -0.273545829 0.082611939
[8,] 0.028956640 -0.095674198
[9,] -0.343703128 0.148523268
[10,] -0.267409591 0.236258325
[11,] -0.362410088 0.278893388
[12,] 0.198170683 -0.282954098
[13,] -0.195901187 0.030789448
[14,] 0.544997090 -0.089702393
[15,] -0.361799979 -0.055526763
[16,] 0.117021002 0.362033198
[17,] -0.385818070 -0.459613063
[18,] -0.182413187 -0.218456938
[19,] 0.386595491 0.337704022
[20,] 0.509929714 0.042702611
You can use the columns of the x component of your prcomp object as the
independent variable in your regression analysis or whatever.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
David W. Roberts office 406-994-4548
Professor and Head FAX 406-994-3190
Department of Ecology email droberts at montana.edu
Montana State University
Bozeman, MT 59717-3460
Christian Jones wrote:> hello R_team
>
> having perfomed a PCA on my fitted model with the function:
>
> data<- na.omit(dataset)
>
> data.pca<-prcomp(data,scale =TRUE),
>
> I??ve decided to aggregate two variables that are highly correlated.
>
> My first question is:
>
> How can I combine the two variables into one new predictor?
>
> and secondly:
>
> How can I predict with the newly created variable in a new dataset?
>
> Guess I need the "predict" and "new data" command but
I??m having problems with the syntax and the help function was not sufficient on
this issue.
>
> many thanks in advance
>
> Christian
>
>
>
>
>
>
>
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>
>
>
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