[R] R vs SPSS output for princomp

[Edgar Acuna]

Hi,
I compared the R's results with those given by MINITAB and SAS and they
are OK. Your problem is with SPSS that unfortunately I have never used it.

Edgar
On Mon, 5 May 2003, James Howison wrote:

> Hi,
>
> I am using R to do a principal components analysis for a class
> which is generally using SPSS - so some of my question relates to
> SPSS output (and this might not be the right place).  I have
> scoured the mailing list and the web but can't get a feel for this.
> It is annoying because they will be marking to the SPSS output.
>
> Basically I'm getting different values for the component loadings
> in SPSS and in R - I suspect that there is some normalization or
> scaling going on that I don't understand (and there is plenty I
> don't understand).  The scree-plots (and thus eigen values for each
> component) and Proportion of Variance figures are identical - but
> the factor loadings are an order of magnitude different.  Basically
> the SPSS loadings are much higher than those shown by R.
>
> Should the loadings returned by the R princomp function and the
> SPSS "Component Matrix" be the same?
>
> And subsidiary question would be:  How does one approximate the
> "Kaiser's little jiffy" test for extracting the components (SPSS
> by default eliminates those components with eigen values below 1)?
> I've been doing this by loadings(DV.prcomped)[,1:x] after inspecting
> the scree plot (to set x) - but is there another way?
>
> The full R commands and SPSS syntax follow below along with the
> differing output.
>
> Thanks, James
> http://freelancepropaganda.com
>
> R analysis
> ===========
> I run:
>
>  > library(mva)
>  > DVfmla
> ~webeval1 + webeval2 + webeval3 + webeval4 + webeval5 + webeval6 +
>      webeval7 + webeval8
>  > loadings(DV.pca <- princomp(DVfmla, scale=T, cor=T))
>
> Loadings:
>           Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8
> webeval1 -0.357  0.258 -0.202  0.458  0.629 -0.350  0.112 -0.159
> webeval2 -0.340  0.510         0.255 -0.305  0.651  0.136 -0.143
> webeval3 -0.319  0.316 -0.276 -0.797  0.244        -0.145
> webeval4  0.247  0.633  0.681               -0.248
> webeval5  0.391  0.150 -0.357 -0.183 -0.158 -0.185  0.584 -0.513
> webeval6  0.392  0.252 -0.282  0.140               -0.756 -0.334
> webeval7 -0.382  0.128 -0.162        -0.651 -0.596 -0.114  0.121
> webeval8  0.377  0.268 -0.428  0.158                0.143  0.746
>
> <snip SS loadings>
>
>  >plot(DV.pca)  # This is exactly the same as the SPSS scree-plot.
>
> SPSS Analysis
> =============
>
> FACTOR
>    /VARIABLES webeval1 webeval2 webeval3 webeval4
>               webeval5 webeval6 webeval7 webeval8
>    /MISSING LISTWISE
>    /ANALYSIS webeval1 webeval2 webeval3 webeval4
> 			webeval5 webeval6 webeval7 webeval8
>    /PRINT INITIAL EXTRACTION
>    /PLOT EIGEN
>    /CRITERIA FACTORS(8) ITERATE(25)
>    /EXTRACTION PC
>    /ROTATION NOROTATE
>    /METHOD=CORRELATION .
>
> As mentioned the proportions of varience explained and the scree
> plot are identical.  However SPSS produces this "Component Matrix"
> which we, in class, have been calling "the loadings":
>
> WEBEVAL1  -0.798  0.253  0.178  0.317 -0.370  0.167 -0.033 -0.037
> WEBEVAL2  -0.764  0.487  0.026  0.188  0.186 -0.309 -0.108 -0.043
> WEBEVAL3  -0.719  0.309  0.217 -0.564 -0.125 -0.040  0.043  0.052
> WEBEVAL4   0.558  0.591 -0.563 -0.063 -0.029  0.131  0.030 -0.019
> WEBEVAL5   0.864  0.161  0.313 -0.128  0.075  0.138 -0.221 -0.200
> WEBEVAL6   0.876  0.252  0.237  0.100  0.008  0.017 -0.088  0.308
> WEBEVAL7  -0.858  0.128  0.133  0.054  0.349  0.308  0.090  0.037
> WEBEVAL8   0.847  0.256  0.316  0.111  0.000 -0.087  0.296 -0.094
>
> Can anyone tell me why these are different (It seems likely that
> this is a scaling of some kind as the SPSS ones just look to have
> been made larger in some way).  Or is it that SPSS is reporting
> cumulatively while R is not?
>
> Thanks in advance,
> James
>
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