Difference between proc princomp in SAS and princomp command in R?

Question

I am currently trying to obtain equivalent results with the proc princomp command in SAS and the princomp() command in R (in the stats package). The results I am getting are very similar, leading me to suspect that this isn't a problem with different options settings in the two commands. However, the outpus are also different enough that the component scores for each data row are notably different. They are also sign-reversed, but this doesn't matter, of course.

The end goal of this analysis is to produce a set of coefficients from the PCA to score data outside the PCA routine (i.e. a formula that can be applied to new datasets to easily produce scored data).

Without posting all my data, I'm hoping someone can provide some information on how these two commands may differ in their calculations. I don't know enough about the PCA math to determine if this is a conceptual difference in the processes or just something like an internal rounding difference. For simplicity, I'll post the eigenvectors for PC1 and PC2 only.

In SAS:

proc princomp data=climate out=pc_out outstat=pc_outstat; 
var MAT MWMT MCMT logMAP logMSP CMI cmiJJA DD_5 NFFD; 
run;

returns

Eigenvectors
       Prin1  Prin2  Prin3  Prin4  Prin5  Prin6  Prin7  Prin8  Prin9 
MAT    0.372  0.257  -.035  -.033  -.106  0.270  -.036  0.216  -.811 
MWMT   0.381  0.077  0.160  -.261  0.627  0.137  -.054  0.497  0.302 
MCMT   0.341  0.324  -.229  0.046  -.544  0.421  0.045  0.059  0.493 
logMAP -.184  0.609  -.311  -.357  -.041  -.548  0.183  0.183  0.000 
logMSP -.205  0.506  0.747  -.137  -.040  0.159  -.156  -.266  0.033 
CMI    -.336  0.287  -.451  0.096  0.486  0.499  0.050  -.318  -.031 
cmiJJA -.365  0.179  0.112  0.688  -.019  0.012  0.015  0.588  0.018 
DD_5   0.379  0.142  0.173  0.368  0.183  -.173  0.725  -.282  0.007 
NFFD   0.363  0.242  -.136  0.402  0.158  -.351  -.637  -.264  0.052 

In R:

PCA.model <- princomp(climate[,c("MAT","MWMT","MCMT","logMAP","logMSP","CMI","cmiJJA","DD.5","NFFD")], scores=T, cor=T)
PCA.model$loadings

returns

Eigenvectors
       Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9
MAT    -0.372 -0.269         0.126        -0.250         0.270  0.789
MWMT   -0.387        -0.171         0.675                0.494 -0.325
MCMT   -0.339 -0.332  0.250  0.164 -0.500 -0.414               -0.510
logMAP  0.174 -0.604  0.309  0.252         0.619 -0.213  0.125       
logMSP  0.202 -0.501 -0.727  0.223        -0.162  0.175 -0.268       
CMI     0.334 -0.293  0.459 -0.222  0.471 -0.495        -0.271       
cmiJJA  0.365 -0.199 -0.174 -0.612 -0.247                0.590       
DD.5   -0.382 -0.143 -0.186 -0.421               -0.695 -0.360       
NFFD   -0.368 -0.227        -0.487         0.309  0.655 -0.205  

As you can see, the values are similar (sign reversed), but not identical. The differences matter in the scored data, the first row of which looks like this:

     Prin1  Prin2  Prin3  Prin4  Prin5  Prin6  Prin7  Prin8  Prin9 
SAS  -1.95   1.68  -0.54   0.72  -1.07   0.10  -0.66  -0.02   0.05
R     1.61  -1.99   0.52  -0.42  -1.13  -0.16   0.79   0.12  -0.09

If I use a GLM (in SAS) or lm() (in R) to calculate the coefficients from the scored data, I get very similar numbers (inverse sign), with the exception of the intercept. Like so:

in SAS:

proc glm order=data data=pc_out;
model Prin1 = MAT MWMT MCMT logMAP logMSP CMI cmiJJA DD_5 NFFD;
run;

in R:

scored <- cbind(PCA.model$scores, climate)
pca.lm <- lm(Comp.1~MAT+MWMT+MCMT+logMAP+logMSP+CMI+cmiJJA+DD.5+NFFD, data=scored)

returns

    Coefficients:
    (Int)  MAT    MWMT   MCMT   logMAP  logMSP  CMI     cmiJJA  DD.5     NFFD 
SAS  0.42   0.04   0.06   0.03  -0.65   -0.69   -0.003  -0.01    0.0002   0.004
R   -0.59  -0.04  -0.06  -0.03   0.62    0.68    0.004   0.02   -0.0002  -0.004

So it would seem that the model intercept is changing the value in the scored data. Any thoughts on why this happens (why the intercept is different) would be appreciated.

Answer 1

Thanks again to all those who commented. Embarrassingly, the differences I found between the SAS proc princomp and R princomp() procedures was actually a product of a data error that I made. Sorry to those who took time to help answer.

But rather than let this question go to waste, I will offer what I found to be statistically equivalent procedures for SAS and R when running a principal component analysis (PCA).

The following procedures are statistically equivalent, with data named 'mydata' and variables named 'Var1', 'Var2', and 'Var3'.

In SAS:

* Run the PCA on your data;
proc princomp data=mydata out=pc_out outstat=pc_outstat; 
var Var1 Var2 Var3; 
run;
* Use GLM on the individual components to obtain the coefficients to calculate the PCA scoring;
proc glm order=data data=pc_out;
model Prin1 = Var1 Var2 Var3;
run;

In R:

PCA.model <- princomp(mydata[,c("Var1","Var2","Var3")], scores=T, cor=T)
scored <- predict(PCA.model, mydata)
scored <- cbind(PCA.model$scores, mydata)
lm(Comp.1~Var1+Var2+Var3, data=scored)