What is the difference between the usage of proc factor and princomp for doing PCA analysis?

[sdhilip]

I am doing PCA analysis in SAS Guide. I have a Pizza dataset which has a brand, ID and 7 variables.

Method: 1

I am using proc factor and finding the eigenvalue and interpret the result from the rotated factor pattern. The method used: Principal

proc factor data=work.pizza1
method=principal scree mineigen=0 NFACTORS=7 ROTATE=varimax REORDER OUT=PIZZA1;
var mois--cal;
run;

Result:

I reduced three variables from the rotated factor pattern.

Method: 2

I am using proc princomp.

proc princomp data=work.pizza1 out=new;
var mois--cal;
run;

I am getting eigen value same for both the methods as it is derived from the correlation matrix. However, value is different for rotated factor pattern and eigen vector .

Result:

My question:

1) What is the difference between princomp vs proc factor while using PCA analysis?

2) I am interpreting the result from rotated factor pattern in proc factor. How to interpret the result from princomp? Should I use eigenvector to interpret the result? If so, it shows a different one compared to proc factor.

3) Are the results same if I run both factor and princomp?

4) How to use the method in princomp? Here, I used varimax. 

I attached the output and sas code.

Please advise.

[PaigeMiller]

The difference is that you asked PROC FACTOR to rotate the components. The PROC PRINCOMP output does not rotate the components.

[sdhilip]

Thanks @PaigeMiller  Which result should I take proc factor are princomp? Which one should I go if I asked to do a PCA analysis? Please advise.

[PaigeMiller]

There is no possible answer to this without a lot more information about the goals of the analysis. Rotated or unrotated components both have usefulness, depending on what you are trying to do with the components.