Cautions
- The amount of time that FACTOR takes is roughly
proportional to the cube of the number of variables.
Factoring 100 variables, therefore, takes about
1000 times as long as factoring 10 variables.
Iterative methods (PRINIT, ALPHA, ULS, ML)
can also take 100 times as long as noniterative
methods (PRINCIPAL, IMAGE, HARRIS).
- No computer program is capable of reliably
determining the optimal number of factors
since the decision is ultimately subjective.
You should not blindly accept the number of
factors obtained by default; instead, use
your own judgment to make a decision.
- Singular correlation matrices cause
problems with the options PRIORS=SMC and METHOD=ML.
Singularities can result from using a variable
that is the sum of other variables, coding too
many dummy variables from a classification variable,
or having more variables than observations.
- If you use the CORR procedure to compute the correlation
matrix and there are missing data and the NOMISS option
is not specified, then the correlation matrix may have
negative eigenvalues.
- If a TYPE=CORR, TYPE=UCORR or TYPE=FACTOR data set is copied
or modified using a DATA step, the new data set does not
automatically have the same TYPE as the old data set.
You must specify the TYPE= data
set option in the DATA statement.
If you try to analyze a data set that has lost its
TYPE=CORR attribute, PROC FACTOR displays a warning message
saying that the data set contains _NAME_ and _TYPE_
variables but analyzes the data set as an ordinary SAS
data set.
- For a TYPE=FACTOR data set, the default
is METHOD=PATTERN, not METHOD=PRIN.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
