BookmarkSubscribeRSS Feed
🔒 This topic is solved and locked. Need further help from the community? Please sign in and ask a new question.
sophie33
Calcite | Level 5

Hello,

 

I am trying to find some correlations between 11 variables (10 continuous variables (with 2 not normally distributed) and 1 categorial variable (1, 2, 3 or 4)). For that, I would like to start with a descriptive analysis using PCA. Regarding the kind of variables (and distributions) I have, I would like to perform a PCA using a matrix of Spearman's correlation. I have seen that we can do it with R software but I'm using SAS and I don't find any information about it, how to do it...

Do you have any idea of how I can do it (in which statement, syntax).

 

Thank you very much.

 

Sophie

1 ACCEPTED SOLUTION

Accepted Solutions
PGStats
Opal | Level 21

Try, for example

 

proc corr data=sashelp.cars nomiss outs=carsSpear;
var invoice--length;
run;

proc princomp data=carsSpear n=2 plots=pattern;
var invoice--length;
run;
PG

View solution in original post

3 REPLIES 3
Reeza
Super User
Look into PROC PRINCOMP and/or PROC FACTOR.
PGStats
Opal | Level 21

Try, for example

 

proc corr data=sashelp.cars nomiss outs=carsSpear;
var invoice--length;
run;

proc princomp data=carsSpear n=2 plots=pattern;
var invoice--length;
run;
PG
sophie33
Calcite | Level 5

YES!!! It's working!!

Thank you very much!

 

Sophie

sas-innovate-white.png

Missed SAS Innovate in Orlando?

Catch the best of SAS Innovate 2025 — anytime, anywhere. Stream powerful keynotes, real-world demos, and game-changing insights from the world’s leading data and AI minds.

 

Register now

What is Bayesian Analysis?

Learn the difference between classical and Bayesian statistical approaches and see a few PROC examples to perform Bayesian analysis in this video.

Find more tutorials on the SAS Users YouTube channel.

SAS Training: Just a Click Away

 Ready to level-up your skills? Choose your own adventure.

Browse our catalog!

Discussion stats
  • 3 replies
  • 2954 views
  • 1 like
  • 3 in conversation