Hi there!
I'm fairly new SAS and I'm trying to run some regressions using proc glm in Enterprise Guide.
I want to run a basic OLS linear regression. The reason I'm using proc glm instead proc reg is so that I can use class variables. I read that proc reg does not support this.
Say I have a sample with 2000 observations, and I want to estimate a series of coeffecients for all the independant variables. So far I'm all good with the following lines of code:
------------------
proc glm data=WORK.INPUT PLOTS=ALL; | |
where group=1 AND NB=0; | |
class C D; | |
model X= A B C D/ | |
solution; | |
output out=WORK.TEST p=yhat r=resid; | |
run;
-------------
Now I have another dataset with an additional 20 000 observations. They all include the independant variables A - D, but lack the dependant X.
How do I predict X in this dataset, using the coefficients from the above stated regression?
PGs example is known as the "missing response trick": The missing value trick for scoring a regression model - The DO Loop
For other ways to score a data set, see Techniques for scoring a regression model in SAS - The DO Loop
For your example, I'd use the STORE statement followed by the PLM procedure.
One way is to append your additional observations to your input dataset and give them a frequency of zero (that way, even if they included dependant values, additional observations would be excluded from the regression)
data FULL / view=FULL;
set INPUT (in=inInput) ADDITIONAL;
where group=1 AND NB=0;
freq = inInput;
run;
proc glm data=FULL PLOTS=ALL;
class C D;
freq freq;
model X= A B C D/ solution;
output out=TEST(where=(not freq)) p=yhat r=resid;
run;
(Untested)
PG
PGs example is known as the "missing response trick": The missing value trick for scoring a regression model - The DO Loop
For other ways to score a data set, see Techniques for scoring a regression model in SAS - The DO Loop
For your example, I'd use the STORE statement followed by the PLM procedure.
Thank you both!
The STORE and PLM procedure is exactly what I was looking for. I found your blog post very useful Rick - thanks again!
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