Proc PLS by variable and CV

[Daisy2]

Hi, I'm running a PLS model with a "by" variable which has 2 levels (2014 vs. 2017). I've run CV to select the correct number of latent factors, but  for each of my "by" variables I need to use a different number of latent factors (3 vs. 5).  How do I tell it that if by=2014 use 3 factors and if by=2017 then use 5?  Here's the code for the cross-validation and the model with the factors. Thanks.

/* Running Model By Year to separate out 2014 from 2017 */
/* Global Model with all bands at R1 */
proc pls data=splitplsr2 cv=random(seed=12345) cvtest varss plots=(diagnostics dmod scores ParmProfiles VIP XLoadingProfiles);
	by yr;
	model meas =
R1Avg_412_d.......etc.
R1Avg_917_d / solution;
	output OUT=test1; 
	ods output VariableImportancePlot=vip;
	title 'Global PLS Full Model';
run;

/*  Model with Factors chosen */
proc pls data=splitplsr2 nfac=3 varss plots=(diagnostics dmod scores ParmProfiles VIP XLoadingProfiles);
	by year;
    model meas = 
R1Avg_412_d....etc.
R1Avg_917_d / solution;
	output OUT=outfile predicted=predR1f press=pressR1f yresidual=yresidR1f ; 
	ods output VariableImportancePlot=vip;
	title 'Global PLS Full Final Model';
run;

[PaigeMiller]

If you want NFAC=3 for one year and NFAC=5 for another year, then you can't fit the models with a BY statement. You need to run PROC PLS twice, once for one year and again for the other year.

[Daisy2 · Accepted solution]

Through some persistence I figured out to use a "Where" line within the Proc allows splitting.  Here's the details of the code.  And below this I have another Proc PLS for 

    where yr='2017';

/* Running Model By Year to separate out 2014 from 2017 */
/* Global Model with all bands at R1 */
proc pls data=splitplsr2 cv=random(seed=12345) cvtest varss plots=(diagnostics dmod scores ParmProfiles VIP XLoadingProfiles);
	by yr;
	model meas =
R1Avg_412_d.......etc.
R1Avg_917_d / solution;
	output OUT=test1; 
	ods output VariableImportancePlot=vip;
	title 'Global PLS Full Model';
run;

/*  Model with Factors chosen */
proc pls data=splitplsr2 nfac=3 varss plots=(diagnostics dmod scores ParmProfiles VIP XLoadingProfiles);
	by year;    
        where yr='2014';
        model meas = 
R1Avg_412_d....etc.
R1Avg_917_d / solution;
	output OUT=outfile predicted=predR1f press=pressR1f yresidual=yresidR1f ; 
	ods output VariableImportancePlot=vip;
	title 'Global PLS Full Final Model';
run;