PROC GAM error

[psh23]

I am trying to fit a DLNM using GAM in SAS. I am getting errors with my outcome variable, which is binary 0/1, I have tried with both character and numeric, and with and without reference value.

ERROR: The response variable ptb_recode has 1 level(s). A binary response must have two levels.

proc gam data=test1;
class ptb_recode;
model ptb_recode = loess(meanpm25_wk)
loess(lag1)
loess(lag2)
loess(lag3)
loess(lag4)
loess(lag5)
loess(lag6)
loess(lag7)
loess(lag8)
loess(lag9)
loess(lag10)
loess(lag11)
loess(lag12)
loess(lag13)
loess(lag14)
loess(lag15)
loess(lag16)
loess(lag17)
loess(lag18)
loess(lag19)
loess(lag20)
loess(lag21)
loess(lag22)
loess(lag23)
loess(lag24)
loess(lag25)
loess(lag26)
loess(lag27)
loess(lag28)
loess(lag29)
loess(lag30)
loess(lag31)
loess(lag32)
loess(lag33)
loess(lag34)
loess(lag35)
loess(lag36)
/ dist=binomial ;

run;

Thanks!!

[Reeza]

Can you show the output of a PROC FREQ on this variable:  ptb_recode ?

proc freq data=test1;
table  ptb_recode ;
run;

[psh23]

ptb_recode	Frequency	Percent	Cumulative	Cumulative
Frequency	Percent
0	216575	89.38	216575	89.38
1	25740	10.62	242315	100

[Reeza]

Do you have missing values in the other variables? To get that error for some reason SAS is including only observations of a single level which usually occurs if you have missing data for some reason?

Otherwise I think we'll need data to replicate the issue.

[WarrenKuhfeld]

@Reeza gave you the information that you need to answer your question.  I can't help but point out that you don't have to type that much syntax to create your model statement.  A simple ad hoc macro can easily do it, particularly when your variables are numbered lists like you have.

data x;
   input y x1-x10 @@;
   datalines;
1 2 3 3 8 8 5 10 7 5 8 1 5 4 8 10 9 8 10 2 1 5 1 5 8 9 5 10 9 8 7 6 2 1
6 6 3 5 9 6 6 6 8 10 1 7 7 7 8 10 3 3 4 1 10 1 7 9 9 9 9 7 1 9 3 8 1 9 7
10 9 7 8 2 7 2 1 1 10 1 3 10 4 9 2 4 4 6 1 10 5 9 8 7 10 6 1 9 5 1 10 10
8 9 10 5 4 8 8 3 2 2 2 7 2 9 10 2 8 10 2 2 3 8 7 7 4 8 9 1 8 8 2 6 1 1 6
10 10 7 2 2 8 2 6 5 10 2 8 9 4 9 1 7 2 7 5 5 6 4 6 10 8 6 1 2 8 8 1 8 9
1 4 5 4 1 2 8 10 9 2 5 3 10 6 2 6 2 9 10 2 6 9 9 3 1 5 9 2 10 3 10 7 9 3
7 10 6 4 2 10 9 1 2 6 8 8 3 5 10
;

proc gam;
   class y;
   model y = %macro l; %do i = 1 %to 10; loess(x&i) %end; %mend; %l;
run;

[Rick_SAS]

I would not use PROC GAM to analyze 242,000 observations and 37 variables. I suggest you switch to PROC GAMPL, which is much faster. You can use univariate splines instead of loess to model the nonlinearities. For an example and references, see "Nonparametric regression for binary response data in SAS."

[Ksharp]

I agreed with Rick. 242,000 observations  is too much for  PROC GAM to analyze.

PROC GAM required the expanse system resource to run Non parameter regression.

If you want fit non-linear effect, why not try  decision tree PROC HPSPLIT ?