Low Rank Radial Smoothing using GLIMMIX and its Scoring

This post was kindly contributed by SAS Programming for Data Mining Applications - go there to comment and to read the full post.

Low Rank Radial Smoothing using GLIMMIX [1], a semiparametric approach to smooth curves [2]. Specifying TYPE=RSMOOTH option in RANDOM statement, we can implement this spline smooth approach. The bast thing is that for future scoring, data preparation is extremely easy by using the OUTDESIGN= & NOFIT options in v9.2 PROC GLIMMIX, then use PROC SCORE twice on this design matrix to score the fixed effects design matrix X and the random effects design matrix Z, respective, add up together is the score from this radial smoothing method.

[Coming soon]

proc glimmix data=train_data  absconv=0.005;
     model y = &covars /s;
     random &z /s type=rsmooth  knotmethod=equal(20);
run;

proc glimmix data=test  nofit  outdesign=test2;
     model y=&covars /s;
     random &z /s type=rsmooth knotmethod=equal(20);
run;


proc score data=test2  score=beta_fix  type=parms  out=score_fix;
     var  &covars;
run;

proc score data=test2 score=beta_random type=parms  out=score_random;
     var _z:;
run;

Reference:

1. SAS Institute, Statistical Analysis with the GLIMMIX procedure Course Notes, SAS Press, SAS Institute
2. D Rupper, M.P. Wand, R.J. Carroll, Semiparametric Regression, Cambridge University Press, Cambridge, 2003

 

This post was kindly contributed by SAS Programming for Data Mining Applications - go there to comment and to read the full post.