Low Rank Radial Smoothing using GLIMMIX and its Scoring

shiyiming · 发表于 2010-10-22 13:39:52

From oloolo's blog on SasProgramming

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<a href="http://feedads.g.doubleclick.net/~a/8xZxOVdT1hM6aoQzqeN6tGQKvv8/1/da"><img src="http://feedads.g.doubleclick.net/~a/8xZxOVdT1hM6aoQzqeN6tGQKvv8/1/di" border="0" ismap="true"></img></a>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] 
 
 
 
<pre style="background-color: #ebebeb; border: 1px dashed rgb(153, 153, 153); color: #000001; font-family: Andale Mono,Lucida Console,Monaco,fixed,monospace; font-size: 12px; line-height: 14px; overflow: auto; padding: 5px; width: 100%;"><code>
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;
</code></pre> 
 
 
 
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 
 
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