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标题: Low Rank Radial Smoothing using GLIMMIX and its Scoring [打印本页]

作者: shiyiming    时间: 2010-10-22 13:39
标题: Low Rank Radial Smoothing using GLIMMIX and its Scoring
From oloolo's blog on SasProgramming


<p><a href="http://feedads.g.doubleclick.net/~a/8xZxOVdT1hM6aoQzqeN6tGQKvv8/0/da"><img src="http://feedads.g.doubleclick.net/~a/8xZxOVdT1hM6aoQzqeN6tGQKvv8/0/di" border="0" ismap="true"></img></a><br/>
<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></p>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= &amp; 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.<br />
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<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 = &amp;covars /s;
     random &amp;z /s type=rsmooth  knotmethod=equal(20);
run;

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


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

proc score data=test2 score=beta_random type=parms  out=score_random;
     var _z:;
run;
</code></pre><br />
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Reference:<br />
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1. SAS Institute, Statistical Analysis with the GLIMMIX procedure Course Notes, SAS Press, SAS Institute<br />
2. D Rupper, M.P. Wand, R.J. Carroll, Semiparametric Regression, Cambridge University Press, Cambridge, 2003<br />
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