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Date:         Wed, 3 Jul 2002 13:57:38 -0400
Reply-To:     mark.k.moran@CENSUS.GOV
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Mark Moran <mark.k.moran@CENSUS.GOV>
Subject:      Re: Proc to Cross-Validate Proc Logistic (Proc or macro)
Comments: To: Dale McLerran <>
Content-type: text/plain; charset=us-ascii

Thank you, Dale, you've been a great help.


Dale McLerran <stringplayer_2@ To: mark.k.moran@CENSUS.GOV,> SAS-L@LISTSERV.UGA.EDU cc: 07/03/2002 01:22 Subject: Re: Proc to Cross-Validate Proc PM Logistic (Proc or macro)


As I indicated in my previous reply, the leave-out-one approach does not fit the full maximum likelihood parameter estimates for each sample. Rather, it uses an approximation to the parameter estimates which can be computed with extreme efficiency. It takes very little time to form the leave-out-one approximate parameter estimates. I don't have time to go into the details of how it is done, but if colleague has a PhD in statistics, he would understand quite quickly the efficiency of the approach if he were to look at the documentation pertaining to the CTABLE option.


--- Mark Moran <mark.k.moran@CENSUS.GOV> wrote: > Obviously I am relatively untutored on crossvalidation. My colleague > has a > PhD and his approach is to divide the data into 10 subsamples, fit a > model > using I suppose 9 of the 10 subsamples to predict the 10th, change to > a > different 9 subsamples to predict the 10th, etc. This would require > only > 10 models. If there are 500,000 records, then by the leave-one-out > method > of crossvalidation how long will it take to create 500,000 models in > PROC > LOGISTIC by this method? Wouldn't this quickly become cumbersome > with so > many observations? I think he is predicting from 5 or 6 predictors. > > Mark Moran >

===== --------------------------------------- Dale McLerran Fred Hutchinson Cancer Research Center mailto: Ph: (206) 667-2926 Fax: (206) 667-5977 ---------------------------------------

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