see ?cv.glm under the heading "Value". The help files tell you what comes
out.
On Fri, May 28, 2010 at 10:19 PM, azam jaafari <azamjaaf...@yahoo.com>wrote:
> Hi
>
>
> Finally, I did leave-one-out cross validation in R for prediction error of
> logistic regression by cv.glm. But I don't know what are the produced
> data(almost 700)? does delta show me error estimation?
>
>
> cost<-function(a,b)mean(abs(a-b))
> #SALIC=binary response
> salic.lr<-glm(profilesample$SALIC~profilesample$wetnessindex ,
> profilesample, family=binomial('logit'))
> >loadpackage(boot)
> > cv.err<-cv.glm(profilesample, salic.lr, cost, K=100)
> > cv.err
>
> $call
> cv.glm(data = profilesample, glmfit = salic.lr, cost = cost,
> K = 100)
>
> $K
> [1] 100
>
> $delta
> 1 1
> 0.4278 0.4278
>
> $seed
> [1] 403 133 1654269195 -1877109783 -961256264 1403523942
> [7] 124639233 261424787 1836448066 1034917620 -13630729 468718317
> [13] 1694379396 1559298986 1935866133 -1450855505 2105396150 1802260960
> [19] 1077391651 539731521 122505520 230898510 -1940184647 1223031755
> [25] -1597886342 -1854140036 -1783225921 1484611221 1365746860 -346485118
> [31] 1206044253 1201793367 956757054 350214264 -1324711077
> .
> .
> .
> please help me
>
> Thanks alot
>
> --- On Wed, 5/26/10, Joris Meys <jorism...@gmail.com> wrote:
>
>
> From: Joris Meys <jorism...@gmail.com>
> Subject: Re: [R] validation logistic regression
> To: "azam jaafari" <azamjaaf...@yahoo.com>
> Cc: r-help@r-project.org
> Date: Wednesday, May 26, 2010, 5:00 AM
>
>
> Hi,
>
> first of all, you shouldn't backtransform your prediction, use the option
> type=response instead :
>
> salichpred<-predict(salic.lr, newdata=profilevalidation,type="response")
>
> limit <- 0.5
> salichpredcat <- ifelse(salichpred<limit,0,1) # prediction of categories.
>
> Read in on sensitivity, specificity and ROC-curves. With changing the
> limit, you can calculate sensitivity and specificity, and you can construct
> a ROC curve that will tell you how well your predictions are. It all depends
> on how much error you allow on the predictions.
>
> Cheers
> Joris
>
>
>
> On Wed, May 26, 2010 at 10:04 AM, azam jaafari <azamjaaf...@yahoo.com>
> wrote:
>
> Hi
>
> I did validation for prediction by logistic regression according to
> following:
>
> validationsize <- 23
> set.seed(1)
> random<-runif(123)
> order(random)
> nrprofilesinsample<-sort(order(random)[1:100])
> profilesample <- data[nrprofilesinsample,]
> profilevalidation <- data[-nrprofilesinsample,]
> salich<-profilesample$SALIC.H.1
> salic.lr<-glm(salich~wetnessindex, profilesample,
> family=binomial('logit'))
> summary(salic.lr)
> salichpred<-predict(salic.lr, newdata=profilevalidation)
> expsalichpred<-exp(salichpred)
> salichprediction<-(expsalichpred/(1+expsalichpred))
>
> So,
> table(salichprediction, profilevalidation$SALIC.H.1)
>
> in result:
> salichprediction 0 1
> 0.0408806327422231 1 0
> 0.094509645033899 1 0
> 0.118665480273383 1 0
> 0.129685441514168 1 0
> 0.13545295569511 1 0
> 0.137580612201769 1 0
> 0.197265822234215 1 0
> 0.199278585548248 0 1
> 0.202436276322278 1 0
> 0.211278767985746 1 0
> 0.261036846823867 1 0
> 0.283792703256058 1 0
> 0.362229486187581 0 1
> 0.362795636267779 1 0
> 0.409067386115694 1 0
> 0.410860613509484 0 1
> 0.423960962956254 1 0
> 0.428164288793652 1 0
> 0.448509687866763 0 1
> 0.538401659478058 0 1
> 0.557282539294224 1 0
> 0.603881788227797 0 1
> 0.63633478460736 0 1
>
> So, I have salichprediction between 0 to 1 and binary variable(observed
> values) 0 or 1. I want to compare these data together and I want to know is
> ok this model(logistic regression) for prediction or no?
>
> please help me?
>
> Thanks alot
>
> Azam
>
>
>
>
> [[alternative HTML version deleted]]
>
>
> ______________________________________________
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> PLEASE do read the posting guide
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> and provide commented, minimal, self-contained, reproducible code.
>
>
>
>
> --
> Joris Meys
> Statistical Consultant
>
> Ghent University
> Faculty of Bioscience Engineering
> Department of Applied mathematics, biometrics and process control
>
> Coupure Links 653
> B-9000 Gent
>
> tel : +32 9 264 59 87
> joris.m...@ugent.be
> -------------------------------
> Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php
>
>
>
>
> [[alternative HTML version deleted]]
>
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
>
--
Joris Meys
Statistical Consultant
Ghent University
Faculty of Bioscience Engineering
Department of Applied mathematics, biometrics and process control
Coupure Links 653
B-9000 Gent
tel : +32 9 264 59 87
joris.m...@ugent.be
-------------------------------
Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php
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