Andreu Ferrero Gmail
2016-Mar-24 13:34 UTC
[R] glmer() -> corrected AUC optimism by bootstraping technic bootMer() [internal validation of a mixed-effects-model]
> > > I would like to do an internal validation of a discriminative ability of a mixed effects models. > > Here is my scrip: > > ########################### > ####bootMer-> boot AUC##### > ########################### > > library(lme4) > library(lattice) > data(cbpp) > > #fit a model > > cbpp$Y<-cbpp$incidence>=1 > glmm<-glmer(Y~period + size + (1|herd), family=binomial, data=cbpp) > glmm > > ##### funcio: versio 3 - no cal posar endpoint en la funcio > ########################################################## > > > > AUCFun <- function(fit) { > library(pROC) > pred<-predict(fit, type="response") > AUC<-as.numeric(auc(fit at resp$y, pred)) > } > > > #test > > (AUCFun(glmm)) > > ###run bootMer: AUCFun > > > > system.time(AUC.boot <- bootMer(glmm,nsim=100,FUN=AUCFun,seed=1982, use.u=TRUE, > type="parametric", parallel="multicore", ncpus=2)) > > > #... > > (boot.ci(AUC.boot, index =c(1,1), type="norm")) > > roc(cbpp$Y, predict(glmm, type="response")) > > > #Now it seems more reasonable, bias as "optimism"... but still do not know #if I am just doing a AUC with bootstrap CI > ************************************************************************************************************************************** > > >
Bert Gunter
2016-Mar-24 17:21 UTC
[R] glmer() -> corrected AUC optimism by bootstraping technic bootMer() [internal validation of a mixed-effects-model]
1. I cannot find a question here. Maybe I missed it. Maybe you should be clearer. 2. You should most this on the mixed-models list, rather than here: https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Thu, Mar 24, 2016 at 6:34 AM, Andreu Ferrero Gmail <fromnorden at gmail.com> wrote:> > >> >> >> I would like to do an internal validation of a discriminative ability of a mixed effects models. >> >> Here is my scrip: >> >> ########################### >> ####bootMer-> boot AUC##### >> ########################### >> >> library(lme4) >> library(lattice) >> data(cbpp) >> >> #fit a model >> >> cbpp$Y<-cbpp$incidence>=1 >> glmm<-glmer(Y~period + size + (1|herd), family=binomial, data=cbpp) >> glmm >> >> ##### funcio: versio 3 - no cal posar endpoint en la funcio >> ########################################################## >> >> >> >> AUCFun <- function(fit) { >> library(pROC) >> pred<-predict(fit, type="response") >> AUC<-as.numeric(auc(fit at resp$y, pred)) >> } >> >> >> #test >> >> (AUCFun(glmm)) >> >> ###run bootMer: AUCFun >> >> >> >> system.time(AUC.boot <- bootMer(glmm,nsim=100,FUN=AUCFun,seed=1982, use.u=TRUE, >> type="parametric", parallel="multicore", ncpus=2)) >> >> >> #... >> >> (boot.ci(AUC.boot, index =c(1,1), type="norm")) >> >> roc(cbpp$Y, predict(glmm, type="response")) >> >> >> #Now it seems more reasonable, bias as "optimism"... but still do not know #if I am just doing a AUC with bootstrap CI >> ************************************************************************************************************************************** >> >> >> > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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.