MMIX-package {MMIX} | R Documentation |
Implement different types of model mixing and model selection methods for linear or logistic models.
Package: | MMIX |
Type: | Package |
Version: | 2.9.1. |
Date: | 2009-07-30 |
License: | GPL (>= 2) |
LazyLoad: | yes |
This package implements three model mixing methods, two model selection procedures and three functions for model evaluation.
Marie Morfin and David Makowski
Maintainer: Who to complain to <makowski@grignon.inra.fr>
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###Data #Explanatory variables X1<-c(-0.2,-2.4,-0.7,1.2,0.0,-1.1,-2.1,-0.3,2.0,-1.7,1.4,-1.3,-3.4,0.4,-1.3, -4.8) X2<- c(-3, 2, 1, -2, -2, -4, 0, 1, 1, -1, -1, -4, 0, 2, 0, -4) X3<-c(2,1,0,-2,1,-2, 0, -1, -4, 1, -3, -3, -3, -1, 0, 2) ##Linear model Y1<- c(8.7, 6, 9.1, 10.4, 7.6 ,10.4, 7.9, 11.9, 18, 10.5, 16.5, 8.8, 7.7, 13.5, 8.2, 0.8) data1<-data.frame(Y1,X1,X2,X3) ##Logistic model Y2<-c(1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1) data2<-data.frame(Y2,X1,X2,X3) ##bootFreq #Stepwise selection method bootStep1<-bootFreq(data=data1,family=gaussian("identity"),nboot=50,method=2, criterion="bic",trace=0) bootStep1 summary(bootStep1) plot(bootStep1) bootStep2<-bootFreq(data=data2,family=binomial("logit"),method=2, criterion="bic",nboot=20,trace=0) bootStep2 summary(bootStep2) plot(bootStep2) ##ARMS method armsResult1<-arms(data=data1,family=gaussian("identity"),nbest=5,nsample=10, criterion="both",weight="aic") armsResult1 summary(armsResult1) plot(armsResult1) armsResult2<-arms(data=data2,family=binomial("logit"),nbest=5,nsample=10, criterion="both",weight="aic") armsResult2 summary(armsResult2) plot(armsResult2) ##Root Mean Square Error by cross-validation #Stepwise selection with BIC pmseStepBic<-pmseCV(data=data1,method=2,np=1,random=FALSE,direction="both", criterion="bic",trace=0) pmseStepBic #BMA pmseBMA<-pmseCV(data=data1,method=3,np=1,random=FALSE) pmseBMA ##Area Under ROC Curve by cross-validation aucStepBic<-aucCV(data=data2,method=2,np=1,random=FALSE,direction="both", criterion="bic",trace=0) aucStepBic aucBMA<-aucCV(data=data2,method=3,np=1,random=FALSE) aucBMA