MMIX-package {MMIX}R Documentation

Model selection uncertainty and model mixing

Description

Implement different types of model mixing and model selection methods for linear or logistic models.

Details

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.

Author(s)

Marie Morfin and David Makowski

Maintainer: Who to complain to <makowski@grignon.inra.fr>

References

Barbottin A., Makowski D., Le Bail M., Jeuffroy M.-H., Bouchard C., Barrier C. (2008) Comparison of models and indicators for categorizing soft wheat fields according to their grain protein contents, European Journal of Agronomy 29, 159-183.

Buckland, S.T., Burnham, K.P. and Augustin, N.H. (1997) Model selection: an integral part of inference, Biometrics 53, 603-618.

Burnham, K.P. and Anderson, D.R. (2002) Model selection and multimodel inference: a practical information-theoretic approach, Springer-Verlag, New York.

Casagrande, M., David, C., Valantin-Morison, M., Makowski, D. and Jeuffroy, M.-H. (2009) Factors limiting protein content of organic winter wheat in south- eastern France: a mixed-model approach, Agronomy for Sustainable Development, In press.

Chatfield, C. (1995) Model uncertainty, data mining and statistical inference, Journal of the Royal Statistical Society /A 158, 419-466.

Efron, B. (1979) Bootstrap methods : another look at the jackknife, American Statistician 7, 1-26.

Efron, B. and Tibshirani, R.J. (1993) An introduction to the bootstrap, Chapman & Hall.

Hammersley, J.M. and Handscomb, D.C. (1964) Monte Carlo Methods, Chapman & Hall, London & New York.

Hoeting, JA., Madigan, D., Raftery, A.E. and Volinsky, C.T. (1999), Bayesian model averaging: a tutorial, Statistical Science, 14, 382-417.

Miller A. (2002) Subset selection in regression, 2nd edition Chapman & Hall/CRC, New York.

Mooney, C.Z. and Duval, R.D. (1993) Bootstrapping : a nonparametric approach to statistical inference, Sage Publications, London.

Prost, L., Makowski, D. and Jeuffroy, M.-H. (2006) Comparison of stepwise selection and Bayesian model averaging for yield gap analysis, Ecological Modelling 219, 66-76.

Raftery, A.E., Madigan D. and Hoeting J.A. (1997) Bayesian model averaging for linear regression models, Journal of the American Statistical Association 92, 179-191.

Sing, T., Sander, O., Beerenwinkel, N. and Lengauer, T. (2005) ROCR: visualizing classifier performance, Bioinformatics applications note 21, 3940-3941.

Viallefont, V., Raftery A.E. and Richardson, S. (2001) Variable selection and Bayesian model averaging in case-control studies, Statistics in medicine 20, 3215-3230.

Yuan, Z. and Ghosh, D. (2008) Combining Multiple Biomarker Models in Logistic Regression, Biometrics 64, 431-439.

Yuan, Z. and Yang, Y. (2005) Combining Linear Regression Models: When and How?, Journal of the American Statistical Association 100, 1202-1214.

Examples


###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



[Package MMIX version 1.1 Index]