bestglm: Best Subset GLM
Provides new information criterion BICq as well as AIC, BIC and EBIC for selecting the best model. Additionally, various CV algorithms are also provided.
| Package: | bestglm |
| Type: | Package |
| Version: | 0.33 |
| Date: | 2011-11-03 |
| License: | GLP 2.0 or greater |
| LazyData: | yes |
| LazyLoad: | yes |
bestglm is the main function. All other functions are utility functions and are not normally invoked.
Many examples are provided in the vignettes accompanying this package.
The vignettes are produced using the R package Sweave and so R scripts
can easily be extracted.
The R package xtable is needed for the vignette in SimExperimentBICq.Rnw.
A.I. McLeod and Changjiang Xu
Xu, C. and McLeod, A.I. (2009). Bayesian Information Criterion with Bernouilli Prior.
## Not run: data(zprostate) train<-(zprostate[zprostate[,10],])[,-10] #Best subset using AIC bestglm(train, IC="AIC") #Best subset using BIC bestglm(train, IC="BIC") #Best subset using EBIC bestglm(train, IC="BICg") #Best subset using BICg with g=0.5 (tuning parameter) bestglm(train, IC="BICg", t=0.5) #Best subset using BICq. Note BICq with q=0.25 is default. bestglm(train, IC="BICq") #Best subset using BICq with q=0.5 (equivalent to BIC) bestglm(train, IC="BICq", t=0.5) #Remark: set seed since CV depends on it set.seed(123321123) bestglm(train, IC="CV", t=10) #using HTF method bestglm(train, IC="CV", CVArgs=list(Method="HTF", K=10, REP=1)) #Best subset, logistic regression data(SAheart) bestglm(SAheart, IC="BIC", family=binomial) #Best subset, factor variables with more than 2 levels data(AirQuality) #subset bestglm(AirQuality, IC="BICq") ## End(Not run)
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