R: bestglm: Best Subset GLM

bestglm-package {bestglm} | R Documentation

## bestglm: Best Subset GLM

### Description

Provides new information criterion BICq as well as AIC, BIC and EBIC for selecting the best model. Additionally, various CV algorithms are also provided.

### Details

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`.

### Author(s)

A.I. McLeod and Changjiang Xu

### References

Xu, C. and McLeod, A.I. (2009). Bayesian Information Criterion with Bernouilli Prior.

### See Also

`leaps`

### Examples

    ## 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)

[Package bestglm version 0.36 Index]