BIC-methods: Log likelihoods and model selection for mle2 objects In bbmle: Tools for General Maximum Likelihood Estimation

Description

Various functions for likelihood-based and information-theoretic model selection of likelihood models

Usage

 ```1 2 3 4 5 6``` ```## S4 method for signature 'ANY,mle2,logLik' AICc(object,...,nobs,k=2) ## S4 method for signature 'ANY,mle2,logLik' qAIC(object,...,k=2) ## S4 method for signature 'ANY,mle2,logLik' qAICc(object,...,nobs,k=2) ```

Arguments

 `object` A `logLik` or `mle2` object `...` An optional list of additional `logLik` or `mle2` objects (fitted to the same data set). `nobs` Number of observations (sometimes obtainable as an attribute of the fit or of the log-likelihood) `k` penalty parameter (nearly always left at its default value of 2)

Details

Further arguments to `BIC` can be specified in the `...` list: `delta` (logical) specifies whether to include a column for delta-BIC in the output.

Value

A table of the BIC values, degrees of freedom, and possibly delta-BIC values relative to the minimum-BIC model

Methods

logLik

`signature(object = "mle2")`: Extract maximized log-likelihood.

AIC

`signature(object = "mle2")`: Calculate Akaike Information Criterion

AICc

`signature(object = "mle2")`: Calculate small-sample corrected Akaike Information Criterion

anova

`signature(object="mle2")`: Likelihood Ratio Test comparision of different models

Note

This is implemented in an ugly way and could probably be improved!

Examples

 ```1 2 3 4 5 6 7 8``` ``` d <- data.frame(x=0:10,y=c(26, 17, 13, 12, 20, 5, 9, 8, 5, 4, 8)) (fit <- mle2(y~dpois(lambda=ymax/(1+x/xhalf)), start=list(ymax=25,xhalf=3),data=d)) (fit2 <- mle2(y~dpois(lambda=(x+1)*slope), start=list(slope=1),data=d)) BIC(fit) BIC(fit,fit2) ```

bbmle documentation built on Nov. 17, 2017, 6:42 a.m.