R: Log likelihoods and model selection for mle2 objects

BIC-methods {bbmle} | R Documentation

## Log likelihoods and model selection for mle2 objects

### Description

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

### Usage

    ## 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

      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)

[Package bbmle version 1.0.19 Index]