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':
BIC(object,...)
## 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
BIC
signature(object = "mle2"): Calculate Bayesian (Schwarz) Information Criterion
BIC
signature(object = "logLik"): Calculate Bayesian (Schwarz) Information Criterion
BIC
signature(object = "ANY"): Calculate Bayesian (Schwarz) 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

  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)))
  (fit2 <- mle2(y~dpois(lambda=(x+1)*slope),
  start=list(slope=1)))
  BIC(fit,nobs=length(x))
  BIC(fit,fit2,nobs=length(x))
  

[Package bbmle version 0.9.0 Index]