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 |

`...` |
An optional list of additional |

`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)
```

*bbmle*version 1.0.25.1 Index]