BIC-methods | R Documentation |

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

## 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)

`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) |

Further arguments to `BIC`

can be specified
in the `...`

list: `delta`

(logical)
specifies whether to include a column for delta-BIC
in the output.

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

- 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

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

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)

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