Log likelihoods and model selection for mle2 objects
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
signature(object = "mle2"): Extract maximized
log-likelihood.
signature(object = "mle2"): Calculate
Akaike Information Criterion
signature(object = "mle2"): Calculate
small-sample corrected Akaike Information Criterion
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.