BIC-methods {bbmle} | R Documentation |
Various functions for likelihood-based and information-theoretic model selection of likelihood models
## 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)
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) |
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 Criterionsignature(object = "mle2")
: Calculate
small-sample corrected Akaike Information Criterionsignature(object = "mle2")
: Calculate
Bayesian (Schwarz) Information Criterionsignature(object = "logLik")
: Calculate
Bayesian (Schwarz) Information Criterionsignature(object = "ANY")
: Calculate
Bayesian (Schwarz) Information Criterionsignature(object="mle2")
: Likelihood Ratio Test
comparision of different modelsThis is implemented in an ugly way and could probably be improved!
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))