bbmle (version 1.0.23.1)

ICtab: Compute table of information criteria and auxiliary info

Description

Computes information criteria for a series of models, optionally giving information about weights, differences between ICs, etc.

Usage

ICtab(…, type=c("AIC","BIC","AICc","qAIC","qAICc"),
    weights = FALSE, delta = TRUE, base = FALSE,
logLik=FALSE, sort = TRUE,
nobs=NULL, dispersion = 1, mnames, k = 2)
AICtab(…,mnames)
BICtab(…,mnames)
AICctab(…,mnames)
# S3 method for ICtab
print(x,…,min.weight)

Arguments

…

a list of (logLik or?) mle objects; in the case of AICtab etc., could also include other arguments to ICtab

type

specify information criterion to use

base

(logical) include base IC (and log-likelihood) values?

weights

(logical) compute IC weights?

logLik

(logical) include log-likelihoods in the table?

delta

(logical) compute differences among ICs (and log-likelihoods)?

sort

(logical) sort ICs in increasing order?

nobs

(integer) number of observations: required for type="BIC" or type="AICc" unless objects have a nobs method

dispersion

overdispersion estimate, for computing qAIC: required for type="qAIC" or type="qAICc" unless objects have a "dispersion" attribute

mnames

names for table rows: defaults to names of objects passed

k

penalty term (largely unused: left at default of 2)

x

an ICtab object

min.weight

minimum weight for exact reporting (smaller values will be reported as "<[min.weight]")

Value

A data frame containing:

IC

information criterion

df

degrees of freedom/number of parameters

dIC

difference in IC from minimum-IC model

weights

exp(-dIC/2)/sum(exp(-dIC/2))

References

Burnham and Anderson 2002

Examples

# NOT RUN {
  set.seed(101)
  d <- data.frame(x=1:20,y=rpois(20,lambda=2))
  m0 <- glm(y~1,data=d)
  m1 <- update(m0,.~x)
  m2 <- update(m0,.~poly(x,2))
  AICtab(m0,m1,m2,mnames=LETTERS[1:3])
  AICtab(m0,m1,m2,base=TRUE,logLik=TRUE)
  AICtab(m0,m1,m2,logLik=TRUE)
  AICctab(m0,m1,m2,weights=TRUE)
  print(AICctab(m0,m1,m2,weights=TRUE),min.weight=0.1)
# }