ICtab  R Documentation 
Computes information criteria for a series of models, optionally giving information about weights, differences between ICs, etc.
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 class 'ICtab' print(x,...,min.weight)
... 
a list of (logLik or?) mle objects; in the case of

type 
specify information criterion to use 
base 
(logical) include base IC (and loglikelihood) values? 
weights 
(logical) compute IC weights? 
logLik 
(logical) include loglikelihoods in the table? 
delta 
(logical) compute differences among ICs (and loglikelihoods)? 
sort 
(logical) sort ICs in increasing order? 
nobs 
(integer) number of observations: required for

dispersion 
overdispersion estimate, for computing qAIC:
required for 
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]") 
A data frame containing:
IC 
information criterion 
df 
degrees of freedom/number of parameters 
dIC 
difference in IC from minimumIC model 
weights 
exp(dIC/2)/sum(exp(dIC/2)) 
(1) The print method uses sensible defaults; all ICs are rounded
to the nearest 0.1, and IC weights are printed using
format.pval
to print an inequality for
values <0.001. (2) The computation of degrees of freedom/number of
parameters (e.g., whether
variance parameters are included in the total) varies enormously
between packages. As long as the df computations
for a given set of models is consistent, differences
don't matter, but one needs to be careful with log likelihoods
and models taken from different packages. If necessary
one can change the degrees of freedom manually by
saying attr(obj,"df") < df.new
, where df.new
is the desired number of parameters.
(3) Defaults have changed to sort=TRUE
, base=FALSE
,
delta=TRUE
, to match my conviction that it rarely makes
sense to report the overall values of information criteria
Ben Bolker
Burnham and Anderson 2002
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)
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