ICtab {bbmle} | 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 = FALSE, sort = FALSE, nobs, dispersion = 1, mnames, k = 2) AICtab(...) BICtab(...) AICctab(...) ## S3 method for class 'ICtab': print(x,...)
... |
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 |
weights |
(logical) compute IC weights? |
delta |
(logical) compute differences among ICs? |
sort |
(logical) sort ICs in increasing order? |
nobs |
(integer) number of observations: required for
type="BIC" or type="AICc" unless objects have
an "nobs" attribute |
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 |
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)) |
(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.
Ben Bolker
Burnham and Anderson 2002