get.IC

A character vector of predictors of the form "c1", "c2"
for main effects, or "c12" for an interaction. The predictors to be used in
a log-linear model.  For example, "c1", "c2" for main effects, or "c12" for
an interaction.

predictors

A data frame that is the design matrix for a log-linear model.

ddat

The information criterion to be computed.  Currently the AIC,
AICc, BIC, BICpi are implemented.

The vector of log-linear coefficients that were previously
estimated.

beta


Given the fitted parameter values for a log-linear model, compute an
information criterion.


Applies local log-linear capture-recapture models (LLLMs) for
closed populations, as described in the doctoral thesis of Zachary Kurtz.
The method is relevant when there are 3-5 capture occasions, with auxiliary
covariates available for all capture occasions.  As part of estimating the
number of missing population units, the method estimates the "rate of
missingness" as it varies over the covariate space.  In addition,
user-friendly functions are provided to recreate (approximately) the method
of Zwane and van der Heijden (2004), which applied the VGAM package
in a way that is closely related to LLLMs.

get.IC: Compute an information criterion

Description

Usage

Arguments

Value

Details

References