ic.fit

A matrix with one row and 2^k-1 column containing cell counts
or empirical cell probabilities corresponding to all the possible capture
patterns.

densi

A list of character vectors, with each vector containing
column names from the associated log-linear design matrix.
For example, see the output of <code><a rd-options="" href="/link/make.hierarchical.term.sets?package=lllcrc&version=1.2" data-mini-rdoc="lllcrc::make.hierarchical.term.sets">make.hierarchical.term.sets</a>()</code>.

models

If you multiply <code>densi</code> by <code>N</code> and then sum over the
resulting vector, you should get the effective sample size.

The information criterion, such as AIC, AICc, BIC, or BICpi.

Logical: TRUE means that we use information criterion
scores to do model averaging.

averaging

Logical: TRUE means that beta0 will be adjusted so that
the log-linear model corresponds to cell probabilities instead of expected
cell counts.

normalized

Logical: TRUE means that the Rasch model is a candidate.

rasch


Use an information criterion to select a local log-linear model


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.

ic.fit: Select and fit an LLM

Description

Usage

Arguments

Value

Details