apply.ic.fit

A matrix with 2^k-1 columns, one for each capture pattern.
Each row sums to 1; these are empirical capture pattern probabilities.

ydens

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

A vector of effective sample sizes, one for each row of ydens.

The number of population units that were observed for each row of
ydens.

The chosen information criterion.  Currently implemented: "AIC",
"AICc", "BIC", "BICpi".

Logical: TRUE means that the cell adjustment of Evans and
Bonet (1995) is applied.

cell.adj

Logical: TRUE means that the information criterion weights
are used to do model averaging, locally.

averaging

Logical: TRUE means that the progress is noted by printing the
number of the row of ydens currently being processed.

loud


Select LLLMs for each row of the input data.


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.

apply.ic.fit: Select an LLLM at each point

Description

Usage

Arguments

Value

Details

References