flat.IC

A data.frame containing CRC data as output of <code><a rd-options="" href="/link/formatdata?package=lllcrc&version=1.2" data-mini-rdoc="lllcrc::formatdata">formatdata</a></code>.

A list of models -- or an expression that returns a
list of models -- to be considered in local model search.  The default is <code>NULL</code>,
and in this case <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>(k = attributes(dat)$k)</code>
is called to generate all hierarchical models that include all main effects.

models

Logical: Should the Rasch model (most basic version, Darroch
et. al. 1993) be considered, in addition to standard models?  <code>FALSE</code> by default.

rasch

Character string specifying the information criterion to use for
model selection.  Currently AIC, AICc, BIC, and BICpi are implemented.

Logical: Should we adjust the cells as in Evans and Bonett
(1995)?

adjust

Logical: Should we use model averaging based on the
information criterion scores?

averaging


Without using covariates (i.e., with capture probabilities assumed flat over
the covariate space), select the best log-linear model for the marginal
contingency table of capture pattern counts.


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.

flat.IC: Select an LLM

Description

Usage

Arguments

Value

References