flat.IC

A data.frame containing CRC data as output of
  <code><a href="/link/formatdata?package=lllcrc&version=1.1" rd-options="" 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 href="/link/make.hierarchical.term.sets%3C%2F?package=lllcrc&version=1.1" rd-options="" data-mini-rdoc="lllcrc::make.hierarchical.term.sets</">make.hierarchical.term.sets</</a></code>

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) a for
    closed populations, as described in the ongoing doctoral thesis work of
    Zachary Kurtz in the department of statistics at Carnegie Mellon
    University.  The method is relevant when there are 3-4 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