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) 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.

get.IC: Compute an information criterion

Description

Usage

Arguments

Value

Details

References