micro.post.stratify

A vector of names of variables to be
  rounded for the purpose of collapsing the data.

round.vars

A vector of scalars that determines
  how much each corresponding variable in <code>round.vars</code>
  is to be rounded.  For example, the first variable
  <code>round.vars[1]</code> will be divided by
  <code>rounding.scale[1]</code>, then rounded to th

rounding.scale

Rounding continuous covariates creates "micro-post-strata"
and therefore tends to reduce the number of distinct
covariate vectors.  After rounding, the data is collapsed
so that there is exactly one row for each distinct
covariate vector, and a column called <code>mct</code> (for
multinomial cell count) is appended with that contains the
number of records corresponding to each row.

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.

micro.post.stratify: Collapse CRC data through micro post-stratification

Description

Usage

Arguments

Value

Details