smooth.patterns

The capture-recapture data in the form that is returned by
<code><a rd-options="" href="/link/formatdata?package=lllcrc&version=1.2" data-mini-rdoc="lllcrc::formatdata">formatdata</a></code> or <code><a rd-options="" href="/link/micro.post.stratify?package=lllcrc&version=1.2" data-mini-rdoc="lllcrc::micro.post.stratify">micro.post.stratify</a></code>.

The approximate fraction of the data that is included in the
support of the kernel for the local averages.

kfrac

A matrix a single column, with rownames that match the covariate
names in <code>dat</code>.  The values in the column are scalars that are used in
constructing distances between covariate vectors.  Raw differences are
divided by the corresponding scalars before being squared in the context of
a Euclidean metric.


A nearest-neighbors procedure is used in conjunction with the Epanechnikov
kernel to define a kernel smooth of multinomial outcomes across the
covariate space


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.

smooth.patterns: Local averaging for LLLMs

Description

Usage

Arguments

Value

Details

References