se.propratio.ps

Computes a large-sample standard error of a paired-samples log proportion
ratio using the frequency counts from a 2 x 2 contingency table. The log
proportion ratio and standard error output from this function can be used
as input in the meta.ave.gen.log function in applications
where compatible proportion ratios from a combination of 2-group and
paired-samples studies are used in the meta-analysis.

Implements functions for varying coefficient meta-analysis methods.
These methods do not assume effect size homogeneity. Subgroup effect size
comparisons, general linear effect size contrasts, and linear models of
effect sizes based on varying coefficient methods can be used to describe
effect size heterogeneity. Varying coefficient meta-analysis methods do not
require the unrealistic assumptions of the traditional fixed-effect and
random-effects meta-analysis methods. For details see:
Statistical Methods for Psychologists, Volume 5, <https://dgbonett.sites.ucsc.edu/>.

Douglas G. Bonett

vcmeta

Varying Coefficient Meta-Analysis

Robert J. Calin-Jageman

se.propratio.ps function

<dl><dt>f00</dt>
<dd>number of participants with y = 0 and x = 0</dd>
<dt>f01</dt>
<dd>number of participants with y = 0 and x = 1</dd>
<dt>f10</dt>
<dd>number of participants with y = 1 and x = 0</dd>
<dt>f11</dt>
<dd>number of participants with y = 1 and x = 1</dd></dl>

Arguments

Computes the estimate and standard error for a paired-samples log
proportion ratio — se.propratio.ps

<dl>

<dt>f00</dt>
<dd>number of participants with y = 0 and x = 0</dd>


<dt>f01</dt>
<dd>number of participants with y = 0 and x = 1</dd>


<dt>f10</dt>
<dd>number of participants with y = 1 and x = 0</dd>


<dt>f11</dt>
<dd>number of participants with y = 1 and x = 1</dd>

</dl>

se.propratio.ps: Computes the estimate and standard error for a paired-samples log proportion ratio

Description

Usage

Value

Arguments

Examples