process_results

This internal function processes results from cluster-specific model fittings
for cluster-robust inference functions. It combines results, computes
variance-covariance matrix, standard errors, t-statistics, p-values, and
confidence intervals for each independent variable.

internal

Simulation results detailed in Esarey and Menger (2019) <doi:10.1017/psrm.2017.42>
demonstrate that cluster adjusted t statistics (CATs) are an effective method
for correcting standard errors in scenarios with a small number of clusters.
The 'mmiCATs' package offers a suite of tools for working with CATs. The
mmiCATs() function initiates a 'shiny' web application, facilitating
the analysis of data utilizing CATs, as implemented in the cluster.im.glm()
function from the 'clusterSEs' package. Additionally, the pwr_func_lmer()
function is designed to simplify the process of conducting simulations to
compare mixed effects models with CATs models. For educational purposes, the
CloseCATs() function launches a 'shiny' application card game, aimed at enhancing
users' understanding of the conditions under which CATs should be preferred
over random intercept models.

Mackson Ncube

mmiCATs

Cluster Adjusted t Statistic Applications

mightymetrika, LLC 

process_results function

<dl><dt>results</dt>
<dd>A list of results from cluster-specific model fittings.</dd>
<dt>ind_variables</dt>
<dd>A vector of independent variable names for which the
results are computed.</dd>
<dt>ci.level</dt>
<dd>Confidence level for the confidence intervals.</dd>
<dt>drop</dt>
<dd>Logical; if TRUE, clusters with failed model fits are omitted
from the results.</dd>
<dt>return.vcv</dt>
<dd>Logical; if TRUE, returns the variance-covariance matrix of
the cluster-averaged coefficients.</dd></dl>

Arguments

Process Cluster-Adjusted Robust Inference Results — process_results

<dl>

<dt>results</dt>
<dd>A list of results from cluster-specific model fittings.</dd>


<dt>ind_variables</dt>
<dd>A vector of independent variable names for which the
results are computed.</dd>


<dt>ci.level</dt>
<dd>Confidence level for the confidence intervals.</dd>


<dt>drop</dt>
<dd>Logical; if TRUE, clusters with failed model fits are omitted
from the results.</dd>


<dt>return.vcv</dt>
<dd>Logical; if TRUE, returns the variance-covariance matrix of
the cluster-averaged coefficients.</dd>

</dl>

process_results: Process Cluster-Adjusted Robust Inference Results

Description

Usage

Value

Arguments

Details