theta_back_2_data

This function performs data augmentation on the provided dataset.

internal

Fits generalized linear models (GLMs) when there is missing data in both the response and categorical covariates. The functions implement likelihood-based methods using the Expectation and Maximization (EM) algorithm and optionally apply Firth’s bias correction for improved inference. See Pradhan, Nychka, and Bandyopadhyay (2025) <https:>, Maiti and Pradhan (2009) <doi:10.1111/j.1541-0420.2008.01186.x>, Maity, Pradhan, and Das (2019) <doi:10.1080/00031305.2017.1407359> for further methodological details.

Vivek Pradhan

glmfitmiss

Fitting GLMs with Missing Data in Both Responses and Covariates

Douglas Nychka

Soutir Bandyopadhyay

theta_back_2_data function

<dl><dt>theta</dt>
<dd>A data frame containing the theta.</dd>
<dt>data</dt>
<dd>A data where theta to be merged.</dd>
<dt>MissingVars</dt>
<dd>A variable with missing data</dd></dl>

Arguments

This function performs data augmentation on the provided dataset. — theta_back_2_data

<dl>

<dt>theta</dt>
<dd>A data frame containing the theta.</dd>


<dt>data</dt>
<dd>A data where theta to be merged.</dd>


<dt>MissingVars</dt>
<dd>A variable with missing data</dd>

</dl>

theta_back_2_data: This function performs data augmentation on the provided dataset.

Description

Usage

Value

Arguments