simulateCovariateData

This function generates a simulated data with independent categorical covariates. The first two covariates namely x1 and x2 are generated using random normal rnorm(n, 40, 20) and random poisson rpois(n, lambda = 4).The remaining covariates are generated at random with categories 0,1,2.

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

simulateCovariateData function

<dl><dt>n</dt>
<dd>numner of observations to be generated for the data</dd>
<dt>nCov</dt>
<dd>4+ number of covariates to be generated for the data, the first 4 covariates generated based on pre-specified distributions</dd></dl>

Arguments

Simulate data with independent categorical covariates — simulateCovariateData

<dl>

<dt>n</dt>
<dd>numner of observations to be generated for the data</dd>


<dt>nCov</dt>
<dd>4+ number of covariates to be generated for the data, the first 4 covariates generated based on pre-specified distributions</dd>

</dl>

simulateCovariateData: Simulate data with independent categorical covariates

Description

Usage

Value

Arguments

Examples