results_k25_n1000

datasets

Implementation of Expectation Maximization (EM) regression of general linear models.
The package currently supports Poisson and Logistic regression with variable weights, with underlying theory included in the vignettes.
New users are recommended to look at the em.glm() and small.em() functions - the outputs of which are supported by AIC(), BIC(),  and logLik() calls.
Several plot functions have been included for useful diagnostics and model exploration.
Methods are based on the theory of Dempster et al (1977, ISBN:00359246), and follow the methods of Hastie et al. (2009) <doi:10.1007/978-0-387-21606-5_7>  and A.
Zeileis et al (2017) <doi:10.18637/jss.v027.i08>.

Robert M. Cook

emax.glm

General Tools for Building GLM Expectation-Maximization Models

results_k25_n1000 function

<p>An object of class <code>matrix</code> with 3 rows and 50 columns.</p>

results_k25_n1000: Simulated data set

Description

Usage

Arguments

Format