marg.hom

print.marginal

<code>marg.hom</code> fits a marginal homogeneity model to a contingency table
that has two margins of equal size.

models

Various functions to fit models for non-normal repeated
measurements, such as Binary Random Effects Models with Two Levels of Nesting,
Bivariate Beta-binomial Regression Models, Marginal Bivariate Binomial Regression Models,
Cormack capture-recapture models, Continuous-time Hidden Markov Chain Models,
Discrete-time Hidden Markov Chain Models,
Changepoint Location Models using a Continuous-time Two-state Hidden Markov Chain,
generalized nonlinear autoregression models, multivariate Gaussian copula models,
generalized non-linear mixed models with one random effect,
generalized non-linear mixed models using h-likelihood for one random effect,
Repeated Measurements Models for Counts with Frailty or Serial Dependence,
Repeated Measurements Models for Continuous Variables with Frailty or Serial Dependence,
Ordinal Random Effects Models with Dropouts, marginal homogeneity models for square
contingency tables, correlated negative binomial models with Kalman update.
References include Lindsey's text books,
JK Lindsey (2001) <isbn:10-0198508123> and JK Lindsey (1999) <isbn:10-0198505590>.

Bruce Swihart

repeated

Non-Normal Repeated Measurements Models

Jim Lindsey

T.R. Ten Have

Richard Cook

Iain MacDonald

Walter Zucchini

Burton Garbow

Euginia Zharichenko

marg.hom function

<dl><dt>freq</dt>
<dd>Vector of frequencies</dd>
<dt>marg1</dt>
<dd>Factor variable for the first margin</dd>
<dt>marg2</dt>
<dd>Factor variable for the second margin</dd></dl>

Arguments

Author

Marginal Homogeneity Models — marg.hom

<dl>

<dt>freq</dt>
<dd>Vector of frequencies</dd>


<dt>marg1</dt>
<dd>Factor variable for the first margin</dd>


<dt>marg2</dt>
<dd>Factor variable for the second margin</dd>

</dl>

Marginal Homogeneity Models

marg.hom: Marginal Homogeneity Models

Description

Usage

Value

Arguments

Author

Examples