These data were analyzed by Goodman (1979) and concern the cross-classification of a sample of fathers and their sons according to the occupational status.

datasets

Bivariate additive categorical regression via penalized maximum likelihood.
Under a multinomial framework, the method fits bivariate models where both
responses are nominal, ordinal, or a mix of the two. Partial proportional
odds models are supported, with flexible (non-)uniform association structures.
Various logit types and parametrizations can be specified for both marginals
and the association, including Dale’s model. The association structure can
be regularized using polynomial-type penalty terms. Additive effects are
modeled using P-splines. Standard methods such as summary(), residuals(),
and predict() are available.

Marco ENEA

pblm

Bivariate Additive Marginal Regression for Categorical Responses

Marco Enea

Mikis Stasinopoulos

Robert Rigby

bms function

A data frame with 49 observations and 3 variables.<dl>
 <dt><code>fathers</code></dt>
<dd>fathers'occupational status. A factor with levels from 1 to 7.</dd> <dt><code>sons</code></dt>
<dd>sons'occupational status. A factor with levels from 1 to 7.</dd> <dt><code>freq</code></dt>
<dd>a vector of integers representing the number of people cross-classified according to the occupational status of <code>fathers</code> and <code>sons</code></dd> 
</dl>

Format

A British male sample on occupational status. — bms

A data frame with 49 observations and 3 variables.<dl>
 <dt><code>fathers</code></dt>
<dd>fathers'occupational status. A factor with levels from 1 to 7.</dd>

 <dt><code>sons</code></dt>
<dd>sons'occupational status. A factor with levels from 1 to 7.</dd>

 <dt><code>freq</code></dt>
<dd>a vector of integers representing the number of people cross-classified according to the occupational status of <code>fathers</code> and <code>sons</code></dd>

 
</dl>

bms: A British male sample on occupational status.

Description

Usage

Arguments

Format

Examples