multicolumn

This function transforms a grouped two-column response data frame into a multi-column one, another data format accepted by <code>pblm</code>

 regression

 nonparametric

 multivariate

 smooth

 models

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

multicolumn function

<dl> <dt>formula</dt>
<dd>a two-side formula with counts in the left side.</dd> <dt>data</dt>
<dd>a data frame with two categorical responses, covariates (if any)
 and a count variable.</dd></dl>

Arguments

Author

transforming bivariate data in a multi-column format — multicolumn

<dl>

 <dt>formula</dt>
<dd>a two-side formula with counts in the left side.</dd>

 <dt>data</dt>
<dd>a data frame with two categorical responses, covariates (if any)
 and a count variable.</dd>

</dl>

transforming bivariate data in a multi-column format

multicolumn: transforming bivariate data in a multi-column format

Description

Usage

Value

Arguments

Author

Examples