drinks

A one-year categorical time series of daily sales of soft-drinks.

datasets

Functions for specifying and fitting marginal models for contingency tables proposed
by Bergsma and Rudas (2002) here called hierarchical multinomial marginal models (hmmm) and their extensions presented by Bartolucci et al.
(2007); multinomial Poisson homogeneous (mph) models and homogeneous linear predictor (hlp) models for contingency
tables proposed by Lang (2004) and (2005); hidden Markov models where the distribution of the observed variables
is described by a marginal model.
Inequality constraints on the parameters are allowed and can be tested.

Colombi Roberto

hmmm

Hierarchical Multinomial Marginal Models

drinks function

A five-variate time series of sale levels of soft-drinks on 269 days<dl class="dl-horizontal">
 <dt><code>lemon.tea</code></dt><dd>A factor with levels: 1 = low, 2 = medium, 3 = high</dd>
 <dt><code>orange.juice</code></dt><dd>A factor with levels: 1 = low, 2 = medium, 3 = high</dd>
 <dt><code>apple.juice</code></dt><dd>A factor with levels: 1 = low, 2 = medium, 3 = high</dd>
 <dt><code>mint.tea</code></dt><dd>A factor with levels: 1 = low, 2 = high</dd>
 <dt><code>soya.milk</code></dt><dd>A factor with levels: 1 = low, 2 = high</dd>
 </dl>

Format

soft-drinks data — drinks

A five-variate time series of sale levels of soft-drinks on 269 days<dl class='dl-horizontal'>
 <dt><code>lemon.tea</code></dt><dd>A factor with levels: 1 = low, 2 = medium, 3 = high</dd>
 <dt><code>orange.juice</code></dt><dd>A factor with levels: 1 = low, 2 = medium, 3 = high</dd>
 <dt><code>apple.juice</code></dt><dd>A factor with levels: 1 = low, 2 = medium, 3 = high</dd>
 <dt><code>mint.tea</code></dt><dd>A factor with levels: 1 = low, 2 = high</dd>
 <dt><code>soya.milk</code></dt><dd>A factor with levels: 1 = low, 2 = high</dd>
 </dl>

drinks: soft-drinks data

Description

Usage

Arguments

Format

References

Examples