cycling

A dataset containing information about cycling sessions

datasets

Different regression models with functional features are implemented: for continuous response, categorical response and ordinal response. The model for ordinal response has been published in: 'Analyzing cycling sensors data through ordinal logistic regression with functional covariates, Jacques J. and Samardzic S., Journal of the Royal Statistical Society, Series C, 1-18, 2022'.

Julien Jacques

FREG

Functional Regression Models

Sanja Samardzic

cycling function

A list of 9 elements. The first element of the list is time whereas the other 8 elements are functional variables.
The performance of 216 cyclists is recorded during one hour. Thus, all functional variables are expressed in a form of 216 x 3600 matrix<dl class="dl-horizontal">
<dt>SECS</dt><dd>3600 seconds (1 hour)</dd>
<dt>KM</dt><dd>kilometers</dd>
<dt>WATTS</dt><dd>power</dd>
<dt>CAD</dt><dd>cadence</dd>
<dt>KPH</dt><dd>kilometers per hour</dd>
<dt>HR</dt><dd>heart rate</dd>
<dt>ALT</dt><dd>altitude</dd>
<dt>SLOPE</dt><dd>slope</dd>
<dt>TEMP</dt><dd>temperature</dd>
</dl>

Format

Cycling dataset — cycling

A list of 9 elements. The first element of the list is time whereas the other 8 elements are functional variables.
The performance of 216 cyclists is recorded during one hour. Thus, all functional variables are expressed in a form of 216 x 3600 matrix<dl class='dl-horizontal'>
<dt>SECS</dt><dd>3600 seconds (1 hour)</dd>
<dt>KM</dt><dd>kilometers</dd>
<dt>WATTS</dt><dd>power</dd>
<dt>CAD</dt><dd>cadence</dd>
<dt>KPH</dt><dd>kilometers per hour</dd>
<dt>HR</dt><dd>heart rate</dd>
<dt>ALT</dt><dd>altitude</dd>
<dt>SLOPE</dt><dd>slope</dd>
<dt>TEMP</dt><dd>temperature</dd>
</dl>

cycling: Cycling dataset

Description

Usage

Arguments

Format