The ART data set mimics the GBSG breast cancer study in terms of the
distribution of predictors and correlation structure.

data

Multivariable fractional polynomial algorithm simultaneously selects variables and functional forms in both generalized linear models and Cox proportional hazard models. Key references for this algorithm are Royston and Altman (1994)<doi:10.2307/2986270> and Sauerbrei and Royston (2008, ISBN:978-0-470-02842-1). In addition, it can model a 'sigmoid' relationship between variable x and an outcome variable y using the approximate cumulative distribution transformation proposed by Royston (2014) <doi:10.1177/1536867X1401400206>. This feature distinguishes it from a standard fractional polynomial function, which lacks the ability to achieve such modeling.

Edwin Kipruto

mfp2

Multivariable Fractional Polynomial Models with Extensions

Michael Kammer

Patrick Royston

Willi Sauerbrei

art function

The dataset has 250 observations and 10 covariates<dl>
<dt>y</dt>
<dd>Continuous response variable.</dd><dt>x1, x3, x5-x7, x10</dt>
<dd>Continuous covariates.</dd><dt>x2</dt>
<dd>Binary variable.</dd><dt>x4</dt>
<dd>Ordinal variable with 3 levels.</dd><dt>x8</dt>
<dd>Binary variable.</dd><dt>x9</dt>
<dd>Nominal variable with 3 levels.</dd>
</dl>

Format

Artificial dataset with continuous response — art

The dataset has 250 observations and 10 covariates<dl>
<dt>y</dt>
<dd>Continuous response variable.</dd>

<dt>x1, x3, x5-x7, x10</dt>
<dd>Continuous covariates.</dd>

<dt>x2</dt>
<dd>Binary variable.</dd>

<dt>x4</dt>
<dd>Ordinal variable with 3 levels.</dd>

<dt>x8</dt>
<dd>Binary variable.</dd>

<dt>x9</dt>
<dd>Nominal variable with 3 levels.</dd>


</dl>

art: Artificial dataset with continuous response

Description

Usage

Arguments

Format