find_best_fp1_for_acd

To be used in <code>fit_acd()</code>.

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

find_best_fp1_for_acd function

<dl><dt>x</dt>
<dd>a numeric vector.</dd>
<dt>y</dt>
<dd>normal cdf of rank transform of <code>x</code>.</dd>
<dt>powers</dt>
<dd>a vector of allowed FP powers. The default value is <code>NULL</code>,
meaning that the set \(S = (-2, -1, -0.5, 0, 0.5, 1, 2, 3)\) is used.</dd></dl>

Arguments

Function to fit univariable FP1 models for acd transformation — find_best_fp1_for_acd

<dl>

<dt>x</dt>
<dd>a numeric vector.</dd>


<dt>y</dt>
<dd>normal cdf of rank transform of <code>x</code>.</dd>


<dt>powers</dt>
<dd>a vector of allowed FP powers. The default value is <code>NULL</code>,
meaning that the set \(S = (-2, -1, -0.5, 0, 0.5, 1, 2, 3)\) is used.</dd>

</dl>

find_best_fp1_for_acd: Function to fit univariable FP1 models for acd transformation

Description

Usage

Value

Arguments