find_scale_factor

Function that calculates an integer used to scale predictor

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_scale_factor function

<dl><dt>x</dt>
<dd>a numeric vector already shifted to positive values (see
<code>find_shift_factor()</code>). This function requires at least 2 distinct values to
work.
#' @examples
x = 1:1000
find_scale_factor(x)</dd></dl>

Arguments

Function that calculates an integer used to scale predictor — find_scale_factor

<dl>

<dt>x</dt>
<dd>a numeric vector already shifted to positive values (see
<code>find_shift_factor()</code>). This function requires at least 2 distinct values to
work.
#' @examples
x = 1:1000
find_scale_factor(x)</dd>

</dl>

find_scale_factor: Function that calculates an integer used to scale predictor

Description

Usage

Value

Arguments

Details

References