reset_acd

To be used in <code>fit_mfp()</code>.
This function resets the <code>acdx</code> parameter (logical vector) of variables with
less than 5 distinct values to <code>FALSE</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

reset_acd function

<dl><dt>x</dt>
<dd>a design matrix of dimension nobs x nvars where nvars is the number
of predictors excluding an intercept.</dd>
<dt>acdx</dt>
<dd>a named logical vector of length nvars indicating which continuous
variables should undergo the approximate cumulative distribution (ACD)
transformation. May be ordered differently than the columns of <code>x</code>.</dd></dl>

Arguments

Helper to reset acd transformation for variables with few values — reset_acd

<dl>

<dt>x</dt>
<dd>a design matrix of dimension nobs x nvars where nvars is the number
of predictors excluding an intercept.</dd>


<dt>acdx</dt>
<dd>a named logical vector of length nvars indicating which continuous
variables should undergo the approximate cumulative distribution (ACD)
transformation. May be ordered differently than the columns of <code>x</code>.</dd>

</dl>

reset_acd: Helper to reset acd transformation for variables with few values

Description

Usage

Value

Arguments