calculate_standard_error

To be used in <code>predict.mfp2()</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

calculate_standard_error function

<dl><dt>model</dt>
<dd>fitted <code>mfp2</code> object.</dd>
<dt>X</dt>
<dd>transformed input matrix with variables of interest for partial predictor.</dd>
<dt>xref</dt>
<dd>transformed reference value for variable of interest. Default is
<code>NULL</code>, in which case this function computes standard errors without reference
values.</dd></dl>

Arguments

Helper function to compute standard error of a partial predictor — calculate_standard_error

<dl>

<dt>model</dt>
<dd>fitted <code>mfp2</code> object.</dd>


<dt>X</dt>
<dd>transformed input matrix with variables of interest for partial predictor.</dd>


<dt>xref</dt>
<dd>transformed reference value for variable of interest. Default is
<code>NULL</code>, in which case this function computes standard errors without reference
values.</dd>

</dl>

calculate_standard_error: Helper function to compute standard error of a partial predictor

Description

Usage

Value

Arguments

Details

References