deviance_gaussian

Deviance computations as used in mfp in stata

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

deviance_gaussian function

<dl><dt>rss</dt>
<dd>residual sum of squares.</dd>
<dt>weights</dt>
<dd>numeric vector of weights used in computation of <code>rss</code>.</dd>
<dt>n</dt>
<dd>number of observations used to compute <code>rss</code>.</dd></dl>

Arguments

Deviance computations as used in mfp in stata — deviance_gaussian

<dl>

<dt>rss</dt>
<dd>residual sum of squares.</dd>


<dt>weights</dt>
<dd>numeric vector of weights used in computation of <code>rss</code>.</dd>


<dt>n</dt>
<dd>number of observations used to compute <code>rss</code>.</dd>

</dl>

deviance_gaussian: Deviance computations as used in mfp in stata

Description

Usage

Value

Arguments

Details