%bt%

getFpTransforms

getTransformName

Transform a variable according to FP transformation formula and attach
proper names to the resulting design matrix. The binary function
<code>bt</code> is the Box-Tidwell transform.

internal

utilities

Implements the Bayesian paradigm for fractional
polynomial models under the assumption of normally distributed error terms, see
Sabanes Bove, D. and Held, L. (2011) <doi:10.1007/s11222-010-9170-7>.

Daniel Sabanes Bove

Bayesian Fractional Polynomials

Isaac Gravestock

Robert Davies

Stephen Moshier

Gareth Ambler

Axel Benner

getFpTransforms function

<dl> <dt>x</dt>
<dd>numeric vector</dd> <dt>pow</dt>
<dd>one single power (not vectorized in this argument)</dd> <dt>vec</dt>
<dd>positive (== already shifted and scaled) column vector (!) with
 proper colname</dd>
 
 <dt>powers</dt>
<dd>power vector with at least one element</dd> <dt>center</dt>
<dd>should the columns be centered around zero? (default)</dd> <dt>name</dt>
<dd>name of the covariate to be transformed with <code>bt</code></dd></dl>

Arguments

Author

Internal functions to handle FP transforms. — getFpTransforms

<dl>

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

 <dt>pow</dt>
<dd>one single power (not vectorized in this argument)</dd>

 <dt>vec</dt>
<dd>positive (== already shifted and scaled) column vector (!) with
 proper colname</dd>
 
 <dt>powers</dt>
<dd>power vector with at least one element</dd>

 <dt>center</dt>
<dd>should the columns be centered around zero? (default)</dd>

 <dt>name</dt>
<dd>name of the covariate to be transformed with <code>bt</code></dd>

</dl>

Internal functions to handle FP transforms.

getFpTransforms: Internal functions to handle FP transforms.

Description

Usage

Arguments

Author

See Also

Examples