cv.inform

<code>cv.inform</code> carries out cross validation to help choosing the set of informative covariates.

survival data

Regression modelling

Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association, endogeneity, non-random sample selection or partial observability.

Giampiero Marra

GJRM

Generalised Joint Regression Modelling

cv.inform function

<dl>
 <dt>x</dt>
<dd>A fitted <code>gamlss</code> object as produced by the respective fitting function.</dd>
<dt>K</dt>
<dd>No. of folds.</dd>
 <dt>data</dt>
<dd>Data.</dd>
 <dt>informative</dt>
<dd>If no then cv is carried out for the case of no informative censoring. This is useful for comparison purposes.</dd>
 </dl>

Arguments

Maintainer: Giampiero Marra <a href="/link/giampiero.marra%40ucl.ac.uk?package=GJRM&version=0.2-6.5" data-mini-rdoc="GJRM::giampiero.marra@ucl.ac.uk">giampiero.marra@ucl.ac.uk</a>

Author

Cross validation for informative censoring univariate survival models — cv.inform

<dl>
 

<dt>x</dt>
<dd>A fitted <code>gamlss</code> object as produced by the respective fitting function.</dd>


<dt>K</dt>
<dd>No. of folds.</dd>
 

<dt>data</dt>
<dd>Data.</dd>
 

<dt>informative</dt>
<dd>If no then cv is carried out for the case of no informative censoring. This is useful for comparison purposes.</dd>
 



</dl>

Maintainer: Giampiero Marra <a href='mailto:giampiero.marra@ucl.ac.uk'>giampiero.marra@ucl.ac.uk</a>

cv.inform: Cross validation for informative censoring univariate survival models

Description

Usage

Value

Arguments

Author

Details

See Also