.cens.draw3

<p>Logical vector of length <code>length(y)</code> indicating the the subset
<code>y[ry]</code> of elements in y to which the imputation model is fitted.
The <code>ry</code> generally distinguishes the observed (<code>TRUE</code>) and
missing values (<code>FALSE</code>) in y.</p>

<p>Numeric design matrix with <code>length(y)</code> rows with predictors
for <code>y</code>. Matrix <code>x</code> may have no missing values.</p>

lod.j

internal

Algorithms for (i) unsupervised learning for dataset with
missing data and/or left-censored data, using multiple imputation and
consensus clustering ; (ii) semi-supervised learning with a survival
endpoint (right-censored) for complete or incomplete datasets, using
multiple imputation and consensus clustering in the latter case. The
methods are described in Faucheux et al. (2021)
<doi:10.1002/bimj.201900366> and Faucheux et al. (2021)
<doi:10.1002/bimj.202000365>, respectively.

Lilith Faucheux

doMIsaul

Do Multiple Imputation-Based Semi-Supervised and Unsupervised
Learning

Sylvie Chevret

Matthieu Resche-Rigon

Marie Perrot-Dock<c3><a8>s

Eric Han

.cens.draw3 function

Base function for imputing left censored data with MICE — .cens.draw3

Base function for imputing left censored data with MICE

.cens.draw3: Base function for imputing left censored data with MICE

Description

Usage

Arguments

Value