fill_data

Impute missing data by optimizing the likelihood function

An implementation of the LUCID model (Peng (2019) <doi:10.1093/bioinformatics/btz667>).
LUCID conducts integrated clustering using exposures, omics data (and outcome
as an option). An EM algorithm is implemented to estimate MLE of the LUCID model.
LUCIDus features integrated variable selection, incorporation of missing omics
data, bootstrap inference, prediction and visualization of the model.

Yinqi Zhao

LUCIDus

Latent Unknown Clustering Integrating Multi-View Data

David Conti

Jesse Goodrich

Cheng Peng

fill_data function

<dl><dt>obs</dt>
<dd>a vector of length M</dd>
<dt>mu</dt>
<dd>a matrix of size M x K</dd>
<dt>sigma</dt>
<dd>a matrix of size M x M x K</dd>
<dt>p</dt>
<dd>a vector of length K</dd>
<dt>index</dt>
<dd>a vector of length M, indicating whether a value is missing 
or not in the raw data</dd></dl>

Arguments

Impute missing data by optimizing the likelihood function — fill_data

<dl>

<dt>obs</dt>
<dd>a vector of length M</dd>


<dt>mu</dt>
<dd>a matrix of size M x K</dd>


<dt>sigma</dt>
<dd>a matrix of size M x M x K</dd>


<dt>p</dt>
<dd>a vector of length K</dd>


<dt>index</dt>
<dd>a vector of length M, indicating whether a value is missing 
or not in the raw data</dd>

</dl>

fill_data: Impute missing data by optimizing the likelihood function

Description

Usage

Value

Arguments