UpdateRPlee11

This an internal function to update coefficients of the Bayesian Cox Lasso Model

An implementation of Bayesian survival models with graph-structured selection priors for sparse identification of omics features predictive of survival (Madjar et al., 2021 <doi:10.1186/s12859-021-04483-z>) and its extension to use a fixed graph via a Markov Random Field (MRF) prior for capturing known structure of omics features, e.g. disease-specific pathways from the Kyoto Encyclopedia of Genes and Genomes database (Hermansen et al., 2025 <doi:10.48550/arXiv.2503.13078>).

Zhi Zhao

BayesSurvive

Bayesian Survival Models for High-Dimensional Data

Waldir Leoncio

Katrin Madjar

Tobias Østmo Hermansen

Manuela Zucknick

Jörg Rahnenführer

UpdateRPlee11 function

<dl><dt>sobj</dt>
<dd>a list containing observed data from <code>n</code> subjects;
<code>t</code>, <code>di</code>, <code>X</code>. See details for more information</dd>
<dt>hyperpar</dt>
<dd>a list containing prior parameter values</dd>
<dt>ini</dt>
<dd>a list containing prior parameters' initial values</dd>
<dt>S</dt>
<dd>the number of subgroups</dd>
<dt>method</dt>
<dd>a method option from
<code>c("Pooled", "CoxBVSSL", "Sub-struct")</code></dd>
<dt>MRF_G</dt>
<dd>logical value. <code>MRF_G = TRUE</code> is to fix the MRF graph which
is provided in the argument <code>hyperpar</code>, and <code>MRF_G = FALSE</code> is to
use graphical model for learning the MRF graph</dd>
<dt>cpp</dt>
<dd>logical, whether to use C++ code for faster computation</dd></dl>

Arguments

Update coefficients of Bayesian Cox Models — UpdateRPlee11

<dl>

<dt>sobj</dt>
<dd>a list containing observed data from <code>n</code> subjects;
<code>t</code>, <code>di</code>, <code>X</code>. See details for more information</dd>


<dt>hyperpar</dt>
<dd>a list containing prior parameter values</dd>


<dt>ini</dt>
<dd>a list containing prior parameters' initial values</dd>


<dt>S</dt>
<dd>the number of subgroups</dd>


<dt>method</dt>
<dd>a method option from
<code>c("Pooled", "CoxBVSSL", "Sub-struct")</code></dd>


<dt>MRF_G</dt>
<dd>logical value. <code>MRF_G = TRUE</code> is to fix the MRF graph which
is provided in the argument <code>hyperpar</code>, and <code>MRF_G = FALSE</code> is to
use graphical model for learning the MRF graph</dd>


<dt>cpp</dt>
<dd>logical, whether to use C++ code for faster computation</dd>

</dl>

UpdateRPlee11: Update coefficients of Bayesian Cox Models

Description

Usage

Value

Arguments