func_MCMC_graph

This an internal function for MCMC sampling

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.

Zhi Zhao

BayesSurvive

Bayesian Survival Models for High-Dimensional Data

Katrin Madjar

Tobias Østmo Hermansen

Manuela Zucknick

Jörg Rahnenführer

func_MCMC_graph 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' ini 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_2b</dt>
<dd>two different b in MRF prior for subgraphs G_ss and G_rs</dd></dl>

Arguments

Function to learn MRF graph — func_MCMC_graph

<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' ini 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_2b</dt>
<dd>two different b in MRF prior for subgraphs G_ss and G_rs</dd>

</dl>

func_MCMC_graph: Function to learn MRF graph

Description

Usage

Value

Arguments