Commit f774c2a9 authored by Gilles Kratzer's avatar Gilles Kratzer
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update website

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......@@ -10,7 +10,7 @@ Authors@R: c(person("Gilles", "Kratzer", role = c("aut", "cre"),
Author: Gilles Kratzer [aut, cre] (<https://orcid.org/0000-0002-5929-8935>),
Reinhard Furrer [ctb] (<https://orcid.org/0000-0002-6319-2332>)
Maintainer: Gilles Kratzer <gilles.kratzer@math.uzh.ch>
Description: Flexible implementation of a structural MCMC sampler for Directed Acyclic Graphs (DAGs). It supports the new edge reversal move from Grzegorczyk and Husmeier (2008) and the Markov blanket resampling from Su and Borsuk (2016). It supports three priors: the one from Koivisto and Sood (2004), an uninformative prior and a user defined prior. The three main problems that can be addressed by this R package is selecting the most probable structure based on a cache of pre-computed scores, controlling for overfitting and sampling the landscape of high scoring structures which can be used as an alternative to parametric bootstrapping and allow the end user to make model averaging with DAGs.
Description: Flexible implementation of a structural MCMC sampler for Directed Acyclic Graphs (DAGs). It supports the new edge reversal move from Grzegorczyk and Husmeier (2008) <https://doi.org/10.1007/s10994-008-5057-7> and the Markov blanket resampling from Su and Borsuk (2016) <http://jmlr.org/papers/v17/su16a.html>. It supports three priors: a prior controlling for structure complexity from Koivisto and Sood (2004) <http://dl.acm.org/citation.cfm?id=1005332.1005352>, an uninformative prior and a user defined prior. The three main problems that can be addressed by this R package are selecting the most probable structure based on a cache of pre-computed scores, controlling for overfitting and sampling the landscape of high scoring structures. It allows to quantify the marginal impact of relationships of interest by marginalising out over structures or nuisance dependencies. Structural MCMC seems a very elegant and natural way to estimate the true marginal impact, so one can determine if it's magnitude is big enough to consider as a worthwhile intervention.
Depends: R (>= 3.5.0)
License: GPL-3
Encoding: UTF-8
......
mc3<-function(n.var, dag.tmp, max.parents, sc ,score.cache, score, prior.choice,prior.lambda, verbose){
mc3<-function(n.var, dag.tmp, max.parents, sc ,score.cache, score, prior.choice,prior.lambda,prior.dag, verbose){
##construction of neighbours list
neighbours.list <- NULL
......@@ -124,8 +124,8 @@ mc3<-function(n.var, dag.tmp, max.parents, sc ,score.cache, score, prior.choice,
#user prior
if(prior.choice==3){
prior.G <- sum(prior.lambda * exp(-prior.lambda*abs(dag.tmp[a,]-prior.lambda[a,])),prior.G)
prior.Gprime <-sum(1/choose(n = (n.var-1),k = sum(dag.gprime[a,])),prior.Gprime)
prior.G <- sum(exp(-prior.lambda*abs(dag.tmp[a,]-prior.dag[a,])),prior.G)
prior.Gprime <-sum(exp(-prior.lambda*abs(dag.gprime[a,]-prior.dag[a,])),prior.Gprime)
}
sc.tmp <- sc[score.cache$children==a,]
......
......@@ -124,7 +124,7 @@ mcmcabn <- function(score.cache = NULL,
method.choice <- sample(x = c("MC3","REV","MBR"),size = 1,prob = c(prob.mc3,prob.rev,prob.mbr))
switch (method.choice, "MC3" = {
out <- mc3(n.var,(dag.tmp),max.parents,sc,score.cache,score,prior.choice,prior.lambda,verbose)
out <- mc3(n.var,(dag.tmp),max.parents,sc,score.cache,score,prior.choice,prior.lambda,prior.dag,verbose)
dag.tmp <- out$dag.tmp
score <- out$score
},
......@@ -160,7 +160,7 @@ switch (method.choice, "MC3" = {
method.choice <- sample(x = c("MC3","REV","MBR"),size = 1,prob = c(prob.mc3,prob.rev,prob.mbr))
switch (method.choice, "MC3" = {
out <- mc3(n.var,(dag.tmp),max.parents,sc,score.cache,score,prior.choice,prior.lambda,verbose)
out <- mc3(n.var,(dag.tmp),max.parents,sc,score.cache,score,prior.choice,prior.lambda,prior.dag,verbose)
dag.tmp <- out$dag.tmp
score <- out$score
alpha <- out$alpha
......
......@@ -19,7 +19,7 @@ knitr::opts_chunk$set(
___
# mcmcabn: An R Package for sampling DAGs using structural MCMC
# mcmcabn: a structural MCMC sampler for DAGs learned from observed systemic datasets
## Installation
......@@ -29,7 +29,17 @@ You can install `mcmabn` with:
install.packages("mcmcabn")
```
mcmcabn is a flexible implementation of a structural MCMC sampler for Directed Acyclic Graphs (DAGs). It supports the new edge reversal move from Grzegorczyk and Husmeier (2008) and the Markov blanket resampling from Su and Borsuk (2016). It supports three priors: the one from Koivisto and Sood (2004), an uninformative prior and a user defined prior. The three main problems that can be addressed by this R package is selecting the most probable structure based on a cache of pre-computed scores, controlling for overfitting and sampling the landscape of high scoring structures which can be used as an alternative to parametric bootstrapping and allow the end user to make model averaging with DAGs.
The three main problems addressed by this R package are:
- selecting the most probable structure based on a cache of pre-computed scores.
- controlling for overfitting.
- sampling the landscape of high scoring structures.
The latter could be very useful in an applied perspective to avoid reducing the richeness of Bayesian network modelling to only one structure. Indeed, it allows user to quantify the marginal impact of relationships of interest by marginalising out over structures or nuisance dependencies. Structural MCMC seems a very elegant and natural way to estimate the true marginal impact, so one can determine if it’s magnitude is big enough to consider as a worthwhile intervention.
## Description
mcmcabn is a flexible implementation of a structural MCMC sampler for Directed Acyclic Graphs (DAGs). It supports the new edge reversal move from Grzegorczyk and Husmeier (2008) <https://doi.org/10.1007/s10994-008-5057-7> and the Markov blanket resampling from Su and Borsuk (2016) <http://jmlr.org/papers/v17/su16a.html>. It supports three priors: a prior controlling for structure complexity from Koivisto and Sood (2004) <http://dl.acm.org/citation.cfm?id=1005332.1005352>, an uninformative prior and a user defined prior. The three main problems that can be addressed by this R package are selecting the most probable structure based on a cache of pre-computed scores, controlling for overfitting and sampling the landscape of high scoring structures. It allows to quantify the marginal impact of relationships of interest by marginalising out over structures or nuisance dependencies. Structural MCMC seems a very elegant and natural way to estimate the true marginal impact, so one can determine if it's magnitude is big enough to consider as a worthwhile intervention.
___
## What's New
......
......@@ -12,6 +12,6 @@ install.packages("https://git.math.uzh.ch/gkratz/mcmcabn/raw/master/mcmcabn_0.1.
## Description
Flexible implementation of a structural MCMC sampler for Directed Acyclic Graphs (DAGs). It supports the new edge reversal move from Grzegorczyk and Husmeier (2008) and the Markov blanket resampling from Su and Borsuk (2016). It supports three priors: the one from Koivisto and Sood (2004), an uninformative prior and a user defined prior. The three main problems that can be addressed by this R package is selecting the most probable structure based on a cache of pre-computed scores, controlling for overfitting and sampling the landscape of high scoring structures which can be used as an alternative to parametric bootstrapping and allow the end user to make model averaging with DAGs.
Flexible implementation of a structural MCMC sampler for Directed Acyclic Graphs (DAGs). It supports the new edge reversal move from Grzegorczyk and Husmeier (2008) <https://doi.org/10.1007/s10994-008-5057-7> and the Markov blanket resampling from Su and Borsuk (2016) <http://jmlr.org/papers/v17/su16a.html>. It supports three priors: a prior controlling for structure complexity from Koivisto and Sood (2004) <http://dl.acm.org/citation.cfm?id=1005332.1005352>, an uninformative prior and a user defined prior. The three main problems that can be addressed by this R package are selecting the most probable structure based on a cache of pre-computed scores, controlling for overfitting and sampling the landscape of high scoring structures. It allows to quantify the marginal impact of relationships of interest by marginalising out over structures or nuisance dependencies. Structural MCMC seems a very elegant and natural way to estimate the true marginal impact, so one can determine if it's magnitude is big enough to consider as a worthwhile intervention.
## Future implementations (ordered by urgency)
......@@ -10,12 +10,19 @@ navbar:
left:
- icon: fa-home
href: index.html
- text: "Vignette"
href: articles/mcmcabn.html
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href: reference/index.html
right:
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href: https://git.math.uzh.ch/gkratz/mcmcabn
- icon: fa-lg fa-twitter
text: "twitter"
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......@@ -60,7 +60,7 @@
</button>
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......@@ -72,6 +72,9 @@
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......@@ -72,6 +72,9 @@
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......@@ -124,14 +133,14 @@
journal = {arXiv preprint arXiv:1902.06641},
}</pre>
<p>Kratzer G, Furrer R (2019).
<em>mcmcabn: an R package for structural MCMC sampler for DAGs learned from observed systemic datasets</em>.
R package version 0.0.0.9000, <a href="https://CRAN.R-project.org/package=mcmcabn">https://CRAN.R-project.org/package=mcmcabn</a>.
<em>mcmcabn: a structural MCMC sampler for DAGs learned from observed systemic datasets</em>.
R package version 0.1, <a href="https://CRAN.R-project.org/package=mcmcabn">https://CRAN.R-project.org/package=mcmcabn</a>.
</p>
<pre>@Manual{,
title = {mcmcabn: an R package for structural MCMC sampler for DAGs learned from observed systemic datasets},
title = {mcmcabn: a structural MCMC sampler for DAGs learned from observed systemic datasets},
author = {Gilles Kratzer and Reinhard Furrer},
year = {2019},
note = {R package version 0.0.0.9000},
note = {R package version 0.1},
url = {https://CRAN.R-project.org/package=mcmcabn},
}</pre>
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<title>Flexible Implementation of a Structural MCMC Sampler for DAGs • mcmcabn</title>
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......@@ -30,7 +30,7 @@
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......@@ -42,6 +42,9 @@
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Flexible implementation of a structural MCMC sampler for DAGs. It supports the new edge reversal move from Grzegorczyk and Husmeier (2008) &lt;<a href="https://doi.org/10.1007/s10994-008-5057-7">doi:10.1007/s10994-008-5057-7</a>&gt; and the Markov blanket resampling from Su and Borsuk (2016) &lt;doi:&gt;. The two main problems that can be addressed by this package is selecting the most probable structure based on a cache of pre-computed scores and the sampling of the landscape of high scoring networks which can be used as an alternative to parametric bootstrapping.
<div class="col-md-9 contents">
<!-- README.md is generated from README.Rmd. Please edit that file -->
<hr>
<div id="mcmcabn-a-structural-mcmc-sampler-for-dags-learned-from-observed-systemic-datasets" class="section level1">
<div class="page-header"><h1 class="hasAnchor">
<a href="#mcmcabn-a-structural-mcmc-sampler-for-dags-learned-from-observed-systemic-datasets" class="anchor"></a>mcmcabn: a structural MCMC sampler for DAGs learned from observed systemic datasets</h1></div>
<div id="installation" class="section level2">
<h2 class="hasAnchor">
<a href="#installation" class="anchor"></a>Installation</h2>
<p>You can install <code>mcmabn</code> with:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="https://www.rdocumentation.org/packages/utils/topics/install.packages">install.packages</a></span>(<span class="st">"mcmcabn"</span>)</code></pre></div>
<p>The three main problems addressed by this R package are:</p>
<ul>
<li>selecting the most probable structure based on a cache of pre-computed scores.</li>
<li>controlling for overfitting.</li>
<li>sampling the landscape of high scoring structures. The latter could be very useful in an applied perspective to avoid reducing the richeness of Bayesian network modelling to only one structure. Indeed, it allows user to quantify the marginal impact of relationships of interest by marginalising out over structures or nuisance dependencies. Structural MCMC seems a very elegant and natural way to estimate the true marginal impact, so one can determine if it’s magnitude is big enough to consider as a worthwhile intervention.</li>
</ul>
</div>
<div id="description" class="section level2">
<h2 class="hasAnchor">
<a href="#description" class="anchor"></a>Description</h2>
<p>mcmcabn is a flexible implementation of a structural MCMC sampler for Directed Acyclic Graphs (DAGs). It supports the new edge reversal move from Grzegorczyk and Husmeier (2008) <a href="https://doi.org/10.1007/s10994-008-5057-7" class="uri">https://doi.org/10.1007/s10994-008-5057-7</a> and the Markov blanket resampling from Su and Borsuk (2016) <a href="http://jmlr.org/papers/v17/su16a.html" class="uri">http://jmlr.org/papers/v17/su16a.html</a>. It supports three priors: a prior controlling for structure complexity from Koivisto and Sood (2004) <a href="http://dl.acm.org/citation.cfm?id=1005332.1005352" class="uri">http://dl.acm.org/citation.cfm?id=1005332.1005352</a>, an uninformative prior and a user defined prior. The three main problems that can be addressed by this R package are selecting the most probable structure based on a cache of pre-computed scores, controlling for overfitting and sampling the landscape of high scoring structures. It allows to quantify the marginal impact of relationships of interest by marginalising out over structures or nuisance dependencies. Structural MCMC seems a very elegant and natural way to estimate the true marginal impact, so one can determine if it’s magnitude is big enough to consider as a worthwhile intervention.</p>
<hr>
</div>
<div id="whats-new" class="section level2">
<h2 class="hasAnchor">
<a href="#whats-new" class="anchor"></a>What’s New</h2>
<ul>
<li><p>23/04/2019 - mcmcabn is available on CRAN (v 0.1)</p></li>
<li><p>18/02/2019 - new pre-print <a href="https://arxiv.org/pdf/1902.06641.pdf">Is a single unique Bayesian network enough to accurately represent your data?</a> on arXiv</p></li>
</ul>
<hr>
<p><strong><code>mcmcabn</code> is developed and maintained by <a href="https://gilleskratzer.netlify.com/">Gilles Kratzer</a> and <a href="https://user.math.uzh.ch/furrer/">Prof. Dr. Reinhard Furrer</a> from <a href="https://www.math.uzh.ch/as/index.php?id=as">Applied Statistics Group</a> from the University of Zurich.</strong> ___</p>
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......@@ -100,9 +149,20 @@ Flexible implementation of a structural MCMC sampler for DAGs. It supports the n
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<h2>Dev status</h2>
<ul class="list-unstyled">
<li><a href="http://cran.rstudio.com/web/packages/mcmcabn/index.html"><img src="https://www.r-pkg.org/badges/version-ago/mcmcabn"></a></li>
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<li><a href="http://cran.rstudio.com/web/packages/mcmcabn/index.html"><img src="http://cranlogs.r-pkg.org/badges/mcmcabn" alt="Downloads"></a></li>
<li><a href="http://www.gnu.org/licenses/gpl-3.0"><img src="https://img.shields.io/badge/License-GPL%20v3-blue.svg" alt="License: GPL v3"></a></li>
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<p>Developed by <a href="https://gilleskratzer.netlify.com/">Gilles Kratzer</a>.</p>
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......@@ -60,7 +60,7 @@
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......@@ -72,6 +72,9 @@
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<h1>Cache of pre-computed scores related to the asia dataset</h1>
<div class="hidden name"><code>bsc-compute-asia.Rd</code></div>
</div>
<div class="ref-description">
<p>This dataframe contains a cache of pre-computed scores with a maximum of two parents per node for the asia dataset.</p>
</div>
<pre class="usage"><span class='fu'><a href='https://www.rdocumentation.org/packages/utils/topics/data'>data</a></span>(<span class='st'>"mcmc_run_asia"</span>)</pre>
<h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>
<p>The data contains a cache of pre-computed scores with a maximum of two parents per node.</p><ul>
<li><p><code>bsc.compute.asia</code>: cache of score with a maximum of two parents per node.</p></li>
</ul>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><span class='co'># NOT RUN {</span>
<span class='co'>## This data set was generated using the following code:</span>
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/library'>library</a></span>(<span class='no'>bnlearn</span>) <span class='co'>#for the dataset</span>
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/library'>library</a></span>(<span class='no'>abn</span>) <span class='co'>#for the cache of score function</span>
<span class='co'>#renaming columns of the dataset</span>
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/colnames'>colnames</a></span>(<span class='no'>asia</span>) <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"Asia"</span>,
<span class='st'>"Smoking"</span>,
<span class='st'>"Tuberculosis"</span>,
<span class='st'>"LungCancer"</span>,
<span class='st'>"Bronchitis"</span>,
<span class='st'>"Either"</span>,
<span class='st'>"XRay"</span>,
<span class='st'>"Dyspnea"</span>)
<span class='co'>#lets define the distribution list</span>
<span class='no'>dist.asia</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>Asia</span> <span class='kw'>=</span> <span class='st'>"binomial"</span>,
<span class='kw'>Smoking</span> <span class='kw'>=</span> <span class='st'>"binomial"</span>,
<span class='kw'>Tuberculosis</span> <span class='kw'>=</span> <span class='st'>"binomial"</span>,
<span class='kw'>LungCancer</span> <span class='kw'>=</span> <span class='st'>"binomial"</span>,
<span class='kw'>Bronchitis</span> <span class='kw'>=</span> <span class='st'>"binomial"</span>,
<span class='kw'>Either</span> <span class='kw'>=</span> <span class='st'>"binomial"</span>,
<span class='kw'>XRay</span> <span class='kw'>=</span> <span class='st'>"binomial"</span>,
<span class='kw'>Dyspnea</span> <span class='kw'>=</span> <span class='st'>"binomial"</span>)
<span class='no'>bsc.compute.asia</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/abn/topics/build_score_cache'>buildscorecache</a></span>(<span class='kw'>data.df</span> <span class='kw'>=</span> <span class='no'>asia</span>,