Commit ff8e78e0 authored by Gilles Kratzer's avatar Gilles Kratzer
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## Quick start
To install `mcmabn` you need two R packages: [abn](https://CRAN.R-project.org/package=abn) and [gRbase](https://CRAN.R-project.org/package=gRbase) that requires libraries stored not stored on [CRAN](https://cran.r-project.org/) but on [bioconductor](http://www.bioconductor.org/). Hence you **must** install these packages **before** installing `mcmcabn`:
To install `mcmabn` you need two R packages: [abn](https://CRAN.R-project.org/package=abn) and [gRbase](https://CRAN.R-project.org/package=gRbase) which requires libraries not stored on [CRAN](https://cran.r-project.org/) but on [bioconductor](http://www.bioconductor.org/). Hence you **must** install these packages **before** installing `mcmcabn`:
``` r
if (!requireNamespace("BiocManager", quietly = TRUE))
......@@ -39,7 +39,7 @@ The three main problems addressed by this R package are:
- 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.
The latter could be very useful in an applied perspective to avoid reducing the richeness of Bayesian network modelling to report 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
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......@@ -14,6 +14,6 @@ Website: https://www.math.uzh.ch/pages/mcmcabn/
## 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) <doi.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.
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)
......@@ -99,7 +99,7 @@
<div id="quick-start" class="section level2">
<h2 class="hasAnchor">
<a href="#quick-start" class="anchor"></a>Quick start</h2>
<p>To install <code>mcmabn</code> you need two R packages: <a href="https://CRAN.R-project.org/package=abn">abn</a> and <a href="https://CRAN.R-project.org/package=gRbase">gRbase</a> that requires libraries stored not stored on <a href="https://cran.r-project.org/">CRAN</a> but on <a href="http://www.bioconductor.org/">bioconductor</a>. Hence you <strong>must</strong> install these packages <strong>before</strong> installing <code>mcmcabn</code>:</p>
<p>To install <code>mcmabn</code> you need two R packages: <a href="https://CRAN.R-project.org/package=abn">abn</a> and <a href="https://CRAN.R-project.org/package=gRbase">gRbase</a> which requires libraries not stored on <a href="https://cran.r-project.org/">CRAN</a> but on <a href="http://www.bioconductor.org/">bioconductor</a>. Hence you <strong>must</strong> install these packages <strong>before</strong> installing <code>mcmcabn</code>:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="cf">if</span> (<span class="op">!</span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/ns-load">requireNamespace</a></span>(<span class="st">"BiocManager"</span>, <span class="dt">quietly =</span> <span class="ot">TRUE</span>))
<span class="kw"><a href="https://www.rdocumentation.org/packages/utils/topics/install.packages">install.packages</a></span>(<span class="st">"BiocManager"</span>)
BiocManager<span class="op">::</span><span class="kw"><a href="https://www.rdocumentation.org/packages/BiocManager/topics/install">install</a></span>(<span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="st">"RBGL"</span>,<span class="st">"Rgraphviz"</span>,<span class="st">"graph"</span>), <span class="dt">version =</span> <span class="st">"3.8"</span>)
......@@ -111,7 +111,7 @@ BiocManager<span class="op">::</span><span class="kw"><a href="https://www.rdocu
<li>controlling for overfitting.</li>
<li>sampling the landscape of high scoring structures.</li>
</ul>
<p>The latter could be very useful in an applied perspective to avoid reducing the richeness of Bayesian network modelling to only <strong>one</strong> 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.</p>
<p>The latter could be very useful in an applied perspective to avoid reducing the richeness of Bayesian network modelling to report only <strong>one</strong> 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.</p>
</div>
<div id="description" class="section level2">
<h2 class="hasAnchor">
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<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Named list of distribution to analyze asia dataset — dist.asia • mcmcabn</title>
<title>Named list of distributions to analyze asia dataset — dist.asia • mcmcabn</title>
<!-- jquery -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js" integrity="sha256-FgpCb/KJQlLNfOu91ta32o/NMZxltwRo8QtmkMRdAu8=" crossorigin="anonymous"></script>
......@@ -30,9 +30,9 @@
<meta property="og:title" content="Named list of distribution to analyze asia dataset — dist.asia" />
<meta property="og:title" content="Named list of distributions to analyze asia dataset — dist.asia" />
<meta property="og:description" content="Named list of distribution to analyze asia dataset" />
<meta property="og:description" content="Named list of distribution to analyze asia dataset." />
<meta name="twitter:card" content="summary" />
......@@ -121,14 +121,14 @@
<div class="row">
<div class="col-md-9 contents">
<div class="page-header">
<h1>Named list of distribution to analyze asia dataset</h1>
<h1>Named list of distributions to analyze asia dataset</h1>
<div class="hidden name"><code>dist-asia.Rd</code></div>
</div>
<div class="ref-description">
<p>Named list of distribution to analyze asia dataset</p>
<p>Named list of distribution to analyze asia dataset.</p>
</div>
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......@@ -147,7 +147,7 @@
<td>
<p><code><a href="dist-asia.html">mcmc_run_asia</a></code> </p>
</td>
<td><p>Named list of distribution to analyze asia dataset</p></td>
<td><p>Named list of distributions to analyze asia dataset</p></td>
</tr><tr>
<td>
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<meta property="og:title" content="MCMC search from the synthetic asia dataset for use with mcmcabn library examples — mcmc_run_asia" />
<meta property="og:description" content="10^5 MCMC runs with 1000 burn in run from the asia synthetic datasets from Lauritzen and Spiegelhalter (1988) provided by Scutari (2010)." />
<meta property="og:description" content="10^5 MCMC runs with 1000 burn-in runs from the asia synthetic dataset from Lauritzen and Spiegelhalter (1988) provided by Scutari (2010)." />
<meta name="twitter:card" content="summary" />
......@@ -128,7 +128,7 @@
<div class="ref-description">
<p>10^5 MCMC runs with 1000 burn in run from the asia synthetic datasets from Lauritzen and Spiegelhalter (1988) provided by Scutari (2010).</p>
<p>10^5 MCMC runs with 1000 burn-in runs from the asia synthetic dataset from Lauritzen and Spiegelhalter (1988) provided by Scutari (2010).</p>
</div>
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......@@ -159,7 +159,7 @@ terms, `.` replaces all the variables in name. Additional, when one want to excl
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
<p>A probability</p>
<p>A frequency for the requested query. Alternatively a matrix with arc-wise frequencies.</p>
<h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>
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......@@ -151,7 +151,7 @@ summary(object,
</tr>
<tr>
<th>lag.max</th>
<td><p>maximum lag at which to calculate the acf. Default is set to 10.</p></td>
<td><p>maximum lag at which to calculate the <a href='https://www.rdocumentation.org/packages/stats/topics/acf'>acf</a>. Default is set to 10.</p></td>
</tr>
<tr>
<th>&#8230;</th>
......@@ -161,11 +161,11 @@ summary(object,
<h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
<p>The summary function for mcmcabn objects returns multiple summary metrics for assesing the quality of the MCMC run.</p>
<p>The summary function for <code>mcmcabn</code> objects returns multiple summary metrics for assesing the quality of the MCMC run. Thinning is the number of thinned MCMC steps for one MCMC returned.</p>
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
<p>This method print: the number of Burn in steps, the number of MCMC steps, the thinning, the maximum achieved score, the empirical mean of the MCMC samples, the empirical standard deviation of the MCMC samples, the user defined quantiles of the posterior network score, the global acceptance rate, a table of the accepted and rejected moves in function of the methods used, the sample size adjusted for autocorrelation and the autocorrelations by lag.</p>
<p>This method prints: the number of burn-in steps, the number of MCMC steps, the thinning, the maximum achieved score, the empirical mean of the MCMC samples, the empirical standard deviation of the MCMC samples, the user defined quantiles of the posterior network score, the global acceptance rate, a table of the accepted and rejected moves in function of the methods used, the sample size adjusted for autocorrelation and the autocorrelations by lag.</p>
<h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>
......
model{
###-----------------------
###Binomial nodes
###-----------------------
A ~ dbern(p.A); #Binary response
logit(p.A) <- 0 + 1*C; #Logistic regression
###-----------------------
###Binomial nodes
###-----------------------
B ~ dbern(p.B); #Binary response
logit(p.B) <- 1 + 1*A + 1*E; #Logistic regression
###-----------------------
###Binomial nodes
###-----------------------
C ~ dbern(p.C); #Binary response
logit(p.C) <- 0; #Logistic regression
###-----------------------
###Binomial nodes
###-----------------------
D ~ dbern(p.D); #Binary response
logit(p.D) <- 0 + 1*E; #Logistic regression
###-----------------------
###Binomial nodes
###-----------------------
E ~ dbern(p.E); #Binary response
logit(p.E) <- 1 + 1*C; #Logistic regression
}
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