Deploying to gh-pages from @ 22f4be6 🚀

docs/devel/pages/beta_diversity.html

@@ -1127,7 +1127,7 @@ <h1 class="title"><span id="sec-community-similarity" class="quarto-section-iden
 <td style="text-align: right;">6</td>
 <td style="text-align: right;">1.1157</td>
 <td style="text-align: right;">1.940</td>
-<td style="text-align: right;">0.037</td>
+<td style="text-align: right;">0.028</td>
 <td style="text-align: right;">3.991</td>
 <td style="text-align: right;">0.2795</td>
 </tr>
@@ -1136,7 +1136,7 @@ <h1 class="title"><span id="sec-community-similarity" class="quarto-section-iden
 <td style="text-align: right;">4</td>
 <td style="text-align: right;">0.5837</td>
 <td style="text-align: right;">1.522</td>
-<td style="text-align: right;">0.128</td>
+<td style="text-align: right;">0.126</td>
 <td style="text-align: right;">3.991</td>
 <td style="text-align: right;">0.1463</td>
 </tr>
@@ -1145,7 +1145,7 @@ <h1 class="title"><span id="sec-community-similarity" class="quarto-section-iden
 <td style="text-align: right;">1</td>
 <td style="text-align: right;">0.1679</td>
 <td style="text-align: right;">1.751</td>
-<td style="text-align: right;">0.102</td>
+<td style="text-align: right;">0.117</td>
 <td style="text-align: right;">3.991</td>
 <td style="text-align: right;">0.0421</td>
 </tr>
@@ -1254,7 +1254,7 @@ <h1 class="title"><span id="sec-community-similarity" class="quarto-section-iden
 <td style="text-align: right;">0.0628</td>
 <td style="text-align: right;">2.7440</td>
 <td style="text-align: right;">999</td>
-<td style="text-align: right;">0.108</td>
+<td style="text-align: right;">0.125</td>
 <td style="text-align: right;">1.0288</td>
 <td style="text-align: right;">0.2440</td>
 </tr>
@@ -1265,7 +1265,7 @@ <h1 class="title"><span id="sec-community-similarity" class="quarto-section-iden
 <td style="text-align: right;">0.0103</td>
 <td style="text-align: right;">0.4158</td>
 <td style="text-align: right;">999</td>
-<td style="text-align: right;">0.527</td>
+<td style="text-align: right;">0.518</td>
 <td style="text-align: right;">0.9283</td>
 <td style="text-align: right;">0.0111</td>
 </tr>
@@ -1276,7 +1276,7 @@ <h1 class="title"><span id="sec-community-similarity" class="quarto-section-iden
 <td style="text-align: right;">0.0113</td>
 <td style="text-align: right;">17.0255</td>
 <td style="text-align: right;">999</td>
-<td style="text-align: right;">0.425</td>
+<td style="text-align: right;">0.419</td>
 <td style="text-align: right;">0.3319</td>
 <td style="text-align: right;">0.9860</td>
 </tr>

docs/devel/pages/extra_material.html

@@ -1296,7 +1296,7 @@ <h1 class="title"><span id="sec-extras" class="quarto-section-identifier">Append
 <p>Printing a summary about the posterior:</p>
 <div class="cell" data-layout-align="center">
 <div class="sourceCode" id="cb40"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://jsilve24.github.io/fido/reference/ppc_summary.html">ppc_summary</a></span><span class="op">(</span><span class="va">posterior</span><span class="op">)</span></span>
-<span><span class="co">##  Proportions of Observations within 95% Credible Interval: 0.9982747</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span><span class="co">##  Proportions of Observations within 95% Credible Interval: 0.9966644</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Plotting the summary of the posterior distributions of the regression parameters:</p>
 <div class="cell" data-layout-align="center">
@@ -1491,8 +1491,8 @@ <h1 class="title"><span id="sec-extras" class="quarto-section-identifier">Append
 <span><span class="co">##  </span></span>
 <span><span class="co">##  First  5  Cluster sizes:</span></span>
 <span><span class="co">##                     BC 1 BC 2 BC 3 BC 4 BC 5</span></span>
-<span><span class="co">##  Number of Rows:      16   14   15    2    6</span></span>
-<span><span class="co">##  Number of Columns:   13   14   10   13   11</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span><span class="co">##  Number of Rows:      16   14   18    4    4</span></span>
+<span><span class="co">##  Number of Columns:   13   14    9   11    8</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>The object includes cluster information. However compared to <code>cobiclust</code>, <code>biclust</code> object includes only information about clusters that were found, not general cluster.</p>
 <p>Meaning that if one cluster size of 5 features was found out of 20 features, those 15 features do not belong to any cluster. That is why we have to create an additional cluster for features/samples that are not assigned into any cluster.</p>

docs/devel/pages/integrated_learner.html

@@ -751,7 +751,7 @@ <h1 class="title"><span id="sec-multi-omics-integration" class="quarto-section-i
 <span><span class="co">##  Running base model for layer  2 ... </span></span>
 <span><span class="co">##  Running stacked model...</span></span>
 <span><span class="co">##  Running concatenated model...</span></span>
-<span><span class="co">##  Time for model fit : 1.6 minutes </span></span>
+<span><span class="co">##  Time for model fit : 1.623 minutes </span></span>
 <span><span class="co">##  ========================================</span></span>
 <span><span class="co">##  Model fit for individual layers: SL.randomForest </span></span>
 <span><span class="co">##  Model fit for stacked layer: SL.nnls.auc </span></span>
@@ -842,7 +842,7 @@ <h1 class="title"><span id="sec-multi-omics-integration" class="quarto-section-i
 <p>The model appears to be performing well the the accuracy being 93.16%. The model seems to precict correctly almost all IBD patients. It is also worth noting that over three-quarters of the controls are classified correctly</p>
 <p>Lastly, we may be interested in identifying the features that contribute most to predicting the outcome. To do this, we first extract feature importance scores from the individual models. Next, we scale these importance scores based on the weights assigned to each layer in the stacked model. This process provides us with the overall importance of each feature in the final model.</p>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org">ggplot2</a></span><span class="op">)</span></span>
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va">miaViz</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># Get individual models</span></span>
 <span><span class="va">models</span> <span class="op">&lt;-</span> <span class="va">fit</span><span class="op">$</span><span class="va">model_fits</span><span class="op">$</span><span class="va">model_layers</span></span>
@@ -854,23 +854,11 @@ <h1 class="title"><span id="sec-multi-omics-integration" class="quarto-section-i
 <span>  <span class="va">temp</span> <span class="op">&lt;-</span> <span class="va">temp</span> <span class="op">*</span> <span class="va">fit</span><span class="op">$</span><span class="va">weights</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span></span>
 <span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html">return</a></span><span class="op">(</span><span class="va">temp</span><span class="op">)</span></span>
 <span>  <span class="op">}</span><span class="op">)</span></span>
-<span><span class="co"># Combine and order to most important features</span></span>
+<span><span class="co"># Combine the feature importances</span></span>
 <span><span class="va">importances</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/do.call.html">do.call</a></span><span class="op">(</span><span class="va">rbind</span>, <span class="va">importances</span><span class="op">)</span></span>
-<span><span class="va">importances</span> <span class="op">&lt;-</span> <span class="va">importances</span><span class="op">[</span></span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/base/order.html">order</a></span><span class="op">(</span><span class="va">importances</span>, decreasing <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>, , drop <span class="op">=</span> <span class="cn">FALSE</span><span class="op">]</span></span>
-<span><span class="co"># Add features to column</span></span>
-<span><span class="va">importances</span> <span class="op">&lt;-</span> <span class="va">importances</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/r/base/as.data.frame.html">as.data.frame</a></span><span class="op">(</span><span class="op">)</span></span>
-<span><span class="va">importances</span><span class="op">[[</span><span class="st">"Feature"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span></span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/base/colnames.html">rownames</a></span><span class="op">(</span><span class="va">importances</span><span class="op">)</span>, levels <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/colnames.html">rownames</a></span><span class="op">(</span><span class="va">importances</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="co"># Convert to 0-1 scale</span></span>
-<span><span class="va">importances</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="va">importances</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span> <span class="op">/</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">importances</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span>
-<span><span class="co"># Get top 20 importances</span></span>
-<span><span class="va">top_n</span> <span class="op">&lt;-</span> <span class="fl">20</span></span>
-<span><span class="va">importances</span> <span class="op">&lt;-</span> <span class="va">importances</span><span class="op">[</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span><span class="op">(</span><span class="va">top_n</span><span class="op">)</span>, <span class="op">]</span></span>
 <span></span>
-<span><span class="co"># Plot as a bar plot</span></span>
-<span><span class="va">p</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html">ggplot</a></span><span class="op">(</span><span class="va">importances</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">MeanDecreaseGini</span>, y <span class="op">=</span> <span class="va">Feature</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
-<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_bar.html">geom_bar</a></span><span class="op">(</span>stat <span class="op">=</span> <span class="st">"identity"</span><span class="op">)</span></span>
+<span><span class="co"># Plot feature importances</span></span>
+<span><span class="va">p</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/miaViz/man/plotLoadings.html">plotLoadings</a></span><span class="op">(</span><span class="va">importances</span>, ncomponents <span class="op">=</span> <span class="fl">1</span>, n <span class="op">=</span> <span class="fl">20</span>, show.color <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
 <span><span class="va">p</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
@@ -879,7 +867,7 @@ <h1 class="title"><span id="sec-multi-omics-integration" class="quarto-section-i
 </div>
 </div>
 </div>
-<p>From the plot, we can observe that <em>Alistipes putredinis</em> and <em>Alistipes putredinis</em> appear to have the greatest predictive power among all the features in determining the outcome. However, the predictive power appears to be fairly evenly distributed across all features.</p>
+<p>From the plot, we can observe that <em>Alistipes putredinis</em> and <em>Firmicutes bacterium CAG:83</em> appear to have the greatest predictive power among all the features in determining the outcome. However, the predictive power appears to be fairly evenly distributed across all features.</p>
 <div class="callout callout-style-default callout-tip callout-titled">
 <div class="callout-header d-flex align-content-center">
 <div class="callout-icon-container">

docs/devel/pages/machine_learning.html

@@ -708,26 +708,26 @@ <h1 class="title"><span id="sec-machine_learning" class="quarto-section-identifi
 <span><span class="co">##  </span></span>
 <span><span class="co">##            Reference</span></span>
 <span><span class="co">##  Prediction T2D healthy</span></span>
-<span><span class="co">##     T2D      94      50</span></span>
-<span><span class="co">##     healthy  42     105</span></span>
+<span><span class="co">##     T2D      87      51</span></span>
+<span><span class="co">##     healthy  49     104</span></span>
 <span><span class="co">##                                          </span></span>
-<span><span class="co">##                 Accuracy : 0.684         </span></span>
-<span><span class="co">##                   95% CI : (0.627, 0.737)</span></span>
+<span><span class="co">##                 Accuracy : 0.656         </span></span>
+<span><span class="co">##                   95% CI : (0.599, 0.711)</span></span>
 <span><span class="co">##      No Information Rate : 0.533         </span></span>
-<span><span class="co">##      P-Value [Acc &gt; NIR] : 1.09e-07      </span></span>
+<span><span class="co">##      P-Value [Acc &gt; NIR] : 1.26e-05      </span></span>
 <span><span class="co">##                                          </span></span>
-<span><span class="co">##                    Kappa : 0.367         </span></span>
+<span><span class="co">##                    Kappa : 0.31          </span></span>
 <span><span class="co">##                                          </span></span>
-<span><span class="co">##   Mcnemar's Test P-Value : 0.466         </span></span>
+<span><span class="co">##   Mcnemar's Test P-Value : 0.92          </span></span>
 <span><span class="co">##                                          </span></span>
-<span><span class="co">##              Sensitivity : 0.691         </span></span>
-<span><span class="co">##              Specificity : 0.677         </span></span>
-<span><span class="co">##           Pos Pred Value : 0.653         </span></span>
-<span><span class="co">##           Neg Pred Value : 0.714         </span></span>
+<span><span class="co">##              Sensitivity : 0.640         </span></span>
+<span><span class="co">##              Specificity : 0.671         </span></span>
+<span><span class="co">##           Pos Pred Value : 0.630         </span></span>
+<span><span class="co">##           Neg Pred Value : 0.680         </span></span>
 <span><span class="co">##               Prevalence : 0.467         </span></span>
-<span><span class="co">##           Detection Rate : 0.323         </span></span>
-<span><span class="co">##     Detection Prevalence : 0.495         </span></span>
-<span><span class="co">##        Balanced Accuracy : 0.684         </span></span>
+<span><span class="co">##           Detection Rate : 0.299         </span></span>
+<span><span class="co">##     Detection Prevalence : 0.474         </span></span>
+<span><span class="co">##        Balanced Accuracy : 0.655         </span></span>
 <span><span class="co">##                                          </span></span>
 <span><span class="co">##         'Positive' Class : T2D           </span></span>
 <span><span class="co">##  </span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>