@@ -3,8 +3,8 @@ Type: Package

 Title: A framework for cross-validated classification problems, with

        applications to differential variability and differential

        distribution testing

-Version: 3.3.12

-Date: 2023-02-10

+Version: 3.3.13

+Date: 2023-02-13

 Authors@R:

     c(

     person(given = "Dario", family = "Strbenac", email = "dario.strbenac@sydney.edu.au", role = c("aut", "cre")),

@@ -473,9 +473,7 @@ setClassUnion("SelectParamsOrNULL", c("SelectParams", "NULL"))

 #' @docType class

 #' @section Constructor:

 #' \describe{

-#' \item

-#'     {\preformatted{SelectParams(featureRanking, characteristics = DataFrame(), minPresence = 1, intermediate = character(0),subsetToSelections = TRUE, tuneParams = list(nFeatures = seq(10, 100, 10), performanceType = "Balanced Accuracy"), ...)}}

-#'     {Creates a \code{SelectParams} object which stores the function(s) which will do the selection and parameters that the function will use.\cr

+#' \item{\preformatted{SelectParams(featureRanking, characteristics = DataFrame(), minPresence = 1, intermediate = character(0),subsetToSelections = TRUE, tuneParams = list(nFeatures = seq(10, 100, 10), performanceType = "Balanced Accuracy"), ...)}}{Creates a \code{SelectParams} object which stores the function(s) which will do the selection and parameters that the function will use.\cr

 #'     \describe{

 #'         \item{\code{featureRanking}}{A character keyword referring to a registered feature ranking function. See \code{\link{available}} for valid keywords.}

 #'         \item{\code{characteristics}}{A \code{\link{DataFrame}} describing the characteristics of feature selection to be done. First column must be named \code{"charateristic"} and second column must be named \code{"value"}. If using wrapper functions for feature selection in this package, the feature selection name will automatically be generated and therefore it is not necessary to specify it.}

@@ -163,7 +163,6 @@ setMethod("crossValidate", "DataFrame",

                   # 3) Multi assays individually

                   # We should probably transition this to use grid instead.

-                  

                   resClassifier <-

                       sapply(assayIDs, function(assayIndex) {

                           # Loop over assays

@@ -212,7 +211,7 @@ setMethod("crossValidate", "DataFrame",

                   if(!is.list(assayCombinations) && assayCombinations[1] == "all") assayCombinations <- do.call("c", sapply(seq_along(assayIDs), function(nChoose) combn(assayIDs, nChoose, simplify = FALSE)))

                   result <- sapply(assayCombinations, function(assayIndex){

-                      CV(measurements = measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayIndex],

+                      CV(measurements = measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayIndex, drop = FALSE],

                          outcome = outcome, assayIDs = assayIndex,

                          nFeatures = nFeatures[assayIndex],

                          selectionMethod = selectionMethod[assayIndex],

@@ -246,7 +245,7 @@ setMethod("crossValidate", "DataFrame",

                   }

                   result <- sapply(assayCombinations, function(assayIndex){

-                      CV(measurements = measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayIndex],

+                      CV(measurements = measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayIndex, drop = FALSE],

                          outcome = outcome, assayIDs = assayIndex,

                          nFeatures = nFeatures[assayIndex],

                          selectionMethod = selectionMethod[assayIndex],

@@ -281,7 +280,7 @@ setMethod("crossValidate", "DataFrame",

                   result <- sapply(assayCombinations, function(assayIndex){

-                      CV(measurements = measurements[, S4Vectors::mcols(measurements)$assay %in% assayIndex],

+                      CV(measurements = measurements[, S4Vectors::mcols(measurements)$assay %in% assayIndex, drop = FALSE],

                          outcome = outcome, assayIDs = assayIndex,

                          nFeatures = nFeatures[assayIndex],

                          selectionMethod = selectionMethod[assayIndex],

@@ -563,7 +562,7 @@ generateCrossValParams <- function(nRepeats, nFolds, nCores, selectionOptimisati

 }

-# Returns a 

+# Returns a single parameter set.

 generateModellingParams <- function(assayIDs,

                                     measurements,

                                     nFeatures,

@@ -722,7 +721,7 @@ generateMultiviewParams <- function(assayIDs,

         if(length(classifier) > 1) classifier <- classifier[[1]]

         # Split measurements up by assay.

-        assayTrain <- sapply(assayIDs, function(assayID) if(assayID == 1) measurements else measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayID], simplify = FALSE)

+        assayTrain <- sapply(assayIDs, function(assayID) if(assayID == 1) measurements else measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayID, drop = FALSE], simplify = FALSE)

         # Generate params for each assay. This could be extended to have different selectionMethods for each type

         paramsAssays <- mapply(generateModellingParams,

@@ -764,7 +763,7 @@ generateMultiviewParams <- function(assayIDs,

     if(multiViewMethod == "prevalidation"){

         # Split measurements up by assay.

-        assayTrain <- sapply(assayIDs, function(assayID) measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayID], simplify = FALSE)

+        assayTrain <- sapply(assayIDs, function(assayID) measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayID, drop = FALSE], simplify = FALSE)

         # Generate params for each assay. This could be extended to have different selectionMethods for each type

         paramsAssays <- mapply(generateModellingParams,

@@ -794,7 +793,7 @@ generateMultiviewParams <- function(assayIDs,

     if(multiViewMethod == "PCA"){

         # Split measurements up by assay.

-        assayTrain <- sapply(assayIDs, function(assayID) measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayID], simplify = FALSE)

+        assayTrain <- sapply(assayIDs, function(assayID) measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayID, drop = FALSE], simplify = FALSE)

         # Generate params for each assay. This could be extended to have different selectionMethods for each type

         paramsClinical <-  list(clinical = generateModellingParams(

@@ -1039,7 +1038,7 @@ train.DataFrame <- function(x, outcomeTrain, selectionMethod = "auto", nFeatures

               ### Merging or binding to combine data

               if(multiViewMethod == "merge"){

-                  measurementsUse <- measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayIDs]

+                  measurementsUse <- measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayIDs, drop = FALSE]

                   model <- .doTrain(measurementsUse, outcomeTrain, NULL, NULL, crossValParams, modellingParams, verbose = 0)[["model"]]

                   class(model) <- c("trainedByClassifyR", class(model))

               }

@@ -1048,7 +1047,7 @@ train.DataFrame <- function(x, outcomeTrain, selectionMethod = "auto", nFeatures

               ### Prevalidation to combine data

               if(multiViewMethod == "prevalidation"){

                 # Split measurements up by assay.

-                 assayTrain <- sapply(assayIDs, function(assayID) measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayID], simplify = FALSE)

+                 assayTrain <- sapply(assayIDs, function(assayID) measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayID, drop = FALSE], simplify = FALSE)

                # Generate params for each assay. This could be extended to have different selectionMethods for each type

                  paramsAssays <- mapply(generateModellingParams,

@@ -1071,10 +1070,10 @@ train.DataFrame <- function(x, outcomeTrain, selectionMethod = "auto", nFeatures

               ### Principal Components Analysis to combine data

               if(multiViewMethod == "PCA"){

-                measurementsUse <- measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayIDs]

+                measurementsUse <- measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayIDs, drop = FALSE]

                 paramsClinical <-  list(clinical = generateModellingParams(

                                         assayIDs = "clinical",

-                                        measurements = measurements[, S4Vectors::mcols(measurements)[["assay"]] == "clinical"],

+                                        measurements = measurements[, S4Vectors::mcols(measurements)[["assay"]] == "clinical", drop = FALSE],

                                         classifier = classifier["clinical"],

                                         multiViewMethod = "none"))

@@ -6,11 +6,12 @@ differentMeansRanking <- function(measurementsTrain, classesTrain, verbose = 3)

   pValues <- NULL

   categorical <- sapply(measurementsTrain, class) %in% c("character", "factor")

-  if(any(categorical)){

+  if(any(categorical)) {

      pValues[categorical] <- sapply(which(categorical), function(featureIndex){

-       pval <- 1

-      if(length(unique(measurementsTrain[, featureIndex])>1)) pval <- chisq.test(measurementsTrain[, featureIndex], classesTrain)$p.value

-       pval

+                             pval <- 1

+                             if(length(unique(measurementsTrain[, featureIndex])) > 1)

+                               pval <- chisq.test(measurementsTrain[, featureIndex], classesTrain)$p.value

+                             pval

     })

   }

new file mode 100644

@@ -0,0 +1,2 @@

+template:

+  bootstrap: 5

@@ -6,12 +6,12 @@

 <meta http-equiv="X-UA-Compatible" content="IE=edge">

 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">

 <meta name="description" content="ClassifyR">

-<title>An Introduction to **ClassifyR** • ClassifyR</title>

+<title>Getting Started with ClassifyR • ClassifyR</title>

 <script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">

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+<!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Getting Started with ClassifyR">

 <meta property="og:description" content="ClassifyR">

 <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>

 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>

@@ -22,11 +22,11 @@

     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>

-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-dark"><div class="container">

+    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">

     <a class="navbar-brand me-2" href="../index.html">ClassifyR</a>

-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.3.2</small>

+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.3.13</small>

     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

@@ -41,8 +41,16 @@

 <li class="nav-item">

   <a class="nav-link" href="../reference/index.html">Reference</a>

 </li>

-<li class="nav-item">

-  <a class="nav-link" href="../articles/index.html">Articles</a>

+<li class="nav-item dropdown">

+  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>

+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">

+    <a class="dropdown-item" href="../articles/DevelopersGuide.html">**ClassifyR** Developer's Guide</a>

+    <a class="dropdown-item" href="../articles/incorporateNew.html">Creating a Wrapper for New Functionality and Registering It</a>

+    <a class="dropdown-item" href="../articles/introduction.html">Introduction to the Concepts of ClassifyR</a>

+    <a class="dropdown-item" href="../articles/multiViewMethods.html">Multi-view Methods for Modelling of Multiple Data Views</a>

+    <a class="dropdown-item" href="../articles/performanceEvaluation.html">Performance Evaluation of Fitted Models</a>

+    <a class="dropdown-item" href="../articles/presets.html">Parameter Tuning Presets for crossValidate and Their Customisation</a>

+  </div>

 </li>

       </ul>

 <form class="form-inline my-2 my-lg-0" role="search">

@@ -61,7 +69,7 @@

 <div class="row">

   <main id="main" class="col-md-9"><div class="page-header">

-      <img src="" class="logo" alt=""><h1>An Introduction to ClassifyR</h1>

+      <img src="" class="logo" alt=""><h1>Getting Started with ClassifyR</h1>

                         <h4 data-toc-skip class="author">Dario Strbenac,

 Ellis Patrick, Graham Mann, Jean Yang, John Ormerod <br> The University

 of Sydney, Australia.</h4>

@@ -125,7 +133,7 @@ Brush-based Classifier of Asthma Identified by Machine Learning Analysis

 of Nasal RNA Sequence Data</a>.</p>

 <p>Load the package.</p>

 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">

-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://sydneybiox.github.io/ClassifyR/">ClassifyR</a></span><span class="op">)</span></span></code></pre></div>

+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://sydneybiox.github.io/ClassifyR/" class="external-link">ClassifyR</a></span><span class="op">)</span></span></code></pre></div>

 <p>A glimpse at the RNA measurements and sample classes.</p>

 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">

 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">asthma</span><span class="op">)</span> <span class="co"># Contains measurements and classes variables.</span></span>

@@ -260,8 +268,8 @@ of <em>measurements</em>.</p>

 using <em>mcols</em>. In the column metadata, each feature must have an

 <em>assay</em> and a <em>feature</em> name.</p>

 <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">

-<code class="sourceCode R"><span><span class="va">measurementsDF</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/S4Vectors/man/DataFrame-class.html" class="external-link">DataFrame</a></span><span class="op">(</span><span class="va">measurements</span><span class="op">)</span></span>

-<span><span class="fu"><a href="https://rdrr.io/pkg/S4Vectors/man/Vector-class.html" class="external-link">mcols</a></span><span class="op">(</span><span class="va">measurementsDF</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>

+<code class="sourceCode R"><span><span class="va">measurementsDF</span> <span class="op">&lt;-</span> <span class="fu">DataFrame</span><span class="op">(</span><span class="va">measurements</span><span class="op">)</span></span>

+<span><span class="fu">mcols</span><span class="op">(</span><span class="va">measurementsDF</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>

 <span>  assay <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"assay_1"</span>, <span class="st">"assay_2"</span><span class="op">)</span>, times <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">1990</span><span class="op">)</span><span class="op">)</span>,</span>

 <span>  feature <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">colnames</a></span><span class="op">(</span><span class="va">measurementsDF</span><span class="op">)</span></span>

 <span><span class="op">)</span></span>

@@ -700,7 +708,7 @@ because some classifiers or feature selection functions will have some

 element of randomisation. One example that works on all operating

 systems, but is best-suited to Windows is:</p>

 <div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">

-<code class="sourceCode R"><span><span class="va">CVparams</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/CrossValParams-class.html">CrossValParams</a></span><span class="op">(</span>parallelParams <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocParallel/man/SnowParam-class.html" class="external-link">SnowParam</a></span><span class="op">(</span><span class="fl">16</span>, RNGseed <span class="op">=</span> <span class="fl">123</span><span class="op">)</span><span class="op">)</span></span>

+<code class="sourceCode R"><span><span class="va">CVparams</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/CrossValParams-class.html">CrossValParams</a></span><span class="op">(</span>parallelParams <span class="op">=</span> <span class="fu">SnowParam</span><span class="op">(</span><span class="fl">16</span>, RNGseed <span class="op">=</span> <span class="fl">123</span><span class="op">)</span><span class="op">)</span></span>

 <span><span class="va">CVparams</span></span></code></pre></div>

 <p>For the actual operations to do to the data to build a model of it,

 each of the stages should be specified by an object of class

@@ -782,12 +790,12 @@ samples can be displayed visually by <em>plotFeatureClasses</em>.</p>

 <span><span class="co">##   Please report the issue to the authors.</span></span></code></pre>

 <p><img src="ClassifyR_files/figure-html/unnamed-chunk-11-1.png" width="768"></p>

 <pre><code><span><span class="co">## allFeaturesText</span></span>

-<span><span class="co">## ARHGAP39 C10orf95 C19orf51 C6orf108 C6orf154  C6orf27 </span></span>

-<span><span class="co">##       60       96       48        8       12        8 </span></span>

+<span><span class="co">##    ANKK1   ANKMY1 ARHGAP39     B9D1     B9D2 C10orf95 </span></span>

+<span><span class="co">##        4        8       72        4        4      100 </span></span>

 <span><span class="co">## allFeaturesText</span></span>

-<span><span class="co">##    SSBP4   ZDHHC1 C10orf95    CROCC  TMEM190    CTXN1 </span></span>

-<span><span class="co">##      100      100       96       96       76       72</span></span></code></pre>

-<p>The means of the abundance levels of SSBP4 are substantially

+<span><span class="co">## C10orf95    SSBP4   ZDHHC1    CROCC    CTXN1    NAT14 </span></span>

+<span><span class="co">##      100      100      100       96       88       76</span></span></code></pre>

+<p>The means of the abundance levels of C10orf95 are substantially

 different between the people with and without asthma.

 <em>plotFeatureClasses</em> can also plot categorical data, such as may

 be found in a clinical data table, as a bar chart.</p>

@@ -815,7 +823,7 @@ performance metrics which may be calculated.</p>

 <code class="sourceCode R"><span><span class="fu"><a href="../reference/ClassifyResult-class.html">performance</a></span><span class="op">(</span><span class="va">DMresults</span><span class="op">)</span></span></code></pre></div>

 <pre><code><span><span class="co">## $`Balanced Accuracy`</span></span>

 <span><span class="co">##         1         2         3         4         5 </span></span>

-<span><span class="co">## 0.8047410 0.7997312 0.7926442 0.8047410 0.7931329</span></span></code></pre>

+<span><span class="co">## 0.8128055 0.8042522 0.8002199 0.7810362 0.7810362</span></span></code></pre>

 <p>The error rate is about 20%. If only a vector of predictions and a

 vector of actual classes is available, such as from an old study which

 did not use <strong>ClassifyR</strong> for cross-validation, then

@@ -850,16 +858,13 @@ change in location or in scale between classes.</p>

 distance between class densities but it can instead use the horizontal

 distance to the nearest non-zero density cross-over point to confidently

 classify samples in the tails of the densities.</p>

-<p>Now, the classification error for each sample is also calculated for

-both the differential means and differential distribution classifiers

-and both <em>ClassifyResult</em> objects generated so far are plotted

-with <em>samplesMetricMap</em>.</p>

+<p>The per-sample classification accuracy is automatically calculated

+for both the differential means and differential distribution

+classifiers and plotted with <em>samplesMetricMap</em>.</p>

 <div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">

-<code class="sourceCode R"><span><span class="va">DMresults</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/calcPerformance.html">calcCVperformance</a></span><span class="op">(</span><span class="va">DMresults</span>, <span class="st">"Sample Error"</span><span class="op">)</span></span>

-<span><span class="va">DDresults</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/calcPerformance.html">calcCVperformance</a></span><span class="op">(</span><span class="va">DDresults</span>, <span class="st">"Sample Error"</span><span class="op">)</span></span>

-<span><span class="va">resultsList</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>Abundance <span class="op">=</span> <span class="va">DMresults</span>, Distribution <span class="op">=</span> <span class="va">DDresults</span><span class="op">)</span></span>

-<span><span class="fu"><a href="../reference/samplesMetricMap.html">samplesMetricMap</a></span><span class="op">(</span><span class="va">resultsList</span>, metric <span class="op">=</span> <span class="st">"Sample Error"</span>, xAxisLabel <span class="op">=</span> <span class="st">"Sample"</span>,</span>

-<span>                              showXtickLabels <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>

+<code class="sourceCode R"><span><span class="va">resultsList</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>Abundance <span class="op">=</span> <span class="va">DMresults</span>, Distribution <span class="op">=</span> <span class="va">DDresults</span><span class="op">)</span></span>

+<span><span class="fu"><a href="../reference/samplesMetricMap.html">samplesMetricMap</a></span><span class="op">(</span><span class="va">resultsList</span>, showXtickLabels <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>

+<pre><code><span><span class="co">## Warning in .local(results, ...): Sample Accuracy not found in all elements of results. Calculating it now.</span></span></code></pre>

 <pre><code><span><span class="co">## Warning: Removed 2 rows containing missing values (`geom_tile()`).</span></span></code></pre>

 <p><img src="ClassifyR_files/figure-html/unnamed-chunk-14-1.png" width="960"></p>

 <pre><code><span><span class="co">## TableGrob (2 x 1) "arrange": 2 grobs</span></span>

@@ -877,7 +882,7 @@ plots, but violin plots could also be made. The default performance

 metric to plot is balanced accuracy. If it’s not already calculated for

 all classifications, as in this case for DD, it will be done

 automatically.</p>

-<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">

+<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r">

 <code class="sourceCode R"><span><span class="fu"><a href="../reference/performancePlot.html">performancePlot</a></span><span class="op">(</span><span class="va">resultsList</span><span class="op">)</span></span></code></pre></div>

 <pre><code><span><span class="co">## Warning in .local(results, ...): Balanced Accuracy not found in all elements of results. Calculating it now.</span></span></code></pre>

 <p><img src="ClassifyR_files/figure-html/unnamed-chunk-15-1.png" width="700"></p>

@@ -889,7 +894,7 @@ can be compared within and between classifiers by the plotting functions

 visually representing how consistent the feature rankings of the top 100

 different features were for the differential distribution classifier for

 all 5 folds in the 5 cross-validations.</p>

-<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r">

+<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">

 <code class="sourceCode R"><span><span class="fu"><a href="../reference/rankingPlot.html">rankingPlot</a></span><span class="op">(</span><span class="va">DDresults</span>, topRanked <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">100</span>, xLabelPositions <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">100</span>, <span class="fl">10</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>

 <p><img src="ClassifyR_files/figure-html/unnamed-chunk-16-1.png" width="700"></p>

 <p>The top-ranked features are fairly similar between all pairs of the

@@ -923,7 +928,7 @@ and one line is drawn per classification. Alternatively, setting

 separately, average them and also calculate and draw confidence

 intervals. The default interval is a 95% interval and is customisable by

 setting <em>interval</em>.</p>

-<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">

+<div class="sourceCode" id="cb38"><pre class="downlit sourceCode r">

 <code class="sourceCode R"><span><span class="fu"><a href="../reference/ROCplot.html">ROCplot</a></span><span class="op">(</span><span class="va">resultsList</span>, fontSizes <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">24</span>, <span class="fl">12</span>, <span class="fl">12</span>, <span class="fl">12</span>, <span class="fl">12</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>

 <p><img src="ClassifyR_files/figure-html/unnamed-chunk-17-1.png" width="576"></p>

 <p>This ROC plot shows the classifiability of the asthma data set is

@@ -986,7 +991,7 @@ the tuning variables, except for one which is named

 for picking the parameter values. Any of the non-sample-specific

 performance metrics which <em>calcCVperformance</em> calculates can be

 optimised.</p>

-<div class="sourceCode" id="cb38"><pre class="downlit sourceCode r">

+<div class="sourceCode" id="cb39"><pre class="downlit sourceCode r">

 <code class="sourceCode R"><span><span class="va">tuneList</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>cost <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.01</span>, <span class="fl">0.1</span>, <span class="fl">1</span>, <span class="fl">10</span><span class="op">)</span><span class="op">)</span></span>

 <span><span class="va">SVMparams</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/ModellingParams-class.html">ModellingParams</a></span><span class="op">(</span>trainParams <span class="op">=</span> <span class="fu"><a href="../reference/TrainParams-class.html">TrainParams</a></span><span class="op">(</span><span class="st">"SVM"</span>, kernel <span class="op">=</span> <span class="st">"linear"</span>, tuneParams <span class="op">=</span> <span class="va">tuneList</span><span class="op">)</span>,</span>

 <span>                             predictParams <span class="op">=</span> <span class="fu"><a href="../reference/PredictParams-class.html">PredictParams</a></span><span class="op">(</span><span class="st">"SVM"</span><span class="op">)</span><span class="op">)</span></span>

@@ -995,49 +1000,49 @@ optimised.</p>

 parameters and their associated performance metric, are stored for every

 validation, and can be accessed with the <em>tunedParameters</em>

 function.</p>

-<div class="sourceCode" id="cb39"><pre class="downlit sourceCode r">

+<div class="sourceCode" id="cb40"><pre class="downlit sourceCode r">

 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="fu"><a href="../reference/ClassifyResult-class.html">tunedParameters</a></span><span class="op">(</span><span class="va">SVMresults</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>

 <pre><code><span><span class="co">## [1] 25</span></span></code></pre>

-<div class="sourceCode" id="cb41"><pre class="downlit sourceCode r">

+<div class="sourceCode" id="cb42"><pre class="downlit sourceCode r">

 <code class="sourceCode R"><span><span class="fu"><a href="../reference/ClassifyResult-class.html">tunedParameters</a></span><span class="op">(</span><span class="va">SVMresults</span><span class="op">)</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">]</span></span></code></pre></div>

 <pre><code><span><span class="co">## [[1]]</span></span>

 <span><span class="co">## [[1]]$tuneCombinations</span></span>

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-<span><span class="co">## 1    10  0.01         0.7746331</span></span>

-<span><span class="co">## 2    20  0.01         0.7847341</span></span>

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-<span><span class="co">## 4    40  0.01         0.8167524</span></span>

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-<span><span class="co">## 6    60  0.01         0.8457214</span></span>

-<span><span class="co">## 7    70  0.01         0.8551553</span></span>

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-<span><span class="co">## 9    90  0.01         0.8645893</span></span>

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-<span><span class="co">## 11   10  0.10         0.8218029</span></span>

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+<span><span class="co">## 23   30  1.00         0.8854584</span></span>

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 <span><span class="co">## 30  100  1.00         1.0000000</span></span>

-<span><span class="co">## 31   10 10.00         0.8652563</span></span>

-<span><span class="co">## 32   20 10.00         0.8672575</span></span>

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-<span><span class="co">## 34   40 10.00         0.9477797</span></span>

-<span><span class="co">## 35   50 10.00         0.9949495</span></span>

+<span><span class="co">## 31   10 10.00         0.8746903</span></span>

+<span><span class="co">## 32   20 10.00         0.8652563</span></span>

+<span><span class="co">## 33   30 10.00         0.9006099</span></span>

+<span><span class="co">## 34   40 10.00         0.9855155</span></span>

+<span><span class="co">## 35   50 10.00         1.0000000</span></span>

 <span><span class="co">## 36   60 10.00         1.0000000</span></span>

 <span><span class="co">## 37   70 10.00         1.0000000</span></span>

 <span><span class="co">## 38   80 10.00         1.0000000</span></span>

@@ -1051,138 +1056,138 @@ function.</p>

 <span><span class="co">## [[2]]</span></span>

 <span><span class="co">## [[2]]$tuneCombinations</span></span>

 <span><span class="co">##    topN  cost Balanced Accuracy</span></span>

-<span><span class="co">## 1    10  0.01         0.8218029</span></span>

-<span><span class="co">## 2    20  0.01         0.8029350</span></span>

-<span><span class="co">## 3    30  0.01         0.8180865</span></span>

-<span><span class="co">## 4    40  0.01         0.8180865</span></span>

-<span><span class="co">## 5    50  0.01         0.8268534</span></span>

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+<span><span class="co">## 1    10  0.01         0.8073185</span></span>

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+<span><span class="co">## 3    30  0.01         0.8167524</span></span>

+<span><span class="co">## 4    40  0.01         0.8066514</span></span>

+<span><span class="co">## 5    50  0.01         0.8167524</span></span>

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 <span><span class="co">## 10  100  0.01         0.8746903</span></span>

-<span><span class="co">## 11   10  0.10         0.8073185</span></span>

-<span><span class="co">## 12   20  0.10         0.8180865</span></span>

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-<span><span class="co">## 14   40  0.10         0.8891748</span></span>

-<span><span class="co">## 15   50  0.10         0.9420621</span></span>

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-<span><span class="co">## 17   70  0.10         0.9420621</span></span>

-<span><span class="co">## 18   80  0.10         0.9420621</span></span>

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 <span><span class="co">## 39   90 10.00         1.0000000</span></span>

 <span><span class="co">## 40  100 10.00         1.0000000</span></span>

 <span><span class="co">## </span></span>

 <span><span class="co">## [[2]]$bestIndex</span></span>

-<span><span class="co">## [1] 37</span></span>

+<span><span class="co">## [1] 29</span></span>

 <span><span class="co">## </span></span>

 <span><span class="co">## </span></span>

 <span><span class="co">## [[3]]</span></span>

 <span><span class="co">## [[3]]$tuneCombinations</span></span>

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+<span><span class="co">## 35   50 10.00         0.9716981</span></span>

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 <span><span class="co">## 39   90 10.00         1.0000000</span></span>

 <span><span class="co">## 40  100 10.00         1.0000000</span></span>

 <span><span class="co">## </span></span>

 <span><span class="co">## [[3]]$bestIndex</span></span>

-<span><span class="co">## [1] 30</span></span>

+<span><span class="co">## [1] 37</span></span>

 <span><span class="co">## </span></span>

 <span><span class="co">## </span></span>

 <span><span class="co">## [[4]]</span></span>

 <span><span class="co">## [[4]]$tuneCombinations</span></span>

 <span><span class="co">##    topN  cost Balanced Accuracy</span></span>

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-<span><span class="co">## 2    20  0.01         0.8174195</span></span>

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-<span><span class="co">## 4    40  0.01         0.8362874</span></span>

-<span><span class="co">## 5    50  0.01         0.8457214</span></span>

-<span><span class="co">## 6    60  0.01         0.8457214</span></span>

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-<span><span class="co">## 9    90  0.01         0.8784067</span></span>

-<span><span class="co">## 10  100  0.01         0.8689727</span></span>

-<span><span class="co">## 11   10  0.10         0.7638651</span></span>

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+<span><span class="co">## 28   80  1.00         0.9949495</span></span>

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+<span><span class="co">## 31   10 10.00         0.8558224</span></span>

+<span><span class="co">## 32   20 10.00         0.8810749</span></span>

+<span><span class="co">## 33   30 10.00         0.9006099</span></span>

+<span><span class="co">## 34   40 10.00         0.9949495</span></span>

+<span><span class="co">## 35   50 10.00         1.0000000</span></span>

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@@ -1195,41 +1200,41 @@ function.</p>

 <span><span class="co">## [[5]]</span></span>

 <span><span class="co">## [[5]]$tuneCombinations</span></span>

 <span><span class="co">##    topN  cost Balanced Accuracy</span></span>

-<span><span class="co">## 1    10  0.01         0.8050000</span></span>

-<span><span class="co">## 2    20  0.01         0.8053846</span></span>

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-<span><span class="co">## 7    70  0.01         0.9223077</span></span>

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-<span><span class="co">## 9    90  0.01         0.9273077</span></span>

-<span><span class="co">## 10  100  0.01         0.9273077</span></span>

-<span><span class="co">## 11   10  0.10         0.8392308</span></span>

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-<span><span class="co">## 22   20  1.00         0.8592308</span></span>

-<span><span class="co">## 23   30  1.00         0.9419231</span></span>

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-<span><span class="co">## 29   90  1.00         1.0000000</span></span>

+<span><span class="co">## 1    10  0.01         0.8057692</span></span>

+<span><span class="co">## 2    20  0.01         0.7861538</span></span>

+<span><span class="co">## 3    30  0.01         0.7861538</span></span>

+<span><span class="co">## 4    40  0.01         0.8342308</span></span>

+<span><span class="co">## 5    50  0.01         0.8534615</span></span>

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+<span><span class="co">## 7    70  0.01         0.8442308</span></span>

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+<span><span class="co">## 14   40  0.10         0.9073077</span></span>

+<span><span class="co">## 15   50  0.10         0.9126923</span></span>

+<span><span class="co">## 16   60  0.10         0.9273077</span></span>

+<span><span class="co">## 17   70  0.10         0.9273077</span></span>

+<span><span class="co">## 18   80  0.10         0.9226923</span></span>

+<span><span class="co">## 19   90  0.10         0.9130769</span></span>

+<span><span class="co">## 20  100  0.10         0.9323077</span></span>

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+<span><span class="co">## 24   40  1.00         0.9176923</span></span>

+<span><span class="co">## 25   50  1.00         0.9465385</span></span>

+<span><span class="co">## 26   60  1.00         0.9176923</span></span>

+<span><span class="co">## 27   70  1.00         0.9515385</span></span>

+<span><span class="co">## 28   80  1.00         0.9853846</span></span>

+<span><span class="co">## 29   90  1.00         0.9853846</span></span>

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-<span><span class="co">## 31   10 10.00         0.7826923</span></span>

-<span><span class="co">## 32   20 10.00         0.8980769</span></span>

-<span><span class="co">## 33   30 10.00         0.9565385</span></span>

-<span><span class="co">## 34   40 10.00         1.0000000</span></span>

-<span><span class="co">## 35   50 10.00         1.0000000</span></span>

+<span><span class="co">## 31   10 10.00         0.8784615</span></span>

+<span><span class="co">## 32   20 10.00         0.9076923</span></span>

+<span><span class="co">## 33   30 10.00         0.9030769</span></span>

+<span><span class="co">## 34   40 10.00         0.8457692</span></span>

+<span><span class="co">## 35   50 10.00         0.9853846</span></span>

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 <span><span class="co">## 37   70 10.00         1.0000000</span></span>

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@@ -1237,7 +1242,7 @@ function.</p>

 <span><span class="co">## 40  100 10.00         1.0000000</span></span>

 <span><span class="co">## </span></span>

 <span><span class="co">## [[5]]$bestIndex</span></span>

-<span><span class="co">## [1] 28</span></span></code></pre>

+<span><span class="co">## [1] 30</span></span></code></pre>

 <p>The cost value of 1 or 10 appears to often be chosen.</p>

 </div>

 </div>

@@ -1275,7 +1280,7 @@ Research</em>, 44(13):e119</p>

 <div class="pkgdown-footer-right">

   <p></p>

-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>

+<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>

 </div>

     </footer>

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@@ -1,68 +1,51 @@

 <!DOCTYPE html>

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