@@ -7,4 +7,5 @@ vignettes/introduction.Rmd

 vignettes/performanceEvaluation.Rmd

 vignettes/multiViewMethods.Rmd

 vignettes/incorporateNew.Rmd

+vignettes/presets.Rmd

 ^\.github$

@@ -3,7 +3,7 @@ Type: Package

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

        applications to differential variability and differential

        distribution testing

-Version: 3.3.11

+Version: 3.3.12

 Date: 2023-02-10

 Authors@R:

     c(

@@ -21,7 +21,8 @@

 #' of survival information, time must be in first column and event status in the second.

 #' @param extraParams A list of parameters that will be used to overwrite default settings of transformation, selection, or model-building functions or

 #' parameters which will be passed into the data cleaning function. The names of the list must be one of \code{"prepare"},

-#' \code{"select"}, \code{"train"}, \code{"predict"}.

+#' \code{"select"}, \code{"train"}, \code{"predict"}. To remove one of the defaults (see the article titled Parameter Tuning Presets for crossValidate and Their Customisation on

+#' the website), specify the list element to be \code{NULL}.

 #' @param nFeatures The number of features to be used for classification. If this is a single number, the same number of features will be used for all comparisons

 #' or assays. If a numeric vector these will be optimised over using \code{selectionOptimisation}. If a named vector with the same names of multiple assays, 

 #' a different number of features will be used for each assay. If a named list of vectors, the respective number of features will be optimised over. 

@@ -622,10 +623,15 @@ generateModellingParams <- function(assayIDs,

         {

           if(is.null(classifierParams$trainParams@otherParams)) classifierParams$trainParams@otherParams <- extraParams[["train"]][paramIndex]

           else classifierParams$trainParams@otherParams[parameterName] <- parameter

-        } else {

+        } else if(length(parameter) > 1) {

           if(is.null(classifierParams$trainParams@tuneParams)) classifierParams$trainParams@tuneParams <- extraParams[["train"]][paramIndex]

           else classifierParams$trainParams@tuneParams[parameterName] <- parameter # Multiple values, so tune them.

-        }

+        } else { # Remove the parameter

+          inOther <- match(parameterName, names(classifierParams$trainParams@otherParams))

+          inTune <- match(parameterName, names(classifierParams$trainParams@tuneParams))

+          if(!is.na(inOther)) classifierParams$trainParams@otherParams <- classifierParams$trainParams@otherParams[-inOther]

+          if(!is.na(inTune)) classifierParams$trainParams@tuneParams <- classifierParams$trainParams@tuneParams[-inTune]

+        } 

       }

     }

     if(!is.null(extraParams) && "predict" %in% names(extraParams))

@@ -638,10 +644,15 @@ generateModellingParams <- function(assayIDs,

         {

           if(is.null(classifierParams$predictParams@otherParams)) classifierParams$predictParams@otherParams <- extraParams[["predict"]][paramIndex]

           else classifierParams$predictParams@otherParams[parameterName] <- parameter

-        } else {

+        } else if(length(parameter) > 1) {

           if(is.null(classifierParams$predictParams@tuneParams)) classifierParams$predictParams@tuneParams <- extraParams[["predict"]][paramIndex]

           else classifierParams$predictParams@tuneParams[parameterName] <- parameter # Multiple values, so tune them.

-        }

+        } else { # Remove the parameter

+          inOther <- match(parameterName, names(classifierParams$predictParams@otherParams))

+          inTune <- match(parameterName, names(classifierParams$predictParams@tuneParams))

+          if(!is.na(inOther)) classifierParams$predictParams@otherParams <- classifierParams$predictParams@otherParams[-inOther]

+          if(!is.na(inTune)) classifierParams$predictParams@tuneParams <- classifierParams$predictParams@tuneParams[-inTune]

+        } 

       }

     }    

@@ -661,9 +672,14 @@ generateModellingParams <- function(assayIDs,

           {

             if(is.null(classifierParams$selectParams@otherParams)) classifierParams$selectParams@otherParams <- extraParams[["select"]][paramIndex]

             else classifierParams$selectParams@otherParams[parameterName] <- parameter

-          } else {

+          } else if(length(parameter) > 1) {

             if(is.null(classifierParams$selectParams@tuneParams)) classifierParams$selectParams@tuneParams <- extraParams[["select"]][paramIndex]

             else classifierParams$selectParams@tuneParams[parameterName] <- parameter # Multiple values, so tune them.

+          } else { # Remove the parameter

+             inOther <- match(parameterName, names(classifierParams$selectParams@otherParams))

+             inTune <- match(parameterName, names(classifierParams$selectParams@tuneParams))

+             if(!is.na(inOther)) classifierParams$selectParams@otherParams <- classifierParams$selectParams@otherParams[-inOther]

+             if(!is.na(inTune)) classifierParams$selectParams@tuneParams <- classifierParams$selectParams@tuneParams[-inTune]

           }

         }

       }

@@ -890,7 +906,7 @@ train.data.frame <- function(x, outcomeTrain, ...)

 #' @method train DataFrame

 #' @export

 train.DataFrame <- function(x, outcomeTrain, selectionMethod = "auto", nFeatures = 20, classifier = "auto", performanceType = "auto",

-                            multiViewMethod = "none", assayIDs = "all", extraParams = NULL)

+                            multiViewMethod = "none", assayIDs = "all", extraParams = NULL, ...)

                    {

               prepParams <- list(x, outcomeTrain)

               if(!is.null(extraParams) && "prepare" %in% names(extraParams))

@@ -950,9 +966,14 @@ train.DataFrame <- function(x, outcomeTrain, selectionMethod = "auto", nFeatures

                                         {

                                           if(is.null(classifierParams$trainParams@otherParams)) classifierParams$trainParams@otherParams <- extraParams[["train"]][paramIndex]

                                           else classifierParams$trainParams@otherParams[parameterName] <- parameter

-                                        } else {

+                                        } else if (length(parameter) > 1) {

                                           if(is.null(classifierParams$trainParams@tuneParams)) classifierParams$trainParams@tuneParams <- extraParams[["train"]][paramIndex]

                                           else classifierParams$trainParams@tuneParams[parameterName] <- parameter # Multiple values, so tune them.

+                                        } else { # Remove the parameter

+                                          inOther <- match(parameterName, names(classifierParams$trainParams@otherParams))

+                                          inTune <- match(parameterName, names(classifierParams$trainParams@tuneParams))

+                                          if(!is.na(inOther)) classifierParams$trainParams@otherParams <- classifierParams$trainParams@otherParams[-inOther]

+                                          if(!is.na(inTune)) classifierParams$trainParams@tuneParams <- classifierParams$trainParams@otherParams[-inTune]

                                         }

                                       }

                                     }

@@ -966,10 +987,15 @@ train.DataFrame <- function(x, outcomeTrain, selectionMethod = "auto", nFeatures

                                         {

                                           if(is.null(classifierParams$predictParams@otherParams)) classifierParams$predictParams@otherParams <- extraParams[["predict"]][paramIndex]

                                           else classifierParams$predictParams@otherParams[parameterName] <- parameter

-                                        } else {

+                                        } else if (length(parameter) > 1) {

                                           if(is.null(classifierParams$predictParams@tuneParams)) classifierParams$predictParams@tuneParams <- extraParams[["predict"]][paramIndex]

                                           else classifierParams$predictParams@tuneParams[parameterName] <- parameter # Multiple values, so tune them.

-                                        }

+                                        } else { # Remove the parameter

+                                          inOther <- match(parameterName, names(classifierParams$predictParams@otherParams))

+                                          inTune <- match(parameterName, names(classifierParams$predictParams@tuneParams))

+                                          if(!is.na(inOther)) classifierParams$predictParams@otherParams <- classifierParams$predictParams@otherParams[-inOther]

+                                          if(!is.na(inTune)) classifierParams$predictParams@tuneParams <- classifierParams$predictParams@otherParams[-inTune]

+                                        } 

                                       }

                                     }

@@ -1,77 +1,106 @@

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

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+

+      

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+      </header><div class="row">

+  <div class="col-md-9 contents">

+    <div class="page-header toc-ignore">

+      <h1 data-toc-skip>Parameter Tuning Presets for crossValidate and

+Their Customisation</h1>

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

+<br> The University of Sydney, Australia.</h4>

+            

+      

+      

+      <div class="hidden name"><code>presets.Rmd</code></div>

+

+    </div>

+

+    

+    

+<p>When a choice about a model to use is made and specified to

+<code>crossValidate</code>, performance tuning of a range of typical

+values is automatically made. These values can be changed or removed.

+Below is a table of models and their default values, which can be

+changed by specifying the <code>extraParams</code> parameter. It is a

+list with named elements. Each element must be named one of

+<code>"select"</code>, <code>"train"</code> or <code>"predict"</code>

+(as well as <code>"prepare"</code> for data preparation which is not a

+parameter tuning operation, so is not discussed here), to identify which

+modelling the parameter corresponds to.</p>

+<table class="table">

+<colgroup>

+<col width="18%">

+<col width="27%">

+<col width="27%">

+<col width="27%">

+</colgroup>

+<thead><tr class="header">

+<th>Model</th>

+<th align="center">Keyword</th>

+<th align="center">Default Parameters</th>

+<th align="center">Other Parameters</th>

+</tr></thead>

+<tbody>

+<tr class="odd">

+<td>Random Forest</td>

+<td align="center"><code>"randomForest"</code></td>

+<td align="center">

+<code>mTryProportion = c(0.25, 0.33, 0.50, 0.66, 0.75, 1.00)</code>

+and <code>num.trees = c(10, seq(100, 500, 100)</code>

+</td>

+<td align="center">See <code><a href="https://rdrr.io/pkg/ranger/man/ranger.html" class="external-link">?ranger::ranger</a></code>

+</td>

+</tr>

+<tr class="even">

+<td>Random Survival Forest</td>

+<td align="center"><code>"randomSurvivalForest"</code></td>

+<td align="center">

+<code>mTryProportion = c(0.25, 0.33, 0.50, 0.66, 0.75, 1.00)</code>

+and <code>num.trees = c(10, seq(100, 500, 100)</code>

+</td>

+<td align="center">See <code><a href="https://www.randomforestsrc.org//reference/rfsrc.html" class="external-link">?randomForestSRC::rfsrc</a></code>

+</td>

+</tr>

+<tr class="odd">

+<td>Extreme Gradient Boosting</td>

+<td align="center"><code>"XGB"</code></td>

+<td align="center">

+<code>mTryProportion = c(0.25, 0.33, 0.50, 0.66, 0.75, 1.00)</code>

+and <code>nrounds = c(5, 10, 15)</code>

+</td>

+<td align="center">See <code><a href="https://rdrr.io/pkg/xgboost/man/xgb.train.html" class="external-link">?xgboost::xgboost</a></code>

+</td>

+</tr>

+<tr class="even">

+<td>k Nearest Neighbours</td>

+<td align="center"><code>"kNN"</code></td>

+<td align="center"><code>k = 1:5</code></td>

+<td align="center">See <code><a href="https://rdrr.io/pkg/class/man/knn.html" class="external-link">?class::knn</a></code>

+</td>

+</tr>

+<tr class="odd">

+<td>Support Vector Machine</td>

+<td align="center"><code>"SVM"</code></td>

+<td align="center">

+<code>kernel = c("linear", "polynomial", "radial", "sigmoid")</code>

+and <code>cost = 10^(-3:3))</code>

+</td>

+<td align="center">See <code><a href="https://rdrr.io/pkg/e1071/man/svm.html" class="external-link">?e1071::svm</a></code>

+</td>

+</tr>

+<tr class="even">

+<td>naive Bayes Kernel</td>

+<td align="center"><code>"naiveBayes"</code></td>

+<td align="center"><code>difference = c("unweighted", "weighted")</code></td>

+<td align="center">

+<code>weighting = c("height difference", "crossover distance")</code>

+and <code>minDifference</code>, a number 0 or higher.</td>

+</tr>

+<tr class="odd">

+<td>Mixtures of Normals</td>

+<td align="center"><code>"mixturesNormals"</code></td>

+<td align="center"><code>nbCluster = 1:2</code></td>

+<td align="center">

+<code>difference = c("unweighted", "weighted")</code>

+and

+<code>weighting = c("height difference", "crossover distance")</code>

+and <code>minDifference</code>, a number 0 or higher.</td>

+</tr>

+</tbody>

+</table>

+<p>Note that the last two models are not wrappers but custom

+implementations in this package. The weighting parameter specifies how

+the differences between classes are calculated. Either the height

+difference of a class’s fitted density to another class’s nearest

+density is used or the horizontal distance from the measurement value to

+the nearest position where the density of that class crosses any other

+class. The distances are only used if <code>difference</code> is

+<code>weighted</code>. <code>minDifference</code> specifies the minimum

+distance between densities for a feature to have a vote for a particular

+class. The default of 0 means that every feature votes for the class

+which has the highest density at that point, regardless how close the

+density of any other class is to it.</p>

+  </div>

+

+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">

+

+      </div>

+

+</div>

+

+

+

+      <footer><div class="copyright">

+  <p></p>

+<p>Developed by Dario Strbenac, Ellis Patrick, Sourish Iyengar, Harry Robertson, Andy Tran, John Ormerod, Graham Mann, Jean Yang.</p>

+</div>

+

+<div class="pkgdown">

+  <p></p>

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

+</div>

+

+      </footer>

+</div>

+

+  

+

+

+  

+

+  </body>

+</html>

@@ -116,7 +116,8 @@ crossValidate(measurements, outcome, ...)

   performanceType = "auto",

   multiViewMethod = "none",

   assayIDs = "all",

-  extraParams = NULL

+  extraParams = NULL,

+  ...

 )

 \method{train}{list}(x, outcomeTrain, ...)

@@ -170,7 +171,8 @@ with each element being a vector of assays to combine. Special value \code{"all"

 \item{extraParams}{A list of parameters that will be used to overwrite default settings of transformation, selection, or model-building functions or

 parameters which will be passed into the data cleaning function. The names of the list must be one of \code{"prepare"},

-\code{"select"}, \code{"train"}, \code{"predict"}.}

+\code{"select"}, \code{"train"}, \code{"predict"}. To remove one of the defaults (see the article titled Parameter Tuning Presets for crossValidate and Their Customisation on

+the website), specify the list element to be \code{NULL}.}

 \item{x}{Same as \code{measurements} but only training samples.}

new file mode 100644

@@ -0,0 +1,23 @@

+---

+title: "Parameter Tuning Presets for crossValidate and Their Customisation"

+author: Dario Strbenac <br>

+        The University of Sydney, Australia.

+output: 

+    BiocStyle::html_document:

+        css: style.css

+        toc: false

+---

+

+When a choice about a model to use is made and specified to `crossValidate`, performance tuning of a range of typical values is automatically made. These values can be changed or removed. Below is a table of models and their default values, which can be changed by specifying the `extraParams` parameter. It is a list with named elements. Each element must be named one of `"select"`, `"train"` or `"predict"` (as well as `"prepare"` for data preparation which is not a parameter tuning operation, so is not discussed here), to identify which modelling the parameter corresponds to.

+

+Model | Keyword | Default Parameters | Other Parameters

+----|:----:|:----:|:----:

+Random Forest | `"randomForest"` | `mTryProportion = c(0.25, 0.33, 0.50, 0.66, 0.75, 1.00)` and `num.trees = c(10, seq(100, 500, 100)` | See `?ranger::ranger` 

+Random Survival Forest | `"randomSurvivalForest"` | `mTryProportion = c(0.25, 0.33, 0.50, 0.66, 0.75, 1.00)` and `num.trees = c(10, seq(100, 500, 100)` | See `?randomForestSRC::rfsrc`

+Extreme Gradient Boosting | `"XGB"` | `mTryProportion = c(0.25, 0.33, 0.50, 0.66, 0.75, 1.00)` and `nrounds = c(5, 10, 15)` | See `?xgboost::xgboost`

+k Nearest Neighbours | `"kNN"` | `k = 1:5` | See `?class::knn`

+Support Vector Machine | `"SVM"` | `kernel = c("linear", "polynomial", "radial", "sigmoid")` and  `cost = 10^(-3:3))` | See `?e1071::svm`

+naive Bayes Kernel | `"naiveBayes"` | `difference = c("unweighted", "weighted")` | `weighting = c("height difference", "crossover distance")` and `minDifference`, a number 0 or higher.

+Mixtures of Normals | `"mixturesNormals"` | `nbCluster = 1:2` | `difference = c("unweighted", "weighted")` and `weighting = c("height difference", "crossover distance")` and `minDifference`, a number 0 or higher.

+

+Note that the last two models are not wrappers but custom implementations in this package. The weighting parameter specifies how the differences between classes are calculated. Either the height difference of a class's fitted density to another class's nearest density is used or the horizontal distance from the measurement value to the nearest position where the density of that class crosses any other class. The distances are only used if `difference` is `weighted`. `minDifference` specifies the minimum distance between densities for a feature to have a vote for a particular class. The default of 0 means that every feature votes for the class which has the highest density at that point, regardless how close the density of any other class is to it.

\ No newline at end of file