@@ -26,7 +26,7 @@ Description: The software formalises a framework for classification in R.

              may be developed by the user, by creating an interface to the framework.

 License: GPL-3

 Packaged: 2014-10-18 11:16:55 UTC; dario

-RoxygenNote: 7.2.0

+RoxygenNote: 7.2.1

 SystemRequirements: C++14

 Collate:

     'ROCplot.R'

new file mode 100644

@@ -0,0 +1,74 @@

+# Generated by roxygen2: do not edit by hand

+

+export(ClassifyResult)

+export(FeatureSetCollection)

+export(ROCplot)

+export(actualOutcome)

+export(allFeatureNames)

+export(available)

+export(calcCVperformance)

+export(calcExternalPerformance)

+export(chosenFeatureNames)

+export(crossValidate)

+export(distribution)

+export(edgesToHubNetworks)

+export(featureSetSummary)

+export(generateCrossValParams)

+export(generateModellingParams)

+export(getLocationsAndScales)

+export(interactorDifferences)

+export(models)

+export(performance)

+export(performancePlot)

+export(plotFeatureClasses)

+export(predictions)

+export(prepareData)

+export(rankingPlot)

+export(sampleNames)

+export(samplesMetricMap)

+export(selectionPlot)

+export(totalPredictions)

+export(tunedParameters)

+exportClasses(ClassifyResult)

+exportClasses(FeatureSetCollection)

+exportMethods("[")

+exportMethods("[[")

+exportMethods(ClassifyResult)

+exportMethods(FeatureSetCollection)

+exportMethods(ROCplot)

+exportMethods(actualOutcome)

+exportMethods(allFeatureNames)

+exportMethods(calcCVperformance)

+exportMethods(calcExternalPerformance)

+exportMethods(chosenFeatureNames)

+exportMethods(crossValidate)

+exportMethods(distribution)

+exportMethods(featureSetSummary)

+exportMethods(getLocationsAndScales)

+exportMethods(interactorDifferences)

+exportMethods(length)

+exportMethods(models)

+exportMethods(performance)

+exportMethods(performancePlot)

+exportMethods(plotFeatureClasses)

+exportMethods(predict)

+exportMethods(predictions)

+exportMethods(prepareData)

+exportMethods(rankingPlot)

+exportMethods(sampleNames)

+exportMethods(samplesMetricMap)

+exportMethods(selectionPlot)

+exportMethods(show)

+exportMethods(totalPredictions)

+exportMethods(tunedParameters)

+import(BiocParallel)

+import(grid)

+import(utils)

+importFrom(S4Vectors,as.data.frame)

+importFrom(dplyr,mutate)

+importFrom(dplyr,n)

+importFrom(rlang,sym)

+importFrom(survival,Surv)

+importFrom(survival,concordance)

+importFrom(tidyr,gather)

+useDynLib(ClassifyR, .registration = TRUE)

@@ -59,7 +59,7 @@

 #'   actual <- factor(c(rep("Healthy", 10), rep("Cancer", 10)), levels = c("Healthy", "Cancer"))

 #'   result1 <- ClassifyResult(DataFrame(characteristic = c("Data Set", "Selection Name", "Classifier Name", "Cross-validation"),

 #'                             value = c("Melanoma", "t-test", "Random Forest", "2-fold")),

-#'                             LETTERS[1:20], list(paste("Gene", LETTERS[1:10]), paste("Gene", LETTERS[c(5:1, 6:10)])),

+#'                             LETTERS[1:20], paste("Gene", LETTERS[1:10]), list(paste("Gene", LETTERS[1:10]), paste("Gene", LETTERS[c(5:1, 6:10)])),

 #'                             list(paste("Gene", LETTERS[1:3]), paste("Gene", LETTERS[1:5])),

 #'                             list(function(oracle){}), NULL, predicted, actual)

 #'   

@@ -67,7 +67,7 @@

 #'   predicted[c(2, 6), "Cancer"] <- c(0.60, 0.40)

 #'   result2 <- ClassifyResult(DataFrame(characteristic = c("Data Set", "Selection Name", "Classifier Name", "Cross-validation"),

 #'                                       value = c("Example", "Bartlett Test", "Differential Variability", "2-fold")),

-#'                             LETTERS[1:20], list(paste("Gene", LETTERS[1:10]), paste("Gene", LETTERS[c(5:1, 6:10)])),

+#'                             LETTERS[1:20], paste("Gene", LETTERS[1:10]), list(paste("Gene", LETTERS[1:10]), paste("Gene", LETTERS[c(5:1, 6:10)])),

 #'                             list(paste("Gene", LETTERS[1:3]), paste("Gene", LETTERS[1:5])),

 #'                             list(function(oracle){}), NULL, predicted, actual)

 #'   ROCplot(list(result1, result2), plotTitle = "Cancer ROC")

@@ -5,13 +5,13 @@

 #' known classes and a vector of predicted classes determined outside of the

 #' ClassifyR package, a single metric value is calculated. If

 #' \code{calcCVperformance} is used, annotates the results of calling

-#' \code{\link{runTests}} with one of the user-specified performance measures.

+#' \code{\link{crossValidate}} with one of the user-specified performance measures.

 #' 

 #' All metrics except Matthews Correlation Coefficient are suitable for

 #' evaluating classification scenarios with more than two classes and are

 #' reimplementations of those available from Intel DAAL.

 #' 

-#' If \code{\link{runTests}} was run in resampling mode, one performance

+#' If \code{\link{crossValidate}} was run in resampling mode, one performance

 #' measure is produced for every resampling. If the leave-k-out mode was used,

 #' then the predictions are concatenated, and one performance measure is

 #' calculated for all classifications.

@@ -75,7 +75,7 @@

 #'                             class = factor(sample(LETTERS[1:2], 50, replace = TRUE)))

 #'   actual <- factor(sample(LETTERS[1:2], 10, replace = TRUE))                             

 #'   result <- ClassifyResult(DataFrame(characteristic = "Data Set", value = "Example"),

-#'                            paste("A", 1:10, sep = ''), list(paste("Gene", 1:50), paste("Gene", 1:50)), list(paste("Gene", 1:5), paste("Gene", 1:10)),

+#'                            paste("A", 1:10, sep = ''), paste("Gene", 1:50), list(paste("Gene", 1:50), paste("Gene", 1:50)), list(paste("Gene", 1:5), paste("Gene", 1:10)),

 #'                            list(function(oracle){}), NULL, predictTable, actual)

 #'   result <- calcCVperformance(result) 

 #'   performance(result)

@@ -12,105 +12,35 @@

 # Delete when sparsediscrim is restored to CRAN.

-#' Trained dlda Object

-#' 

-#' Enables S4 method dispatching on it.

-#' 

-#' 

-#' @name dlda-class

-#' @aliases dlda dlda-class

-#' @docType class

-#' @author Dario Strbenac

-#' @usage NULL

+# Trained dlda Object

 dlda <- function(x, ...) {

   UseMethod("dlda")

 }

-

 setOldClass("dlda")

-

-#' Trained pamr Object

-#' 

-#' Enables S4 method dispatching on it.

-#' 

-#' 

-#' @name pamrtrained-class

-#' @aliases pamrtrained pamrtrained-class

-#' @docType class

-#' @author Dario Strbenac

+# Trained pamr Object

 setOldClass("pamrtrained")

-#' Trained svm Object

-#' 

-#' Enables S4 method dispatching on it.

-#' 

-#' 

-#' @name svm-class

-#' @aliases svm svm-class

-#' @docType class

-#' @author Dario Strbenac

+# Trained svm Object

 setOldClass("svm")

-#' Trained multnet Object

-#' 

-#' Enables S4 method dispatching on it.

-#' 

-#' 

-#' @name multnet-class

-#' @aliases multnet multnet-class

-#' @docType class

-#' @author Dario Strbenac

+# Trained multnet Object

 setOldClass("multnet")

-#' @name coxnet-class

-#' @aliases coxnet coxnet-class

-#' @docType class

-#' @author Dario Strbenac

+# Trained coxnet Object

 setOldClass("coxnet")

-

-

-#' Trained randomForest Object

-#' 

-#' Enables S4 method dispatching on it.

-#' 

-#' 

-#' @name randomForest-class

-#' @aliases randomForest randomForest-class

-#' @docType class

-#' @author Dario Strbenac

+# Trained randomForest Object

 setOldClass("randomForest")

-

-#' Trained coxph Object

-#' 

-#' Enables S4 method dispatching on it.

-#' 

-#' 

-#' @name coxph-class

-#' @aliases coxph coxph-class

-#' @docType class

+# Trained coxph Object

 setOldClass("coxph")

-#' Survival Data Container

-#' 

-#' Enables S4 method dispatching on it.

-#' 

-#' 

-#' @name Surv-class

-#' @aliases Surv Surv-class

-#' @docType class

+# Survival Data Container

 setOldClass("Surv")

-#' Survival Forest Data Container

-#' 

-#' Enables S4 method dispatching on it.

-#' 

-#' 

-#' @name rfsrc-class

-#' @aliases rfsrc rfsrc-class

-#' @docType class

+# Survival Forest Data Container

 setOldClass("rfsrc")

 ################################################################################

@@ -120,122 +50,29 @@ setOldClass("rfsrc")

 ################################################################################

-#' Union of A Function and NULL

-#' 

-#' Allows a slot to be either a function or empty.

-#' 

-#' 

-#' @name functionOrNULL-class

-#' @aliases functionOrNULL functionOrNULL-class

-#' @docType class

-#' @author Dario Strbenac

-#' @examples

-#' 

-#'   PredictParams(NULL) # Training function does both tasks.

-#'   PredictParams(DLDApredictInterface)

-#' 

+# Union of A Function and NULL

 setClassUnion("functionOrNULL", c("function", "NULL"))

-#' Union of Functions and List of Functions

-#' 

-#' Allows a slot to be either a function or a list of functions.

-#' 

-#' 

-#' @name functionOrList-class

-#' @aliases functionOrList functionOrList-class

-#' @docType class

-#' @author Dario Strbenac

-#' @examples

-#' 

-#'   SelectParams(limmaRanking)

-#'   SelectParams(list(limmaRanking, differentMeansRanking)) # Ensemble selection.

-#' 

+# Union of Functions and List of Functions. Useful for allowing ensemble feature selection.

 setClassUnion("functionOrList", c("function", "list"))

-#' Union of A Numeric Value and NULL

-#' 

-#' Allows a slot to be either a numeric value or empty. No constructor.

-#' 

-#' 

-#' @name numericOrNULL-class

-#' @aliases numericOrNULL numericOrNULL-class

-#' @docType class

-#' @author Dario Strbenac

+# Union of A Numeric Value and NULL

 setClassUnion("numericOrNULL", c("numeric", "NULL"))

-

-#' Union of a Character and a DataFrame

-#' 

-#' Allows a slot to be either a character or a DataFrame.

-#' 

-#' 

-#' @name characterOrDataFrame-class

-#' @aliases characterOrDataFrame characterOrDataFrame-class

-#' @docType class

-#' @author Dario Strbenac

-#' @examples

-#' 

-#'   setClass("Selections", representation(features = "characterOrDataFrame"))

-#'   selections <- new("Selections", features = c("BRAF", "NRAS"))

-#'   featuresTable <- DataFrame(assay = c("RNA-seq", "Mass spectrometry"),

-#'                              feature = c("PD-1", "MITF"))

-#'   omicsSelections <- new("Selections", features = featuresTable)

+# Union of a Character and a DataFrame

 setClassUnion("characterOrDataFrame", c("character", "DataFrame"))

-#' Union of a Surv class and a factor

-#' 

-#' Enables different functionality to be executed depending on whether

-#' input data dependent variable is survival or categorical classes.

-#' 

-#' 

-#' @name characterOrDataFrame-class

-#' @aliases characterOrDataFrame characterOrDataFrame-class

-#' @docType class

-#' @author Dario Strbenac

-#' @examples

-#' 

-#'   setClass("Selections", representation(features = "characterOrDataFrame"))

-#'   selections <- new("Selections", features = c("BRAF", "NRAS"))

-#'   featuresTable <- DataFrame(assay = c("RNA-seq", "Mass spectrometry"),

-#'                              feature = c("PD-1", "MITF"))

-#'   omicsSelections <- new("Selections", features = featuresTable)

+# Union of a Surv class and a factor

 setClassUnion("factorOrSurv", c("factor", "Surv"))

-

-

-#' Union of a List and NULL

-#' 

-#' Allows a slot to be either a list or a NULL.

-#' 

-#' 

-#' @name listOrNULL-class

-#' @aliases listOrNULL listOrNULL-class

-#' @docType class

-#' @author Dario Strbenac

-#' @examples

-#' 

-#'   setClass("EasyClassifier", representation(model = "listOrNULL"))

-#'   classifier <- new("EasyClassifier", model = NULL) # Optimistic classifier.

-#' 

+# Union of a List and NULL

 setClassUnion("listOrNULL", c("list", "NULL"))

-#' Union of NULL and DataFrame Class

-#' 

-#' Allows cross-validation to accept data as either a \code{DataFrame} (for a

-#' single data set) or \code{DataFrameList} (for a list of tables of related

-#' measurements, such as different projects measuring the same outcome and the

-#' same kind of measurements). No constructor.

-#' 

-#' 

-#' @name DataFrameOrNULL-class

-#' @aliases DataFrameOrNULL DataFrameOrNULL-class

-#' @docType class

-#' @author Dario Strbenac

+# Union of NULL and DataFrame Class

 setClassUnion("DataFrameOrNULL", c("DataFrame", "NULL"))

-

 ################################################################################

 #

 # Params

@@ -244,56 +81,8 @@ setClassUnion("DataFrameOrNULL", c("DataFrame", "NULL"))

 ##### CrossValParams #####

-#' Parameters for Cross-validation Specification

-#' 

-#' Collects and checks necessary parameters required for cross-validation by

-#' \code{\link{runTests}}.

-#' 

-#' 

-#' @name CrossValParams

-#' @rdname CrossValParams-class

-#' @aliases CrossValParams CrossValParams-class

-#' @docType class

-#' 

-#' @param samplesSplits Default: "Permute k-Fold". A character value

-#' specifying what kind of sample splitting to do.

-#' @param permutations Default: 100. Number of times to permute the

-#' data set before it is split into training and test sets. Only relevant if

-#' \code{samplesSplits} is either \code{"Permute k-Fold"} or \code{"Permute

-#' Percentage Split"}.

-#' @param percentTest The percentage of the data

-#' set to assign to the test set, with the remainder of the samples belonging

-#' to the training set. Only relevant if \code{samplesSplits} is \code{"Permute

-#' Percentage Split"}.

-#' @param folds The number of approximately equal-sized folds to partition

-#' the samples into. Only relevant if \code{samplesSplits} is \code{"Permute k-Fold"}

-#' or \code{"k-Fold"}.

-#' @param leave The number of samples to generate all possible

-#' combination of and use as the test set.  Only relevant if \code{samplesSplits} is

-#' \code{"Leave-k-Out"}. If set to 1, it is the traditional leave-one-out cross-validation,

-#' sometimes written as LOOCV.

-#' @param tuneMode Default: Resubstitution. The scheme to use for selecting any tuning parameters.

-#' @param adaptiveResamplingDelta Default: \code{NULL}. If not null, adaptive resampling of training

-#' samples is performed and this number is the difference in consecutive iterations that the

-#' class probability or risk of all samples must change less than for the iterative process to stop. 0.01

-#' was used in the original publication.

-#' @param parallelParams An instance of \code{\link{BiocParallelParam}} specifying

-#' the kind of parallelisation to use. Default is to use two cores less than the total number of

-#' cores the computer has, if it has four or more cores, otherwise one core, as is the

-#' default of \code{\link{bpparam}}. To make results fully reproducible, please

-#' choose a specific back-end depending on your operating system and also set

-#' \code{RNGseed} to a number.

-#' 

-#' @author Dario Strbenac

-#' @examples

-#' 

-#'   CrossValParams() # Default is 100 permutations and 5 folds of each.

-#'   snow <- SnowParam(workers = 4, RNGseed = 999)

-#'   CrossValParams("Leave-k-Out", leave = 2, parallelParams = snow)

-#'   # Fully reproducible Leave-2-out cross-validation on 4 cores,

-#'   # even if feature selection or classifier use random sampling.

-#' 

-#' @exportClass CrossValParams

+# Parameters for Cross-validation Specification

+

 setClass("CrossValParams", representation(

     samplesSplits = "character",

     permutations = "numericOrNULL",

@@ -306,9 +95,7 @@ setClass("CrossValParams", representation(

 )

 )

-# CrossValParams constructor is an ordinary function for performance reasons.

-#' @export

-#' @rdname CrossValParams-class

+# CrossValParams constructor is an ordinary function and not S4 method for performance reasons.

 CrossValParams <- function(samplesSplits = c("Permute k-Fold", "Permute Percentage Split", "Leave-k-Out", "k-Fold"),

                            permutations = 100, percentTest = 25, folds = 5, leave = 2,

                            tuneMode = c("Resubstitution", "Nested CV", "none"), adaptiveResamplingDelta = NULL, parallelParams = bpparam())

@@ -339,43 +126,18 @@ CrossValParams <- function(samplesSplits = c("Permute k-Fold", "Permute Percenta

 ##### StageParams #####

-#' StageParams Virtual Class

-#' 

-#' A class for any one of \code{\link{TransformParams}},

-#' \code{\link{SelectParams}}, \code{\link{TrainParams}} or

-#' \code{\link{PredictParams}}. Allows a method to dispatch on any of the

-#' parameter objects specifying any stage of cross-validation.

-#' 

-#' 

-#' @name StageParams-class

-#' @aliases StageParams StageParams-class

-#' @author Dario Strbenac

-setClass("StageParams", representation("VIRTUAL"))

-

+# StageParams Virtual Class. A class for any one of TransformParams,

+# SelectParams, TrainParams, PredictParams. Allows a method to dispatch on

+# any of the parameter objects specifying any stage of cross-validation.

-#' Union of A StageParams Object and NULL

-#' @name StageParamsOrMissing-class

-#' @rdname StageParamsOrElse

-#' @aliases StageParamsOrMissing StageParamsOrMissing-class

-#' @author Dario Strbenac

+setClass("StageParams", representation("VIRTUAL"))

 setClassUnion("StageParamsOrMissing", c("StageParams", "missing"))

-

-#' Union of A StageParams Object and NULL

-#' 

-#' StageParamsOrMissing: Allows a slot to be either a class that has StageParams as its virtual

-#' parent class or empty. No constructor.

-#' StageParamsOrMissingOrNULL: Allows a slot to be either a class that has StageParams as its virtual

-#' parent class or empty or NULL. No constructor.

-#' 

-#' @name StageParamsOrMissingOrNULL-class

-#' @rdname StageParamsOrElse

-#' @aliases StageParamsOrMissingOrNULL StageParamsOrMissingOrNULL-class

+# Union of a StageParams object and NULL for parameter that's optional.

 setClassUnion("StageParamsOrMissingOrNULL", c("StageParams", "missing", "NULL"))

 ##### TransformParams #####

-#' @exportClass TransformParams

 setClass("TransformParams", representation(

   transform = "function",

   characteristics = "DataFrame",

@@ -383,80 +145,14 @@ setClass("TransformParams", representation(

   otherParams = "list"), contains = "StageParams"

 )

-#' Union of A TransformParams Object and NULL

-#' 

-#' Allows a slot to be either a TransformParams class object or empty. No

-#' constructor.

-#' 

-#' 

-#' @name TransformParamsOrNULL-class

-#' @aliases TransformParamsOrNULL TransformParamsOrNULL-class

-#' @docType class

-#' @author Dario Strbenac

-#' @examples

-#' 

-#'   ModellingParams(transformParams = NULL)

-#'   ModellingParams(transformParams = TransformParams(subtractFromLocation),

-#'                   selectParams = SelectParams(leveneRanking))

-#' 

+# Union of a TransformParams pbject and NULL. 

 setClassUnion("TransformParamsOrNULL", c("TransformParams", "NULL"))

+# Parameters for Data Transformation within CV.

-#' Parameters for Data Transformation

-#' 

-#' Collects and checks necessary parameters required for transformation within CV. The

-#' empty constructor is for when no data transformation is desired. See

-#' \code{\link{subtractFromLocation}} for an example of such a function.

-#' 

-#' 

-#' @name TransformParams

-#' @rdname TransformParams-class

-#' @aliases TransformParams TransformParams-class TransformParams,ANY-method

-#' TransformParams,function-method show,TransformParams-method

-#' @docType class

-#' @usage NULL

-#' @section Constructor:

-#' \describe{

-#' \item{}{

-#' \code{TransformParams(transform, characteristics = DataFrame(), intermediate = character(0), ...)} 

-#' Creates a \code{TransformParams} object which stores the function which will do the

-#' transformation and parameters that the function will use.

-#' \describe{

-#' \item{\code{transform}}{A function which will do the transformation. The

-#' first argument must be a \code{\link{DataFrame}} object.}

-#' \item{\code{characteristics}}{A \code{\link{DataFrame}} describing the

-#' characteristics of data transformation to be done. First column must be

-#' named \code{"charateristic"} and second column must be named \code{"value"}.

-#' If using wrapper functions for data transformation in this package, the data

-#' transformation name will automatically be generated and therefore it is not

-#' necessary to specify it.}

-#' \item{\code{intermediate}}{Character vector. Names of any variables created in

-#' prior stages by \code{\link{runTest}} that need to be passed to a feature selection

-#' function.}

-#' \item{\code{...}}{Other named parameters which will be used by the transformation function.}

-#' } } }

-#' 

-#' @section Summary:

-#' \code{transformParams} is a \code{TransformParams} object.

-#' \describe{

-#' \item{}{

-#'     \code{show(transformParams)}: Prints a short summary of what \code{transformParams} contains.

-#'  }}

-#' 

-#' @author Dario Strbenac

-#' @examples

-#' 

-#'   transformParams <- TransformParams(subtractFromLocation, location = "median")

-#'   # Subtract all values from training set median, to obtain absolute deviations.

-#' 

-#' @export

-#' @usage NULL

 setGeneric("TransformParams", function(transform, ...)

 standardGeneric("TransformParams"))

-#' @rdname TransformParams-class

-#' @usage NULL

-#' @export

 setMethod("TransformParams", "function",

           function(transform, characteristics = S4Vectors::DataFrame(), intermediate = character(0), ...)

           {

@@ -468,8 +164,10 @@ setMethod("TransformParams", "function",

                 intermediate = intermediate, otherParams = list(...))

           })

-#' @usage NULL

+#' Inspect Data Transformation Details

+#'

 #' @rdname TransformParams-class

+#' @param object An object of class \code{TransformParams} to inspect.

 #' @export

 setMethod("show", "TransformParams",

           function(object)

@@ -492,6 +190,7 @@ setMethod("show", "TransformParams",

 ##### FeatureSetCollection #####

 #' @docType class

 #' @exportClass FeatureSetCollection

+#' @rdname FeatureSetCollection

 setClass("FeatureSetCollection", representation(sets = "list"))

 #'

 #' Container for Storing A Collection of Sets

@@ -601,6 +300,7 @@ setMethod("length", c("FeatureSetCollection"),

 })

 #' @usage NULL

+#' @rdname FeatureSetCollection

 #' @export

 setMethod("show", "FeatureSetCollection",

           function(object)

@@ -673,7 +373,6 @@ setMethod("show", "FeatureSetCollection",

           }

 )

-#' @usage NULL

 #' @export

 setMethod("[", c("FeatureSetCollection", "numeric", "missing", "ANY"),

     function(x, i, j, ..., drop = TRUE)

@@ -681,7 +380,6 @@ setMethod("[", c("FeatureSetCollection", "numeric", "missing", "ANY"),

     new("FeatureSetCollection", sets = x@sets[i])

 })

-#' @usage NULL

 #' @export

 setMethod("[[", c("FeatureSetCollection", "ANY", "missing"),

     function(x, i, j, ...)

@@ -689,29 +387,10 @@ setMethod("[[", c("FeatureSetCollection", "ANY", "missing"),

     x@sets[[i]]

 })

-

-#' Union of a FeatureSetCollection and NULL

-#' 

-#' Allows a slot to be either a FeatureSetCollectionOrNULL object or empty.

-#' 

-#' 

-#' @name FeatureSetCollectionOrNULL-class

-#' @aliases FeatureSetCollectionOrNULL FeatureSetCollectionOrNULL-class

-#' @docType class

-#' @author Dario Strbenac

-#' @examples

-#' 

-#'   TrainParams(DLDAtrainInterface, transform = NULL) # Use the input data as-is.

-#' 

 setClassUnion("FeatureSetCollectionOrNULL", c("FeatureSetCollection", "NULL"))

-

-

-

-

 ##### SelectParams #####

-#' @exportClass SelectParams

 setClass("SelectParams", representation(

   featureRanking = "functionOrList",

   characteristics = "DataFrame",

@@ -722,97 +401,13 @@ setClass("SelectParams", representation(

   otherParams = "listOrNULL"), contains = "StageParams"

 )

-

-#' Union of A SelectParams Object and NULL

-#' 

-#' Allows a slot to be either a SelectParams class object or empty. No

-#' constructor.

-#' 

-#' 

-#' @name SelectParamsOrNULL-class

-#' @aliases SelectParamsOrNULL SelectParamsOrNULL-class

-#' @docType class

-#' @author Dario Strbenac

-#' @examples

-#' 

-#'   ModellingParams(selectParams = NULL)

-#'   ModellingParams(selectParams = SelectParams(differentMeansRanking))

-#' 

 setClassUnion("SelectParamsOrNULL", c("SelectParams", "NULL"))

-

-#' Parameters for Feature Selection

-#' 

-#' Collects and checks necessary parameters required for feature selection.

-#' Either one function is specified or a list of functions to perform ensemble

-#' feature selection. The empty constructor is provided for convenience.

-#' 

-#' 

-#' @name SelectParams

-#' @rdname SelectParams-class

-#' @aliases SelectParams SelectParams-class SelectParams,missing-method

-#' SelectParams,functionOrList-method

-#' @docType class

-#' @section Constructor:

-#' \describe{ \item{}{

-#' \code{SelectParams()} Creates a default \code{SelectParams} object. This uses either

-#' an ordinary t-test or ANOVA (depending on the number of classes) and tries the

-#' top 10 to top 100 features in increments of 10, and picks the number of features

-#' with the best resubstitution balanced error rate. Users should create an appropriate

-#' \code{SelectParams} object for the characteristics of their data.}

-#' \item{}{\preformatted{SelectParams(featureSelection, characteristics = DataFrame(), minPresence = 1, intermediate = character(0),

-#' subsetToSelections = TRUE, tuneParams = list(nFeatures = seq(10, 100, 10), performanceType = "Balanced Error"), ...)} Creates a \code{SelectParams}

-#' object which stores the function(s) which will do the selection and parameters that the

-#' function will use.

-#' \describe{\item{\code{featureRanking}}{Either a function which will rank the features

-#' from most promising to least promising or a list of such functions. For a particular function,

-#' the first argument must be an \code{\link{DataFrame}} object. The function's return value must

-#' be a vector of indices.}

-#' \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.}

-#' \item{\code{minPresence}}{If a list of functions was provided, how many of

-#' those must a feature have been selected by to be used in classification. 1

-#' is equivalent to a set union and a number the same length as

-#' \code{featureSelection} is equivalent to set intersection.}

-#' \item{\code{intermediate}}{Character vector. Names of any variables created

-#' in prior stages by \code{\link{runTest}} that need to be passed to a feature

-#' selection function.}

-#' \item{\code{subsetToSelections}}{Whether to subset the data table(s), after feature selection has been done.}

-#' \item{\code{tuneParams}}{A list specifying tuning parameters required during feature selection. The names of

-#' the list are the names of the parameters and the vectors are the values of the parameters to try. All possible

-#' combinations are generated. Two elements named \code{nFeatures} and \code{performanceType} are mandatory, to

-#' define the performance metric which will be used to select features and how many top-ranked features to try.}

-#' \item{\code{...}}{Other named parameters which will be used by the

-#' selection function. If \code{featureSelection} was a list of functions,

-#' this must be a list of lists, as long as \code{featureSelection}.} } } }

-#' @section Summary:

-#' \code{selectParams} is a \code{SelectParams} object.

-#' \describe{

-#' \item{}{

-#'   \code{show(SelectParams)}: Prints a short summary of what \code{selectParams} contains.

-#' }}

-#' @author Dario Strbenac

-#' @examples

-#' 

-#'   #if(require(sparsediscrim))

-#'   #{

-#'     SelectParams(differentMeansRanking)

-#'     

-#'     # Ensemble feature selection.

-#'     SelectParams(list(differentMeansRanking, pairsDifferencesRanking))

-#'   #}

-#' 

-#' @export

-#' @usage NULL

+# Parameters for Feature Selection

 setGeneric("SelectParams", function(featureRanking, ...)

 standardGeneric("SelectParams"))

-#' @rdname SelectParams-class

-#' @usage NULL

-#' @export

+# Default constructor.

 setMethod("SelectParams", "missing", function()

 {

   new("SelectParams", featureRanking = differentMeansRanking,

@@ -820,9 +415,7 @@ setMethod("SelectParams", "missing", function()

       minPresence = 1, intermediate = character(0), subsetToSelections = TRUE,

       tuneParams = list(nFeatures = seq(10, 100, 10), performanceType = "Balanced Error"))

 })

-#' @rdname SelectParams-class

-#' @usage NULL

-#' @export

+

 setMethod("SelectParams", c("functionOrList"),

           function(featureRanking, characteristics = DataFrame(), minPresence = 1, 

                    intermediate = character(0), subsetToSelections = TRUE, tuneParams = list(nFeatures = seq(10, 100, 10), performanceType = "Balanced Error"), ...)

@@ -844,8 +437,10 @@ setMethod("SelectParams", c("functionOrList"),

                 tuneParams = tuneParams, otherParams = others)

           })

-#' @usage NULL

+#' Container for Storing Details of Feature Selection Function(s)

+#' 

 #' @rdname SelectParams-class

+#' @param object An object of class \code{SelectParams} to inspect.

 #' @export

 setMethod("show", "SelectParams",

           function(object)

@@ -873,84 +468,17 @@ setMethod("show", "SelectParams",

 ##### TrainParams #####

-#' @exportClass TrainParams

 setClass("TrainParams", representation(

   classifier = "function",

   characteristics = "DataFrame",

   intermediate = "character",

   tuneParams = "listOrNULL",

   otherParams = "listOrNULL",

-  getFeatures = "functionOrNULL"

-), contains = "StageParams"

-)

-

+  getFeatures = "functionOrNULL"), contains = "StageParams")

-#' Parameters for Classifier Training

-#' 

-#' Collects and checks necessary parameters required for classifier training.

-#' The empty constructor is provided for convenience.

-#' 

-#' 

-#' @name TrainParams

-#' @rdname TrainParams-class

-#' @aliases TrainParams TrainParams-class TrainParams,missing-method

-#' TrainParams,function-method show,TrainParams-method

-#' @docType class

-#' @section Constructor:

-#' \describe{

-#' \item{}{\code{TrainParams()} Creates a default \code{TrainParams} object.

-#' The classifier function is \code{dlda} for Diagonal LDA. Users should create

-#' an appropriate \code{TrainParams} object for the characteristics of their data,

-#' once they are familiar with this software.}

-#' \item{}{\preformatted{TrainParams(classifier, characteristics = DataFrame(),

-#' intermediate = character(0), getFeatures = NULL, ...)}

-#' Creates a \code{TrainParams} object which stores the function which will do the

-#' classifier building and parameters that the function will use.

-#' \describe{

-#' \item{\code{classifier}}{A function which will construct a classifier, and

-#' also possibly make the predictions. The first argument must be a

-#' \code{\link{DataFrame}} object. The second argument must be a vector of

-#' classes. If the function also makes predictions and the value of the

-#' \code{predictor} setting of \code{PredictParams} is therefore \code{NULL},

-#' the third argument must be a \code{DataFrame} of test data. The function

-#' must also accept a parameter named \code{verbose}. The function's return

-#' value can be either a trained classifier if the function only does training

-#' or a vector or data frame of class predictions if it also does prediction

-#' with the test set samples.}

-#' \item{\code{characteristics}}{A \code{\link{DataFrame}} describing the

-#' characteristics of the classifier used. First column must be named \code{"charateristic"}

-#' and second column must be named \code{"value"}. If using wrapper functions for classifiers

-#' in this package, a classifier name will automatically be generated and

-#' therefore it is not necessary to specify it.}

-#' \item{\code{intermediate}}{Character vector. Names of any variables created

-#' in prior stages by \code{\link{runTest}} that need to be passed to

-#' \code{classifier}.}

-#' \item{\code{getFeatures}}{A function may be specified that extracts the selected

-#' features from the trained model. This is relevant if using a classifier that does

-#' feature selection within training (e.g. random forest). The function must return a

-#' list of two vectors. The first vector contains the ranked features (or empty if the

-#' training algorithm doesn't produce rankings) and the second vector contains the selected

-#' features.}

-#' \item{\code{...}}{Other named parameters which will be used by the classifier.} } } }

-#' @section Summary:

-#' \code{trainParams} is a \code{TrainParams} object.

-#' \describe{

-#' \item{}{

-#'   \code{show(trainParams)}: Prints a short summary of what \code{trainParams} contains.

-#' }}

-#' @author Dario Strbenac

-#' @examples

-#' 

-#' #if(require(sparsediscrim))

-#'   trainParams <- TrainParams(DLDAtrainInterface)

-#' 

-#' @usage NULL

-#' @export

+# Parameters for Classifier Training

 setGeneric("TrainParams", function(classifier, ...) standardGeneric("TrainParams"))

-#' @usage NULL

-#' @rdname TrainParams-class

-#' @export

 setMethod("TrainParams", "missing", function()

 {