@@ -85,7 +85,6 @@ exportMethods(selectionPlot)

 exportMethods(show)

 exportMethods(totalPredictions)

 exportMethods(tunedParameters)

-import(BiocParallel)

 import(MultiAssayExperiment)

 import(grid)

 import(methods)

@@ -521,17 +521,6 @@ Using an ordinary GLM instead.")

     classifier

 }

-######################################

-######################################

-#' A function to generate a CrossValParams object

-#'

-#' @inheritParams crossValidate

-#'

-#' @return CrossValParams object

-#'

-#' @examples

-#' CVparams <- generateCrossValParams(nRepeats = 20, nFolds = 5, nCores = 8, selectionOptimisation = "none")

-#' @import BiocParallel

 generateCrossValParams <- function(nRepeats, nFolds, nCores, selectionOptimisation){

     seed <- .Random.seed[1]

@@ -554,31 +543,7 @@ generateCrossValParams <- function(nRepeats, nFolds, nCores, selectionOptimisati

     if(!any(tuneMode %in% c("Resubstitution", "Nested CV", "none"))) stop("selectionOptimisation must be Nested CV or Resubstitution or none")

     CrossValParams(permutations = nRepeats, folds = nFolds, parallelParams = BPparam, tuneMode = tuneMode)

 }

-######################################

-######################################

-#' A function to generate a ModellingParams object

-#'

-#' @inheritParams crossValidate

-#' @param assayIDs A vector of data set identifiers as long at the number of data sets.

-#'

-#' @return ModellingParams object

-#'

-#' @examples

-#' data(asthma)

-#' # First make a toy example assay with multiple data types. We'll randomly assign different features to be clinical, gene or protein.

-#' set.seed(51773)

-#' measurements <- DataFrame(measurements, check.names = FALSE) 

-#' mcols(measurements)$assay <- c(rep("clinical",20),sample(c("gene", "protein"), ncol(measurements)-20, replace = TRUE))

-#' mcols(measurements)$feature <- colnames(measurements)

-#' modellingParams <- generateModellingParams(assayIDs = c("clinical", "gene", "protein"),

-#'                                           measurements = measurements, 

-#'                                           nFeatures = list(clinical = 10, gene = 10, protein = 10),

-#'                                           selectionMethod = list(clinical = "t-test", gene = "t-test", protein = "t-test"),

-#'                                           selectionOptimisation = "none",

-#'                                           classifier = "randomForest",

-#'                                           multiViewMethod = "merge")

-#' @import BiocParallel

 generateModellingParams <- function(assayIDs,

                                     measurements,

                                     nFeatures,

deleted file mode 100644

@@ -1,26 +0,0 @@

-% Generated by roxygen2: do not edit by hand

-% Please edit documentation in R/crossValidate.R

-\name{generateCrossValParams}

-\alias{generateCrossValParams}

-\title{A function to generate a CrossValParams object}

-\usage{

-generateCrossValParams(nRepeats, nFolds, nCores, selectionOptimisation)

-}

-\arguments{

-\item{nRepeats}{A numeric specifying the the number of repeats or permutations to use for cross-validation.}

-

-\item{nFolds}{A numeric specifying the number of folds to use for cross-validation.}

-

-\item{nCores}{A numeric specifying the number of cores used if the user wants to use parallelisation.}

-

-\item{selectionOptimisation}{A character of "Resubstitution", "Nested CV" or "none" specifying the approach used to optimise \code{nFeatures}.}

-}

-\value{

-CrossValParams object

-}

-\description{

-A function to generate a CrossValParams object

-}

-\examples{

-CVparams <- generateCrossValParams(nRepeats = 20, nFolds = 5, nCores = 8, selectionOptimisation = "none")

-}

deleted file mode 100644

@@ -1,63 +0,0 @@

-% Generated by roxygen2: do not edit by hand

-% Please edit documentation in R/crossValidate.R

-\name{generateModellingParams}

-\alias{generateModellingParams}

-\title{A function to generate a ModellingParams object}

-\usage{

-generateModellingParams(

-  assayIDs,

-  measurements,

-  nFeatures,

-  selectionMethod,

-  selectionOptimisation,

-  performanceType = "auto",

-  classifier,

-  multiViewMethod = "none"

-)

-}

-\arguments{

-\item{assayIDs}{A vector of data set identifiers as long at the number of data sets.}

-

-\item{measurements}{Either a \code{\link{DataFrame}}, \code{\link{data.frame}}, \code{\link{matrix}}, \code{\link{MultiAssayExperiment}} 

-or a list of these objects containing the data.}

-

-\item{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. 

-Set to NULL or "all" if all features should be used.}

-

-\item{selectionMethod}{Default: "auto". A character vector of feature selection methods to compare. If a named character vector with names corresponding to different assays, 

-and performing multiview classification, the respective classification methods will be used on each assay. If \code{"auto"} t-test (two categories) / F-test (three or more categories) ranking

-and top \code{nFeatures} optimisation is done. Otherwise, the ranking method is per-feature Cox proportional hazards p-value.}

-

-\item{selectionOptimisation}{A character of "Resubstitution", "Nested CV" or "none" specifying the approach used to optimise \code{nFeatures}.}

-

-\item{performanceType}{Performance metric to optimise if classifier has any tuning parameters.}

-

-\item{classifier}{Default: \code{"auto"}. A character vector of classification methods to compare. If a named character vector with names corresponding to different assays, 

-and performing multiview classification, the respective classification methods will be used on each assay. If \code{"auto"}, then a random forest is used for a classification

-task or Cox proportional hazards model for a survival task.}

-

-\item{multiViewMethod}{A character vector specifying the multiview method or data integration approach to use.}

-}

-\value{

-ModellingParams object

-}

-\description{

-A function to generate a ModellingParams object

-}

-\examples{

-data(asthma)

-# First make a toy example assay with multiple data types. We'll randomly assign different features to be clinical, gene or protein.

-set.seed(51773)

-measurements <- DataFrame(measurements, check.names = FALSE) 

-mcols(measurements)$assay <- c(rep("clinical",20),sample(c("gene", "protein"), ncol(measurements)-20, replace = TRUE))

-mcols(measurements)$feature <- colnames(measurements)

-modellingParams <- generateModellingParams(assayIDs = c("clinical", "gene", "protein"),

-                                          measurements = measurements, 

-                                          nFeatures = list(clinical = 10, gene = 10, protein = 10),

-                                          selectionMethod = list(clinical = "t-test", gene = "t-test", protein = "t-test"),

-                                          selectionOptimisation = "none",

-                                          classifier = "randomForest",

-                                          multiViewMethod = "merge")

-}