inst/doc/wrapper.R

In ClassifyR: A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing

## ---- echo = FALSE, results = "asis"--------------------------------------------------------------------------------------------
options(width = 130)

## ---- eval = FALSE--------------------------------------------------------------------------------------------------------------
#  setGeneric("kNNinterface", function(measurements, ...) {standardGeneric("kNNinterface")})
#  
#  setMethod("kNNinterface", "DataFrame", function(measurements, classes, test, ..., verbose = 3)
#  {
#    splitDataset <- .splitDataAndClasses(measurements, classes)
#    trainingMatrix <- as.matrix(splitDataset[["measurements"]])
#    isNumeric <- sapply(measurements, is.numeric)
#    measurements <- measurements[, isNumeric, drop = FALSE]
#    isNumeric <- sapply(test, is.numeric)
#    test <- test[, isNumeric, drop = FALSE]
#  
#    if(!requireNamespace("class", quietly = TRUE))
#      stop("The package 'class' could not be found. Please install it.")
#    if(verbose == 3)
#      message("Fitting k Nearest Neighbours classifier to data and predicting classes.")
#  
#    class::knn(as.matrix(measurements), as.matrix(test), classes, ...)
#  })

## ---- eval = FALSE--------------------------------------------------------------------------------------------------------------
#  setMethod("kNNinterface", "matrix",
#            function(measurements, classes, test, ...)
#  {
#    kNNinterface(DataFrame(t(measurements), check.names = FALSE),
#                 classes,
#                 DataFrame(t(test), check.names = FALSE), ...)
#  })
#  
#  setMethod("kNNinterface", "MultiAssayExperiment",
#  function(measurements, test, targets = names(measurements), ...)
#  {
#    tablesAndClasses <- .MAEtoWideTable(measurements, targets)
#    trainingTable <- tablesAndClasses[["dataTable"]]
#    classes <- tablesAndClasses[["classes"]]
#    testingTable <- .MAEtoWideTable(test, targets)
#  
#    .checkVariablesAndSame(trainingTable, testingTable)
#    kNNinterface(trainingTable, classes, testingTable, ...)
#  })

## ---- message = FALSE-----------------------------------------------------------------------------------------------------------
classes <- factor(rep(c("Healthy", "Disease"), each = 5), levels = c("Healthy", "Disease"))
measurements <- matrix(c(rnorm(50, 10), rnorm(50, 5)), ncol = 10)
colnames(measurements) <- paste("Sample", 1:10)
rownames(measurements) <- paste("mRNA", 1:10)

library(ClassifyR)
trainParams <- TrainParams(kNNinterface)
predictParams <- PredictParams(NULL)
classified <- runTests(measurements, classes, datasetName = "Example",
                       classificationName = "kNN", validation = "leaveOut", leave = 1,
                       params = list(trainParams, predictParams))
classified
cbind(predictions(classified)[[1]], known = actualClasses(classified))