@@ -86,4 +86,4 @@ Collate:

     'simpleParams.R'

     'subtractFromLocation.R'

     'utilities.R'

-URL: https://sydneybiox.github.io/ClassifyR/

\ No newline at end of file

+URL: https://sydneybiox.github.io/ClassifyR/

@@ -86,7 +86,7 @@ setMethod("ROCplot", "ClassifyResult", function(results, ...) {

 #' @rdname ROCplot

 #' @export

-setMethod("ROCplot", "list", 

+setMethod("ROCplot", "list",

           function(results, mode = c("merge", "average"), interval = 95,

                    comparison = "auto", lineColours = "auto",

                    lineWidth = 1, fontSizes = c(24, 16, 12, 12, 12), labelPositions = seq(0.0, 1.0, 0.2),

@@ -101,10 +101,18 @@ setMethod("ROCplot", "list",

   if(comparison == "auto")

   {

     if(max(characteristicsCounts) == length(results))

-      comparison <- names(characteristicsCounts)[characteristicsCounts == max(characteristicsCounts)][1]

-    else

+    { # Choose a characteristic which varies the most across the results.

+      candidates <- names(characteristicsCounts)[characteristicsCounts == length(results)]

+      allCharacteristics <- do.call(rbind, lapply(results, function(result) result@characteristics))

+      distinctValues <- by(allCharacteristics[, "value"], allCharacteristics[, "characteristic"], function(values) length(unique(values)))

+      comparison <- names(distinctValues)[which.max(distinctValues)][1]

+    } else {

       stop("No characteristic is present for all results but must be.")

+    }

   }

+  resultsWithComparison <- sum(sapply(results, function(result) any(result@characteristics[, "characteristic"] == comparison)))

+  if(resultsWithComparison < length(results))

+    stop("Not all results have comparison characteristic ", comparison, ' but need to.')

   ggplot2::theme_set(ggplot2::theme_classic() + ggplot2::theme(panel.border = ggplot2::element_rect(fill = NA)))

   distinctClasses <- levels(actualOutcome(results[[1]]))

@@ -13,7 +13,7 @@

 #' a matrix of pre-calculated metrics, for backwards compatibility.

 #' @param classes If \code{results} is a matrix, this is a factor vector of the

 #' same length as the number of columns that \code{results} has.

-#' @param comparison Default: "Classifier Name". The aspect of the experimental

+#' @param comparison Default: "auto". The aspect of the experimental

 #' design to compare. Can be any characteristic that all results share.

 #' @param metric Default: "Sample Error". The sample-wise metric to plot.

 #' @param featureValues If not NULL, can be a named factor or named numeric

@@ -82,11 +82,17 @@

 setGeneric("samplesMetricMap", function(results, ...)

 standardGeneric("samplesMetricMap"))

+#' @rdname samplesMetricMap

+#' @export

+setMethod("samplesMetricMap", "ClassifyResult", function(results, ...) {

+    samplesMetricMap(list(assay = results), ...)

+})

+

 #' @rdname samplesMetricMap

 #' @export

 setMethod("samplesMetricMap", "list", 

           function(results,

-                   comparison = "Classifier Name",

+                   comparison = "auto",

                    metric = c("Sample Error", "Sample Accuracy", "Sample C-index"),

                    featureValues = NULL, featureName = NULL,

                    metricColours = list(c("#3F48CC", "#6F75D8", "#9FA3E5", "#CFD1F2", "#FFFFFF"),

@@ -103,6 +109,20 @@ setMethod("samplesMetricMap", "list",

     stop("The package 'gridExtra' could not be found. Please install it.")       

   if(!requireNamespace("gtable", quietly = TRUE))

     stop("The package 'gtable' could not be found. Please install it.")

+  

+  characteristicsCounts <- table(unlist(lapply(results, function(result) result@characteristics[["characteristic"]])))

+  if(comparison == "auto")

+  {

+    if(max(characteristicsCounts) == length(results))

+    { # Choose a characteristic which varies the most across the results.

+      candidates <- names(characteristicsCounts)[characteristicsCounts == length(results)]

+      allCharacteristics <- do.call(rbind, lapply(results, function(result) result@characteristics))

+      distinctValues <- by(allCharacteristics[, "value"], allCharacteristics[, "characteristic"], function(values) length(unique(values)))

+      comparison <- names(distinctValues)[which.max(distinctValues)][1]

+    } else {

+      stop("No characteristic is present for all results but must be.")

+    }

+  }

   resultsWithComparison <- sum(sapply(results, function(result) any(result@characteristics[, "characteristic"] == comparison)))

   if(resultsWithComparison < length(results))

     stop("Not all results have comparison characteristic ", comparison, ' but need to.')

@@ -4,11 +4,14 @@

 \alias{samplesMetricMap}

 \alias{samplesMetricMap,list-method}

 \alias{samplesMetricMap,matrix-method}

+\alias{samplesMetricMap,ClassifyResult-method}

 \title{Plot a Grid of Sample Error Rates or Accuracies}

 \usage{

+\S4method{samplesMetricMap}{ClassifyResult}(results, ...)

+

 \S4method{samplesMetricMap}{list}(

   results,

-  comparison = "Classifier Name",

+  comparison = "auto",

   metric = c("Sample Error", "Sample Accuracy", "Sample C-index"),

   featureValues = NULL,

   featureName = NULL,

@@ -55,7 +58,7 @@

 \item{results}{A list of \code{\link{ClassifyResult}} objects. Could also be

 a matrix of pre-calculated metrics, for backwards compatibility.}

-\item{comparison}{Default: "Classifier Name". The aspect of the experimental

+\item{comparison}{Default: "auto". The aspect of the experimental

 design to compare. Can be any characteristic that all results share.}

 \item{metric}{Default: "Sample Error". The sample-wise metric to plot.}