Plot Receiver Operating Curve Graphs for Classification Results

Description

The average pair-wise overlap is computed for every pair of cross-validations. The overlap is converted to a percentage and plotted as lineplots.

Usage

  ## S4 method for signature 'list'
ROCplot(results, nBins = sapply(results, totalPredictions),
                   lineColourVariable = c("classificationName", "datasetName", "selectionName", "validation", "None"), lineColours = NULL,
                   lineWidth = 1, fontSizes = c(24, 16, 12, 12, 12), labelPositions = seq(0.0, 1.0, 0.2),
                   plotTitle = "ROC", legendTitle = NULL, xLabel = "False Positive Rate", yLabel = "True Positive Rate",
                   plot = TRUE, showAUC = TRUE)

Arguments

Details

Possible values for slot names are "datasetName", "classificationName", and "validation". If "None", then any lines drawn will be black.

The scores stored in the results should be higher if the sample is more likely to be from the second class, based on the levels of the actual classes. The scores must be in a column named "score".

For cross-validated classification, all predictions from all iterations are considered simultaneously, to calculate one curve per classification.

The number of bins determines how many pairs of TPR and FPR points will be used to draw the plot. A higher number will result in a smoother ROC curve.

The AUC is calculated using the trapezoidal rule.

Value

An object of class ggplot and a plot on the current graphics device, if plot is TRUE.

Author(s)

Dario Strbenac

Examples

  predicted <- list(data.frame(sample = c(1, 8, 15, 3, 11, 20, 19, 18), score = c(0.11, 0.32, 0.47, 0.24, 0.87, 0.80, 0.40, 0.75)),
                    data.frame(sample = c(11, 18, 15, 4, 6, 10, 11, 12), score = c(0.55, 0.44, 0.67, 0.44, 0.67, 0.80, 0.40, 0.60)))
  actual <- factor(c(rep("Healthy", 10), rep("Cancer", 10)), levels = c("Healthy", "Cancer"))
  result1 <- ClassifyResult("Example", "Differential Expression", "t-test", LETTERS[1:10], LETTERS[10:1], list(1:100, c(1:9, 11:101)), list(sample(10, 10), sample(10, 10)),
                            predicted, actual, list("fold", 2, 1))
  predicted[[1]][, "score"][c(2, 6)] <- c(0.60, 0.40)
  result2 <- ClassifyResult("Example", "Differential Variability", "F-test", LETTERS[1:10], LETTERS[10:1], list(1:100, c(1:5, 11:105)), list(sample(10, 10), sample(10, 10)),
                            predicted, actual, validation = list("fold", 2, 1))
  ROCplot(list(result1, result2), lineColourVariable = "classificationName", plotTitle = "Ovarian Cancer ROC")

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(ClassifyR)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: BiocParallel
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/ClassifyR/ROCplot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ROCplot
> ### Title: Plot Receiver Operating Curve Graphs for Classification Results
> ### Aliases: ROCplot ROCplot,list-method
> 
> ### ** Examples
> 
>   predicted <- list(data.frame(sample = c(1, 8, 15, 3, 11, 20, 19, 18), score = c(0.11, 0.32, 0.47, 0.24, 0.87, 0.80, 0.40, 0.75)),
+                     data.frame(sample = c(11, 18, 15, 4, 6, 10, 11, 12), score = c(0.55, 0.44, 0.67, 0.44, 0.67, 0.80, 0.40, 0.60)))
>   actual <- factor(c(rep("Healthy", 10), rep("Cancer", 10)), levels = c("Healthy", "Cancer"))
>   result1 <- ClassifyResult("Example", "Differential Expression", "t-test", LETTERS[1:10], LETTERS[10:1], list(1:100, c(1:9, 11:101)), list(sample(10, 10), sample(10, 10)),
+                             predicted, actual, list("fold", 2, 1))
>   predicted[[1]][, "score"][c(2, 6)] <- c(0.60, 0.40)
>   result2 <- ClassifyResult("Example", "Differential Variability", "F-test", LETTERS[1:10], LETTERS[10:1], list(1:100, c(1:5, 11:105)), list(sample(10, 10), sample(10, 10)),
+                             predicted, actual, validation = list("fold", 2, 1))
>   ROCplot(list(result1, result2), lineColourVariable = "classificationName", plotTitle = "Ovarian Cancer ROC")
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>