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Last data update: 2014.03.03

R: Plot Performance Measures for Various Classifications

performancePlot

R Documentation

Plot Performance Measures for Various Classifications

Description

Draws a graphical summary of a particular performance measure for a list of classifications

Usage

  ## S4 method for signature 'list'
performancePlot(results,
                   aggregate = character(),
                   xVariable = c("classificationName", "datasetName", "selectionName", "validation"),
                   performanceName = NULL,
                   boxFillColouring = c("classificationName", "datasetName", "selectionName", "validation", "None"),
                   boxFillColours = NULL,
                   boxLineColouring = c("classificationName", "datasetName", "selectionName", "validation", "None"),
                   boxLineColours = NULL,
                   rowVariable = c("None", "validation", "datasetName", "classificationName", "selectionName"),
                   columnVariable = c("datasetName", "classificationName", "validation", "selectionName", "None"),
                   yLimits = c(0, 1), fontSizes = c(24, 16, 12, 12), title = NULL,
                   xLabel = "Analysis", yLabel = performanceName,
                   margin = grid::unit(c(0, 0, 0, 0), "lines"), rotate90 = FALSE, showLegend = TRUE, plot = TRUE)

Arguments

`results`	A list of `ClassifyResult` objects.
`aggregate`	A character vector of the levels of `xVariable` to aggregate to a single number by taking the mean. This is partciularly meaningful when the cross-validation is leave-k-out, when k is small.
`xVariable`	The factor to make separate boxes for.
`performanceName`	The name of the performance measure to make comparisons of. This is one of the names printed in the Performance Measures field when a `ClassifyResult` object is printed.
`boxFillColouring`	A factor to colour the boxes by.
`boxFillColours`	A vector of colours, one for each level of `boxFillColouring`.
`boxLineColouring`	A factor to colour the box lines by.
`boxLineColours`	A vector of colours, one for each level of `boxLineColouring`.
`rowVariable`	The slot name that different levels of are plotted as separate rows of boxplots.
`columnVariable`	The slot name that different levels of are plotted as separate columns of boxplots.
`yLimits`	The minimum and maximum value of the performance metric to plot.
`fontSizes`	A vector of length 4. The first number is the size of the title. The second number is the size of the axes titles. The third number is the size of the axes values. The fourth number is the font size of the titles of grouped plots, if any are produced. In other words, when `rowVariable` or `columnVariable` are not `NULL`.
`title`	An overall title for the plot.
`xLabel`	Label to be used for the x-axis.
`yLabel`	Label to be used for the y-axis of overlap percentages.
`margin`	The margin to have around the plot.
`rotate90`	Logical. IF `TRUE`, the plot is horizontal.
`showLegend`	If `TRUE`, a legend is plotted next to the plot. If FALSE, it is hidden.
`plot`	Logical. IF `TRUE`, a plot is produced on the current graphics device.

Details

Possible values for slot names are "datasetName", "classificationName", and "validation". If "None", then that graphic element is not used.

If there are multiple values for a performance measure in a single result object, it is plotted as a boxplot, unless aggregate is TRUE, in which case the all predictions in a single result object are considered simultaneously, so that only one performance number is calculated, and a barchart is plotted.

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 = sample(10, 20, replace = TRUE),
                              label = rep(c("Healthy", "Cancer"), each = 10)),
                    data.frame(sample = sample(10, 20, replace = TRUE),
                               label = rep(c("Healthy", "Cancer"), each = 10)),
                    data.frame(sample = sample(10, 20, replace = TRUE),
                               label = rep(c("Healthy", "Cancer"), each = 10)),
                    data.frame(sample = sample(10, 20, replace = TRUE),
                               label = rep(c("Healthy", "Cancer"), each = 10)))
  actual <- factor(rep(c("Healthy", "Cancer"), each = 5))
  result1 <- ClassifyResult("Example", "Differential Expression", "t-test", LETTERS[1:10], LETTERS[10:1], list(1:100, c(1:9, 11:101)), list(c(1:3), c(2, 5, 6), c(1:4), c(5:8), 1:5),
                            predicted, actual, list("fold", 2, 2))
  result1 <- calcPerformance(result1, "f")
  predicted <- data.frame(sample = sample(10, 100, replace = TRUE),
                          label = rep(c("Healthy", "Cancer"), each = 50))
  result2 <- ClassifyResult("Example", "Differential Variability", "F-test", LETTERS[1:10], LETTERS[10:1], list(1:100, c(1:5, 11:105)), list(c(1:3), c(4:6), c(1, 6, 7, 9), c(5:8), c(1, 5, 10)),
                            list(predicted), actual, validation = list("leave", 1))
  result2 <- calcPerformance(result2, "f")                            
  performancePlot(list(result1, result2), performanceName = "Precision-Recall F measure", title = "Comparison", boxLineColouring = "None")

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/performancePlot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: performancePlot
> ### Title: Plot Performance Measures for Various Classifications
> ### Aliases: performancePlot performancePlot,list-method
> 
> ### ** Examples
> 
>   predicted <- list(data.frame(sample = sample(10, 20, replace = TRUE),
+                               label = rep(c("Healthy", "Cancer"), each = 10)),
+                     data.frame(sample = sample(10, 20, replace = TRUE),
+                                label = rep(c("Healthy", "Cancer"), each = 10)),
+                     data.frame(sample = sample(10, 20, replace = TRUE),
+                                label = rep(c("Healthy", "Cancer"), each = 10)),
+                     data.frame(sample = sample(10, 20, replace = TRUE),
+                                label = rep(c("Healthy", "Cancer"), each = 10)))
>   actual <- factor(rep(c("Healthy", "Cancer"), each = 5))
>   result1 <- ClassifyResult("Example", "Differential Expression", "t-test", LETTERS[1:10], LETTERS[10:1], list(1:100, c(1:9, 11:101)), list(c(1:3), c(2, 5, 6), c(1:4), c(5:8), 1:5),
+                             predicted, actual, list("fold", 2, 2))
>   result1 <- calcPerformance(result1, "f")
>   predicted <- data.frame(sample = sample(10, 100, replace = TRUE),
+                           label = rep(c("Healthy", "Cancer"), each = 50))
>   result2 <- ClassifyResult("Example", "Differential Variability", "F-test", LETTERS[1:10], LETTERS[10:1], list(1:100, c(1:5, 11:105)), list(c(1:3), c(4:6), c(1, 6, 7, 9), c(5:8), c(1, 5, 10)),
+                             list(predicted), actual, validation = list("leave", 1))
>   result2 <- calcPerformance(result2, "f")                            
>   performancePlot(list(result1, result2), performanceName = "Precision-Recall F measure", title = "Comparison", boxLineColouring = "None")
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>