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R: Selection of Differential Distributions with...

KolmogorovSmirnovSelection

R Documentation

Selection of Differential Distributions with Kolmogorov-Smirnov Distance

Description

Ranks features by largest Kolmogorov-Smirnov distance and chooses the features which have best resubstitution performance.

Usage

  ## S4 method for signature 'matrix'
KolmogorovSmirnovSelection(expression, classes, ...)
  ## S4 method for signature 'ExpressionSet'
KolmogorovSmirnovSelection(expression, datasetName, trainParams,
                                       predictParams, resubstituteParams, ...,
                                       selectionName, verbose = 3)

Arguments

`expression`	Either a `matrix` or `ExpressionSet` containing the training data. For a matrix, the rows are features, and the columns are samples.
`classes`	A vector of class labels.
`datasetName`	A name for the dataset used. Stored in the result.
`trainParams`	A container of class `TrainParams` describing the classifier to use for training.
`predictParams`	A container of class `PredictParams` describing how prediction is to be done.
`resubstituteParams`	An object of class `ResubstituteParams` describing the performance measure to consider and the numbers of top features to try for resubstitution classification.
`...`	For the `matrix` method, variables passed to the `ExpressionSet` method. For the `ExpressionSet` method, the options to be passed to function `ks.test`.
`selectionName`	A name to identify this selection method by. Stored in the result.
`verbose`	A number between 0 and 3 for the amount of progress messages to give. This function only prints progress messages if the value is 3.

Details

Features are sorted in order of biggest distance to smallest. The top number of features is used in a classifier, to determine which number of features has the best resubstitution performance.

Value

An object of class SelectResult or a list of such objects, if the classifier which was used for determining resubstitution error rate made a number of prediction varieties.

Author(s)

Dario Strbenac

Examples

  if(require(sparsediscrim))
  {
    # First 20 features have bimodal distribution for Poor class. Other 80 features have normal distribution for
    # both classes.
    genesMatrix <- sapply(1:25, function(sample) c(rnorm(20, sample(c(8, 12), 20, replace = TRUE), 1), rnorm(80, 10, 1)))
    genesMatrix <- cbind(genesMatrix, sapply(1:25, function(sample) rnorm(100, 10, 1)))
    classes <- factor(rep(c("Poor", "Good"), each = 25))
    KolmogorovSmirnovSelection(genesMatrix, classes, "Example",
                               trainParams = TrainParams(naiveBayesKernel, FALSE, doesTests = TRUE),
                               predictParams = PredictParams(function(){}, FALSE, getClasses = function(result) result),
                               resubstituteParams = ResubstituteParams(nFeatures = seq(10, 100, 10), performanceType = "balanced", better = "lower"))
  }

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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Platform: x86_64-pc-linux-gnu (64-bit)

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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/KolmogorovSmirnovSelection.Rd_%03d_medium.png", width=480, height=480)
> ### Name: KolmogorovSmirnovSelection
> ### Title: Selection of Differential Distributions with Kolmogorov-Smirnov
> ###   Distance
> ### Aliases: KolmogorovSmirnovSelection
> ###   KolmogorovSmirnovSelection,matrix-method
> ###   KolmogorovSmirnovSelection,ExpressionSet-method
> 
> ### ** Examples
> 
>   if(require(sparsediscrim))
+   {
+     # First 20 features have bimodal distribution for Poor class. Other 80 features have normal distribution for
+     # both classes.
+     genesMatrix <- sapply(1:25, function(sample) c(rnorm(20, sample(c(8, 12), 20, replace = TRUE), 1), rnorm(80, 10, 1)))
+     genesMatrix <- cbind(genesMatrix, sapply(1:25, function(sample) rnorm(100, 10, 1)))
+     classes <- factor(rep(c("Poor", "Good"), each = 25))
+     KolmogorovSmirnovSelection(genesMatrix, classes, "Example",
+                                trainParams = TrainParams(naiveBayesKernel, FALSE, doesTests = TRUE),
+                                predictParams = PredictParams(function(){}, FALSE, getClasses = function(result) result),
+                                resubstituteParams = ResubstituteParams(nFeatures = seq(10, 100, 10), performanceType = "balanced", better = "lower"))
+   }
Loading required package: sparsediscrim
Selecting features by Kolmogorov Smirnov distance
Fitting densities.
Calculating crossover points of class densities.
Calculating vertical differences between densities.
Calculating class scores and determining class labels.
Training and classification completed.
Prediction completed.
Prediction completed.
Prediction completed.
Prediction completed.
Fitting densities.
Calculating crossover points of class densities.
Calculating vertical differences between densities.
Calculating class scores and determining class labels.
Training and classification completed.
Prediction completed.
Prediction completed.
Prediction completed.
Prediction completed.
Fitting densities.
Calculating crossover points of class densities.
Calculating vertical differences between densities.
Calculating class scores and determining class labels.
Training and classification completed.
Prediction completed.
Prediction completed.
Prediction completed.
Prediction completed.
Fitting densities.
Calculating crossover points of class densities.
Calculating vertical differences between densities.
Calculating class scores and determining class labels.
Training and classification completed.
Prediction completed.
Prediction completed.
Prediction completed.
Prediction completed.
Fitting densities.
Calculating crossover points of class densities.
Calculating vertical differences between densities.
Calculating class scores and determining class labels.
Training and classification completed.
Prediction completed.
Prediction completed.
Prediction completed.
Prediction completed.
Fitting densities.
Calculating crossover points of class densities.
Calculating vertical differences between densities.
Calculating class scores and determining class labels.
Training and classification completed.
Prediction completed.
Prediction completed.
Prediction completed.
Prediction completed.
Fitting densities.
Calculating crossover points of class densities.
Calculating vertical differences between densities.
Calculating class scores and determining class labels.
Training and classification completed.
Prediction completed.
Prediction completed.
Prediction completed.
Prediction completed.
Fitting densities.
Calculating crossover points of class densities.
Calculating vertical differences between densities.
Calculating class scores and determining class labels.
Training and classification completed.
Prediction completed.
Prediction completed.
Prediction completed.
Prediction completed.
Fitting densities.
Calculating crossover points of class densities.
Calculating vertical differences between densities.
Calculating class scores and determining class labels.
Training and classification completed.
Prediction completed.
Prediction completed.
Prediction completed.
Prediction completed.
Fitting densities.
Calculating crossover points of class densities.
Calculating vertical differences between densities.
Calculating class scores and determining class labels.
Training and classification completed.
Prediction completed.
Prediction completed.
Prediction completed.
Prediction completed.
Features selected.
$`weighted=unweighted`
An object of class 'SelectResult'.
Dataset Name: Example.
Feature Selection Name: Kolmogorov-Smirnov Test.
Features Considered: 100.
Selections: List of length 1.
Selection Size : 30 features.

$`weighted=weighted,weight=crossover distance`
An object of class 'SelectResult'.
Dataset Name: Example.
Feature Selection Name: Kolmogorov-Smirnov Test.
Features Considered: 100.
Selections: List of length 1.
Selection Size : 20 features.

$`weighted=weighted,weight=height difference`
An object of class 'SelectResult'.
Dataset Name: Example.
Feature Selection Name: Kolmogorov-Smirnov Test.
Features Considered: 100.
Selections: List of length 1.
Selection Size : 40 features.

$`weighted=weighted,weight=sum differences`
An object of class 'SelectResult'.
Dataset Name: Example.
Feature Selection Name: Kolmogorov-Smirnov Test.
Features Considered: 100.
Selections: List of length 1.
Selection Size : 40 features.

> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>