Parameters for Feature Selection

Description

Collects and checks necessary parameters required for feature selection. The empty constructor is provided for convenience.

Constructor

SelectParams() Creates a default SelectParams object. This uses a limma t-test and tries 100, 200, 300, 400, 500 features, and picks the number of features with the best resubstitution error rate. Users should create an appropriate SelectParams object for the characteristics of their data, once they are familiar with this software.

SelectParams(featureSelection, selectionName, minPresence = 1, intermediate = character(0), subsetExpressionData = TRUE, ...) Creates a SelectParams object which stores the function which will do the selection and parameters that the function will use.

featureSelection

Either a function which will do the selection or a list of such functions. For a particular function, the first argument must be an ExpressionSet object. The function's return value must be a vector of row indices of genes that were selected.

selectionName

A name to identify this selection method by.

minPresence

If a list of functions was provided, how many of those must a feature have been selected by to be used in classification. 1 is equivalent to a set union and a number the same length as featureSelection is equivalent to set intersection.

intermediate

Character vector. Names of any variables created in prior stages by runTest that need to be passed to a feature selection function.

subsetExpressionData

Whether to subset the expression data, after selection has been done.

...

Other named parameters which will be used by the selection function. If featureSelection was a list of functions, this must be a list of lists, as long as featureSelection.

Author(s)

Dario Strbenac

Examples

  if(require(sparsediscrim))
  {
    SelectParams(limmaSelection, "t-test",
                 trainParams = TrainParams(), predictParams = PredictParams(),
                 resubstituteParams = ResubstituteParams())
    
    # For pamr shrinkage selection.
    SelectParams(nearestShrunkenCentroidSelectionInterface, datasetName = "Ovarian Cancer",
                 intermediate = "trained", subsetExpressionData = FALSE)
  }

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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> 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/SelectParams-class.Rd_%03d_medium.png", width=480, height=480)
> ### Name: SelectParams
> ### Title: Parameters for Feature Selection
> ### Aliases: SelectParams SelectParams-class SelectParams,ANY-method
> ###   SelectParams,functionOrList-method
> 
> ### ** Examples
> 
>   if(require(sparsediscrim))
+   {
+     SelectParams(limmaSelection, "t-test",
+                  trainParams = TrainParams(), predictParams = PredictParams(),
+                  resubstituteParams = ResubstituteParams())
+     
+     # For pamr shrinkage selection.
+     SelectParams(nearestShrunkenCentroidSelectionInterface, datasetName = "Ovarian Cancer",
+                  intermediate = "trained", subsetExpressionData = FALSE)
+   }
Loading required package: sparsediscrim
An object of class "SelectParams"
Slot "featureSelection":
function (expression, ...) 
{
    standardGeneric("nearestShrunkenCentroidSelectionInterface")
}
<environment: 0x412eab0>
attr(,"generic")
[1] "nearestShrunkenCentroidSelectionInterface"
attr(,"generic")attr(,"package")
[1] "ClassifyR"
attr(,"package")
[1] "ClassifyR"
attr(,"group")
list()
attr(,"valueClass")
character(0)
attr(,"signature")
[1] "expression"
attr(,"default")
`01NULL01`
attr(,"skeleton")
(function (expression, ...) 
stop("invalid call in method dispatch to 'nearestShrunkenCentroidSelectionInterface' (no default method)", 
    domain = NA))(expression, ...)
attr(,"class")
[1] "nonstandardGenericFunction"
attr(,"class")attr(,"package")
[1] "methods"

Slot "selectionName":
[1] "Shrunken Centroids"

Slot "minPresence":
[1] 1

Slot "intermediate":
[1] "trained"

Slot "subsetExpressionData":
[1] FALSE

Slot "otherParams":
$datasetName
[1] "Ovarian Cancer"


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> dev.off()
null device 
          1 
>