Reproducibly Run Various Kinds of Cross-Validation
runTests.Rd
Enables doing classification schemes such as ordinary 10-fold, 100
permutations 5-fold, and leave one out cross-validation. Processing in
parallel is possible by leveraging the package BiocParallel
.
Usage
# S4 method for matrix
runTests(measurements, outcome, ...)
# S4 method for DataFrame
runTests(
measurements,
outcome,
crossValParams = CrossValParams(),
modellingParams = ModellingParams(),
characteristics = S4Vectors::DataFrame(),
...,
verbose = 1
)
# S4 method for MultiAssayExperiment
runTests(measurements, outcome, ...)
Arguments
- measurements
Either a
matrix
,DataFrame
orMultiAssayExperiment
containing all of the data. For amatrix
orDataFrame
, the rows are samples, and the columns are features.- ...
Variables not used by the
matrix
nor theMultiAssayExperiment
method which are passed into and used by theDataFrame
method or passed onwards toprepareData
.- outcome
Either a factor vector of classes, a
Surv
object, or a character string, or vector of such strings, containing column name(s) of column(s) containing either classes or time and event information about survival. Ifmeasurements
is aMultiAssayExperiment
, the names of the column (class) or columns (survival) in the table extracted bycolData(data)
that contain(s) the samples' outcome to use for prediction. If column names of survival information, time must be in first column and event status in the second.- crossValParams
An object of class
CrossValParams
, specifying the kind of cross-validation to be done.- modellingParams
An object of class
ModellingParams
, specifying the class rebalancing, transformation (if any), feature selection (if any), training and prediction to be done on the data set.- characteristics
A
DataFrame
describing the characteristics of the classification used. First column must be named"charateristic"
and second column must be named"value"
. Useful for automated plot annotation by plotting functions within this package. Transformation, selection and prediction functions provided by this package will cause the characteristics to be automatically determined and this can be left blank.- verbose
Default: 1. A number between 0 and 3 for the amount of progress messages to give. A higher number will produce more messages as more lower-level functions print messages.
Value
An object of class ClassifyResult
.
Examples
#if(require(sparsediscrim))
#{
data(asthma)
CVparams <- CrossValParams(permutations = 5)
tuneList <- list(nFeatures = seq(5, 25, 5), performanceType = "Balanced Error")
selectParams <- SelectParams("t-test", tuneParams = tuneList)
modellingParams <- ModellingParams(selectParams = selectParams)
runTests(measurements, classes, CVparams, modellingParams,
DataFrame(characteristic = c("Assay Name", "Classifier Name"),
value = c("Asthma", "Different Means"))
)
#> Processing sample set 10.
#> Processing sample set 20.
#> An object of class 'ClassifyResult'.
#> Characteristics:
#> characteristic value
#> Assay Name Asthma
#> Classifier Name Different Means
#> Selection Name Difference in Means
#> Cross-validation 5 Permutations, 5 Folds
#> Features: List of length 25 of feature identifiers.
#> Predictions: A data frame of 950 rows.
#> Performance Measures: None calculated yet.
#}