NSCpredictInterface: Interface for 'pamr.predict' Function from 'pamr' CRAN...

In ClassifyR: A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing

Description

Restructures variables from ClassifyR framework to be compatible with pamr.predict definition.

Usage

  ## S4 method for signature 'pamrtrained,matrix'
NSCpredictInterface(trained, test, ...)
  ## S4 method for signature 'pamrtrained,DataFrame'
NSCpredictInterface(trained, test, classes = NULL, ..., returnType = c("class", "score", "both"), verbose = 3)  
  ## S4 method for signature 'pamrtrained,MultiAssayExperiment'
NSCpredictInterface(trained, test, targets = names(test), ...)

Arguments

trained	An object of class pamrtrained.
test	An object of the same class as measurements with no samples in common with measurements used in the training stage and the same number of features as it. Also, if a DataFrame, the class column must be absent.
classes	Either NULL or a character vector of length 1, specifying the column name to remove.
targets	If test is a MultiAssayExperiment, the names of the data tables to be used. "clinical" is also a valid value and specifies that numeric variables from the clinical data table will be used.
...	Variables not used by the matrix nor the MultiAssayExperiment method which are passed into and used by the DataFrame method or optional settings that are passed to pamr.predict.
returnType	Default: "class". Either "class", "score" or "both". Sets the return value from the prediction to either a vector of class labels, score for a sample belonging to the second class, as determined by the factor levels, or both labels and scores in a data.frame.
verbose	Default: 3. 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

This function is an interface between the ClassifyR framework and pamr.predict. It selects the highest threshold that gives the minimum error rate in the training data.

Value

Either a factor vector of predicted classes, a matrix of scores for each class, or a table of both the class labels and class scores, depending on the setting of returnType.

Author(s)

Dario Strbenac

See Also

pamr.predict for the function that was interfaced to.

Examples

  if(require(pamr))
  {
    # Samples in one class with differential expression to other class.
    genesMatrix <- sapply(1:25, function(geneColumn) c(rnorm(100, 9, 1)))
    genesMatrix <- cbind(genesMatrix, sapply(1:25, function(geneColumn)
                                 c(rnorm(75, 9, 1), rnorm(25, 14, 1))))
    classes <- factor(rep(c("Poor", "Good"), each = 25))
    
    fit <- NSCtrainInterface(genesMatrix[, c(1:20, 26:45)], classes[c(1:20, 26:45)])
    NSCpredictInterface(fit, genesMatrix[, c(21:25, 46:50)])
  }

ClassifyR documentation built on Nov. 8, 2020, 6:53 p.m.