pRoloc (version 1.12.4)

plsdaClassification: plsda classification

Description

Classification using the partial least square distcriminant analysis algorithm.

Usage

plsdaClassification(object, assessRes, scores = c("prediction", "all", "none"), ncomp, fcol = "markers", ...)

Arguments

object

An instance of class "MSnSet".

assessRes

An instance of class "GenRegRes", as generated by plsdaOptimisation.

scores

One of "prediction", "all" or "none" to report the score for the predicted class only, for all cluster or none.

ncomp

If assessRes is missing, a ncomp must be provided.

fcol

The feature meta-data containing marker definitions. Default is markers.

...

Additional parameters passed to plsda from package caret.

Value

An instance of class "MSnSet" with plsda and plsda.scores feature variables storing the classification results and scores respectively.

Examples

## not running this one for time considerations
library(pRolocdata)
data(dunkley2006)
## reducing parameter search space and iterations 
params <- plsdaOptimisation(dunkley2006, ncomp = c(3, 10),  times = 2)
params
plot(params)
f1Count(params)
levelPlot(params)
getParams(params)
res <- plsdaClassification(dunkley2006, params)
getPredictions(res, fcol = "plsda")
getPredictions(res, fcol = "plsda", t = 0.9)
plot2D(res, fcol = "plsda")