plsdaClassification: plsda classification

In pRoloc: A unifying bioinformatics framework for spatial proteomics

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 classes 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.

Author(s)

Laurent Gatto

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")

pRoloc documentation built on Nov. 8, 2020, 6:26 p.m.