plsdaClassification {pRoloc} | R Documentation

## 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 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")

[Package pRoloc version 1.30.0 Index]