plsdaClassification {pRoloc} | R Documentation |
Classification using the partial least square distcriminant analysis algorithm.
plsdaClassification(object, assessRes, scores = c("prediction", "all", "none"), ncomp, fcol = "markers", ...)
object |
An instance of class |
assessRes |
An instance of class
|
scores |
One of |
ncomp |
If |
fcol |
The feature meta-data containing marker definitions.
Default is |
... |
Additional parameters passed to |
An instance of class "MSnSet"
with
plsda
and plsda.scores
feature variables storing
the classification results and scores respectively.
Laurent Gatto
## 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")