pRoloc (version 1.12.4)

perTurboClassification: perTurbo classification

Description

Classification using the PerTurbo algorithm.

Usage

perTurboClassification(object, assessRes, scores = c("prediction", "all", "none"), pRegul, sigma, inv, reg, fcol = "markers")

Arguments

object

An instance of class "MSnSet".

assessRes

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

scores

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

pRegul

If assessRes is missing, a pRegul must be provided. See perTurboOptimisation for details.

sigma

If assessRes is missing, a sigma must be provided. See perTurboOptimisation for details.

inv

The type of algorithm used to invert the matrix. Values are : "Inversion Cholesky" (chol2inv), "Moore Penrose" (ginv), "solve" (solve), "svd" (svd). Default value is "Inversion Cholesky".

reg

The type of regularisation of matrix. Values are "none", "trunc" or "tikhonov". Default value is "tikhonov".

fcol

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

Value

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

References

N. Courty, T. Burger, J. Laurent. "PerTurbo: a new classification algorithm based on the spectrum perturbations of the Laplace-Beltrami operator", The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2011), D. Gunopulos et al. (Eds.): ECML PKDD 2011, Part I, LNAI 6911, pp. 359 - 374, Athens, Greece, September 2011.

Examples

library(pRolocdata)
data(dunkley2006)
## reducing parameter search space 
params <- perTurboOptimisation(dunkley2006,
                               pRegul = 2^seq(-2,2,2),
                               sigma = 10^seq(-1, 1, 1),
                               inv = "Inversion Cholesky",
                               reg ="tikhonov",
                               times = 3)
params
plot(params)
f1Count(params)
levelPlot(params)
getParams(params)
res <- perTurboClassification(dunkley2006, params)
getPredictions(res, fcol = "perTurbo")
getPredictions(res, fcol = "perTurbo", t = 0.75)
plot2D(res, fcol = "perTurbo")