perTurboClassification: perTurbo classification

In pRoloc: A unifying bioinformatics framework for spatial proteomics

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 classes 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.

Author(s)

Thomas Burger and Samuel Wieczorek

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")

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