perTurboClassification {pRoloc} | R Documentation |
Classification using the PerTurbo algorithm.
perTurboClassification(object, assessRes, scores = c("prediction", "all", "none"), pRegul, sigma, inv, reg, fcol = "markers")
object |
An instance of class |
assessRes |
An instance of class
|
scores |
One of |
pRegul |
If |
sigma |
If |
inv |
The type of algorithm used to invert the matrix.
Values are : "Inversion Cholesky" ( |
reg |
The type of regularisation of matrix. Values are
"none", "trunc" or "tikhonov". Default value is
|
fcol |
The feature meta-data containing marker definitions.
Default is |
An instance of class "MSnSet"
with
perTurbo
and perTurbo.scores
feature variables
storing the classification results and scores respectively.
Thomas Burger and Samuel Wieczorek
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.
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")