# # Settings file for executing model_AST.R with a Leave-One-Out-Cross-Validation # optimization. # Uses Forward selection of parameters # # Use example: # R --vanilla --slave -f model_AST.R --args qda '(Quadratic discriminant analysis)' classification LOOCV_data.R # # Copyright (C) 2012-2013 NKI-AVL, R. J. J. H. van Son # R.J.J.H.vanSon@gmail.com # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see . # # Full license text is available at: # http://www.gnu.org/licenses/gpl-3.0.html # # #################################################### # # # The original classifications # evaluationData <- "example_acoustics.tsv"; # # Training data # trainingData <- "example_acoustics.tsv"; # # Test data # testData <- "example_acoustics.tsv"; # # # Wrapper Foreward search or Backward elimination (default value) # BackwardElimination <- FALSE; # #################################################### # # The original classifications evaluationData <- "example_acoustics.tsv"; # Training data trainingData <- "example_acoustics.tsv"; # Test data testData <-"example_acoustics.tsv"; # Wrapper Foreward search or Backward elimination BackwardElimination <- FALSE; # Select worst factor in each round # E.g., together with BackwardElimination useful to determine # most important factors. SelectWorst <- FALSE; # Minimize cost based on: OptimizeCorr <- FALSE; OptimizeClass <- TRUE; OptimizeRMSE <- FALSE; # Type of Cross Validation # Default is Leave-One-Out-Cross-Validation LOOCValidation <- TRUE; # Bootstrap Validation BootstrapValidation <- FALSE; bootstrapiterations <- 40; # What factors to merge? # e.g., MergeFactors <- c(1,2.4); #MergeFactors <- c();