sota.svm: sota.svm
In predomics/predomicspkg: Interpretable Prediction in Omics Data

sota.svm

R Documentation

sota.svm

Description

sota.svm is a wrapper that executes svm using the same framework as for the predomics package.

Usage

sota.svm(
  sparsity = c(1:30),
  objective = "auc",
  max.nb.features = 1000,
  intercept = 0,
  language = "svm",
  evalToFit = "auc_",
  k_penalty = 0,
  scaled = TRUE,
  type = NULL,
  kernel = "rbfdot",
  kpar = "automatic",
  C = c(1e-04, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000),
  nu = 0.2,
  epsilon.hp = 0.1,
  prob.model = FALSE,
  class.weights = NULL,
  fit = TRUE,
  cache = 40,
  tol = 0.001,
  shrinking = TRUE,
  na.action = na.omit,
  popSaveFile = "NULL",
  seed = "NULL",
  nCores = 4,
  verbose = TRUE,
  plot = FALSE,
  warnings = FALSE,
  debug = FALSE,
  print_ind_method = "short",
  experiment.id = NULL,
  experiment.description = NULL,
  experiment.save = "nothing"
)

Arguments

`language`	is the language that is used by the different algorithms bin, bininter, ter, terinter, ratio, (default:"sota")
`sparsity:`	number of features in a given model. This is a vector with multiple lengths.
`objective:`	prediction mode (default: auc)
`max.nb.features:`	create the glmnet object using only the top most significant features (default:1000)
`intercept:`	(Interceot for the a given model) (default:NULL)
`evalToFit:`	Which model property will be used to select the best model among different k_sparsities (default: auc_)
`k_penalty:`	Penalization of the fit by the k_sparsity (default: 0)
`scaled:`	??
`type:`	??
`kernel:`	??
`kpar:`	??
`C:`	(??)
`nu:`	??
`epsilon.hp:`	(??) (for the SVM)
`prob.model:`	??
`class.weights:`	??
`fit:`	??
`cache:`	(??)
`tol:`	??
`shrinking:`	??
`na.action:`	??
`popSaveFile:`	(??)
`seed:`	the seed to be used for reproductibility. If seed=NULL than it is not taken into account (default:NULL).
`nCores:`	the number of CPUs to run the program in parallel
`plot:`	Plot graphics indicating the evolution of the simulation (default:FALSE)
`verbose:`	print out information on the progress of the algorithm (default:TRUE)
`warnings:`	Print out warnings when runnig (default:FALSE).
`debug:`	print out information on the progress of the algorithm (default:FALSE)
`print_ind_method:`	One of c("short","graphical") indicates how to print a model and subsequently a population during the run (default:"short").
`experiment.id:`	The id of the experiment that is to be used in the plots and comparitive analyses (default is the learner's name, when not specified)
`experiment.description:`	A longer description of the experiment. This is important when many experiments are run and can also be printed in by the printExperiment function.
`experiment.save:`	Data from an experiment can be saved with different levels of completness, with options to be selected from c("nothing", "minimal", "full"), default is "minimal"