terBeam: terbeam
In predomics/predomicspkg: Interpretable Prediction in Omics Data

terBeam

R Documentation

terbeam

Description

terbeam is a model search algorithm on a beam search approach.

Usage

terBeam(
  sparsity = 1:5,
  max.nb.features = 1000,
  maxNbOfModels = 10000,
  nbBest = round(maxNbOfModels/10),
  nbVeryBest = round(maxNbOfModels/100),
  final.pop.perc = 100,
  popSaveFile = "NULL",
  saveFiles = FALSE,
  language = "terinter",
  scoreFormula = scoreRatio,
  epsilon = "NULL",
  objective = "auc",
  k_penalty = 0,
  evalToFit = "auc_",
  estimate_coefs = FALSE,
  intercept = "NULL",
  testAllSigns = FALSE,
  plot = FALSE,
  verbose = TRUE,
  warnings = FALSE,
  debug = FALSE,
  print_ind_method = "short",
  parallelize.folds = TRUE,
  nCores = 4,
  seed = "NULL",
  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:"terinter")
`sparsity:`	number of features in a given model. This is a vector with multiple lengths.
`maxNbOfModels:`	number of models to be explored for a given k_sparsity. This is equivalent to a population size in terga.
`nbVeryBest:`	is the number of features to be kept that appear in the very best models. They will be kept even if they are not frequent in the best models (default: 1 percent of maxNbOfModels).
`nbBest:`	is the number of features that will be used to build the k+1 sparsity combinations (default: 10 percent of maxNbOfModels).
`final.pop.perc:`	a percentage of nbVeryBest translates in a number of models to be kept for k_sparsity.
`popSaveFile:`	(??)
`saveFiles:`	??
`scoreFormula:`	a Function that contains the ratio Formula or other specific ones
`epsilon:`	a small value to be used with the ratio language (useCustomLanguage) (default: NULL). When null it is going to be calculated by the minimum value of X divided by 10.
`objective:`	this can be auc, cor or aic. Terga can also predict regression, other than class prediction. (default:auc)
`max.nb.features:`	focuses only on the subset of top most significant features (default:1000)
`estimate_coefs:`	non ternary solution for the aic objective (default:FALSE)
`evalToFit:`	The model performance attribute to use as fitting score (default:"fit_"). Other choices are c("auc_","accuracy_","precision_","recall_","f_score_")
`k_penalty:`	Penalization of the fit by the k_sparsity (default: 0)
`intercept:`	(??) (default:NULL)
`testAllSigns:`	??
`plot:`	Plot different graphics (default:FALSE).
`verbose:`	print out information on the progress of the algorithm (default:TRUE)
`warnings:`	Print out warnings when runnig (default:FALSE).
`debug:`	print debug information (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").
`nCores:`	the number of cores to execute the program. If nCores=1 than the program runs in a non parallel mode
`parallelize.folds:`	parallelize folds when cross-validating (default:TRUE)
`seed:`	the seed to be used for reproductibility. If seed=NULL than it is not taken into account (default:NULL).
`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"
`parallel:`	parallel