terda: terda
In predomics/predomicspkg: Interpretable Prediction in Omics Data

terda

R Documentation

terda

Description

terbeam is a model search algorithm.

Usage

terda(
  sparsity = 5,
  nIterations = 5,
  max.nb.features = 1000,
  kBest = "NULL",
  method = "glmnetRR",
  kStep = "NULL",
  vartype = "real",
  gamma = 0.7,
  nRR = 1,
  lb = -1,
  ub = 1,
  language = "terinter",
  scoreFormula = scoreRatio,
  epsilon = "NULL",
  nblambdas = 1000,
  objective = "auc",
  evalToFit = "auc_",
  k_penalty = 0,
  intercept = "NULL",
  popSaveFile = "NULL",
  final.pop.perc = 100,
  alpha = 0.5,
  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.
`nIterations:`	??
`max.nb.features:`	create the glmnet object using only the top most significant features (default:1000)
`kBest:`	??
`method:`	??
`kStep:`	??
`vartype:`	(??)
`gamma:`	??
`nRR:`	(??) (default:FALSE)
`lb:`	??
`ub:`	??
`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)
`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)
`popSaveFile:`	(??)
`final.pop.perc:`	??
`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 out debug infotmation when activated (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").
`parallelize.folds:`	parallelize folds when cross-validating (default:TRUE)
`nCores:`	the number of cores to execute the program. If nCores=1 than the program runs in a non parallel mode
`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"