PruneExhaustive: Given multiple composite predictors, evaluate each...

In ncats/MultiOmicsGraphPrediction: Network-Based Ensemble Prediction from IntLIM Predictors

Given multiple composite predictors, evaluate each combination exhaustively.

Description

Given multiple composite predictors, evaluate each combination exhaustively.

Usage

PruneExhaustive(
  pairs,
  targets,
  sources,
  modelResults,
  minCutoff,
  maxCutoff,
  useCutoff,
  weights,
  pred,
  verbose,
  mapping,
  trueVal,
  previousModels,
  tolerance,
  i,
  pruningMethod = "error.t.test",
  averaging = FALSE,
  zeroOut = zeroOut
)

Arguments

pairs	A list of pairs to include in the composite model.
targets	A list of targets to include in the composite model.
sources	A list of sources to include in the composite model.
modelResults	The ModelResults object that will be filled in during training, obtained from DoModelSetup().
minCutoff	Mininum cutoff for the prediction.
maxCutoff	Maximum cutoff for the prediction.
useCutoff	Whether or not to use the cutoff for prediction. Default is FALSE.
weights	The weights for each predictor, calculated using ComputeMetaFeatureWeights()
pred	The predicted values using each individual predictor.
verbose	Whether or not to print out each step.
mapping	A mapping from models to composite models for the next stage of pooling.
trueVal	The true values of the phenotype.#'
previousModels	A list of the previous models that were consolidated.
tolerance	Tolerance factor when computing equality of two numeric values.
i	Model index
pruningMethod	The method to use for pruning. Right now, only "error.t.test" is valid.
averaging	If TRUE, then averaging is used to combine predictors rather than retaining the same functional form for both the input and the output.
zeroOut	This parameter zeros out predictors outside of the allowed range.

Value

A ModelIDSet with the retained models.

ncats/MultiOmicsGraphPrediction documentation built on Aug. 23, 2023, 9:19 a.m.