ObtainUnweightedModel: Obtain the combination of predictors without including any...

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

Obtain the combination of predictors without including any weights.

Description

Obtain the combination of predictors without including any weights.

Usage

ObtainUnweightedModel(
  modelResults,
  verbose = TRUE,
  modelRetention = "stringent",
  useCutoff = FALSE,
  pruningTechnique = "backward.stepwise",
  doPooling = TRUE,
  doPruning = TRUE,
  averaging = FALSE,
  zeroOut = FALSE
)

Arguments

modelResults	An object of the ModelResults class.
verbose	Whether to print results as you run the model.
modelRetention	Strategy for model retention. "stringent" (the default) retains only models that improve the prediction score. "lenient" also retains models that neither improve nor reduce the prediction score.
useCutoff	Whether or not to use the cutoff for prediction. Default is FALSE.
pruningTechnique	Pruning technique to use. Possible methods are "backward.stepwise", "forward.stepwise", "individual.performance", and "exhaustive".
doPooling	Whether or not to pool predictors together using the structure of the graph.
doPruning	Whether or not to prune predictors. If pruning is not done, result is a weighted combination of all predictors.
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 ModelResults object, with all of the tracking information from each iteration filled in.

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