DoSignificancePropagation: Obtain a prediction from a composite model, given the pairs...

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

Obtain a prediction from a composite model, given the pairs to include in the model.

Description

Obtain a prediction from a composite model, given the pairs to include in the model.

Usage

DoSignificancePropagation(
  pairs,
  modelResults,
  covar = c(),
  verbose = FALSE,
  pruningMethod = "error.t.test",
  modelRetention = "stringent",
  minCutoff,
  maxCutoff,
  useCutoff = FALSE,
  weights,
  components,
  pruningTechnique = "backward.stepwise",
  doPooling = TRUE,
  averaging = FALSE,
  zeroOut = FALSE,
  feedbackPairs = NULL,
  trimming = "modelwise"
)

Arguments

pairs	A list of pairs to include in the composite model.
modelResults	A ModelResults object.
covar	Covariates in the model
verbose	Whether or not to print out each step.
pruningMethod	The method to use for pruning. Right now, only "error.t.test" is valid.
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.
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	Current weights
components	A Model object containing the list of components in the graph.
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.
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.
feedbackPairs	The pairs to use when calculating the feedback model
trimming	Set to "edgewise" to trim edges at each layer or "modelwise" to trim entire models (neighborhoods, connected components) at each layer.

Value

A final predicted value for each sample in the input data

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