DoTestSetupAndPrediction: Sets up the test data so that it is in the correct format,...

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

Sets up the test data so that it is in the correct format, then runs prediction on the test data. To do this: 1. Run pairwise prediction on the test data. 2. Run pairwise prediction on the training data. 3. Compute metafeatures on the test data. 4. Predict the test values using the learned model.

Description

Sets up the test data so that it is in the correct format, then runs prediction on the test data. To do this: 1. Run pairwise prediction on the test data. 2. Run pairwise prediction on the training data. 3. Compute metafeatures on the test data. 4. Predict the test values using the learned model.

Usage

DoTestSetupAndPrediction(
  inputDataTest,
  model,
  k = 2,
  eigStep = 1,
  colIdInd = "databaseId",
  colIdOut = "databaseId",
  useCutoff = FALSE,
  covar = c(),
  averaging = FALSE,
  zeroOut = FALSE
)

Arguments

inputDataTest	An object of the IntLimData class corresponding to the test set.
model	An object of the ModelResults class corresponding to the optimized model.
k	The number of nearest neighbors to consider in localerr.
eigStep	The number of eigenvectors to step by during the evaluation in localerr. Note that this must be less than the number of samples in localerr. Default = 10.
colIdInd	The ID of the column that has the analyte ID's for the independent variable. If blank, then the existing ID's are used.
colIdOut	The ID of the column that has the analyte ID's for the outcome variable. If blank, then the existing ID's are used.
useCutoff	Whether or not to use the cutoff for prediction. Default is FALSE.
covar	A list of covariates.
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 vector of final prediction values for the test data.

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