SplitTrainingAndTesting: Splits input data into training and testing sets given a...
In ncats/MultiOmicsGraphPrediction: Network-Based Ensemble Prediction from IntLIM Predictors
View source: R/03_PredictUsingLearnedModel.R
SplitTrainingAndTesting R Documentation
Splits input data into training and testing sets given a specified
set of testing samples. Alternatively, separate training and testing
sets can be read in prior to training. However, this function is a
useful helper for cross-validation.
Description
Splits input data into training and testing sets given a specified
set of testing samples. Alternatively, separate training and testing
sets can be read in prior to training. However, this function is a
useful helper for cross-validation.
Usage
SplitTrainingAndTesting(inputData, testingSamples)
Arguments
inputData
An object of the IntLimData class.
testingSamples
Calculated importance metrics.
Value
A list of two IntLimData objects: a training set and a testing set.
ncats/MultiOmicsGraphPrediction documentation built on Aug. 23, 2023, 9:19 a.m.
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View source: R/03_PredictUsingLearnedModel.R
| SplitTrainingAndTesting | R Documentation |
Splits input data into training and testing sets given a specified set of testing samples. Alternatively, separate training and testing sets can be read in prior to training. However, this function is a useful helper for cross-validation.
Description
Splits input data into training and testing sets given a specified set of testing samples. Alternatively, separate training and testing sets can be read in prior to training. However, this function is a useful helper for cross-validation.
Usage
SplitTrainingAndTesting(inputData, testingSamples)
Arguments
inputData |
An object of the IntLimData class. |
testingSamples |
Calculated importance metrics. |
Value
A list of two IntLimData objects: a training set and a testing set.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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