runCrossValidation: Run Cross-Validation for A List of Algoirthms with Peak...
In MetaClean: Detection of Low-Quality Peaks in Untargeted Metabolomics Data

Description Usage Arguments Value Examples

Wrapper function for running cross-validation on up to 8 classification algorithms using one or more of the three available metrics sets.

runCrossValidation(
  trainData,
  k,
  repNum,
  rand.seed = NULL,
  models = "all",
  metricSet = "M11"
)

`trainData`	dataframe. Rows should correspond to peaks, columns should include peak quality metrics and class labels only.
`k`	integer. Number of folds to be used in cross-validation
`repNum`	integer. Number of cross-validation rounds to perform
`rand.seed`	integer. State in which to set the random number generator
`models`	character string or vector. Specifies the classification algorithms to be trained from the eight available: DecisionTree, LogisiticRegression, NaiveBayes, RandomForest, SVM_Linear, AdaBoost, NeuralNetwork, and ModelAveragedNeuralNetwork. "all" specifies the use of all models. Default is "all".
`metricSet`	The metric set(s) to be run with the selected model(s). Select from the following: M4, M7, and M11. Use c() to select multiple metrics. "all" specifics the use of all metrics. Default is "M11".

a list of up to 8 trained models

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# train classification algorithms
models <- trainClassifiers(trainData=pqMetrics_development, k=5, repNum=10,
 rand.seed = 453, models="DecisionTree")