runCrossValidation: Run Cross-Validation for A List of Algoirthms with Peak...

In KelseyChetnik/MetaClean: Detection of Low-Quality Peaks in Untargeted Metabolomics Data

Description

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

Usage

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

Arguments

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".

Value

a list of up to 8 trained models

Examples

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

KelseyChetnik/MetaClean documentation built on May 17, 2021, 5:33 a.m.