ML_error: Application of each ML method to a testing set
In jjsayleraxio/JTIMLmaster: An application of ML methods

Description Usage Arguments Details Value

View source: R/ML_error_function.R

Returns the estimated accurasy measures of Sensitivity, Specificity, and Misclassification using Confusion matrix.

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ML_error(data, newdata, importance_measure, add.vars = NULL, rdit = 4,
  depVar = depVar, indVar = indVar, ML = c("RF", "BOOST",
  "Logistic"))

`data`	A matrix of the whole training data set for coefficients' esimates
`newdata`	A matrix of a testing data
`importance_measure`	A vector of importance measures obtained from Random Forest model
`add.vars`	??
`rdit`	??
`depVar`	An outcome variable
`indVar`	A list of candidate predictors
`ML`	A ML method to apply

This is a generic function. The candidate predictors are a series of forward-in variables which can be picked by the variables ranked by importance measures or user-defined ones in 'depvar' parameter. Their coefficients are estimated or the decision tree is built by by the whole training data.

ML.err.vect A data frame including estimated accuracy measures

jjsayleraxio/JTIMLmaster documentation built on Nov. 4, 2019, 2:57 p.m.