summaryStats: Calculate summary statistics for evaluation measures

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

Description

For repeated cross-validation, find the mean and standard error of N rounds for each model.

Usage

summaryStats(i, evalMeasuresDF, emNames, modelNames)

Arguments

i	An integer representing 1:N where N is the total number of cross-validation rounds.
evalMeasuresDF	A dataframe with the following columns: Model, RepNum, PosClass.FScore, PosClass.Recall, PosClass.Precision, NegClass.FScore, NegClass.Recall, NegClass.Precision, and Accuracy. The rows of the dataframe will correspond to the results of a particular model and a particular round of cross-validation.
emNames	A list of names of the evaluation measures to visualize. Accepts the following: PosClass.FScore, PosClass.Recall, PosClass.Precision, NegClass.FScore, NegClass.Recall, NegClass.Precision, and Accuracy. Default is "All".
modelNames	A list of the models trained.

Value

A dataframe with the following columns: Model, evalMeasure, Mean, and SE (Standard Error).

Examples

summaryStatsList <-  lapply(1:numModels, summaryStats,
evalMeasuresDF=evalMeasuresDF, emNames=emNames, modelNames=modelNames)

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