getCDPlots: Generate Bar Plots for the Seven Evaluation Measures

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

Description

Wrapper function for generating CD plots for each classifiers for each of the seven evaluation measures. Code for CD plots adapted from now archived scmamp R package.

Usage

getCDPlots(evalMeasuresDF, emNames = "All", compareBest = F, use_abbr = T)

Arguments

evalMeasuresDF	A dataframe with the following columns: Model, RepNum, Pass_FScore, Pass_Recall, Pass_Precision, Fail_FScore, Fail_Recall, Fail_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: Pass_FScore, Pass_Recall, Pass_Precision, Fail_FScore, Fail_Recall, Fail_Precision, and Accuracy. Default is "All".
compareBest	Boolean. If T, compare the best performing models from each of the metric sets. Else, compare the models within eachh metric set. Must have at least two metric sets. Default: F.
use_abbr	Boolean. If T, use abbreviations for model names in the CD plot (e.g. DecisionTree = DT). Default: T.

Value

A named list with the following structure: metric_type$plots | rankmatrix$eval_measures, where metric_type is one of the three metric sets (M4, M7, or M11) and eval_measures

Examples

# Create a list of bar plots for each evaluation measure
getCDPlots(evalMeasuresDF = test_evalMeasures, emNames = c("Pass_FScore", "Fail_FScore"))

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