McpAUC: Clasification of area under ROC curve following McClish...

In juanpegarcia/ROCpAI: Receiver Operating Characteristic Partial Area Indexes for evaluating classifiers

Description

Calculate the area under the ROC curve following McClish methodologic from a dataset and a sample from that dataset.

Usage

mcpAUC(
  dataset,
  low.value = NULL,
  up.value = NULL,
  plot = FALSE,
  selection = NULL,
  variable = NULL
)

Arguments

dataset	Dataframe of the complete information of the samples
low.value	lower false positive rate value that the function will use to calculate the pAUC
up.value	upper false positive rate value that the function will use to calculate the pAUC
plot	ROC plot
selection	vector that will only be used if the parameter "dataset" is a RangedSummarizedExperiment object. This parameter is used to select the variables that will be analysed
variable	in case that dataset is a SummarizedExperiment, indicate the Gold Standard

Value

RangedSummarizedExperiment object with the pAUC and the mcpAUC scores,and the TPR and FPR values for each ROC curve generated

Examples

library(fission)
data("fission")
resultsMC <- mcpAUC(fission, low.value = 0, up.value = 0.25, plot = TRUE,
selection = c("SPNCRNA.1080","SPAC186.08c"), variable="strain")

juanpegarcia/ROCpAI documentation built on June 3, 2021, 5:42 a.m.