multiPRC: Precision-recall curve for multi-class prediction

View source: R/assessment.R

multiPRCR Documentation

Precision-recall curve for multi-class prediction

Description

Precision-recall curve for multi-class prediction

Usage

multiPRC(
  prob_mat,
  simu_mat,
  marginal_mode = "best",
  cutoff = NULL,
  multiLabel.rm = TRUE,
  add_cut1 = FALSE
)

Arguments

prob_mat

Probability matrix for each cell to each component

simu_mat

The true identity of assignment from simulation

marginal_mode

A string for the mode to marginalize the column: best, second, or delta

cutoff

A list of cutoff; if NULL use all unique scores

multiLabel.rm

Logical value; if True, remove the samples with multiple labels

add_cut1

Logical value; if True, manually add a cutoff of 1

Value

A list with two components: df, a data.frame containing precision and recall values at various cutoffs and AUC, the overall AUC.


single-cell-genetics/cardelino documentation built on Nov. 22, 2022, 4:05 p.m.