plot_curves: Plot ROC and PRC curves

In SchlossLab/mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines

Plot ROC and PRC curves

Description

Plot ROC and PRC curves

Usage

plot_mean_roc(dat, ribbon_fill = "#C6DBEF", line_color = "#08306B")

plot_mean_prc(
  dat,
  baseline_precision = NULL,
  ribbon_fill = "#C7E9C0",
  line_color = "#00441B"
)

Arguments

dat	sensitivity, specificity, and precision data calculated by calc_mean_roc()
ribbon_fill	ribbon fill color (default: "#D9D9D9")
line_color	line color (default: "#000000")
baseline_precision	baseline precision from calc_baseline_precision()

Functions

• plot_mean_roc(): Plot mean sensitivity over specificity

• plot_mean_prc(): Plot mean precision over recall

Author(s)

Courtney Armour

Kelly Sovacool [e-mail]

Examples

## Not run: 
library(dplyr)
# get performance for multiple models
get_sensspec_seed <- function(seed) {
  ml_result <- run_ml(otu_mini_bin, "glmnet", seed = seed)
  sensspec <- calc_model_sensspec(
    ml_result$trained_model,
    ml_result$test_data,
    "dx"
  ) %>%
    mutate(seed = seed)
  return(sensspec)
}
sensspec_dat <- purrr::map_dfr(seq(100, 102), get_sensspec_seed)

# plot ROC & PRC
sensspec_dat %>%
  calc_mean_roc() %>%
  plot_mean_roc()
baseline_prec <- calc_baseline_precision(otu_mini_bin, "dx", "cancer")
sensspec_dat %>%
  calc_mean_prc() %>%
  plot_mean_prc(baseline_precision = baseline_prec)

## End(Not run)

SchlossLab/mikropml documentation built on March 18, 2023, 6:29 a.m.