plot_mean_roc {mikropml} | R Documentation |
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,
ycol = mean_precision,
ribbon_fill = "#C7E9C0",
line_color = "#00441B"
)
Arguments
dat |
sensitivity, specificity, and precision data calculated by |
ribbon_fill |
ribbon fill color (default: "#D9D9D9") |
line_color |
line color (default: "#000000") |
baseline_precision |
baseline precision from |
ycol |
column for the y axis (Default: |
Functions
-
plot_mean_roc()
: Plot mean sensitivity over specificity -
plot_mean_prc()
: Plot mean precision over recall
Author(s)
Courtney Armour
Kelly Sovacool sovacool@umich.edu
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)
[Package mikropml version 1.6.1 Index]