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roc.R
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roc.R
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# ROC Curve ---------------------------------------------------------------
#' ROC Curve
#'
#' Create a ROC Curve
#'
#' @inheritParams prepare_performance_data
#' @param color_values color palette
#' @param chosen_threshold a chosen threshold to display (for non-interactive)
#' @param interactive whether the plot should be interactive
#' plots
#' @param color_values color palette
#' @param title_included add title to the curve
#' @param size the size of the curve
#'
#'
#' @export
#'
#' @examples
#' \dontrun{
#'
#' create_roc_curve(
#' probs = list(example_dat$estimated_probabilities),
#' reals = list(example_dat$outcome)
#' )
#'
#' create_roc_curve(
#' probs = list(example_dat$estimated_probabilities),
#' reals = list(example_dat$outcome),
#' stratified_by = "ppcr"
#' )
#'
#' create_roc_curve(
#' probs = list(
#' "First Model" = example_dat$estimated_probabilities,
#' "Second Model" = example_dat$random_guess
#' ),
#' reals = list(example_dat$outcome)
#' )
#'
#'
#' create_roc_curve(
#' probs = list(
#' "First Model" = example_dat$estimated_probabilities,
#' "Second Model" = example_dat$random_guess
#' ),
#' reals = list(example_dat$outcome),
#' stratified_by = "ppcr"
#' )
#'
#'
#' create_roc_curve(
#' probs = list(
#' "train" = example_dat %>%
#' dplyr::filter(type_of_set == "train") %>%
#' dplyr::pull(estimated_probabilities),
#' "test" = example_dat %>% dplyr::filter(type_of_set == "test") %>%
#' dplyr::pull(estimated_probabilities)
#' ),
#' reals = list(
#' "train" = example_dat %>% dplyr::filter(type_of_set == "train") %>%
#' dplyr::pull(outcome),
#' "test" = example_dat %>% dplyr::filter(type_of_set == "test") %>%
#' dplyr::pull(outcome)
#' )
#' )
#'
#' create_roc_curve(
#' probs = list(
#' "train" = example_dat %>%
#' dplyr::filter(type_of_set == "train") %>%
#' dplyr::pull(estimated_probabilities),
#' "test" = example_dat %>% dplyr::filter(type_of_set == "test") %>%
#' dplyr::pull(estimated_probabilities)
#' ),
#' reals = list(
#' "train" = example_dat %>% dplyr::filter(type_of_set == "train") %>%
#' dplyr::pull(outcome),
#' "test" = example_dat %>% dplyr::filter(type_of_set == "test") %>%
#' dplyr::pull(outcome)
#' ),
#' stratified_by = "ppcr"
#' )
#' }
create_roc_curve <- function(probs, reals, by = 0.01,
stratified_by = "probability_threshold",
chosen_threshold = NA,
interactive = TRUE,
color_values = c(
"#1b9e77", "#d95f02",
"#7570b3", "#e7298a",
"#07004D", "#E6AB02",
"#FE5F55", "#54494B",
"#006E90", "#BC96E6",
"#52050A", "#1F271B",
"#BE7C4D", "#63768D",
"#08A045", "#320A28",
"#82FF9E", "#2176FF",
"#D1603D", "#585123"
),
title_included = FALSE,
size = NULL) {
check_probs_input(probs)
# check_real_input(reals)
if (!is.na(chosen_threshold)) {
check_chosen_threshold_input(chosen_threshold)
}
prepare_performance_data(
probs = probs,
reals = reals,
by = by,
stratified_by = stratified_by
) %>%
plot_roc_curve(
chosen_threshold = chosen_threshold,
interactive = interactive,
color_values = color_values,
title_included = FALSE,
size = size
)
}
#' ROC Curve from Performance Data
#'
#' Plot a ROC Curve
#'
#' @inheritParams create_roc_curve
#' @param performance_data an rtichoke Performance Data
#'
#' @examples
#' \dontrun{
#'
#' one_pop_one_model %>%
#' plot_roc_curve()
#'
#' one_pop_one_model_by_ppcr %>%
#' plot_roc_curve()
#'
#' multiple_models %>%
#' plot_roc_curve()
#'
#' multiple_models_by_ppcr %>%
#' plot_roc_curve()
#'
#' multiple_populations %>%
#' plot_roc_curve()
#'
#' multiple_populations_by_ppcr %>%
#' plot_roc_curve()
#' }
#'
#' @export
plot_roc_curve <- function(performance_data,
chosen_threshold = NA,
interactive = TRUE,
color_values = c(
"#1b9e77", "#d95f02",
"#7570b3", "#e7298a",
"#07004D", "#E6AB02",
"#FE5F55", "#54494B",
"#006E90", "#BC96E6",
"#52050A", "#1F271B",
"#BE7C4D", "#63768D",
"#08A045", "#320A28",
"#82FF9E", "#2176FF",
"#D1603D", "#585123"
),
title_included = FALSE,
size = NULL) {
rtichoke_curve_list <- performance_data |>
create_rtichoke_curve_list("roc", size = size, color_values = color_values)
if (!is.na(chosen_threshold)) {
check_chosen_threshold_input(chosen_threshold)
}
stratified_by <- check_performance_data_stratification(
performance_data
)
perf_dat_type <-
check_performance_data_type_for_plotly(performance_data = performance_data)
prevalence <-
get_prevalence_from_performance_data(performance_data, perf_dat_type)
if (interactive == FALSE) {
reference_lines <- create_reference_lines_data_frame("roc")
roc_curve <- performance_data %>%
create_ggplot_for_performance_metrics(
"FPR",
"sensitivity", color_values
) %>%
add_reference_lines_to_ggplot(reference_lines) +
ggplot2::xlab("1 - Specificity") +
ggplot2::ylab("Sensitivity")
}
if (interactive == TRUE) {
roc_curve <- rtichoke_curve_list |>
create_plotly_curve()
}
return(roc_curve)
}