Plot a Precision decision Curve
Usage
plot_decision_curve(
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"),
size = NULL,
type = "conventional",
min_p_threshold = 0,
max_p_threshold = 1
)Arguments
- performance_data
an rtichoke Performance Data
- chosen_threshold
a chosen threshold to display (for non-interactive)
- interactive
whether the plot should be interactive plots
- color_values
color palette
- size
the size of the curve
- type
What type of Decision Curve, default choice is "conventional". Alternatives are "interventions avoided" and "combined" for both "conventional" and "interventions avoided" on the same view.
- min_p_threshold
The minimum Probability Threshold value to be displayed
- max_p_threshold
The maximum Probability Threshold value to be displayed
Examples
if (FALSE) {
one_pop_one_model %>%
plot_decision_curve()
one_pop_one_model %>%
plot_decision_curve(type = "interventions avoided")
one_pop_one_model %>%
plot_decision_curve(type = "combined")
multiple_models %>%
plot_decision_curve()
multiple_models %>%
plot_decision_curve(type = "interventions avoided")
multiple_models %>%
plot_decision_curve(type = "combined")
multiple_populations %>%
plot_decision_curve()
multiple_populations %>%
plot_decision_curve(type = "interventions avoided")
multiple_populations %>%
plot_decision_curve(type = "combined")
}