create_decision_curve: Decision Curve

In uriahf/rtichoke: rtichoke

Decision Curve

Description

Create a decision Curve

Usage

create_decision_curve(
  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"),
  size = NULL,
  type = "conventional",
  min_p_threshold = 0,
  max_p_threshold = 1
)

Arguments

probs	a list of vectors of estimated probabilities (one for each model or one for each population)
reals	a list of vectors of binary outcomes (one for each population)
by	number: increment of the sequence.
stratified_by	Performance Metrics can be stratified by Probability Threshold or alternatively by Predicted Positives Condition Rate
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

## Not run: 


create_decision_curve(
  probs = list(example_dat$estimated_probabilities),
  reals = list(example_dat$outcome)
)

create_decision_curve(
  probs = list(example_dat$estimated_probabilities),
  reals = list(example_dat$outcome,
    type = "interventions avoided"
  )
)

create_decision_curve(
  probs = list(example_dat$estimated_probabilities),
  reals = list(example_dat$outcome,
    type = "combined"
  )
)

create_decision_curve(
  probs = list(
    "First Model" = example_dat$estimated_probabilities,
    "Second Model" = example_dat$random_guess
  ),
  reals = list(example_dat$outcome)
)

create_decision_curve(
  probs = list(
    "First Model" = example_dat$estimated_probabilities,
    "Second Model" = example_dat$random_guess
  ),
  reals = list(example_dat$outcome),
  type = "interventions avoided"
)

create_decision_curve(
  probs = list(
    "First Model" = example_dat$estimated_probabilities,
    "Second Model" = example_dat$random_guess
  ),
  reals = list(example_dat$outcome),
  type = "combined"
)

create_decision_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_decision_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)
  ),
  type = "interventions avoided"
)


create_decision_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)
  ),
  type = "combined"
)

## End(Not run)

uriahf/rtichoke documentation built on Nov. 22, 2023, 1:30 a.m.