Create an ROC curve from observed data

Usage

compute_empirical_roc(
  data,
  response,
  predictor,
  direction = "auto",
  best_weights = c(1, 0.5),
  levels = NULL,
  ...
)

Arguments

data: a dataframe containing responses (groupings) and predictor variable
response: a bare column name with the group status (control vs. cases). If the response has more than two groups, the first element of levels is the control group and the second element of levels is the case group.
predictor: a bare column name with the predictor to use for classification
direction: direction for computing case status. pROC::roc()'s direction conventions are supported: "<" (i.e., control < case) and ">" (control > case), or "auto" to have pROC guess the direction (if applicable). Alternatively, more verbose directions are supported: "case-low" or "control-high" if a low score predicts case status (case <= threshold < control, analogous to pROC's ">") and "case-high" or "control-low" if a high score predicts case status (control < threshold <= case, analogous to pROC's "<"). These directions are translated into the pROC conventions.
best_weights: weights for computing the best ROC curve points. Defaults to c(1, .5), which are the defaults used by pROC::coords().
levels: two-element vector c(control, case) where control is the value of response for the control group and case is the value of response for the case group. The ordering matters: The first element of the vector names the control group.
...: additional arguments passed to pROC::roc().

Value

a new dataframe of ROC coordinates is returned with columns for the predictor variable, .sensitivities, .specificities, .auc, .direction, .controls, .cases, .n_controls, .n_cases, .is_best_youden and .is_best_closest_topleft.

Examples

set.seed(100)
x1 <- rnorm(100, 4, 1)
x2 <- rnorm(100, 2, .5)
both <- c(x1, x2)
steps <- seq(min(both), max(both), length.out = 200)
d1 <- dnorm(steps, mean(x1), sd(x1))
d2 <- dnorm(steps, mean(x2), sd(x2))
data <- tibble::tibble(
  y = steps,
  d1 = d1,
  d2 = d2,
  outcome = rbinom(200, 1, prob = 1 - (d1 / (d1 + d2))),
  group = ifelse(outcome, "case", "control")
)

# get an ROC on the fake data
compute_empirical_roc(data, outcome, y)
#> ℹ No `levels` provided. Using `levels = c("0", "1")`.
#> • Setting control to outcome == "0".
#> • Setting case to outcome == "1".
#> Setting direction: controls > cases
# this guess the cases and controls from the group name and gets it wrong
compute_empirical_roc(data, group, y)
#> ℹ No `levels` provided. Using `levels = c("case", "control")`.
#> • Setting control to group == "case".
#> • Setting case to group == "control".
#> Setting direction: controls < cases
# better
compute_empirical_roc(data, group, y, levels = c("control", "case"))
#> Setting direction: controls > cases