Calculate the fraction of positives, i.e. baseline precision for a PRC curve

Usage

calc_baseline_precision(dataset, outcome_colname = NULL, pos_outcome = NULL)

Arguments

dataset: Data frame with an outcome variable and other columns as features.
outcome_colname: Column name as a string of the outcome variable (default NULL; the first column will be chosen automatically).
pos_outcome: the positive outcome from outcome_colname, e.g. "cancer" for the otu_mini_bin dataset.

Value

the baseline precision based on the fraction of positives

Author

Kelly Sovacool, sovacool@umich.edu

Examples

# calculate the baseline precision
data.frame(y = c("a", "b", "a", "b")) %>%
  calc_baseline_precision(
    outcome_colname = "y",
    pos_outcome = "a"
  )
#> Using 'y' as the outcome column.
#> [1] 0.5


calc_baseline_precision(otu_mini_bin,
  outcome_colname = "dx",
  pos_outcome = "cancer"
)
#> Using 'dx' as the outcome column.
#> [1] 0.49


# if you're not sure which outcome was used as the 'positive' outcome during
# model training, you can access it from the trained model and pass it along:
calc_baseline_precision(otu_mini_bin,
  outcome_colname = "dx",
  pos_outcome = otu_mini_bin_results_glmnet$trained_model$levels[1]
)
#> Using 'dx' as the outcome column.
#> [1] 0.49