Compute CARWatch saliva features using sample position and actual time

Usage

compute_features_from_carwatch(
  data,
  saliva_type = "cortisol",
  slope_pairs = NULL,
  group_levels = NULL
)

Arguments

data: Canonical results or sample events.
saliva_type: Measurement column name.
slope_pairs: Sample pairs for slopes.
group_levels: Additional R column names that identify separate curves.

Value

Per participant-day response features.

Examples

fixture <- system.file("extdata", "parity", "v1.0.0", package = "carwatch")
results <- read_study_results(file.path(fixture, "results.csv"))
saliva <- read_saliva(file.path(fixture, "saliva.csv"))
merged <- merge_saliva(results, saliva)
compute_features_from_carwatch(merged)
#> # A tibble: 1 × 14
#>   participant day   day_compliant expected_sample_count recorded_sample_count
#>   <chr>       <chr> <lgl>                         <int>                 <int>
#> 1 VP01        D1    TRUE                              2                     2
#> # ℹ 9 more variables: assessed_sample_count <int>,
#> #   compliant_sample_count <int>, non_compliant_samples <chr>,
#> #   cortisol_auc_g <dbl>, cortisol_auc_i <dbl>, cortisol_ini_val <dbl>,
#> #   cortisol_max_val <dbl>, cortisol_max_inc <dbl>, cortisol_slope12 <dbl>