Explicitly observed behavioural endpoint
Source:vignettes/observed-behavioral-endpoint.Rmd
observed-behavioral-endpoint.RmdObserved endpoint
data <- simulate_gazepoint_governed_data(18L, 6L, 1L, seed = 2301L)
predictors <- c("tracking_ratio", "fixation_duration", "gaze_dispersion")
task <- create_gazepoint_synthetic_task(
data, "observed_behavior", "new_participants"
)
manifest <- create_gazepoint_synthetic_manifest(task$outcome, predictors)
folds <- create_gazepoint_group_folds(
data, task$outcome, predictors, manifest,
task$generalization_target,
task$participant_id, task$unit_id, task$stimulus_id,
v = 3L, repeats = 2L, seed = 2301L
)Evaluate and quantify participant-cluster uncertainty
evaluation <- evaluate_gazepoint_group_folds(
folds, task, predictors, "glm", seed = 2301L
)
predictions <- evaluation$predictions
uncertainty <- bootstrap_gazepoint_metrics_by_unit(
task,
truth = predictions$truth,
prediction = factor(
predictions$prediction,
levels = levels(data$observed_response)
),
probability = predictions$probability,
participant_id = predictions$participant_id,
unit = "participant",
bootstrap = 50L,
seed = 2301L
)
uncertainty
#> <gp3ml_target_uncertainty>
#> Unit: participant
#> Target: new_participants
#> Successful/failed replicates: 50/0
#> metric estimate lower upper successful_replicates
#> accuracy 0.541666667 0.4547454 0.5961806 50
#> balanced_accuracy 0.502703174 0.4348836 0.5700013 50
#> sensitivity 0.202127660 0.1169271 0.3006962 50
#> specificity 0.803278689 0.6919828 0.9101786 50
#> precision 0.441860465 0.2843813 0.6902406 50
#> recall 0.202127660 0.1169271 0.3006962 50
#> f1 0.277372263 0.1800560 0.3836667 50
#> mcc 0.006712597 -0.1529414 0.1867860 50
#> roc_auc 0.511248692 0.4412685 0.6007832 50
#> pr_auc 0.453755329 0.3649841 0.5836588 50
#> brier 0.254567555 0.2413744 0.2722628 50
#> log_loss 0.704706896 0.6760984 0.7414688 50The endpoint is the recorded response only. The analysis does not infer intent, comprehension, cognition, or another latent construct.