The label is the experimentally assigned condition. The workflow assesses whether predeclared measurements discriminate that assignment. It does not establish psychological interpretation or causal mechanism.
data <- simulate_gazepoint_governed_data(18L, 6L, 1L, seed = 2201L)
predictors <- c("fixation_duration", "gaze_dispersion", "pupil_change")
task <- create_gazepoint_synthetic_task(
data, "assigned_condition", "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 = 1L, seed = 2201L
)grid <- create_gazepoint_tuning_grid(
engine = "glm",
preprocessor_grid = list(center = c(TRUE, FALSE), scale = TRUE),
thresholds = c(0.45, 0.55),
complexity = "low",
interpretability = "high"
)
tuning <- tune_gazepoint_model(
folds, task, grid, predictors = predictors, seed = 2201L
)
compare_gazepoint_models(tuning, c("roc_auc", "balanced_accuracy", "brier"))
#> candidate_id
#> 1 candidate_001
#> 2 candidate_001
#> 3 candidate_001
#> 4 candidate_002
#> 5 candidate_002
#> 6 candidate_002
#> 7 candidate_003
#> 8 candidate_003
#> 9 candidate_003
#> 10 candidate_004
#> 11 candidate_004
#> 12 candidate_004
#> label engine
#> 1 glm [engine:default; prep:center=TRUE,scale=TRUE; threshold=0.45] glm
#> 2 glm [engine:default; prep:center=TRUE,scale=TRUE; threshold=0.45] glm
#> 3 glm [engine:default; prep:center=TRUE,scale=TRUE; threshold=0.45] glm
#> 4 glm [engine:default; prep:center=FALSE,scale=TRUE; threshold=0.45] glm
#> 5 glm [engine:default; prep:center=FALSE,scale=TRUE; threshold=0.45] glm
#> 6 glm [engine:default; prep:center=FALSE,scale=TRUE; threshold=0.45] glm
#> 7 glm [engine:default; prep:center=TRUE,scale=TRUE; threshold=0.55] glm
#> 8 glm [engine:default; prep:center=TRUE,scale=TRUE; threshold=0.55] glm
#> 9 glm [engine:default; prep:center=TRUE,scale=TRUE; threshold=0.55] glm
#> 10 glm [engine:default; prep:center=FALSE,scale=TRUE; threshold=0.55] glm
#> 11 glm [engine:default; prep:center=FALSE,scale=TRUE; threshold=0.55] glm
#> 12 glm [engine:default; prep:center=FALSE,scale=TRUE; threshold=0.55] glm
#> threshold complexity interpretability candidate_status success_prop
#> 1 0.45 low high pass 1
#> 2 0.45 low high pass 1
#> 3 0.45 low high pass 1
#> 4 0.45 low high pass 1
#> 5 0.45 low high pass 1
#> 6 0.45 low high pass 1
#> 7 0.55 low high pass 1
#> 8 0.55 low high pass 1
#> 9 0.55 low high pass 1
#> 10 0.55 low high pass 1
#> 11 0.55 low high pass 1
#> 12 0.55 low high pass 1
#> failed_folds error metric mean sd n_folds direction
#> 1 0 <NA> balanced_accuracy 0.6574074 0.08929306 3 maximize
#> 2 0 <NA> roc_auc 0.7345679 0.05136826 3 maximize
#> 3 0 <NA> brier 0.2239127 0.03635743 3 minimize
#> 4 0 <NA> balanced_accuracy 0.6574074 0.08929306 3 maximize
#> 5 0 <NA> roc_auc 0.7345679 0.05136826 3 maximize
#> 6 0 <NA> brier 0.2239127 0.03635743 3 minimize
#> 7 0 <NA> balanced_accuracy 0.6203704 0.05782406 3 maximize
#> 8 0 <NA> roc_auc 0.7345679 0.05136826 3 maximize
#> 9 0 <NA> brier 0.2239127 0.03635743 3 minimize
#> 10 0 <NA> balanced_accuracy 0.6203704 0.05782406 3 maximize
#> 11 0 <NA> roc_auc 0.7345679 0.05136826 3 maximize
#> 12 0 <NA> brier 0.2239127 0.03635743 3 minimizeNo candidate is selected automatically. A selection requires an explicit metric, direction, and human rationale.
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