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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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.