Experimental Process-IRT Methods and Evidence Gates
Source:vignettes/experimental-process-irt-0-7.Rmd
experimental-process-irt-0-7.RmdWhy these functions are gated
Some methods are scientifically attractive but too new, too
estimator-specific, or too computationally demanding to present as
production estimators without a validated implementation. In those cases
eyeprocess provides one of three things:
- a transparent reference model;
- an adapter to an established external package;
- an explicit external-engine gate that refuses to fake the estimator.
Process-state HMM + IRT
hmm <- fit_process_hmm_irt(
data = events,
sequence_id = "person_item",
process_features = c("stem_dwell", "option_dwell", "transition_rate"),
response = "correct",
person = "person_id",
item = "item_id",
n_states = 3
)
process_state_occupancy(hmm)
process_state_transition_summary(hmm)
plot(hmm)The internal HMM is an interpretable two-stage reference engine. State labels are statistical summaries and should not be named as unobserved mental states without independent validation.
Cognitive diagnosis and latent process classes
cdm <- fit_cognitive_diagnosis_process(
response_matrix = response_matrix,
q_matrix = q_matrix,
process_data = process_data,
process_features = process_features
)
mix <- fit_latent_class_process_irt(
data,
process_features = c("fixation_count", "rt", "transition_entropy"),
response = "correct",
person = "person_id",
item = "item_id",
n_classes = 3
)Latent-space IRT
fit_latent_space_irt() is an adapter to
LSMjml, avoiding a home-grown approximation when a current
R implementation exists.
ls <- fit_latent_space_irt(response_matrix, dimensions = 2)
map <- process_residual_map(ls)
plot(ls)
validate_latent_space_process_similarity(
ls,
process_matrix = scanpath_feature_matrix
)This allows a new validation question: do person-item residual proximities agree with independently measured process similarity?
Process-adjusted DIF
surrogate <- process_dif_nuisance_surrogate(
data,
process_features = c("rt", "fixation_count", "stem_revisits")
)
audit_process_adjusted_dif(
data,
response = "correct",
ability = "theta",
group = "group",
item = "item_id",
process_features = c("rt", "fixation_count", "stem_revisits"),
person = "participant_id"
)Process adjustment should be reported transparently: which nuisance surrogate was used, whether conclusions changed, and whether the process channel itself may be group-dependent.
Sequence representations
ngrams <- process_ngram_features(sequences, n = 2:4)
emb <- process_sequence_embedding(sequences, dimensions = 8)
fit_response_process_embedding_irt(
data,
sequences = sequences,
response = "correct",
person = "person_id",
item = "item_id"
)Flexible item-response curves
gp <- fit_gpirt(response_matrix, engine = "spline_reference")
compare_parametric_nonparametric_irf(gp)
audit_irf_shape(gp)
plot(gp)The spline reference is a shape audit, not a
Gaussian-process posterior. Exact GPIRT remains behind
external_engine until a validated engine is chosen.
fit_dynamic_gpirt(data, external_engine = my_validated_dynamic_gpirt)
fit_continuous_time_irt(data, external_engine = my_validated_ct_irt)
fit_flow_mirt(response_matrix, external_engine = my_validated_flow_mirt)
fit_variational_irt(response_matrix, external_engine = my_validated_vi_engine)A missing engine produces a deliberate error instead of silently substituting a different model.
Linking and person fit
link <- equate_irt_scales(reference_parameters, new_parameters,
method = "stocking_lord")
plot(link)
pf <- process_person_fit(
joint_fit,
data = trials,
person = "person_id"
)
plot(pf)Person-fit output describes model-process inconsistency. It must not be relabelled as cheating, deception, disengagement, or pathology without separate evidence.
Process-aware adaptive testing
info <- process_item_information(theta, a, b,
process_information = process_information,
rt_information = rt_information,
weights = c(response = 1, rt = .25, process = .25))
expected_process_information(info)
select_next_item_process(theta, item_bank)
simulate_process_cat(item_bank, true_theta = 0, n_items = 10)The adaptive functions are research utilities. They should not be deployed in a high-stakes adaptive assessment until item-selection bias, exposure, fairness, measurement invariance, and stopping rules have been separately validated.