{"id":"W4405020068","doi":"10.1007/s00184-024-00980-2","title":"Reducing multi-collinearity in GLMS with categorical covariates","year":2024,"lang":"en","type":"article","venue":"Metrika","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Science Foundation Ireland","keywords":"Covariate; Categorical variable; Collinearity; Mathematics; Generalized linear model; Estimator; Statistics; Econometrics; Analysis of covariance; Covariance","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007861474,0.0001280842,0.0002631704,0.0001642401,0.00004513907,0.00004934272,0.00008421101,0.00006878008,0.0000438102],"category_scores_gemma":[0.001987204,0.000090188,0.00003460022,0.0007536311,0.00003967738,0.00008414517,0.00003373373,0.000353832,0.00001690985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008773085,"about_ca_system_score_gemma":0.00005255684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001134613,"about_ca_topic_score_gemma":0.00004310366,"domain_scores_codex":[0.9989089,0.0001179806,0.000248229,0.0003055614,0.0001732802,0.0002460702],"domain_scores_gemma":[0.9976595,0.00202387,0.00002632827,0.0001758105,0.00003866955,0.00007583071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008685135,0.0004143718,0.0002023945,0.000440129,0.00006758716,0.000511006,0.001234023,0.0006674715,0.001145585,0.9444864,0.0005182764,0.05022593],"study_design_scores_gemma":[0.0009304115,0.0001857979,0.0002631308,0.0001883041,0.0000747863,0.00003696948,0.0001487562,0.2996193,0.003908012,0.690639,0.003622934,0.0003826051],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009998919,0.0003861769,0.9883591,0.0001323898,0.0001483925,0.0001929499,0.00001411091,0.0001155058,0.0006524771],"genre_scores_gemma":[0.3002994,0.00001242985,0.6990435,0.00001738818,0.00004481754,0.00002093469,0.000002055295,0.00002231394,0.0005371435],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2989519,"threshold_uncertainty_score":0.367776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1740731186932173,"score_gpt":0.4509770781552029,"score_spread":0.2769039594619856,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}