{"id":"W2053124332","doi":"10.5539/ijsp.v3n4p42","title":"Model Equivalence in General Linear Models: Set-to-Zero, Sum-to-Zero Restrictions, and Extra Sum of Squares Method","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical and numerical algorithms","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Equivalence (formal languages); Mathematics; Parameterized complexity; Covariance; Linear model; Set (abstract data type); Applied mathematics; Generalized least squares; Zero (linguistics); Least-squares function approximation; Variance (accounting); Generalized linear model; Function (biology); Hierarchical generalized linear model; Mathematical optimization; Computer science; Statistics; Algorithm; Discrete mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08044316,0.00242358,0.003686313,0.003442798,0.00149746,0.004735066,0.00561718,0.003044721,0.008001572],"category_scores_gemma":[0.255312,0.001532912,0.004771424,0.004641513,0.006515576,0.00689373,0.01000696,0.0112195,0.001737167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002321981,"about_ca_system_score_gemma":0.006145718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002637252,"about_ca_topic_score_gemma":0.002244238,"domain_scores_codex":[0.8331349,0.1434664,0.003645918,0.006385822,0.01248187,0.0008850566],"domain_scores_gemma":[0.7686296,0.2060886,0.004943572,0.01297286,0.006561842,0.0008035885],"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":[0.0002573096,0.000309886,0.002489293,0.001055775,0.000659926,0.000427804,0.002331716,0.05231671,0.0009401448,0.6677277,0.007118832,0.2643649],"study_design_scores_gemma":[0.00009548177,0.0003716944,0.001128429,0.00036517,0.0001704258,0.0002368971,0.0004551355,0.203054,0.001220232,0.775463,0.01733202,0.0001073962],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001081428,0.0002198051,0.996899,0.0002794395,0.00009910964,0.00009236695,0.00003675754,0.0001935501,0.001098515],"genre_scores_gemma":[0.05067663,0.0006397004,0.9444226,0.0005086885,0.0002902552,0.001460867,0.0003138802,0.0006173291,0.001070129],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.08044316,"threshold_uncertainty_score":0.4254292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09488129367053232,"score_gpt":0.3799223220856128,"score_spread":0.2850410284150805,"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."}}