{"id":"W2061886846","doi":"10.5539/jmr.v4n2p148","title":"Designing a Pseudo R-Squared Goodness-of-Fit Measure in Generalized Linear Models","year":2012,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mathematics; Goodness of fit; Studentized residual; Deviance (statistics); Statistics; Linear model; Generalized linear model; Monotonic function; Residual; Linear regression; Categorical variable; Heteroscedasticity; Log-linear model; Ordinary least squares; Applied mathematics; Econometrics; Mathematical analysis","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":[0.03680864,0.001388445,0.002198405,0.004098983,0.0009007015,0.002346562,0.00251561,0.002237904,0.001944379],"category_scores_gemma":[0.176484,0.0006896881,0.00213601,0.004305392,0.003038747,0.0034241,0.003236197,0.002305535,0.0008379986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00102572,"about_ca_system_score_gemma":0.003155924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001670396,"about_ca_topic_score_gemma":0.001538428,"domain_scores_codex":[0.9456182,0.04169946,0.002072233,0.003106719,0.006813292,0.0006901182],"domain_scores_gemma":[0.8764994,0.09882706,0.004828446,0.008886529,0.01018808,0.0007705444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0009591125,0.0006369775,0.113075,0.001658294,0.001956606,0.001054968,0.00213865,0.2613268,0.00914122,0.1560228,0.00823202,0.4437976],"study_design_scores_gemma":[0.0001762172,0.002449159,0.03606651,0.0002690043,0.0003599277,0.001211529,0.0008539998,0.8091167,0.00805349,0.1311681,0.009927488,0.0003479388],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03810204,0.0002471665,0.9589506,0.0001987124,0.00008191464,0.00021751,0.000131659,0.0004727701,0.001597566],"genre_scores_gemma":[0.4216488,0.0002265329,0.5750474,0.0001556151,0.00007473861,0.001163843,0.0005352909,0.0003220375,0.0008257598],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03680864,"threshold_uncertainty_score":0.194665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6442609157665158,"score_gpt":0.560188904273862,"score_spread":0.08407201149265375,"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."}}