{"id":"W2009736783","doi":"10.2333/bhmk.30.145","title":"Relationships Between two Methods for Dealing with Missing Data in Principal Component Analysis","year":2003,"lang":"en","type":"article","venue":"Behaviormetrika","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Missing data; Principal component analysis; Equating; Homogeneity (statistics); Correspondence analysis; Variety (cybernetics); Computer science; Data mining; Statistics; Econometrics; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05497724,0.002282894,0.001843333,0.005611157,0.001592064,0.004431129,0.002612562,0.002887893,0.001982434],"category_scores_gemma":[0.2382638,0.0009672997,0.002510175,0.004874256,0.002220665,0.006903217,0.002750677,0.004329259,0.0007882888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001019369,"about_ca_system_score_gemma":0.002495108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001754386,"about_ca_topic_score_gemma":0.002637242,"domain_scores_codex":[0.9674695,0.0218858,0.002414288,0.003144213,0.004392162,0.0006940773],"domain_scores_gemma":[0.6327379,0.3312306,0.005449482,0.01363473,0.01583852,0.001108799],"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":[0.002387179,0.001123622,0.05683799,0.001382391,0.002865583,0.0002088343,0.001709121,0.06672339,0.007906321,0.05156162,0.003581344,0.8037127],"study_design_scores_gemma":[0.0004888915,0.0009457312,0.04381789,0.0003493097,0.00158586,0.0006733369,0.0005053452,0.8258162,0.01193036,0.1094384,0.003978435,0.0004702373],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02195263,0.001088564,0.9755339,0.0002934366,0.0001296121,0.0001372104,0.0001331822,0.0003148641,0.0004166508],"genre_scores_gemma":[0.2038941,0.0008532453,0.7930185,0.0001216354,0.0001575866,0.0005115513,0.0003739436,0.0003064215,0.000762953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05497724,"threshold_uncertainty_score":0.2907509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.210272307041433,"score_gpt":0.4587870013282969,"score_spread":0.2485146942868639,"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."}}