reconnect moved files to git repo

venv/lib/python3.11/site-packages/statsmodels/stats/tests/results/binary_constrict.csv

@@ -0,0 +1,40 @@
+Case,diagnosis,rate,volumne,constrict,const,log_rate,log_volumne
+1,constrict,0.825,3.7,1,1.0,-0.19237189264745613,1.308332819650179
+2,constrict,1.09,3.5,1,1.0,0.08617769624105241,1.252762968495368
+3,constrict,2.5,1.25,1,1.0,0.9162907318741551,0.22314355131420976
+4,constrict,1.5,0.75,1,1.0,0.4054651081081644,-0.2876820724517809
+5,constrict,3.2,0.8,1,1.0,1.1631508098056809,-0.2231435513142097
+6,constrict,3.5,0.7,1,1.0,1.252762968495368,-0.35667494393873245
+7,no_constrict,0.75,0.6,0,1.0,-0.2876820724517809,-0.5108256237659907
+8,no_constrict,1.7,1.1,0,1.0,0.5306282510621704,0.09531017980432493
+9,no_constrict,0.75,0.9,0,1.0,-0.2876820724517809,-0.10536051565782628
+10,no_constrict,0.45,0.9,0,1.0,-0.7985076962177716,-0.10536051565782628
+11,no_constrict,0.57,0.8,0,1.0,-0.5621189181535413,-0.2231435513142097
+12,no_constrict,2.75,0.55,0,1.0,1.0116009116784799,-0.5978370007556204
+13,no_constrict,3.0,0.6,0,1.0,1.0986122886681098,-0.5108256237659907
+14,constrict,2.33,1.4,1,1.0,0.8458682675776092,0.3364722366212129
+15,constrict,3.75,0.75,1,1.0,1.3217558399823195,-0.2876820724517809
+16,constrict,1.64,2.3,1,1.0,0.494696241836107,0.8329091229351039
+17,constrict,1.6,3.2,1,1.0,0.47000362924573563,1.1631508098056809
+18,constrict,1.415,0.85,1,1.0,0.34712953109520095,-0.16251892949777494
+19,no_constrict,1.06,1.7,0,1.0,0.058268908123975824,0.5306282510621704
+20,constrict,1.8,1.8,1,1.0,0.5877866649021191,0.5877866649021191
+21,no_constrict,2.0,0.4,0,1.0,0.6931471805599453,-0.916290731874155
+22,no_constrict,1.36,0.95,0,1.0,0.3074846997479607,-0.05129329438755058
+23,no_constrict,1.35,1.35,0,1.0,0.30010459245033816,0.30010459245033816
+24,no_constrict,1.36,1.5,0,1.0,0.3074846997479607,0.4054651081081644
+25,constrict,1.78,1.6,1,1.0,0.5766133643039938,0.47000362924573563
+26,no_constrict,1.5,0.6,0,1.0,0.4054651081081644,-0.5108256237659907
+27,constrict,1.5,1.8,1,1.0,0.4054651081081644,0.5877866649021191
+28,no_constrict,1.9,0.95,0,1.0,0.6418538861723947,-0.05129329438755058
+29,constrict,0.95,1.9,1,1.0,-0.05129329438755058,0.6418538861723947
+30,no_constrict,0.4,1.6,0,1.0,-0.916290731874155,0.47000362924573563
+31,constrict,0.75,2.7,1,1.0,-0.2876820724517809,0.9932517730102834
+32,no_constrict,0.03,2.35,0,1.0,-3.506557897319982,0.8544153281560676
+33,no_constrict,1.83,1.1,0,1.0,0.6043159668533296,0.09531017980432493
+34,constrict,2.2,1.1,1,1.0,0.7884573603642703,0.09531017980432493
+35,constrict,2.0,1.2,1,1.0,0.6931471805599453,0.1823215567939546
+36,constrict,3.33,0.8,1,1.0,1.2029723039923526,-0.2231435513142097
+37,no_constrict,1.9,0.95,0,1.0,0.6418538861723947,-0.05129329438755058
+38,no_constrict,1.9,0.75,0,1.0,0.6418538861723947,-0.2876820724517809
+39,constrict,1.625,1.3,1,1.0,0.4855078157817008,0.26236426446749106

venv/lib/python3.11/site-packages/statsmodels/stats/tests/results/contingency_table_r_results.csv

@@ -0,0 +1,4 @@
+"homog_stat","homog_df","homog_binom_p","homog_cont_p","lbl_stat","lbl_expval","lbl_var","lbl_chi2","lbl_pvalue","lbl2_stat","lbl2_expval","lbl2_var","lbl2_chi2","lbl2_pvalue","bowker_stat","bowker_df","bowker_pvalue"
+0.470588235294118,1,0.60759136127308,0.606905427217951,215,210.136363636364,5.44848959817612,4.34156260215759,0.0371927738063454,369,354.409090909091,49.0364063835851,4.34156260215755,0.0371927738063464,0.470588235294118,1,0.492716677227088
+271.922246968262,3,0,0,1760,1637.12,318.387012391739,47.4249696511547,5.71498404156046e-12,6006,5384.96,8083.23776042639,47.7148752803301,4.9293902293357e-12,297.610989010989,6,2.65851858368065e-61
+13.7647058823529,2,0,0,661,608.890625,45.1014721487451,60.2061714068945,8.54871728961371e-15,1761,1544.046875,804.735991710752,58.4895654377337,2.04281036531029e-14,13.7692307692308,3,0.00323670650323545

venv/lib/python3.11/site-packages/statsmodels/stats/tests/results/data.dat

@@ -0,0 +1,61 @@
+Days      Duration Weight ID
+    0.0      1      1      1
+    2.0      1      1      2
+    1.0      1      1      3
+    3.0      1      1      4
+    0.0      1      1      5
+    2.0      1      1      6
+    0.0      1      1      7
+    5.0      1      1      8
+    6.0      1      1      9
+    8.0      1      1     10
+    2.0      1      2      1
+    4.0      1      2      2
+    7.0      1      2      3
+   12.0      1      2      4
+   15.0      1      2      5
+    4.0      1      2      6
+    3.0      1      2      7
+    1.0      1      2      8
+    5.0      1      2      9
+   20.0      1      2     10
+   15.0      1      3      1
+   10.0      1      3      2
+    8.0      1      3      3
+    5.0      1      3      4
+   25.0      1      3      5
+   16.0      1      3      6
+    7.0      1      3      7
+   30.0      1      3      8
+    3.0      1      3      9
+   27.0      1      3     10
+    0.0      2      1      1
+    1.0      2      1      2
+    1.0      2      1      3
+    0.0      2      1      4
+    4.0      2      1      5
+    2.0      2      1      6
+    7.0      2      1      7
+    4.0      2      1      8
+    0.0      2      1      9
+    3.0      2      1     10
+    5.0      2      2      1
+    3.0      2      2      2
+    2.0      2      2      3
+    0.0      2      2      4
+    1.0      2      2      5
+    1.0      2      2      6
+    3.0      2      2      7
+    6.0      2      2      8
+    7.0      2      2      9
+    9.0      2      2     10
+   10.0      2      3      1
+    8.0      2      3      2
+   12.0      2      3      3
+    3.0      2      3      4
+    7.0      2      3      5
+   15.0      2      3      6
+    4.0      2      3      7
+    9.0      2      3      8
+    6.0      2      3      9
+    1.0      2      3     10

venv/lib/python3.11/site-packages/statsmodels/stats/tests/results/framing.csv

@@ -0,0 +1,266 @@
+"","cond","anx","age","educ","gender","income","emo","p_harm","tone","eth","treat","english","immigr","anti_info","cong_mesg"
+"1","3","a little anxious",45,"high school","male",13,7,6,0,1,0,"Oppose",4,0,1
+"2","4","somewhat anxious",73,"bachelor's degree or higher","male",16,6,3,0,0,0,"Favor",3,0,0
+"3","2","a little anxious",53,"some college","female",3,8,7,1,0,0,"Strongly Oppose",3,0,0
+"4","1","not anxious at all",45,"high school","male",14,9,8,1,1,1,"Strongly Oppose",4,0,1
+"5","3","somewhat anxious",55,"some college","female",12,5,5,0,1,0,"Strongly Oppose",2,0,0
+"6","1","a little anxious",85,"high school","female",3,5,6,1,1,1,"Strongly Oppose",4,0,0
+"7","1","a little anxious",58,"high school","female",10,10,8,1,1,1,"Oppose",4,0,0
+"8","2","a little anxious",53,"some college","male",9,8,7,1,0,0,"Favor",4,1,1
+"9","1","a little anxious",52,"some college","female",14,8,5,1,1,1,"Strongly Oppose",3,0,0
+"10","4","very anxious",42,"some college","male",15,3,2,0,0,0,"Oppose",2,0,0
+"11","4","a little anxious",38,"bachelor's degree or higher","female",9,11,8,0,0,0,"Strongly Oppose",4,1,1
+"12","4","very anxious",38,"high school","male",6,9,8,0,0,0,"Strongly Oppose",4,1,1
+"13","4","a little anxious",26,"bachelor's degree or higher","female",10,8,6,0,0,0,"Oppose",3,0,1
+"14","1","not anxious at all",52,"some college","male",11,10,8,1,1,1,"Strongly Oppose",4,0,1
+"15","1","very anxious",48,"some college","male",19,3,3,1,1,1,"Oppose",2,0,0
+"16","2","not anxious at all",62,"high school","female",10,12,8,1,0,0,"Strongly Oppose",4,1,1
+"17","1","not anxious at all",41,"less than high school","female",7,9,8,1,1,1,"Oppose",4,0,0
+"18","3","somewhat anxious",54,"high school","male",11,6,6,0,1,0,"Strongly Oppose",4,0,0
+"19","1","a little anxious",69,"high school","female",9,8,7,1,1,1,"Strongly Oppose",4,0,0
+"20","1","somewhat anxious",71,"less than high school","male",6,6,6,1,1,1,"Strongly Oppose",1,0,0
+"21","3","somewhat anxious",62,"some college","male",14,4,5,0,1,0,"Oppose",2,0,0
+"22","2","very anxious",41,"some college","male",13,3,6,1,0,0,"Strongly Oppose",3,0,0
+"23","3","a little anxious",60,"high school","female",11,7,7,0,1,0,"Strongly Oppose",4,0,1
+"24","4","somewhat anxious",62,"some college","male",17,6,8,0,0,0,"Oppose",4,0,1
+"25","4","somewhat anxious",31,"bachelor's degree or higher","male",13,7,5,0,0,0,"Strongly Oppose",4,0,0
+"26","3","somewhat anxious",50,"some college","female",13,4,2,0,1,0,"Strongly Oppose",2,0,0
+"27","3","a little anxious",48,"high school","male",7,9,5,0,1,0,"Strongly Oppose",4,0,1
+"28","2","somewhat anxious",29,"some college","male",10,6,4,1,0,0,"Strongly Favor",2,0,0
+"29","3","a little anxious",64,"high school","female",5,9,5,0,1,0,"Oppose",4,0,0
+"30","3","very anxious",65,"high school","female",12,4,6,0,1,0,"Strongly Oppose",4,0,1
+"31","4","not anxious at all",66,"less than high school","female",4,12,8,0,0,0,"Oppose",4,0,0
+"32","1","not anxious at all",68,"high school","female",9,11,8,1,1,1,"Strongly Oppose",4,0,1
+"33","4","somewhat anxious",43,"high school","female",15,6,6,0,0,0,"Favor",3,0,0
+"34","3","a little anxious",57,"bachelor's degree or higher","female",12,7,5,0,1,0,"Oppose",3,0,0
+"35","3","somewhat anxious",41,"high school","male",11,9,4,0,1,0,"Strongly Oppose",4,0,0
+"36","2","somewhat anxious",85,"bachelor's degree or higher","male",13,7,7,1,0,0,"Strongly Oppose",3,0,1
+"37","4","a little anxious",70,"bachelor's degree or higher","male",15,8,8,0,0,0,"Strongly Oppose",4,0,1
+"38","3","a little anxious",65,"bachelor's degree or higher","male",14,8,6,0,1,0,"Strongly Oppose",4,0,0
+"39","2","a little anxious",67,"less than high school","female",11,11,8,1,0,0,"Strongly Oppose",4,1,1
+"40","1","a little anxious",68,"some college","female",9,6,5,1,1,1,"Favor",2,0,0
+"41","2","somewhat anxious",64,"some college","male",13,5,5,1,0,0,"Strongly Oppose",2,0,0
+"42","1","a little anxious",37,"some college","male",13,10,8,1,1,1,"Strongly Oppose",4,0,1
+"43","3","not anxious at all",45,"high school","female",7,10,7,0,1,0,"Strongly Oppose",4,0,0
+"44","3","a little anxious",81,"less than high school","female",14,9,7,0,1,0,"Oppose",4,0,1
+"45","2","somewhat anxious",51,"high school","female",10,6,6,1,0,0,"Strongly Oppose",3,0,0
+"46","1","a little anxious",53,"some college","male",12,10,8,1,1,1,"Strongly Oppose",4,0,1
+"47","4","somewhat anxious",62,"high school","female",9,6,5,0,0,0,"Strongly Oppose",4,0,0
+"48","2","a little anxious",42,"less than high school","male",8,9,8,1,0,0,"Oppose",4,0,1
+"49","1","a little anxious",71,"some college","female",12,8,8,1,1,1,"Strongly Oppose",4,0,0
+"50","1","a little anxious",56,"high school","male",11,11,8,1,1,1,"Strongly Oppose",4,0,1
+"51","1","a little anxious",67,"less than high school","female",8,10,5,1,1,1,"Oppose",4,0,1
+"52","4","a little anxious",77,"some college","female",6,7,6,0,0,0,"Strongly Oppose",2,1,0
+"53","1","not anxious at all",24,"some college","female",15,11,7,1,1,1,"Strongly Oppose",4,1,1
+"54","4","a little anxious",42,"high school","male",3,7,5,0,0,0,"Oppose",2,0,0
+"55","3","very anxious",25,"bachelor's degree or higher","female",11,3,4,0,1,0,"Oppose",2,0,0
+"56","1","a little anxious",60,"high school","female",12,8,6,1,1,1,"Oppose",4,0,0
+"57","1","somewhat anxious",43,"some college","male",14,10,8,1,1,1,"Strongly Oppose",1,0,1
+"58","1","a little anxious",60,"some college","male",6,9,6,1,1,1,"Strongly Oppose",3,0,0
+"59","4","not anxious at all",31,"high school","male",11,11,7,0,0,0,"Strongly Oppose",4,1,0
+"60","2","somewhat anxious",47,"some college","female",13,7,6,1,0,0,"Favor",3,0,0
+"61","1","very anxious",32,"bachelor's degree or higher","female",10,3,2,1,1,1,"Favor",2,0,0
+"62","2","somewhat anxious",66,"some college","male",8,8,7,1,0,0,"Strongly Oppose",3,0,0
+"63","2","very anxious",44,"high school","female",15,6,8,1,0,0,"Strongly Oppose",4,0,0
+"64","2","somewhat anxious",46,"high school","male",17,6,4,1,0,0,"Strongly Oppose",3,0,0
+"65","4","very anxious",41,"high school","female",10,3,3,0,0,0,"Strongly Oppose",2,0,0
+"66","2","a little anxious",75,"high school","female",9,7,4,1,0,0,"Strongly Oppose",3,0,0
+"67","1","not anxious at all",53,"bachelor's degree or higher","male",10,12,8,1,1,1,"Strongly Oppose",4,0,1
+"68","3","very anxious",60,"bachelor's degree or higher","male",12,3,4,0,1,0,"Favor",2,0,0
+"69","3","very anxious",75,"high school","female",2,4,6,0,1,0,"Favor",2,0,0
+"70","2","not anxious at all",45,"high school","male",9,12,8,1,0,0,"Strongly Oppose",4,0,0
+"71","2","not anxious at all",69,"less than high school","female",5,12,8,1,0,0,"Strongly Oppose",4,0,1
+"72","4","somewhat anxious",67,"high school","female",15,6,4,0,0,0,"Strongly Oppose",3,0,0
+"73","4","somewhat anxious",20,"high school","male",9,5,5,0,0,0,"Oppose",2,0,0
+"74","1","somewhat anxious",43,"bachelor's degree or higher","male",16,6,4,1,1,1,"Strongly Oppose",2,0,0
+"75","4","a little anxious",32,"some college","male",10,9,8,0,0,0,"Strongly Oppose",4,0,0
+"76","3","somewhat anxious",39,"high school","male",12,5,4,0,1,0,"Favor",3,0,0
+"77","3","very anxious",53,"bachelor's degree or higher","female",13,4,3,0,1,0,"Favor",2,0,0
+"78","2","not anxious at all",57,"some college","female",2,12,8,1,0,0,"Strongly Oppose",4,1,0
+"79","4","a little anxious",62,"high school","female",7,8,7,0,0,0,"Strongly Oppose",3,1,1
+"80","2","a little anxious",69,"high school","female",7,9,8,1,0,0,"Strongly Oppose",4,0,0
+"81","2","somewhat anxious",44,"high school","female",13,7,6,1,0,0,"Oppose",4,0,0
+"82","1","not anxious at all",57,"high school","male",11,12,8,1,1,1,"Strongly Oppose",4,0,0
+"83","4","a little anxious",27,"some college","female",15,7,4,0,0,0,"Strongly Oppose",2,0,0
+"84","3","somewhat anxious",82,"bachelor's degree or higher","female",17,7,5,0,1,0,"Oppose",2,0,0
+"85","4","somewhat anxious",84,"high school","male",12,6,6,0,0,0,"Strongly Oppose",3,1,1
+"86","3","somewhat anxious",54,"some college","female",13,5,6,0,1,0,"Oppose",2,0,0
+"87","2","very anxious",36,"less than high school","male",11,4,6,1,0,0,"Oppose",4,0,0
+"88","1","not anxious at all",47,"some college","male",13,12,8,1,1,1,"Strongly Oppose",4,1,1
+"89","1","very anxious",28,"bachelor's degree or higher","female",12,3,6,1,1,1,"Strongly Oppose",4,0,0
+"90","2","very anxious",42,"high school","female",14,4,8,1,0,0,"Strongly Oppose",4,0,1
+"91","3","very anxious",60,"high school","male",6,7,8,0,1,0,"Strongly Oppose",4,0,0
+"92","1","very anxious",55,"bachelor's degree or higher","male",15,3,4,1,1,1,"Strongly Oppose",3,0,0
+"93","2","a little anxious",55,"high school","female",6,9,7,1,0,0,"Strongly Oppose",4,0,0
+"94","4","not anxious at all",61,"some college","male",12,12,8,0,0,0,"Strongly Oppose",4,1,1
+"95","3","somewhat anxious",84,"high school","female",11,5,2,0,1,0,"Favor",2,0,0
+"96","4","not anxious at all",45,"high school","female",3,10,8,0,0,0,"Oppose",1,0,1
+"97","1","a little anxious",67,"some college","male",7,8,8,1,1,1,"Oppose",3,0,0
+"98","2","not anxious at all",24,"some college","male",12,11,8,1,0,0,"Strongly Oppose",4,0,0
+"99","2","not anxious at all",64,"less than high school","male",11,10,7,1,0,0,"Strongly Oppose",4,1,1
+"100","1","a little anxious",72,"high school","female",7,10,3,1,1,1,"Strongly Oppose",4,0,0
+"101","2","not anxious at all",40,"high school","female",4,12,8,1,0,0,"Strongly Favor",1,0,1
+"102","4","not anxious at all",35,"bachelor's degree or higher","female",11,9,8,0,0,0,"Strongly Oppose",4,1,1
+"103","1","not anxious at all",69,"high school","female",8,12,8,1,1,1,"Oppose",4,0,1
+"104","1","a little anxious",30,"less than high school","female",6,11,7,1,1,1,"Oppose",4,0,0
+"105","2","a little anxious",45,"high school","female",7,8,8,1,0,0,"Strongly Oppose",3,0,0
+"106","4","very anxious",35,"some college","male",9,3,4,0,0,0,"Oppose",2,0,0
+"107","4","not anxious at all",47,"high school","male",11,10,8,0,0,0,"Oppose",3,1,1
+"108","4","a little anxious",53,"some college","male",11,5,4,0,0,0,"Strongly Oppose",2,0,0
+"109","3","a little anxious",75,"high school","male",4,9,6,0,1,0,"Oppose",2,0,0
+"110","1","somewhat anxious",70,"high school","female",13,8,6,1,1,1,"Favor",3,0,0
+"111","1","somewhat anxious",56,"high school","female",8,7,8,1,1,1,"Strongly Oppose",4,0,1
+"112","3","a little anxious",70,"less than high school","female",7,8,8,0,1,0,"Strongly Oppose",3,0,0
+"113","4","somewhat anxious",35,"bachelor's degree or higher","male",11,4,3,0,0,0,"Strongly Oppose",2,0,0
+"114","1","not anxious at all",54,"bachelor's degree or higher","female",11,12,6,1,1,1,"Strongly Oppose",4,0,0
+"115","2","somewhat anxious",76,"bachelor's degree or higher","male",12,5,6,1,0,0,"Strongly Oppose",2,0,0
+"116","1","a little anxious",65,"high school","female",13,9,6,1,1,1,"Strongly Oppose",4,1,1
+"117","2","somewhat anxious",22,"high school","male",12,5,4,1,0,0,"Oppose",2,0,0
+"118","3","somewhat anxious",28,"high school","male",13,8,8,0,1,0,"Strongly Oppose",4,0,1
+"119","4","very anxious",26,"bachelor's degree or higher","male",11,3,5,0,0,0,"Favor",3,0,0
+"120","1","somewhat anxious",67,"high school","male",13,5,6,1,1,1,"Strongly Oppose",4,0,0
+"121","4","very anxious",58,"high school","male",13,5,4,0,0,0,"Strongly Oppose",3,0,1
+"122","3","somewhat anxious",34,"some college","female",16,6,3,0,1,0,"Strongly Oppose",3,0,0
+"123","2","very anxious",29,"bachelor's degree or higher","female",1,5,6,1,0,0,"Oppose",2,0,0
+"124","3","a little anxious",61,"less than high school","male",13,10,6,0,1,0,"Oppose",4,0,1
+"125","4","not anxious at all",58,"some college","female",6,11,8,0,0,0,"Strongly Oppose",4,0,0
+"126","1","very anxious",46,"some college","female",10,5,4,1,1,1,"Oppose",2,0,0
+"127","3","not anxious at all",73,"less than high school","female",6,8,6,0,1,0,"Strongly Oppose",4,0,1
+"128","4","very anxious",23,"bachelor's degree or higher","male",1,3,3,0,0,0,"Strongly Oppose",1,0,0
+"129","3","very anxious",19,"high school","male",15,4,6,0,1,0,"Strongly Oppose",3,0,0
+"130","2","somewhat anxious",35,"some college","male",8,4,4,1,0,0,"Strongly Oppose",2,0,0
+"131","2","very anxious",29,"bachelor's degree or higher","male",19,4,5,1,0,0,"Strongly Oppose",3,0,0
+"132","2","very anxious",53,"bachelor's degree or higher","male",8,3,3,1,0,0,"Strongly Oppose",3,0,0
+"133","3","a little anxious",72,"bachelor's degree or higher","male",12,7,6,0,1,0,"Oppose",3,0,1
+"134","3","somewhat anxious",36,"bachelor's degree or higher","male",14,5,4,0,1,0,"Favor",1,0,0
+"135","1","not anxious at all",26,"less than high school","male",5,11,8,1,1,1,"Strongly Oppose",3,0,0
+"136","2","a little anxious",46,"bachelor's degree or higher","female",13,7,8,1,0,0,"Strongly Oppose",4,0,1
+"137","3","a little anxious",31,"high school","male",6,9,8,0,1,0,"Strongly Oppose",4,0,0
+"138","2","a little anxious",70,"high school","female",1,8,7,1,0,0,"Strongly Oppose",4,0,0
+"139","4","a little anxious",65,"bachelor's degree or higher","male",13,7,6,0,0,0,"Strongly Oppose",3,0,1
+"140","4","very anxious",69,"less than high school","female",1,4,4,0,0,0,"Oppose",2,0,0
+"141","4","very anxious",72,"bachelor's degree or higher","male",11,4,4,0,0,0,"Oppose",2,0,0
+"142","1","not anxious at all",71,"less than high school","female",13,12,8,1,1,1,"Strongly Oppose",4,1,1
+"143","2","very anxious",62,"some college","male",12,4,6,1,0,0,"Oppose",3,0,0
+"144","3","a little anxious",37,"bachelor's degree or higher","male",13,7,5,0,1,0,"Oppose",3,0,0
+"145","2","very anxious",36,"high school","female",5,3,6,1,0,0,"Oppose",3,0,0
+"146","4","somewhat anxious",47,"some college","female",9,4,4,0,0,0,"Favor",3,0,0
+"147","3","somewhat anxious",47,"bachelor's degree or higher","male",16,5,4,0,1,0,"Oppose",3,0,0
+"148","1","very anxious",28,"bachelor's degree or higher","male",4,3,3,1,1,1,"Oppose",3,0,0
+"149","2","somewhat anxious",34,"bachelor's degree or higher","female",11,5,6,1,0,0,"Oppose",2,0,0
+"150","2","very anxious",69,"bachelor's degree or higher","male",10,3,4,1,0,0,"Favor",2,0,0
+"151","3","a little anxious",47,"high school","male",12,10,8,0,1,0,"Oppose",3,0,1
+"152","1","not anxious at all",73,"high school","male",11,11,8,1,1,1,"Strongly Oppose",4,0,1
+"153","3","very anxious",63,"high school","female",10,3,3,0,1,0,"Strongly Oppose",2,0,0
+"154","2","somewhat anxious",33,"less than high school","male",1,7,4,1,0,0,"Strongly Oppose",2,0,0
+"155","1","somewhat anxious",42,"high school","female",6,7,4,1,1,1,"Oppose",2,0,0
+"156","3","somewhat anxious",43,"high school","female",7,5,6,0,1,0,"Strongly Oppose",4,1,1
+"157","4","very anxious",50,"bachelor's degree or higher","female",16,3,4,0,0,0,"Oppose",2,0,0
+"158","2","somewhat anxious",56,"bachelor's degree or higher","male",10,6,7,1,0,0,"Strongly Oppose",3,0,1
+"159","3","somewhat anxious",28,"some college","female",11,6,6,0,1,0,"Strongly Oppose",2,0,0
+"160","1","somewhat anxious",44,"some college","female",13,8,8,1,1,1,"Oppose",4,0,0
+"161","3","somewhat anxious",38,"bachelor's degree or higher","female",9,5,6,0,1,0,"Oppose",1,0,0
+"162","1","not anxious at all",34,"bachelor's degree or higher","female",14,11,7,1,1,1,"Strongly Oppose",2,0,0
+"163","2","very anxious",56,"some college","male",16,3,3,1,0,0,"Strongly Oppose",2,0,0
+"164","3","somewhat anxious",47,"bachelor's degree or higher","male",11,4,6,0,1,0,"Oppose",2,0,0
+"165","4","somewhat anxious",42,"bachelor's degree or higher","male",7,7,6,0,0,0,"Strongly Oppose",4,0,1
+"166","3","somewhat anxious",53,"some college","female",16,4,5,0,1,0,"Strongly Oppose",2,0,0
+"167","3","very anxious",38,"bachelor's degree or higher","female",11,4,4,0,1,0,"Strongly Favor",1,0,0
+"168","2","somewhat anxious",50,"bachelor's degree or higher","female",11,6,4,1,0,0,"Favor",3,0,0
+"169","3","a little anxious",21,"high school","male",15,8,7,0,1,0,"Strongly Oppose",3,0,1
+"170","4","somewhat anxious",25,"some college","male",18,5,6,0,0,0,"Strongly Oppose",3,0,0
+"171","3","very anxious",58,"high school","female",16,5,8,0,1,0,"Oppose",4,0,1
+"172","4","somewhat anxious",39,"some college","male",10,5,7,0,0,0,"Strongly Oppose",3,1,0
+"173","4","not anxious at all",26,"some college","female",7,12,8,0,0,0,"Strongly Oppose",4,0,0
+"174","4","somewhat anxious",61,"some college","female",8,8,6,0,0,0,"Strongly Oppose",4,0,1
+"175","1","somewhat anxious",29,"some college","female",11,5,4,1,1,1,"Oppose",1,0,0
+"176","3","not anxious at all",39,"some college","female",12,12,7,0,1,0,"Strongly Oppose",4,0,1
+"177","2","somewhat anxious",40,"bachelor's degree or higher","male",11,7,6,1,0,0,"Strongly Oppose",4,0,1
+"178","1","somewhat anxious",30,"bachelor's degree or higher","female",11,4,4,1,1,1,"Strongly Favor",2,0,0
+"179","2","very anxious",64,"bachelor's degree or higher","female",10,3,5,1,0,0,"Oppose",1,0,0
+"180","2","a little anxious",50,"less than high school","male",5,10,7,1,0,0,"Strongly Oppose",3,0,1
+"181","2","somewhat anxious",33,"bachelor's degree or higher","female",12,9,8,1,0,0,"Strongly Oppose",4,0,0
+"182","3","somewhat anxious",35,"high school","male",9,6,6,0,1,0,"Oppose",4,0,1
+"183","1","somewhat anxious",26,"bachelor's degree or higher","female",16,6,5,1,1,1,"Oppose",3,0,0
+"184","1","not anxious at all",44,"some college","male",16,10,8,1,1,1,"Strongly Oppose",4,0,0
+"185","1","somewhat anxious",44,"some college","male",13,6,6,1,1,1,"Oppose",4,0,1
+"186","1","not anxious at all",25,"some college","male",11,12,8,1,1,1,"Strongly Oppose",4,1,1
+"187","3","very anxious",44,"bachelor's degree or higher","male",17,3,4,0,1,0,"Oppose",1,0,0
+"188","2","a little anxious",51,"bachelor's degree or higher","female",17,9,8,1,0,0,"Strongly Oppose",4,1,1
+"189","2","very anxious",62,"bachelor's degree or higher","female",14,5,6,1,0,0,"Strongly Oppose",3,0,0
+"190","4","very anxious",55,"bachelor's degree or higher","female",18,3,8,0,0,0,"Strongly Oppose",4,0,1
+"191","3","somewhat anxious",43,"bachelor's degree or higher","male",13,5,6,0,1,0,"Strongly Oppose",3,0,1
+"192","4","very anxious",53,"bachelor's degree or higher","male",12,3,3,0,0,0,"Strongly Oppose",3,1,1
+"193","3","a little anxious",49,"some college","female",10,6,4,0,1,0,"Oppose",3,0,1
+"194","3","somewhat anxious",34,"some college","male",11,5,4,0,1,0,"Favor",2,0,0
+"195","4","not anxious at all",36,"high school","male",11,9,7,0,0,0,"Strongly Oppose",4,0,0
+"196","2","very anxious",31,"bachelor's degree or higher","male",11,3,3,1,0,0,"Strongly Oppose",3,0,1
+"197","4","somewhat anxious",57,"bachelor's degree or higher","female",15,5,5,0,0,0,"Strongly Oppose",2,0,0
+"198","2","somewhat anxious",18,"less than high school","female",16,6,6,1,0,0,"Oppose",1,0,0
+"199","3","somewhat anxious",37,"bachelor's degree or higher","female",12,5,6,0,1,0,"Strongly Oppose",3,0,0
+"200","3","very anxious",63,"bachelor's degree or higher","male",16,3,4,0,1,0,"Strongly Oppose",1,0,0
+"201","2","a little anxious",48,"bachelor's degree or higher","female",16,8,8,1,0,0,"Oppose",3,0,0
+"202","4","not anxious at all",32,"high school","male",13,12,8,0,0,0,"Strongly Oppose",4,0,1
+"203","2","a little anxious",28,"some college","female",9,7,5,1,0,0,"Oppose",2,0,0
+"204","4","somewhat anxious",45,"high school","female",13,6,5,0,0,0,"Favor",3,0,0
+"205","1","a little anxious",59,"some college","male",13,7,8,1,1,1,"Oppose",4,1,1
+"206","4","not anxious at all",82,"high school","female",10,11,8,0,0,0,"Strongly Oppose",3,1,0
+"207","2","very anxious",62,"bachelor's degree or higher","female",2,4,4,1,0,0,"Favor",2,0,0
+"208","1","very anxious",35,"bachelor's degree or higher","female",19,4,5,1,1,1,"Oppose",3,0,0
+"209","4","very anxious",47,"some college","female",15,4,5,0,0,0,"Oppose",1,0,1
+"210","3","not anxious at all",65,"some college","male",15,11,8,0,1,0,"Strongly Oppose",2,0,0
+"211","1","a little anxious",62,"high school","male",13,8,8,1,1,1,"Strongly Oppose",4,0,0
+"212","4","a little anxious",59,"bachelor's degree or higher","female",18,7,7,0,0,0,"Strongly Oppose",4,0,1
+"213","4","somewhat anxious",51,"bachelor's degree or higher","female",9,6,6,0,0,0,"Oppose",2,0,0
+"214","1","somewhat anxious",33,"high school","female",12,8,6,1,1,1,"Strongly Oppose",4,0,0
+"215","1","a little anxious",27,"some college","female",8,9,6,1,1,1,"Oppose",3,0,0
+"216","2","somewhat anxious",54,"bachelor's degree or higher","female",10,5,3,1,0,0,"Strongly Oppose",2,1,0
+"217","3","very anxious",58,"bachelor's degree or higher","male",5,3,2,0,1,0,"Favor",1,0,0
+"218","2","very anxious",20,"some college","male",3,3,3,1,0,0,"Strongly Favor",2,0,0
+"219","3","very anxious",47,"bachelor's degree or higher","male",5,3,3,0,1,0,"Oppose",3,0,0
+"220","2","a little anxious",29,"high school","male",12,10,7,1,0,0,"Oppose",4,1,1
+"221","3","not anxious at all",54,"high school","female",4,12,8,0,1,0,"Strongly Oppose",4,0,0
+"222","1","a little anxious",24,"bachelor's degree or higher","female",7,7,6,1,1,1,"Oppose",3,0,0
+"223","2","not anxious at all",47,"high school","female",10,12,8,1,0,0,"Strongly Oppose",4,1,1
+"224","3","a little anxious",25,"some college","female",11,7,3,0,1,0,"Oppose",2,0,0
+"225","4","somewhat anxious",28,"high school","male",10,5,6,0,0,0,"Oppose",3,0,0
+"226","2","somewhat anxious",57,"bachelor's degree or higher","male",16,7,6,1,0,0,"Strongly Favor",3,0,1
+"227","4","a little anxious",26,"high school","male",8,10,6,0,0,0,"Strongly Oppose",4,0,0
+"228","1","a little anxious",43,"high school","male",7,7,7,1,1,1,"Strongly Favor",3,0,1
+"229","4","somewhat anxious",35,"high school","male",12,6,6,0,0,0,"Strongly Oppose",4,0,1
+"230","4","a little anxious",29,"bachelor's degree or higher","female",11,7,5,0,0,0,"Oppose",2,0,0
+"231","2","very anxious",26,"some college","female",14,5,4,1,0,0,"Oppose",3,0,0
+"232","3","not anxious at all",18,"high school","female",15,12,8,0,1,0,"Strongly Oppose",4,0,0
+"233","4","very anxious",60,"bachelor's degree or higher","female",12,3,2,0,0,0,"Oppose",1,0,0
+"234","4","very anxious",42,"bachelor's degree or higher","female",15,3,3,0,0,0,"Strongly Oppose",2,0,0
+"235","1","a little anxious",29,"high school","female",15,10,8,1,1,1,"Strongly Oppose",4,0,1
+"236","3","somewhat anxious",57,"bachelor's degree or higher","female",10,5,4,0,1,0,"Favor",1,0,0
+"237","2","not anxious at all",53,"high school","female",14,10,8,1,0,0,"Strongly Oppose",4,0,1
+"238","3","very anxious",27,"some college","male",6,3,6,0,1,0,"Strongly Oppose",3,0,0
+"239","1","a little anxious",25,"bachelor's degree or higher","male",8,9,7,1,1,1,"Strongly Oppose",4,0,1
+"240","3","somewhat anxious",37,"some college","female",7,7,4,0,1,0,"Strongly Oppose",2,0,0
+"241","1","a little anxious",25,"some college","female",12,7,7,1,1,1,"Oppose",3,0,1
+"242","1","very anxious",38,"bachelor's degree or higher","female",13,4,3,1,1,1,"Strongly Oppose",3,0,0
+"243","1","somewhat anxious",39,"bachelor's degree or higher","female",15,5,5,1,1,1,"Strongly Oppose",3,0,0
+"244","1","somewhat anxious",26,"some college","male",7,7,6,1,1,1,"Strongly Oppose",4,0,1
+"245","4","very anxious",45,"bachelor's degree or higher","female",18,5,3,0,0,0,"Strongly Oppose",3,0,0
+"246","2","somewhat anxious",27,"some college","male",13,5,7,1,0,0,"Favor",4,0,0
+"247","3","very anxious",46,"high school","male",12,3,5,0,1,0,"Oppose",2,0,0
+"248","1","somewhat anxious",62,"high school","male",3,5,2,1,1,1,"Strongly Oppose",3,0,0
+"249","3","somewhat anxious",44,"high school","female",12,6,5,0,1,0,"Oppose",2,0,0
+"250","3","somewhat anxious",65,"bachelor's degree or higher","male",9,6,8,0,1,0,"Strongly Oppose",3,0,0
+"251","2","not anxious at all",34,"bachelor's degree or higher","male",8,12,7,1,0,0,"Oppose",3,0,0
+"252","4","somewhat anxious",66,"high school","female",11,7,8,0,0,0,"Oppose",3,1,1
+"253","3","very anxious",44,"high school","female",14,3,4,0,1,0,"Favor",2,0,0
+"254","1","a little anxious",34,"high school","male",13,7,5,1,1,1,"Oppose",3,0,1
+"255","4","a little anxious",43,"some college","male",10,11,8,0,0,0,"Strongly Oppose",4,0,0
+"256","2","a little anxious",30,"high school","male",9,8,7,1,0,0,"Strongly Oppose",4,0,0
+"257","1","not anxious at all",27,"some college","female",17,12,7,1,1,1,"Strongly Oppose",4,0,1
+"258","1","a little anxious",20,"high school","female",17,8,4,1,1,1,"Oppose",2,0,1
+"259","2","a little anxious",56,"high school","female",10,10,8,1,0,0,"Strongly Oppose",3,0,0
+"260","2","not anxious at all",42,"high school","male",12,10,7,1,0,0,"Oppose",4,0,1
+"261","3","somewhat anxious",44,"bachelor's degree or higher","male",17,7,6,0,1,0,"Oppose",1,0,0
+"262","4","somewhat anxious",28,"bachelor's degree or higher","female",14,6,6,0,0,0,"Strongly Oppose",1,0,1
+"263","2","very anxious",38,"bachelor's degree or higher","female",14,3,6,1,0,0,"Favor",2,0,0
+"264","1","somewhat anxious",29,"bachelor's degree or higher","male",10,9,7,1,1,1,"Strongly Oppose",4,0,1
+"265","4","very anxious",52,"some college","female",1,3,2,0,0,0,"Oppose",2,0,0

venv/lib/python3.11/site-packages/statsmodels/stats/tests/results/influence_measures_R.csv

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+"200",FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE
+"201",FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE
+"202",FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE

venv/lib/python3.11/site-packages/statsmodels/stats/tests/results/lilliefors_critical_value_simulation.py

@@ -0,0 +1,150 @@
+"""
+Simulate critical values for finite sample distribution
+and estimate asymptotic expansion parameters for the lilliefors tests
+"""
+import datetime as dt
+import gzip
+import logging
+import pickle
+from collections import defaultdict
+
+import numpy as np
+import pandas as pd
+from scipy import stats
+from yapf.yapflib.yapf_api import FormatCode
+
+import statsmodels.api as sm
+
+NUM_SIM = 10000000
+MAX_MEMORY = 2 ** 28
+SAMPLE_SIZES = [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+                20, 25, 30, 40, 50, 100, 200, 400, 800, 1600]
+MIN_SAMPLE_SIZE = {'normal': 4, 'exp': 3}
+MAX_SIZE = max(SAMPLE_SIZES)
+MAX_SIM_SIZE = MAX_MEMORY // (MAX_SIZE * 8)
+PERCENTILES = [1, 5, 10, 25, 50, 75, 90, 92.5, 95, 97.5, 99, 99.5, 99.7, 99.9]
+seed = 113682199084250344115761738871133961874
+seed = np.array([(seed >> (32 * i)) % 2 ** 32 for i in range(4)],
+                dtype=np.uint32)
+
+
+def simulations(sim_type, save=False):
+    rs = np.random.RandomState(seed)
+    remaining = NUM_SIM
+    results = defaultdict(list)
+    start = dt.datetime.now()
+    while remaining > 0:
+        this_iter = min(remaining, MAX_SIM_SIZE)
+        remaining -= this_iter
+        if sim_type == 'normal':
+            dist = rs.standard_normal
+        else:
+            dist = rs.standard_exponential
+        rvs = dist((MAX_SIZE, this_iter))
+        sample_sizes = [ss for ss in SAMPLE_SIZES if
+                        ss >= MIN_SAMPLE_SIZE[sim_type]]
+        for ss in sample_sizes:
+            sample = rvs[:ss]
+            mu = sample.mean(0)
+            if sim_type == 'normal':
+                std = sample.std(0, ddof=1)
+                z = (sample - mu) / std
+                cdf_fn = stats.norm.cdf
+            else:
+                z = sample / mu
+                cdf_fn = stats.expon.cdf
+            z = np.sort(z, axis=0)
+            nobs = ss
+            cdf = cdf_fn(z)
+            plus = np.arange(1.0, nobs + 1) / nobs
+            d_plus = (plus[:, None] - cdf).max(0)
+            minus = np.arange(0.0, nobs) / nobs
+            d_minus = (cdf - minus[:, None]).max(0)
+            d = np.max(np.abs(np.c_[d_plus, d_minus]), 1)
+            results[ss].append(d)
+        logging.log(
+            logging.INFO,
+            'Completed {}, remaining {}'.format(
+                NUM_SIM - remaining, remaining
+            )
+        )
+        elapsed = dt.datetime.now() - start
+        rem = elapsed.total_seconds() / (NUM_SIM - remaining) * remaining
+        logging.log(logging.INFO,
+                    f'({sim_type}) Time remaining {rem:0.1f}s')
+
+    for key in results:
+        results[key] = np.concatenate(results[key])
+
+    if save:
+        file_name = f'lilliefors-sim-{sim_type}-results.pkl.gz'
+        with gzip.open(file_name, 'wb', 5) as pkl:
+            pickle.dump(results, pkl)
+
+    crit_vals = {}
+    for key in results:
+        crit_vals[key] = np.percentile(results[key], PERCENTILES)
+
+    start = 20
+    num = len([k for k in crit_vals if k >= start])
+    all_x = np.zeros((num * len(PERCENTILES), len(PERCENTILES) + 2))
+    all_y = np.zeros(num * len(PERCENTILES))
+    loc = 0
+    for i, perc in enumerate(PERCENTILES):
+        y = pd.DataFrame(results).quantile(perc / 100.)
+        y = y.loc[start:]
+        all_y[loc:loc + len(y)] = np.log(y)
+        x = y.index.values.astype(float)
+        all_x[loc:loc + len(y), -2:] = np.c_[np.log(x), np.log(x) ** 2]
+        all_x[loc:loc + len(y), i:(i + 1)] = 1
+        loc += len(y)
+    w = np.ones_like(all_y).reshape(len(PERCENTILES), -1)
+    w[6:, -5:] = 3
+    w = w.ravel()
+    res = sm.WLS(all_y, all_x, weights=w).fit()
+    params = []
+    for i in range(len(PERCENTILES)):
+        params.append(np.r_[res.params[i], res.params[-2:]])
+    params = np.array(params)
+
+    df = pd.DataFrame(params).T
+    df.columns = PERCENTILES
+    asymp_crit_vals = {}
+    for col in df:
+        asymp_crit_vals[col] = df[col].values
+
+    code = f'{sim_type}_crit_vals = '
+    code += str(crit_vals).strip() + '\n\n'
+    code += '\n# Coefficients are model '
+    code += 'log(cv) = b[0] + b[1] log(n) + b[2] log(n)**2\n'
+    code += f'{sim_type}_asymp_crit_vals = '
+    code += str(asymp_crit_vals) + '\n\n'
+    return code
+
+
+if __name__ == '__main__':
+    logging.getLogger().setLevel(logging.INFO)
+    header = '"""\nThis file is automatically generated by ' \
+             'littlefors_critical_values.py.\nDo not directly modify' \
+             'this file.\n\nValue based on 10,000,000 simulations."""\n\n'
+    header += 'from numpy import array\n\n'
+    header += 'PERCENTILES = ' + str(PERCENTILES).strip() + '\n\n'
+    header += 'SAMPLE_SIZES = ' + str(SAMPLE_SIZES).strip() + '\n\n'
+    normal = simulations('normal', True)
+    exp = simulations('exp', True)
+    footer = """
+# Critical Value
+critical_values = {'normal': normal_crit_vals,
+                   'exp': exp_crit_vals}
+asymp_critical_values = {'normal': normal_asymp_crit_vals,
+                         'exp': exp_asymp_crit_vals}
+
+"""
+    cv_filename = '../../_lilliefors_critical_values.py'
+    with open(cv_filename, 'w', newline='\n', encoding="utf-8") as cv:
+        cv.write(FormatCode(header)[0])
+        cv.write(FormatCode(normal)[0])
+        cv.write('\n\n')
+        cv.write(FormatCode(exp)[0])
+        cv.write('\n\n')
+        cv.write(FormatCode(footer)[0])

venv/lib/python3.11/site-packages/statsmodels/stats/tests/results/results_influence_logit.csv

@@ -0,0 +1,40 @@
+Case,LogRate,LogVolumne,resid_pearson,resid_deviance,hat_matrix_diagonal,dfb_intercept,dfb_lograte,dfb_logvolumne,Cooks C,Cooks Cbar,d_deviance,d_pearson_chi2
+1,-0.1924,1.3083,0.2205,0.3082,0.0927,-0.0165,0.0193,0.0556,0.00548,0.00497,0.1,0.0536
+2,0.0862,1.2528,0.1349,0.1899,0.0429,-0.0134,0.0151,0.0261,0.000853,0.000816,0.0369,0.019
+3,0.9163,0.2231,0.2923,0.4049,0.0612,-0.0492,0.066,0.0589,0.00593,0.00557,0.1695,0.091
+4,0.4055,-0.2877,3.5181,2.2775,0.0867,1.0734,-0.9302,-1.018,1.2873,1.1756,6.3626,13.5523
+5,1.1632,-0.2231,0.5287,0.7021,0.1158,-0.0832,0.1411,0.0583,0.0414,0.0366,0.5296,0.3161
+6,1.2528,-0.3567,0.609,0.7943,0.1524,-0.0922,0.171,0.0381,0.0787,0.0667,0.6976,0.4376
+7,-0.2877,-0.5108,-0.0328,-0.0464,0.00761,-0.0028,0.00274,0.00265,8.321e-06,8.258e-06,0.00216,0.00109
+8,0.5306,0.0953,-1.0196,-1.1939,0.0559,-0.1444,0.0613,0.057,0.0652,0.0616,1.487,1.1011
+9,-0.2877,-0.1054,-0.0938,-0.1323,0.0342,-0.0178,0.0173,0.0153,0.000322,0.000311,0.0178,0.00911
+10,-0.7985,-0.1054,-0.0293,-0.0414,0.00721,-0.00245,0.00246,0.00211,6.256e-06,6.211e-06,0.00172,0.000862
+11,-0.5621,-0.2231,-0.037,-0.0523,0.00969,-0.00361,0.00358,0.00319,1.4e-05,1.3e-05,0.00274,0.00138
+12,1.0116,-0.5978,-0.5073,-0.6768,0.1481,-0.1173,0.0647,0.1651,0.0525,0.0447,0.5028,0.3021
+13,1.0986,-0.5108,-0.7751,-0.97,0.1628,-0.0931,-0.00946,0.1775,0.1395,0.1168,1.0577,0.7175
+14,0.8459,0.3365,0.2559,0.3562,0.0551,-0.0414,0.0538,0.0527,0.00404,0.00382,0.1307,0.0693
+15,1.3218,-0.2877,0.4352,0.589,0.1336,-0.094,0.1408,0.0643,0.0337,0.0292,0.3761,0.2186
+16,0.4947,0.8329,0.1576,0.2215,0.0402,-0.0198,0.0234,0.0307,0.00108,0.00104,0.0501,0.0259
+17,0.47,1.1632,0.0709,0.1001,0.0172,-0.0063,0.00701,0.00914,8.9e-05,8.8e-05,0.0101,0.00511
+18,0.3471,-0.1625,2.9062,2.1192,0.0954,0.9595,-0.8279,-0.8477,0.9845,0.8906,5.3817,9.3363
+19,0.0583,0.5306,-1.0718,-1.2368,0.1315,-0.2591,0.2024,-0.00488,0.2003,0.174,1.7037,1.3227
+20,0.5878,0.5878,0.2405,0.3353,0.0525,-0.0331,0.0421,0.0518,0.00338,0.0032,0.1156,0.061
+21,0.6931,-0.9163,-0.1076,-0.1517,0.0373,-0.018,0.0158,0.0208,0.000465,0.000448,0.0235,0.012
+22,0.3075,-0.0513,-0.4193,-0.5691,0.1015,-0.1449,0.1237,0.1179,0.0221,0.0199,0.3437,0.1956
+23,0.3001,0.3001,-1.0242,-1.1978,0.0761,-0.1961,0.1275,0.0357,0.0935,0.0864,1.5212,1.1355
+24,0.3075,0.4055,-1.3684,-1.4527,0.0717,-0.1281,0.041,-0.1004,0.1558,0.1447,2.255,2.0171
+25,0.5766,0.47,0.3347,0.4608,0.0587,-0.0403,0.057,0.0708,0.00741,0.00698,0.2193,0.119
+26,0.4055,-0.5108,-0.1595,-0.2241,0.0548,-0.0366,0.0329,0.0373,0.00156,0.00147,0.0517,0.0269
+27,0.4055,0.5878,0.3645,0.4995,0.0661,-0.0327,0.0496,0.0788,0.0101,0.00941,0.2589,0.1423
+28,0.6419,-0.0513,-0.8989,-1.0883,0.0647,-0.1423,0.0617,0.1025,0.0597,0.0559,1.2404,0.8639
+29,-0.0513,0.6419,0.8981,1.0876,0.1682,0.2367,-0.195,0.0286,0.1961,0.1631,1.346,0.9697
+30,-0.9163,0.47,-0.0992,-0.14,0.0507,-0.0224,0.0227,0.0159,0.000554,0.000526,0.0201,0.0104
+31,-0.2877,0.9933,0.6198,0.8064,0.2459,0.1165,-0.0996,0.1322,0.1661,0.1253,0.7755,0.5095
+32,-3.5066,0.8544,-0.00073,-0.00103,2.2e-05,-3.22e-06,3.405e-06,2.48e-06,1.18e-11,1.18e-11,1.065e-06,5.324e-07
+33,0.6043,0.0953,-1.2062,-1.3402,0.051,-0.0882,-0.0137,-0.00216,0.0824,0.0782,1.8744,1.5331
+34,0.7885,0.0953,0.5447,0.7209,0.0601,-0.0425,0.0877,0.0671,0.0202,0.019,0.5387,0.3157
+35,0.6931,0.1823,0.5404,0.7159,0.0552,-0.034,0.0755,0.0711,0.018,0.017,0.5295,0.3091
+36,1.203,-0.2231,0.4828,0.6473,0.1177,-0.0867,0.1381,0.0631,0.0352,0.0311,0.4501,0.2641
+37,0.6419,-0.0513,-0.8989,-1.0883,0.0647,-0.1423,0.0617,0.1025,0.0597,0.0559,1.2404,0.8639
+38,0.6419,-0.2877,-0.4874,-0.6529,0.1,-0.1395,0.1032,0.1397,0.0293,0.0264,0.4526,0.2639
+39,0.4855,0.2624,0.7053,0.8987,0.0531,0.0326,0.019,0.0489,0.0295,0.0279,0.8355,0.5254

venv/lib/python3.11/site-packages/statsmodels/stats/tests/results/results_meta.py

@@ -0,0 +1,491 @@
+"""
+Created on Tue Apr  7 11:31:21 2020
+
+Author: Josef Perktold
+License: BSD-3
+
+"""
+
+import numpy as np
+from statsmodels.tools.testing import Holder
+
+"""
+example from Kacker 2004, computed with R metafor
+
+> y = c(61.0, 61.4 , 62.21, 62.3 , 62.34, 62.6 , 62.7 , 62.84, 65.9)
+> v = c(0.2025, 1.2100, 0.0900, 0.2025, 0.3844, 0.5625, 0.0676, 0.0225, 1.8225)
+> res = rma(y, v, data=dat, method="PM", control=list(tol=1e-9))
+> convert_items(res, prefix="exk1_metafor.")
+
+"""
+
+exk1_metafor = Holder()
+exk1_metafor.b = 62.4076199113286
+exk1_metafor.beta = 62.4076199113286
+exk1_metafor.se = 0.338030602684471
+exk1_metafor.zval = 184.621213037276
+exk1_metafor.pval = 0
+exk1_metafor.ci_lb = 61.7450921043947
+exk1_metafor.ci_ub = 63.0701477182625
+exk1_metafor.vb = 0.114264688351227
+exk1_metafor.tau2 = 0.705395309224248
+exk1_metafor.se_tau2 = 0.51419109758052
+exk1_metafor.tau2_f = 0.705395309224248
+exk1_metafor.k = 9
+exk1_metafor.k_f = 9
+exk1_metafor.k_eff = 9
+exk1_metafor.k_all = 9
+exk1_metafor.p = 1
+exk1_metafor.p_eff = 1
+exk1_metafor.parms = 2
+exk1_metafor.m = 1
+exk1_metafor.QE = 24.801897741835
+exk1_metafor.QEp = 0.00167935146372742
+exk1_metafor.QM = 34084.9923033553
+exk1_metafor.QMp = 0
+exk1_metafor.I2 = 83.7218626490482
+exk1_metafor.H2 = 6.14320900751909
+exk1_metafor.yi = np.array([
+    61, 61.4, 62.21, 62.3, 62.34, 62.6, 62.7, 62.84, 65.9
+    ])
+exk1_metafor.vi = np.array([
+    0.2025, 1.21, 0.09, 0.2025, 0.3844, 0.5625, 0.0676, 0.0225, 1.8225
+    ])
+exk1_metafor.X = np.array([
+    1, 1, 1, 1, 1, 1, 1, 1, 1
+    ]).reshape(9, 1, order='F')
+
+exk1_metafor.yi_f = np.array([
+    61, 61.4, 62.21, 62.3, 62.34, 62.6, 62.7, 62.84, 65.9
+    ])
+exk1_metafor.vi_f = np.array([
+    0.2025, 1.21, 0.09, 0.2025, 0.3844, 0.5625, 0.0676, 0.0225, 1.8225
+    ])
+exk1_metafor.X_f = np.array([
+    1, 1, 1, 1, 1, 1, 1, 1, 1
+    ]).reshape(9, 1, order='F')
+
+exk1_metafor.M = np.array([
+    0.907895309224248, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.91539530922425, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0.795395309224248, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.907895309224248, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.08979530922425, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 1.26789530922425, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.772995309224248, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.727895309224248, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 2.52789530922425
+    ]).reshape(9, 9, order='F')
+
+exk1_metafor.ids = np.array([
+    1, 2, 3, 4, 5, 6, 7, 8, 9
+    ])
+exk1_metafor.slab = np.array([
+    1, 2, 3, 4, 5, 6, 7, 8, 9
+    ])
+exk1_metafor.measure = 'GEN'
+exk1_metafor.method = 'PM'
+exk1_metafor.test = 'z'
+exk1_metafor.s2w = 1
+exk1_metafor.btt = 1
+exk1_metafor.digits = np.array([
+    4, 4, 4, 4, 4, 4, 4, 4, 4
+    ])
+exk1_metafor.level = 0.05
+exk1_metafor.add = 0.5
+exk1_metafor.to = 'only0'
+exk1_metafor.fit_stats = np.array([
+    -12.722152033808, 21.73438033144, 29.4443040676159, 29.8387532222884,
+    31.4443040676159, -11.7892200590463, 23.5784401180925, 27.5784401180925,
+    27.7373232014522, 29.9784401180925
+    ]).reshape(5, 2, order='F')
+
+exk1_metafor.model = 'rma.uni'
+
+
+# > res = rma(y, v, data=dat, method="DL", control=list(tol=1e-9))
+# > convert_items(res, prefix="exk1_dl.")
+
+exk1_dl = Holder()
+exk1_dl.b = 62.3901386044504
+exk1_dl.beta = 62.3901386044504
+exk1_dl.se = 0.245749668040304
+exk1_dl.zval = 253.876797075543
+exk1_dl.pval = 0
+exk1_dl.ci_lb = 61.9084781058787
+exk1_dl.ci_ub = 62.8717991030221
+exk1_dl.vb = 0.0603928993419195
+exk1_dl.tau2 = 0.288049246973751
+exk1_dl.se_tau2 = 0.269366223207558
+exk1_dl.tau2_f = 0.288049246973751
+exk1_dl.k = 9
+exk1_dl.k_f = 9
+exk1_dl.k_eff = 9
+exk1_dl.k_all = 9
+exk1_dl.p = 1
+exk1_dl.p_eff = 1
+exk1_dl.parms = 2
+exk1_dl.m = 1
+exk1_dl.QE = 24.801897741835
+exk1_dl.QEp = 0.00167935146372742
+exk1_dl.QM = 64453.4280933367
+exk1_dl.QMp = 0
+exk1_dl.I2 = 67.744403741711
+exk1_dl.H2 = 3.10023721772938
+
+
+# > res = rma(y, v, data=dat, method="DL", test="knha", control=list(tol=1e-9))
+# > convert_items(res, prefix="exk1_dl_hksj.")
+
+exk1_dl_hksj = Holder()
+exk1_dl_hksj.b = 62.3901386044504
+exk1_dl_hksj.beta = 62.3901386044504
+exk1_dl_hksj.se = 0.29477605699879
+exk1_dl_hksj.zval = 211.652666908108
+exk1_dl_hksj.pval = 2.77938607433693e-16
+exk1_dl_hksj.ci_lb = 61.710383798052
+exk1_dl_hksj.ci_ub = 63.0698934108488
+exk1_dl_hksj.vb = 0.0868929237797541
+exk1_dl_hksj.tau2 = 0.288049246973751
+exk1_dl_hksj.se_tau2 = 0.269366223207558
+exk1_dl_hksj.tau2_f = 0.288049246973751
+exk1_dl_hksj.k = 9
+exk1_dl_hksj.k_f = 9
+exk1_dl_hksj.k_eff = 9
+exk1_dl_hksj.k_all = 9
+exk1_dl_hksj.p = 1
+exk1_dl_hksj.p_eff = 1
+exk1_dl_hksj.parms = 2
+exk1_dl_hksj.m = 1
+exk1_dl_hksj.QE = 24.801897741835
+exk1_dl_hksj.QEp = 0.00167935146372742
+exk1_dl_hksj.QM = 44796.8514093144
+exk1_dl_hksj.QMp = 2.77938607433693e-16
+exk1_dl_hksj.I2 = 67.744403741711
+exk1_dl_hksj.H2 = 3.10023721772938
+
+
+# > res = rma(y, v, data=dat, method="FE", control=list(tol=1e-9))
+# > convert_items(res, prefix="exk1_fe.")
+
+exk1_fe = Holder()
+exk1_fe.b = 62.5833970939982
+exk1_fe.beta = 62.5833970939982
+exk1_fe.se = 0.107845705498231
+exk1_fe.zval = 580.304953311515
+exk1_fe.pval = 0
+exk1_fe.ci_lb = 62.3720233953344
+exk1_fe.ci_ub = 62.7947707926621
+exk1_fe.vb = 0.0116306961944112
+exk1_fe.tau2 = 0
+exk1_fe.tau2_f = 0
+exk1_fe.k = 9
+exk1_fe.k_f = 9
+exk1_fe.k_eff = 9
+exk1_fe.k_all = 9
+exk1_fe.p = 1
+exk1_fe.p_eff = 1
+exk1_fe.parms = 1
+exk1_fe.m = 1
+exk1_fe.QE = 24.801897741835
+exk1_fe.QEp = 0.00167935146372742
+exk1_fe.QM = 336753.838837879
+exk1_fe.QMp = 0
+exk1_fe.I2 = 67.744403741711
+exk1_fe.H2 = 3.10023721772938
+
+
+# > res = rma(y, v, data=dat, method="FE", test="knha", control=list(tol=1e-9))
+# Warning message:
+# In rma(y, v, data = dat, method = "FE", test = "knha",
+#        control = list(tol = 1e-09)) :
+#  Knapp & Hartung method is not meant to be used in the context of FE models.
+# > convert_items(res, prefix="exk1_fe_hksj.")
+
+exk1_fe_hksj = Holder()
+exk1_fe_hksj.b = 62.5833970939982
+exk1_fe_hksj.beta = 62.5833970939982
+exk1_fe_hksj.se = 0.189889223522271
+exk1_fe_hksj.zval = 329.57845597098
+exk1_fe_hksj.pval = 8.04326466920145e-18
+exk1_fe_hksj.ci_lb = 62.1455117593252
+exk1_fe_hksj.ci_ub = 63.0212824286713
+exk1_fe_hksj.vb = 0.0360579172098909
+exk1_fe_hksj.tau2 = 0
+exk1_fe_hksj.tau2_f = 0
+exk1_fe_hksj.k = 9
+exk1_fe_hksj.k_f = 9
+exk1_fe_hksj.k_eff = 9
+exk1_fe_hksj.k_all = 9
+exk1_fe_hksj.p = 1
+exk1_fe_hksj.p_eff = 1
+exk1_fe_hksj.parms = 1
+exk1_fe_hksj.m = 1
+exk1_fe_hksj.QE = 24.801897741835
+exk1_fe_hksj.QEp = 0.00167935146372742
+exk1_fe_hksj.QM = 108621.958640215
+exk1_fe_hksj.QMp = 8.04326466920145e-18
+exk1_fe_hksj.I2 = 67.744403741711
+exk1_fe_hksj.H2 = 3.10023721772938
+
+
+# effect size for proportions, metafor `escalc` function
+
+# > library(metafor)
+# > dat <- dat.fine1993
+# > dat_or <- escalc(measure="OR", ai=e2i, n1i=nei, ci=c2i, n2i=nci, data=dat,
+#                    var.names=c("y2i","v2i"))
+# > r = dat_or[c("y2i", "v2i")]
+# > cat_items(r)
+y_or = np.array([
+    0.13613217432458, 0.768370601797533, 0.374938517449009, 1.65822807660353,
+    0.784954729813068, 0.361663949151077, 0.575364144903562,
+    0.250542525502324, 0.650587566141149, 0.0918075492531228,
+    0.273865253802803, 0.485755524477543, 0.182321556793955,
+    0.980829253011726, 1.31218638896617, -0.259511195485084, 0.138402322859119
+    ])
+v_or = np.array([
+    0.399242424242424, 0.244867149758454, 0.152761481951271, 0.463095238095238,
+    0.189078465394255, 0.0689052107900588, 0.240651709401709,
+    0.142027027027027, 0.280657748049052, 0.210140736456526,
+    0.0373104717196078, 0.0427774287950624, 0.194901960784314,
+    0.509259259259259, 1.39835164835165, 0.365873015873016, 0.108630952380952
+    ])
+
+# > dat_rr <- escalc(measure="RR", ai=e2i, n1i=nei, ci=c2i, n2i=nci, data=dat,
+#                    var.names=c("y2i","v2i"))
+# > r = dat_rr[c("y2i", "v2i")]
+# > cat_items(r)
+y_rr = np.array([
+    0.0595920972022457, 0.434452644981417, 0.279313822781264,
+    0.934309237376833, 0.389960921572199, 0.219327702635984,
+    0.328504066972036, 0.106179852041229, 0.28594445255324,
+    0.0540672212702757, 0.164912297594691, 0.300079561474504,
+    0.0813456394539525, 0.693147180559945, 0.177206456127184,
+    -0.131336002061087, 0.0622131845015728
+    ])
+v_rr = np.array([
+    0.0761562998405104, 0.080905695611578, 0.0856909430438842,
+    0.175974025974026, 0.0551968864468864, 0.0267002515563729,
+    0.074017094017094, 0.0257850995555914, 0.0590338164251208,
+    0.073266499582289, 0.0137191240428942, 0.0179386112192693,
+    0.0400361415752742, 0.3, 0.0213675213675214, 0.0922402159244264,
+    0.021962676962677
+    ])
+
+# > dat_rd <- escalc(measure="RD", ai=e2i, n1i=nei, ci=c2i, n2i=nci, data=dat,
+#                    var.names=c("y2i","v2i"))
+# > r = dat_rd[c("y2i", "v2i")]
+# > cat_items(r)
+y_rd = np.array([
+    0.0334928229665072, 0.186554621848739, 0.071078431372549,
+    0.386363636363636, 0.19375, 0.0860946401581211, 0.14, 0.0611028315946349,
+    0.158888888888889, 0.0222222222222222, 0.0655096935584741,
+    0.114173373020248, 0.045021186440678, 0.2, 0.150793650793651,
+    -0.0647773279352226, 0.0342342342342342
+    ])
+v_rd = np.array([
+    0.0240995805934916, 0.0137648162576944, 0.00539777447807907,
+    0.0198934072126221, 0.0109664132254464, 0.00376813659489987,
+    0.0142233846153846, 0.00842011053321928, 0.0163926076817558,
+    0.0122782676856751, 0.00211164860232433, 0.00219739135615223,
+    0.0119206723560942, 0.016, 0.014339804116826, 0.0226799351233969,
+    0.00663520262409963
+    ])
+
+
+# > dat_as <- escalc(measure="AS", ai=e2i, n1i=nei, ci=c2i, n2i=nci, data=dat,
+#                    var.names=c("y2i","v2i"))
+# > r = dat_as[c("y2i", "v2i")]
+# > cat_items(r)
+y_as = np.array([
+    0.0337617513001424, 0.189280827304914, 0.0815955178338458,
+    0.399912703180945, 0.194987153482868, 0.0882233598093272,
+    0.141897054604164, 0.0618635353537276, 0.160745373792417,
+    0.0225840453649413, 0.0669694915300637, 0.117733830714136,
+    0.0452997410410423, 0.221071594001477, 0.220332915310739,
+    -0.0648275244591966, 0.0344168494848509
+    ])
+v_as = np.array([
+    0.0245215311004785, 0.0144957983193277, 0.00714869281045752,
+    0.0238636363636364, 0.0113839285714286, 0.00402569468666434,
+    0.0146153846153846, 0.00864381520119225, 0.0169444444444444,
+    0.0126984126984127, 0.0022181832395247, 0.00242071803917245,
+    0.0120497881355932, 0.0222222222222222, 0.0317460317460317,
+    0.0227732793522267, 0.00671171171171171
+    ])
+
+eff_prop1 = Holder(y_rd=y_rd, v_rd=v_rd, y_rr=y_rr, v_rr=v_rr,
+                   y_or=y_or, v_or=v_or, y_as=y_as, v_as=v_as)
+
+
+# package meta metabin OR
+NA = np.nan  # for R output
+results_or_dl_hk = Holder()
+# > res_mb_hk = metabin(e2i, nei, c2i, nci, data=dat2, sm="OR",
+#     Q.Cochrane=FALSE, method="Inverse", method.tau="DL", hakn=TRUE,
+#     backtransf=FALSE)
+# > cat_items(res_mb_hk, prefix="results_or_dl_hk.")
+results_or_dl_hk.event_e = np.array([
+    18, 22, 21, 14, 42, 80, 13, 37, 23, 19, 106, 170, 34, 18, 13, 12, 42
+    ])
+results_or_dl_hk.n_e = np.array([
+    19, 34, 72, 22, 70, 183, 26, 61, 36, 45, 246, 386, 59, 45, 14, 26, 74
+    ])
+results_or_dl_hk.event_c = np.array([
+    12, 12, 15, 5, 13, 33, 18, 30, 12, 14, 76, 46, 17, 3, 14, 10, 40
+    ])
+results_or_dl_hk.n_c = np.array([
+    22, 35, 68, 20, 32, 94, 50, 55, 25, 35, 208, 141, 32, 15, 18, 19, 75
+    ])
+results_or_dl_hk.method = 'Inverse'
+results_or_dl_hk.incr = 0.5
+results_or_dl_hk.Q_CMH = 24.9044036917599
+results_or_dl_hk.df_Q_CMH = 1
+results_or_dl_hk.pval_Q_CMH = 6.02446516918864e-07
+results_or_dl_hk.incr_e = np.array([
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
+    ])
+results_or_dl_hk.incr_c = np.array([
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
+    ])
+results_or_dl_hk.studlab = np.array([
+    1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17
+    ])
+results_or_dl_hk.TE = np.array([
+    2.70805020110221, 1.25672336971146, 0.374938517449009, 1.65822807660353,
+    0.784954729813068, 0.361663949151077, 0.575364144903562,
+    0.250542525502324, 0.650587566141149, 0.0918075492531229,
+    0.273865253802803, 0.485755524477543, 0.182321556793955,
+    0.980829253011726, 1.31218638896617, -0.259511195485085, 0.138402322859119
+    ])
+results_or_dl_hk.seTE = np.array([
+    1.1130538571376, 0.505568465186247, 0.390847133738078, 0.680511012471685,
+    0.434831536798166, 0.26249802054503, 0.490562645746402, 0.376864733063505,
+    0.529771411128472, 0.458411099840008, 0.193159187510219,
+    0.206827050443269, 0.441477021807833, 0.713624032148063, 1.18251919576455,
+    0.604874380241894, 0.329592099997789
+    ])
+results_or_dl_hk.lower = np.array([
+    0.526504728259124, 0.265827386227229, -0.391107788138334,
+    0.324451001076142, -0.0672994216535403, -0.152822717130236,
+    -0.386120972920066, -0.488098778345447, -0.387745319709617,
+    -0.806661696546688, -0.104719797000245, 0.0803819545800872,
+    -0.682957505951402, -0.41784814850073, -1.00550864575963,
+    -1.44504319593018, -0.507586322725471
+    ])
+results_or_dl_hk.upper = np.array([
+    4.8895956739453, 2.2476193531957, 1.14098482303635, 2.99200515213092,
+    1.63720888127968, 0.87615061543239, 1.53684926272719, 0.989183829350096,
+    1.68892045199192, 0.990276795052934, 0.65245030460585, 0.891129094374998,
+    1.04760061953931, 2.37950665452418, 3.62988142369196, 0.926020804960014,
+    0.784390968443709
+    ])
+results_or_dl_hk.zval = np.array([
+    2.43299116546472, 2.48576297030017, 0.959297088514073, 2.43673951811695,
+    1.80519273186346, 1.37777781485798, 1.17286578970589, 0.664807564946931,
+    1.22805336882057, 0.200273399324678, 1.41782152499639, 2.34860731919001,
+    0.412980852428863, 1.37443416817026, 1.10965335164625, -0.429033207492279,
+    0.419920024964335
+    ])
+results_or_dl_hk.pval = np.array([
+    0.0149746662574385, 0.012927403688425, 0.337409101687073,
+    0.0148203508441569, 0.0710445278963136, 0.168271897623853,
+    0.240849630303111, 0.50617358344946, 0.21942693438385, 0.841266765353313,
+    0.15624287815226, 0.0188437679780689, 0.67962064241999, 0.169306934699306,
+    0.267148431658854, 0.667899058451397, 0.674543878468201
+    ])
+results_or_dl_hk.w_fixed = np.array([
+    0.807174887892376, 3.91237113402062, 6.54615278162192, 2.15938303341902,
+    5.28880958450166, 14.5126905285409, 4.15538290788013, 7.04091341579448,
+    3.5630585898709, 4.75871559633028, 26.8021269609001, 23.3768140855494,
+    5.1307847082495, 1.96363636363636, 0.715127701375246, 2.73318872017354,
+    9.2054794520548
+    ])
+results_or_dl_hk.w_random = np.array([
+    0.806082838403413, 3.8868480814364, 6.47501150011912, 2.15158503393154,
+    5.24227532998639, 14.1675952027965, 4.12660238520421, 6.95867952431796,
+    3.54187734484692, 4.72100880622866, 25.6483453428049, 22.4942390635843,
+    5.0869781966579, 1.95718594121596, 0.714270384745876, 2.72070780232418,
+    9.06541462543771
+    ])
+results_or_dl_hk.TE_fixed = 0.428036725396544
+results_or_dl_hk.seTE_fixed = 0.0902874968199668
+results_or_dl_hk.lower_fixed = 0.251076483375135
+results_or_dl_hk.upper_fixed = 0.604996967417954
+results_or_dl_hk.zval_fixed = 4.74081949851871
+results_or_dl_hk.pval_fixed = 2.12855503378502e-06
+results_or_dl_hk.TE_random = 0.429520368698268
+results_or_dl_hk.seTE_random = 0.0915952752397692
+results_or_dl_hk.lower_random = 0.235347059333852
+results_or_dl_hk.upper_random = 0.623693678062684
+results_or_dl_hk.zval_random = 4.68932887175579
+results_or_dl_hk.pval_random = 0.000246175101510513
+results_or_dl_hk.null_effect = 0
+results_or_dl_hk.seTE_predict = 0.100339885576592
+results_or_dl_hk.lower_predict = 0.215650965184522
+results_or_dl_hk.upper_predict = 0.643389772212014
+results_or_dl_hk.level_predict = 0.95
+results_or_dl_hk.k = 17
+results_or_dl_hk.Q = 16.181374262823
+results_or_dl_hk.df_Q = 16
+results_or_dl_hk.pval_Q = 0.440375456698129
+results_or_dl_hk.tau2 = 0.0016783981912744
+results_or_dl_hk.se_tau2 = 0.0529437644950009
+results_or_dl_hk.lower_tau2 = 0
+results_or_dl_hk.upper_tau2 = 0.45893520964914
+results_or_dl_hk.tau = 0.0409682583383086
+results_or_dl_hk.lower_tau = 0
+results_or_dl_hk.upper_tau = 0.677447569668045
+results_or_dl_hk.method_tau_ci = 'J'
+results_or_dl_hk.sign_lower_tau = ''
+results_or_dl_hk.sign_upper_tau = ''
+results_or_dl_hk.H = 1.00565197331206
+results_or_dl_hk.lower_H = 1
+results_or_dl_hk.upper_H = 1.43793739981697
+results_or_dl_hk.I2 = 0.0112088293538659
+results_or_dl_hk.lower_I2 = 0
+results_or_dl_hk.upper_I2 = 0.516362418389019
+results_or_dl_hk.Rb = 0.0117679262339789
+results_or_dl_hk.lower_Rb = 0
+results_or_dl_hk.upper_Rb = 0.725656377301252
+results_or_dl_hk.approx_TE = np.array([
+ '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''
+    ])
+results_or_dl_hk.approx_seTE = np.array([
+ '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''
+    ])
+results_or_dl_hk.sm = 'OR'
+results_or_dl_hk.level = 0.95
+results_or_dl_hk.level_comb = 0.95
+results_or_dl_hk.df_hakn = 16
+results_or_dl_hk.method_tau = 'DL'
+results_or_dl_hk.method_bias = 'score'
+results_or_dl_hk.title = ''
+results_or_dl_hk.complab = ''
+results_or_dl_hk.outclab = ''
+results_or_dl_hk.label_e = 'Experimental'
+results_or_dl_hk.label_c = 'Control'
+results_or_dl_hk.label_left = ''
+results_or_dl_hk.label_right = ''
+results_or_dl_hk.data = np.array([
+    1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 34, 72, 22,
+    70, 183, 26, 61, 36, 45, 246, 386, 59, 45, 14, 26, 74, 22, 35, 68, 20, 32,
+    94, 50, 55, 25, 35, 208, 141, 32, 15, 18, 19, 75, 16, 22, 44, 19, 62, 130,
+    24, 51, 30, 43, 169, 279, 56, 42, 14, 21, NA, 20, 22, 40, 12, 27, 65, 30,
+    44, 17, 35, 139, 97, 30, 10, 18, 15, NA, 18, 22, 21, 14, 42, 80, 13, 37,
+    23, 19, 106, 170, 34, 18, 13, 12, 42, 12, 12, 15, 5, 13, 33, 18, 30, 12,
+    14, 76, 46, 17, 3, 14, 10, 40, 4, 15, 10, 5, 26, 47, 5, 19, 13, 8, 67, 97,
+    21, 9, 12, 6, NA, 8, 8, 3, 4, 6, 14, 10, 19, 4, 4, 42, 21, 9, 1, 13, 4,
+    NA, 4, 15, 3, 2, 15, 30, 3, 11, 10, 6, 51, 73, 20, 9, 9, 5, 23, 3, 6, 0,
+    3, 5, 11, 9, 15, 4, 0, 35, 8, 7, 1, 12, 1, 30, 18, 22, 21, 14, 42, 80, 13,
+    37, 23, 19, 106, 170, 34, 18, 13, 12, 42, 19, 34, 72, 22, 70, 183, 26, 61,
+    36, 45, 246, 386, 59, 45, 14, 26, 74, 12, 12, 15, 5, 13, 33, 18, 30, 12,
+    14, 76, 46, 17, 3, 14, 10, 40, 22, 35, 68, 20, 32, 94, 50, 55, 25, 35,
+    208, 141, 32, 15, 18, 19, 75, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
+    14, 15, 16, 17, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
+    0.5, 0.5, 0.5, 0.5, 0.5, 0.5
+    ]).reshape(17, 17, order='F')
+
+results_or_dl_hk.byseparator = ' = '
+results_or_dl_hk.pscale = 1
+results_or_dl_hk.irscale = 1
+results_or_dl_hk.irunit = 'person-years'
+results_or_dl_hk.version = '4.11-0'

venv/lib/python3.11/site-packages/statsmodels/stats/tests/results/results_multinomial_proportions.py

@@ -0,0 +1,78 @@
+"""Test values for multinomial_proportion_confint.
+
+Author: Sébastien Lerique
+"""
+
+
+import collections
+import numpy as np
+
+from statsmodels.tools.testing import Holder
+
+res_multinomial = collections.defaultdict(Holder)
+
+# The following examples come from the Sison & Glaz paper, and the values were
+# computed using the R MultinomialCI package.
+
+# Floating-point arithmetic errors get blown up in the Edgeworth expansion
+# (starting in g1 and g2, but mostly when computing f, because of the
+# polynomials), which explains why we only obtain a precision of 4 decimals
+# when comparing to values computed in R.
+
+# We test with any method name that starts with 'sison', as that is the
+# criterion.
+key1 = ('sison', 'Sison-Glaz example 1')
+res_multinomial[key1].proportions = [56, 72, 73, 59, 62, 87, 58]
+res_multinomial[key1].cis = np.array([
+    [.07922912, .1643361], [.11349036, .1985973],
+    [.11563169, .2007386], [.08565310, .1707601],
+    [.09207709, .1771840], [.14561028, .2307172],
+    [.08351178, .1686187]])
+res_multinomial[key1].precision = 4
+
+key2 = ('sisonandglaz', 'Sison-Glaz example 2')
+res_multinomial[key2].proportions = [5] * 50
+res_multinomial[key2].cis = [0, .05304026] * np.ones((50, 2))
+res_multinomial[key2].precision = 4
+
+key3 = ('sison-whatever', 'Sison-Glaz example 3')
+res_multinomial[key3].proportions = (
+    [1] * 10 + [12] * 10 + [5] * 10 + [3] * 10 + [4] * 10)
+res_multinomial[key3].cis = np.concatenate([
+    [0, .04120118] * np.ones((10, 2)),
+    [.012, .08520118] * np.ones((10, 2)),
+    [0, .05720118] * np.ones((10, 2)),
+    [0, .04920118] * np.ones((10, 2)),
+    [0, .05320118] * np.ones((10, 2))
+])
+res_multinomial[key3].precision = 4
+
+# The examples from the Sison & Glaz paper only include 3 decimals.
+gkey1 = ('goodman', 'Sison-Glaz example 1')
+res_multinomial[gkey1].proportions = [56, 72, 73, 59, 62, 87, 58]
+res_multinomial[gkey1].cis = np.array([
+    [.085, .166],
+    [.115, .204],
+    [.116, .207],
+    [.091, .173],
+    [.096, .181],
+    [.143, .239],
+    [.089, .171]])
+res_multinomial[gkey1].precision = 3
+
+gkey2 = ('goodman', 'Sison-Glaz example 2')
+res_multinomial[gkey2].proportions = [5] * 50
+res_multinomial[gkey2].cis = [.005, .075] * np.ones((50, 2))
+res_multinomial[gkey2].precision = 3
+
+gkey3 = ('goodman', 'Sison-Glaz example 3')
+res_multinomial[gkey3].proportions = (
+    [1] * 10 + [12] * 10 + [5] * 10 + [3] * 10 + [4] * 10)
+res_multinomial[gkey3].cis = np.concatenate([
+    [0, .049] * np.ones((10, 2)),
+    [.019, .114] * np.ones((10, 2)),
+    [.005, .075] * np.ones((10, 2)),
+    [.002, .062] * np.ones((10, 2)),
+    [.004, .069] * np.ones((10, 2))
+])
+res_multinomial[gkey3].precision = 3

venv/lib/python3.11/site-packages/statsmodels/stats/tests/results/results_panelrobust.py

@@ -0,0 +1,91 @@
+import numpy as np
+
+from statsmodels.tools.tools import Bunch
+
+cov_clu_stata = np.array([
+    .00025262993207,
+    -.00065043385106,
+    .20961897960949,
+    -.00065043385106,
+    .00721940994738,
+    -1.2171040967615,
+    .20961897960949,
+    -1.2171040967615,
+    417.18890043724]).reshape(3, 3)
+
+cov_pnw0_stata = np.array([
+    .00004638910396,
+    -.00006781406833,
+    -.00501232990882,
+    -.00006781406833,
+    .00238784043122,
+    -.49683062350622,
+    -.00501232990882,
+    -.49683062350622,
+    133.97367476797]).reshape(3, 3)
+
+cov_pnw1_stata = np.array([
+    .00007381482253,
+    -.00009936717692,
+    -.00613513582975,
+    -.00009936717692,
+    .00341979122583,
+    -.70768252183061,
+    -.00613513582975,
+    -.70768252183061,
+    197.31345000598]).reshape(3, 3)
+
+cov_pnw4_stata = np.array([
+    .0001305958131,
+    -.00022910455176,
+    .00889686530849,
+    -.00022910455176,
+    .00468152667913,
+    -.88403667445531,
+    .00889686530849,
+    -.88403667445531,
+    261.76140136858]).reshape(3, 3)
+
+cov_dk0_stata = np.array([
+    .00005883478135,
+    -.00011241470772,
+    -.01670183921469,
+    -.00011241470772,
+    .00140649264687,
+    -.29263014921586,
+    -.01670183921469,
+    -.29263014921586,
+    99.248049966902]).reshape(3, 3)
+
+cov_dk1_stata = np.array([
+    .00009855800275,
+    -.00018443722054,
+    -.03257408922788,
+    -.00018443722054,
+    .00205106413403,
+    -.3943459697384,
+    -.03257408922788,
+    -.3943459697384,
+    140.50692606398]).reshape(3, 3)
+
+cov_dk4_stata = np.array([
+    .00018052657317,
+    -.00035661054613,
+    -.06728261073866,
+    -.00035661054613,
+    .0024312795189,
+    -.32394785247278,
+    -.06728261073866,
+    -.32394785247278,
+    148.60456447156]).reshape(3, 3)
+
+
+results = Bunch(
+    cov_clu_stata=cov_clu_stata,
+    cov_pnw0_stata=cov_pnw0_stata,
+    cov_pnw1_stata=cov_pnw1_stata,
+    cov_pnw4_stata=cov_pnw4_stata,
+    cov_dk0_stata=cov_dk0_stata,
+    cov_dk1_stata=cov_dk1_stata,
+    cov_dk4_stata=cov_dk4_stata
+)

venv/lib/python3.11/site-packages/statsmodels/stats/tests/results/results_power.py

@@ -0,0 +1,126 @@
+"""
+
+Created on Thu Feb 28 13:23:09 2013
+
+Author: Josef Perktold
+"""
+
+import collections
+
+from statsmodels.tools.testing import Holder
+
+
+# numbers from R package `pwr` pwr.chisq.test
+pwr_chisquare = collections.defaultdict(Holder)
+pwr_chisquare[0].w = 1e-04
+pwr_chisquare[0].N = 5
+pwr_chisquare[0].df = 4
+pwr_chisquare[0].sig_level = 0.05
+pwr_chisquare[0].power = 0.05000000244872708
+pwr_chisquare[0].method = 'Chi squared power calculation'
+pwr_chisquare[0].note = 'N is the number of observations'
+pwr_chisquare[1].w = 0.005
+pwr_chisquare[1].N = 5
+pwr_chisquare[1].df = 4
+pwr_chisquare[1].sig_level = 0.05
+pwr_chisquare[1].power = 0.05000612192891004
+pwr_chisquare[1].method = 'Chi squared power calculation'
+pwr_chisquare[1].note = 'N is the number of observations'
+pwr_chisquare[2].w = 0.1
+pwr_chisquare[2].N = 5
+pwr_chisquare[2].df = 4
+pwr_chisquare[2].sig_level = 0.05
+pwr_chisquare[2].power = 0.05246644635810126
+pwr_chisquare[2].method = 'Chi squared power calculation'
+pwr_chisquare[2].note = 'N is the number of observations'
+pwr_chisquare[3].w = 1
+pwr_chisquare[3].N = 5
+pwr_chisquare[3].df = 4
+pwr_chisquare[3].sig_level = 0.05
+pwr_chisquare[3].power = 0.396188517504065
+pwr_chisquare[3].method = 'Chi squared power calculation'
+pwr_chisquare[3].note = 'N is the number of observations'
+pwr_chisquare[4].w = 1e-04
+pwr_chisquare[4].N = 100
+pwr_chisquare[4].df = 4
+pwr_chisquare[4].sig_level = 0.05
+pwr_chisquare[4].power = 0.05000004897454883
+pwr_chisquare[4].method = 'Chi squared power calculation'
+pwr_chisquare[4].note = 'N is the number of observations'
+pwr_chisquare[5].w = 0.005
+pwr_chisquare[5].N = 100
+pwr_chisquare[5].df = 4
+pwr_chisquare[5].sig_level = 0.05
+pwr_chisquare[5].power = 0.05012248082672883
+pwr_chisquare[5].method = 'Chi squared power calculation'
+pwr_chisquare[5].note = 'N is the number of observations'
+pwr_chisquare[6].w = 0.1
+pwr_chisquare[6].N = 100
+pwr_chisquare[6].df = 4
+pwr_chisquare[6].sig_level = 0.05
+pwr_chisquare[6].power = 0.1054845044462312
+pwr_chisquare[6].method = 'Chi squared power calculation'
+pwr_chisquare[6].note = 'N is the number of observations'
+pwr_chisquare[7].w = 1
+pwr_chisquare[7].N = 100
+pwr_chisquare[7].df = 4
+pwr_chisquare[7].sig_level = 0.05
+pwr_chisquare[7].power = 0.999999999999644
+pwr_chisquare[7].method = 'Chi squared power calculation'
+pwr_chisquare[7].note = 'N is the number of observations'
+pwr_chisquare[8].w = 1e-04
+pwr_chisquare[8].N = 1000
+pwr_chisquare[8].df = 4
+pwr_chisquare[8].sig_level = 0.05
+pwr_chisquare[8].power = 0.0500004897461283
+pwr_chisquare[8].method = 'Chi squared power calculation'
+pwr_chisquare[8].note = 'N is the number of observations'
+pwr_chisquare[9].w = 0.005
+pwr_chisquare[9].N = 1000
+pwr_chisquare[9].df = 4
+pwr_chisquare[9].sig_level = 0.05
+pwr_chisquare[9].power = 0.0512288025485101
+pwr_chisquare[9].method = 'Chi squared power calculation'
+pwr_chisquare[9].note = 'N is the number of observations'
+pwr_chisquare[10].w = 0.1
+pwr_chisquare[10].N = 1000
+pwr_chisquare[10].df = 4
+pwr_chisquare[10].sig_level = 0.05
+pwr_chisquare[10].power = 0.715986350467412
+pwr_chisquare[10].method = 'Chi squared power calculation'
+pwr_chisquare[10].note = 'N is the number of observations'
+pwr_chisquare[11].w = 1
+pwr_chisquare[11].N = 1000
+pwr_chisquare[11].df = 4
+pwr_chisquare[11].sig_level = 0.05
+pwr_chisquare[11].power = 1
+pwr_chisquare[11].method = 'Chi squared power calculation'
+pwr_chisquare[11].note = 'N is the number of observations'
+pwr_chisquare[12].w = 1e-04
+pwr_chisquare[12].N = 30000
+pwr_chisquare[12].df = 4
+pwr_chisquare[12].sig_level = 0.05
+pwr_chisquare[12].power = 0.05001469300301765
+pwr_chisquare[12].method = 'Chi squared power calculation'
+pwr_chisquare[12].note = 'N is the number of observations'
+pwr_chisquare[13].w = 0.005
+pwr_chisquare[13].N = 30000
+pwr_chisquare[13].df = 4
+pwr_chisquare[13].sig_level = 0.05
+pwr_chisquare[13].power = 0.0904799545200348
+pwr_chisquare[13].method = 'Chi squared power calculation'
+pwr_chisquare[13].note = 'N is the number of observations'
+pwr_chisquare[14].w = 0.1
+pwr_chisquare[14].N = 30000
+pwr_chisquare[14].df = 4
+pwr_chisquare[14].sig_level = 0.05
+pwr_chisquare[14].power = 1
+pwr_chisquare[14].method = 'Chi squared power calculation'
+pwr_chisquare[14].note = 'N is the number of observations'
+pwr_chisquare[15].w = 1
+pwr_chisquare[15].N = 30000
+pwr_chisquare[15].df = 4
+pwr_chisquare[15].sig_level = 0.05
+pwr_chisquare[15].power = 1
+pwr_chisquare[15].method = 'Chi squared power calculation'
+pwr_chisquare[15].note = 'N is the number of observations'

venv/lib/python3.11/site-packages/statsmodels/stats/tests/results/results_proportion.py

@@ -0,0 +1,123 @@
+"""
+
+Created on Fri Mar 01 14:48:59 2013
+
+Author: Josef Perktold
+"""
+
+import collections
+import numpy as np
+
+from statsmodels.tools.testing import Holder
+
+# numbers from R package `pwr` pwr.chisq.test
+res_binom = collections.defaultdict(Holder)
+res_binom_methods = ["agresti-coull", "asymptotic", "bayes",  "cloglog",
+                     "exact", "logit", "probit", "profile", "lrt", "prop.test",
+                     "wilson"]
+
+
+# > bci = binom.confint(x = c(18), n = 20, tol = 1e-8)
+# > mkarray2(bci$lower, "res_binom[(18, 20)].ci_low")
+res_binom[(18, 20)].ci_low = np.array([
+    0.6867561125596077, 0.768521618913513, 0.716146742695748,
+    0.656030707261567, 0.6830172859809176, 0.676197991611287,
+    0.7027685414174645, 0.722052946372325, 0.7220576251734515,
+    0.668722403162941, 0.6989663547715128
+    ])
+# > mkarray2(bci$upper, "res_binom[(18, 20)].ci_upp")
+res_binom[(18, 20)].ci_upp = np.array([
+    0.984343760998137, 1.031478381086487, 0.97862751197755,
+    0.974010174395775, 0.9876514728297052, 0.974866415649319,
+    0.978858461808406, 0.982318186566456, 0.982639913376776,
+    0.982487361226571, 0.972133518786232
+    ])
+# >
+# > bci = binom.confint(x = c(4), n = 20, tol = 1e-8)
+# > mkarray2(bci$lower, "res_binom[(4, 20)].ci_low")
+res_binom[(4, 20)].ci_low = np.array([
+    0.0749115102767071, 0.0246954918846837, 0.07152005247873425,
+    0.0623757232566298, 0.05733399705003284, 0.0771334546771001,
+    0.0710801045992076, 0.0668624655835687, 0.0668375191189685,
+    0.0661062308910436, 0.0806576625797981
+    ])
+# > mkarray2(bci$upper, "res_binom[(4, 20)].ci_upp")
+res_binom[(4, 20)].ci_upp = np.array([
+    0.4217635845549845, 0.3753045081153163, 0.4082257625169254,
+    0.393143902056907, 0.436614002996668, 0.427846901518118,
+    0.4147088121599544, 0.405367872119342, 0.405364309586823,
+    0.442686245059445, 0.4160174322518935
+    ])
+# >
+# > bci = binom.confint(x = c(4), n = 200, tol = 1e-8)
+# > mkarray2(bci$lower, "res_binom[(4, 200)].ci_low")
+res_binom[(4, 200)].ci_low = np.array([
+    0.005991954548218395, 0.000597346459104517, 0.00678759879519299,
+    0.006650668467968445, 0.005475565879556443, 0.00752663882411158,
+    0.00705442514086136, 0.00625387073493174, 0.00625223049303646,
+    0.00642601313670221, 0.00780442641634947
+    ])
+# > mkarray2(bci$upper, "res_binom[(4, 200)].ci_upp")
+res_binom[(4, 200)].ci_upp = np.array([
+    0.0520995587739575, 0.0394026535408955, 0.0468465669668423,
+    0.04722535678688564, 0.05041360908989634, 0.05206026227201098,
+    0.04916362085874019, 0.04585048214247203, 0.0458490848884339,
+    0.0537574613520185, 0.05028708690582643
+    ])
+# > bci = binom.confint(x = c(190), n = 200, tol = 1e-8)
+# Warning message:
+# In binom.bayes(x, n, conf.level = conf.level, ...) :
+#  1 confidence interval failed to converge (marked by '*').
+#  Try changing 'tol' to a different value.
+# JP: I replace 0.02094150654714356 by np.nan in Bayes
+# > mkarray2(bci$lower, "res_binom[(190, 200)].ci_low")
+res_binom[(190, 200)].ci_low = np.array([
+    0.909307307911624, 0.919794926420966, np.nan,
+    0.909066091776046, 0.9099724622986486, 0.9095820742314172,
+    0.9118101288857796, 0.913954651984184, 0.913956305842353,
+    0.9073089225133698, 0.910421851861224
+    ])
+# > mkarray2(bci$upper, "res_binom[(190, 200)].ci_upp")
+res_binom[(190, 200)].ci_upp = np.array([
+    0.973731898348837, 0.980205073579034, 1, 0.972780587302479,
+    0.975765834527891, 0.9728891271086528, 0.973671370402242,
+    0.974623779100809, 0.974626983311416, 0.974392083257476,
+    0.972617354399236
+    ])
+
+# > bci = binom.confint(x = c(1), n = 30, tol = 1e-8)
+res_binom[(1, 30)].ci_low = np.array([
+    -8.305484e-03, -3.090070e-02,  6.903016e-05, 2.494567e-03,
+    8.435709e-04,  4.675346e-03,  3.475014e-03, 3.012987e-03,
+    1.932430e-03,  1.742467e-03,  5.908590e-03])
+
+res_binom[(1, 30)].ci_upp = np.array([
+    0.18091798, 0.09756737, 0.12314380, 0.14513807,
+    0.17216946, 0.20200244, 0.16637241, 0.13868254,
+    0.13868375, 0.19053022, 0.16670391])
+
+# > bci = binom.confint(x = c(29), n = 30, tol = 1e-8)
+res_binom[(29, 30)].ci_low = np.array([
+    0.8190820, 0.9024326, 0.8768562, 0.7860836,
+    0.8278305, 0.7979976, 0.8336276, 0.8613175,
+    0.8613162, 0.8094698, 0.8332961])
+res_binom[(29, 30)].ci_upp = np.array([
+    1.0083055, 1.0309007, 0.9999310, 0.9952363,
+    0.9991564, 0.9953247, 0.9965250, 0.9969870,
+    0.9980676, 0.9982575, 0.9940914])
+
+# > bci = binom.confint(x = c(0), n = 30, tol = 1e-8)
+# Note: this ci_low clips one negative value to 0
+res_binom[(0, 30)].ci_low = np.zeros(11)
+res_binom[(0, 30)].ci_upp = np.array([
+    0.13471170, 0.00000000, 0.06151672, 0.11570331,
+    0.11570331, 0.11570331, 0.11570331, 0.10402893,
+    0.06201781, 0.14132048, 0.11351339])
+
+# > bci = binom.confint(x = c(30), n = 30, tol = 1e-8)
+res_binom[(30, 30)].ci_low = np.array([
+    0.8652883, 1.0000000, 0.9384833, 0.8842967,
+    0.8842967, 0.8842967, 0.8842967, 0.8959711,
+    0.9379822, 0.8586795, 0.8864866])
+# Note: this ci_upp clips one value > 1
+res_binom[(30, 30)].ci_upp = np.ones(11)

venv/lib/python3.11/site-packages/statsmodels/stats/tests/results/results_rates.py

@@ -0,0 +1,47 @@
+"""
+Created on Mon May  4 21:21:09 2020
+
+Author: Josef Perktold
+License: BSD-3
+
+"""
+
+import numpy as np
+from statsmodels.tools.testing import Holder
+
+NA = np.nan
+
+# > pe = poisson.exact(c(60, 30), c(51477.5, 54308.7), tsmethod="minlike",
+#                      midp=FALSE)
+# > cat_items(pe, prefix="res.")
+res_pexact_cond = res = Holder()
+res.statistic = 60
+res.parameter = 43.7956463130352
+res.p_value = 0.000675182658686321
+res.conf_int = np.array([
+    1.34983090611567, 3.27764509862914
+    ])
+res.estimate = 2.10999757175465
+res.null_value = 1
+res.alternative = 'two.sided'
+res.method = ('Exact two-sided Poisson test (sum of minimum likelihood'
+              ' method)')
+res.data_name = 'c(60, 30) time base: c(51477.5, 54308.7)'
+
+
+# > pe = poisson.exact(c(60, 30), c(51477.5, 54308.7), tsmethod="minlike",
+#                      midp=TRUE)
+# > cat_items(pe, prefix="res.")
+res_pexact_cond_midp = res = Holder()
+res.statistic = 60
+res.parameter = 43.7956463130352
+res.p_value = 0.000557262406619052
+res.conf_int = np.array([
+    NA, NA
+    ])
+res.estimate = 2.10999757175465
+res.null_value = 1
+res.alternative = 'two.sided'
+res.method = ('Exact two-sided Poisson test (sum of minimum'
+              ' likelihood method), mid-p version')
+res.data_name = 'c(60, 30) time base: c(51477.5, 54308.7)'

venv/lib/python3.11/site-packages/statsmodels/stats/tests/results/wspec1.csv

@@ -0,0 +1,36 @@
+IV1,IV2,DV
+-0.02015,-8.12,-0.00262
+-0.09145,22.6,0.06002
+-0.23417,34.14,0.28942
+-0.11179,-24.21,-0.01991
+0.16144,-14.37,-0.11282
+0.15733,-10.98,-0.15465
+0.05293,-0.61,-0.09055
+0.05686,-3.38,-0.01311
+-0.11524,18.48,0.01793
+0.10973,-12.93,-0.02117
+0.11152,-9.32,-0.08318
+0.05917,5.86,-0.01982
+-0.00468,-6.67,0.03378
+-0.15653,25.27,0.10565
+0.115,-2.55,0.0483
+0.12352,-22.48,-0.07733
+-0.03604,3.69,0.07183
+0.05634,-6.19,-0.05512
+-0.00401,2.25,0.01318
+0.10524,-3.73,-0.00379
+0.02288,1.5,0.00922
+0.05646,-3.48,-0.02355
+0.09566,3.33,-0.06103
+0.01535,1.1,-0.0343
+0.0435,-4.41,-0.02346
+-0.00534,0,-0.05993
+0.04721,9.22,-0.00264
+0.01321,-3.86,-0.02038
+-0.00353,-3.54,-0.00693
+-0.07728,21.89,0.04091
+0.0572,-16.35,0.00791
+0.00372,3.75,0.10267
+0.02087,-2.38,-0.04426
+0.03918,-7.62,-0.05083
+0.03597,4.37,0.01428

venv/lib/python3.11/site-packages/statsmodels/stats/tests/results/wspec2.csv

@@ -0,0 +1,36 @@
+IV1,IV2,IV3,DV
+144.6,0.02846,2.28,-0.00262
+30,0.15082,2.768,0.06002
+-293.1,0.58902,3.323,0.28942
+-166,0.01642,-5.012,-0.01991
+-43.5,-0.23772,-1.471,-0.11282
+43.7,-0.42052,1.129,-0.15465
+182.4,-0.2129,1.837,-0.09055
+114.6,-0.22211,1.686,-0.01311
+207.5,0.02502,0.344,0.01793
+169.1,0.10521,-0.077,-0.02117
+172.5,-0.07982,0.637,-0.08318
+8.2,-0.08353,1.765,-0.01982
+222.5,-0.02544,2.345,0.03378
+126.2,0.28108,2.524,0.10565
+198.2,0.18466,1.465,0.0483
+188.6,-0.09714,1.014,-0.07733
+148,0.01393,1.279,0.07183
+106,-0.04318,0.482,-0.05512
+69.4,-0.14695,1.033,0.01318
+178.1,-0.07366,1.528,-0.00379
+66,0.02879,0.93,0.00922
+207.9,0.01782,-0.271,-0.02355
+250.6,-0.06627,1.253,-0.06103
+31.4,-0.114,0.906,-0.0343
+289.6,-0.08656,1.491,-0.02346
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