diff --git a/docs/_quarto.yml b/docs/_quarto.yml index 752776345..0acdc5350 100644 --- a/docs/_quarto.yml +++ b/docs/_quarto.yml @@ -64,6 +64,7 @@ website: - notebooks/categorical_regression.ipynb - notebooks/circular_regression.ipynb - notebooks/quantile_regression.ipynb + - notebooks/mister_p.ipynb - section: More advanced models contents: - notebooks/distributional_models.ipynb diff --git a/docs/notebooks/data/mr_p_cces18_common_vv.csv.gz b/docs/notebooks/data/mr_p_cces18_common_vv.csv.gz new file mode 100644 index 000000000..7e3a538c0 Binary files /dev/null and b/docs/notebooks/data/mr_p_cces18_common_vv.csv.gz differ diff --git a/docs/notebooks/data/mr_p_poststrat_df.csv b/docs/notebooks/data/mr_p_poststrat_df.csv new file mode 100644 index 000000000..73c032188 --- /dev/null +++ b/docs/notebooks/data/mr_p_poststrat_df.csv @@ -0,0 +1,12001 @@ +"state","eth","male","age","educ","n" +"AL","White",-0.5,"18-29","No HS",23948 +"AL","White",-0.5,"18-29","HS",59378 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College",305 +"ND","Black",-0.5,"40-49","Post-grad",69 +"ND","Black",-0.5,"50-59","No HS",0 +"ND","Black",-0.5,"50-59","HS",42 +"ND","Black",-0.5,"50-59","Some college",92 +"ND","Black",-0.5,"50-59","4-Year College",0 +"ND","Black",-0.5,"50-59","Post-grad",152 +"ND","Black",-0.5,"60-69","No HS",0 +"ND","Black",-0.5,"60-69","HS",0 +"ND","Black",-0.5,"60-69","Some college",131 +"ND","Black",-0.5,"60-69","4-Year College",0 +"ND","Black",-0.5,"60-69","Post-grad",0 +"ND","Black",-0.5,"70+","No HS",0 +"ND","Black",-0.5,"70+","HS",0 +"ND","Black",-0.5,"70+","Some college",0 +"ND","Black",-0.5,"70+","4-Year College",38 +"ND","Black",-0.5,"70+","Post-grad",0 +"ND","Black",0.5,"18-29","No HS",83 +"ND","Black",0.5,"18-29","HS",994 +"ND","Black",0.5,"18-29","Some college",1360 +"ND","Black",0.5,"18-29","4-Year College",388 +"ND","Black",0.5,"18-29","Post-grad",0 +"ND","Black",0.5,"30-39","No HS",155 +"ND","Black",0.5,"30-39","HS",387 +"ND","Black",0.5,"30-39","Some college",346 +"ND","Black",0.5,"30-39","4-Year College",223 +"ND","Black",0.5,"30-39","Post-grad",0 +"ND","Black",0.5,"40-49","No HS",181 +"ND","Black",0.5,"40-49","HS",695 +"ND","Black",0.5,"40-49","Some college",244 +"ND","Black",0.5,"40-49","4-Year College",356 +"ND","Black",0.5,"40-49","Post-grad",125 +"ND","Black",0.5,"50-59","No HS",13 +"ND","Black",0.5,"50-59","HS",158 +"ND","Black",0.5,"50-59","Some college",358 +"ND","Black",0.5,"50-59","4-Year College",118 +"ND","Black",0.5,"50-59","Post-grad",342 +"ND","Black",0.5,"60-69","No HS",13 +"ND","Black",0.5,"60-69","HS",0 +"ND","Black",0.5,"60-69","Some college",68 +"ND","Black",0.5,"60-69","4-Year College",10 +"ND","Black",0.5,"60-69","Post-grad",0 +"ND","Black",0.5,"70+","No HS",0 +"ND","Black",0.5,"70+","HS",62 +"ND","Black",0.5,"70+","Some college",26 +"ND","Black",0.5,"70+","4-Year College",5 +"ND","Black",0.5,"70+","Post-grad",37 +"ND","Other",-0.5,"18-29","No HS",1019 +"ND","Other",-0.5,"18-29","HS",1799 +"ND","Other",-0.5,"18-29","Some college",3375 +"ND","Other",-0.5,"18-29","4-Year College",651 +"ND","Other",-0.5,"18-29","Post-grad",189 +"ND","Other",-0.5,"30-39","No HS",731 +"ND","Other",-0.5,"30-39","HS",1111 +"ND","Other",-0.5,"30-39","Some college",1353 +"ND","Other",-0.5,"30-39","4-Year College",849 +"ND","Other",-0.5,"30-39","Post-grad",295 +"ND","Other",-0.5,"40-49","No HS",420 +"ND","Other",-0.5,"40-49","HS",651 +"ND","Other",-0.5,"40-49","Some college",1303 +"ND","Other",-0.5,"40-49","4-Year College",617 +"ND","Other",-0.5,"40-49","Post-grad",145 +"ND","Other",-0.5,"50-59","No HS",454 +"ND","Other",-0.5,"50-59","HS",648 +"ND","Other",-0.5,"50-59","Some college",1238 +"ND","Other",-0.5,"50-59","4-Year College",531 +"ND","Other",-0.5,"50-59","Post-grad",358 +"ND","Other",-0.5,"60-69","No HS",215 +"ND","Other",-0.5,"60-69","HS",484 +"ND","Other",-0.5,"60-69","Some college",714 +"ND","Other",-0.5,"60-69","4-Year College",174 +"ND","Other",-0.5,"60-69","Post-grad",129 +"ND","Other",-0.5,"70+","No HS",546 +"ND","Other",-0.5,"70+","HS",249 +"ND","Other",-0.5,"70+","Some college",240 +"ND","Other",-0.5,"70+","4-Year College",314 +"ND","Other",-0.5,"70+","Post-grad",60 +"ND","Other",0.5,"18-29","No HS",1361 +"ND","Other",0.5,"18-29","HS",2834 +"ND","Other",0.5,"18-29","Some college",2731 +"ND","Other",0.5,"18-29","4-Year College",325 +"ND","Other",0.5,"18-29","Post-grad",12 +"ND","Other",0.5,"30-39","No HS",862 +"ND","Other",0.5,"30-39","HS",1147 +"ND","Other",0.5,"30-39","Some college",1847 +"ND","Other",0.5,"30-39","4-Year College",365 +"ND","Other",0.5,"30-39","Post-grad",187 +"ND","Other",0.5,"40-49","No HS",344 +"ND","Other",0.5,"40-49","HS",774 +"ND","Other",0.5,"40-49","Some college",1140 +"ND","Other",0.5,"40-49","4-Year College",357 +"ND","Other",0.5,"40-49","Post-grad",245 +"ND","Other",0.5,"50-59","No HS",628 +"ND","Other",0.5,"50-59","HS",976 +"ND","Other",0.5,"50-59","Some college",1270 +"ND","Other",0.5,"50-59","4-Year College",175 +"ND","Other",0.5,"50-59","Post-grad",176 +"ND","Other",0.5,"60-69","No HS",166 +"ND","Other",0.5,"60-69","HS",345 +"ND","Other",0.5,"60-69","Some college",467 +"ND","Other",0.5,"60-69","4-Year College",208 +"ND","Other",0.5,"60-69","Post-grad",88 +"ND","Other",0.5,"70+","No HS",418 +"ND","Other",0.5,"70+","HS",153 +"ND","Other",0.5,"70+","Some college",164 +"ND","Other",0.5,"70+","4-Year College",14 +"ND","Other",0.5,"70+","Post-grad",3 +"ND","Hispanic",-0.5,"18-29","No HS",90 +"ND","Hispanic",-0.5,"18-29","HS",766 +"ND","Hispanic",-0.5,"18-29","Some college",878 +"ND","Hispanic",-0.5,"18-29","4-Year College",98 +"ND","Hispanic",-0.5,"18-29","Post-grad",0 +"ND","Hispanic",-0.5,"30-39","No HS",212 +"ND","Hispanic",-0.5,"30-39","HS",276 +"ND","Hispanic",-0.5,"30-39","Some college",395 +"ND","Hispanic",-0.5,"30-39","4-Year College",75 +"ND","Hispanic",-0.5,"30-39","Post-grad",23 +"ND","Hispanic",-0.5,"40-49","No HS",88 +"ND","Hispanic",-0.5,"40-49","HS",128 +"ND","Hispanic",-0.5,"40-49","Some college",267 +"ND","Hispanic",-0.5,"40-49","4-Year College",84 +"ND","Hispanic",-0.5,"40-49","Post-grad",16 +"ND","Hispanic",-0.5,"50-59","No HS",49 +"ND","Hispanic",-0.5,"50-59","HS",54 +"ND","Hispanic",-0.5,"50-59","Some college",118 +"ND","Hispanic",-0.5,"50-59","4-Year College",152 +"ND","Hispanic",-0.5,"50-59","Post-grad",0 +"ND","Hispanic",-0.5,"60-69","No HS",12 +"ND","Hispanic",-0.5,"60-69","HS",105 +"ND","Hispanic",-0.5,"60-69","Some college",88 +"ND","Hispanic",-0.5,"60-69","4-Year College",12 +"ND","Hispanic",-0.5,"60-69","Post-grad",0 +"ND","Hispanic",-0.5,"70+","No HS",29 +"ND","Hispanic",-0.5,"70+","HS",86 +"ND","Hispanic",-0.5,"70+","Some college",23 +"ND","Hispanic",-0.5,"70+","4-Year College",0 +"ND","Hispanic",-0.5,"70+","Post-grad",0 +"ND","Hispanic",0.5,"18-29","No HS",255 +"ND","Hispanic",0.5,"18-29","HS",1117 +"ND","Hispanic",0.5,"18-29","Some college",1002 +"ND","Hispanic",0.5,"18-29","4-Year College",290 +"ND","Hispanic",0.5,"18-29","Post-grad",0 +"ND","Hispanic",0.5,"30-39","No HS",212 +"ND","Hispanic",0.5,"30-39","HS",658 +"ND","Hispanic",0.5,"30-39","Some college",506 +"ND","Hispanic",0.5,"30-39","4-Year College",143 +"ND","Hispanic",0.5,"30-39","Post-grad",136 +"ND","Hispanic",0.5,"40-49","No HS",33 +"ND","Hispanic",0.5,"40-49","HS",59 +"ND","Hispanic",0.5,"40-49","Some college",336 +"ND","Hispanic",0.5,"40-49","4-Year College",51 +"ND","Hispanic",0.5,"40-49","Post-grad",34 +"ND","Hispanic",0.5,"50-59","No HS",27 +"ND","Hispanic",0.5,"50-59","HS",10 +"ND","Hispanic",0.5,"50-59","Some college",234 +"ND","Hispanic",0.5,"50-59","4-Year College",0 +"ND","Hispanic",0.5,"50-59","Post-grad",26 +"ND","Hispanic",0.5,"60-69","No HS",105 +"ND","Hispanic",0.5,"60-69","HS",82 +"ND","Hispanic",0.5,"60-69","Some college",61 +"ND","Hispanic",0.5,"60-69","4-Year College",0 +"ND","Hispanic",0.5,"60-69","Post-grad",0 +"ND","Hispanic",0.5,"70+","No HS",0 +"ND","Hispanic",0.5,"70+","HS",20 +"ND","Hispanic",0.5,"70+","Some college",54 +"ND","Hispanic",0.5,"70+","4-Year College",4 +"ND","Hispanic",0.5,"70+","Post-grad",0 +"OH","White",-0.5,"18-29","No HS",57744 +"OH","White",-0.5,"18-29","HS",179310 +"OH","White",-0.5,"18-29","Some college",280200 +"OH","White",-0.5,"18-29","4-Year College",129283 +"OH","White",-0.5,"18-29","Post-grad",31460 +"OH","White",-0.5,"30-39","No HS",33095 +"OH","White",-0.5,"30-39","HS",116875 +"OH","White",-0.5,"30-39","Some college",182211 +"OH","White",-0.5,"30-39","4-Year College",127250 +"OH","White",-0.5,"30-39","Post-grad",83078 +"OH","White",-0.5,"40-49","No HS",32099 +"OH","White",-0.5,"40-49","HS",154876 +"OH","White",-0.5,"40-49","Some college",188511 +"OH","White",-0.5,"40-49","4-Year College",125857 +"OH","White",-0.5,"40-49","Post-grad",75591 +"OH","White",-0.5,"50-59","No HS",46713 +"OH","White",-0.5,"50-59","HS",247309 +"OH","White",-0.5,"50-59","Some college",214022 +"OH","White",-0.5,"50-59","4-Year College",119418 +"OH","White",-0.5,"50-59","Post-grad",67283 +"OH","White",-0.5,"60-69","No HS",49144 +"OH","White",-0.5,"60-69","HS",239081 +"OH","White",-0.5,"60-69","Some college",180263 +"OH","White",-0.5,"60-69","4-Year College",87036 +"OH","White",-0.5,"60-69","Post-grad",64007 +"OH","White",-0.5,"70+","No HS",101798 +"OH","White",-0.5,"70+","HS",309431 +"OH","White",-0.5,"70+","Some college",131289 +"OH","White",-0.5,"70+","4-Year College",63469 +"OH","White",-0.5,"70+","Post-grad",39115 +"OH","White",0.5,"18-29","No HS",75171 +"OH","White",0.5,"18-29","HS",240585 +"OH","White",0.5,"18-29","Some college",259258 +"OH","White",0.5,"18-29","4-Year College",105250 +"OH","White",0.5,"18-29","Post-grad",20164 +"OH","White",0.5,"30-39","No HS",42454 +"OH","White",0.5,"30-39","HS",166087 +"OH","White",0.5,"30-39","Some college",166265 +"OH","White",0.5,"30-39","4-Year College",115195 +"OH","White",0.5,"30-39","Post-grad",55771 +"OH","White",0.5,"40-49","No HS",44390 +"OH","White",0.5,"40-49","HS",192261 +"OH","White",0.5,"40-49","Some college",164543 +"OH","White",0.5,"40-49","4-Year College",112030 +"OH","White",0.5,"40-49","Post-grad",57221 +"OH","White",0.5,"50-59","No HS",59006 +"OH","White",0.5,"50-59","HS",261592 +"OH","White",0.5,"50-59","Some college",176552 +"OH","White",0.5,"50-59","4-Year College",109178 +"OH","White",0.5,"50-59","Post-grad",62997 +"OH","White",0.5,"60-69","No HS",47942 +"OH","White",0.5,"60-69","HS",204394 +"OH","White",0.5,"60-69","Some college",158972 +"OH","White",0.5,"60-69","4-Year College",96170 +"OH","White",0.5,"60-69","Post-grad",72245 +"OH","White",0.5,"70+","No HS",74002 +"OH","White",0.5,"70+","HS",179218 +"OH","White",0.5,"70+","Some college",99887 +"OH","White",0.5,"70+","4-Year College",60963 +"OH","White",0.5,"70+","Post-grad",54229 +"OH","Black",-0.5,"18-29","No HS",17361 +"OH","Black",-0.5,"18-29","HS",42021 +"OH","Black",-0.5,"18-29","Some college",57626 +"OH","Black",-0.5,"18-29","4-Year College",12234 +"OH","Black",-0.5,"18-29","Post-grad",1963 +"OH","Black",-0.5,"30-39","No HS",10639 +"OH","Black",-0.5,"30-39","HS",23065 +"OH","Black",-0.5,"30-39","Some college",39652 +"OH","Black",-0.5,"30-39","4-Year College",11467 +"OH","Black",-0.5,"30-39","Post-grad",6669 +"OH","Black",-0.5,"40-49","No HS",8485 +"OH","Black",-0.5,"40-49","HS",23296 +"OH","Black",-0.5,"40-49","Some college",35996 +"OH","Black",-0.5,"40-49","4-Year College",11989 +"OH","Black",-0.5,"40-49","Post-grad",8718 +"OH","Black",-0.5,"50-59","No HS",10850 +"OH","Black",-0.5,"50-59","HS",27486 +"OH","Black",-0.5,"50-59","Some college",38220 +"OH","Black",-0.5,"50-59","4-Year College",10489 +"OH","Black",-0.5,"50-59","Post-grad",6054 +"OH","Black",-0.5,"60-69","No HS",10586 +"OH","Black",-0.5,"60-69","HS",24720 +"OH","Black",-0.5,"60-69","Some college",28096 +"OH","Black",-0.5,"60-69","4-Year College",7254 +"OH","Black",-0.5,"60-69","Post-grad",5691 +"OH","Black",-0.5,"70+","No HS",18367 +"OH","Black",-0.5,"70+","HS",25630 +"OH","Black",-0.5,"70+","Some college",15940 +"OH","Black",-0.5,"70+","4-Year College",3605 +"OH","Black",-0.5,"70+","Post-grad",3504 +"OH","Black",0.5,"18-29","No HS",22834 +"OH","Black",0.5,"18-29","HS",49380 +"OH","Black",0.5,"18-29","Some college",47953 +"OH","Black",0.5,"18-29","4-Year College",8082 +"OH","Black",0.5,"18-29","Post-grad",1205 +"OH","Black",0.5,"30-39","No HS",11585 +"OH","Black",0.5,"30-39","HS",28604 +"OH","Black",0.5,"30-39","Some college",29391 +"OH","Black",0.5,"30-39","4-Year College",8225 +"OH","Black",0.5,"30-39","Post-grad",4397 +"OH","Black",0.5,"40-49","No HS",9693 +"OH","Black",0.5,"40-49","HS",28483 +"OH","Black",0.5,"40-49","Some college",28580 +"OH","Black",0.5,"40-49","4-Year College",8637 +"OH","Black",0.5,"40-49","Post-grad",4490 +"OH","Black",0.5,"50-59","No HS",13021 +"OH","Black",0.5,"50-59","HS",33713 +"OH","Black",0.5,"50-59","Some college",24091 +"OH","Black",0.5,"50-59","4-Year College",7782 +"OH","Black",0.5,"50-59","Post-grad",4453 +"OH","Black",0.5,"60-69","No HS",9329 +"OH","Black",0.5,"60-69","HS",23736 +"OH","Black",0.5,"60-69","Some college",20155 +"OH","Black",0.5,"60-69","4-Year College",5518 +"OH","Black",0.5,"60-69","Post-grad",3234 +"OH","Black",0.5,"70+","No HS",11729 +"OH","Black",0.5,"70+","HS",13946 +"OH","Black",0.5,"70+","Some college",9175 +"OH","Black",0.5,"70+","4-Year College",2535 +"OH","Black",0.5,"70+","Post-grad",2583 +"OH","Other",-0.5,"18-29","No HS",6596 +"OH","Other",-0.5,"18-29","HS",15468 +"OH","Other",-0.5,"18-29","Some college",22585 +"OH","Other",-0.5,"18-29","4-Year College",7558 +"OH","Other",-0.5,"18-29","Post-grad",2736 +"OH","Other",-0.5,"30-39","No HS",3418 +"OH","Other",-0.5,"30-39","HS",6250 +"OH","Other",-0.5,"30-39","Some college",11650 +"OH","Other",-0.5,"30-39","4-Year College",6883 +"OH","Other",-0.5,"30-39","Post-grad",5623 +"OH","Other",-0.5,"40-49","No HS",3060 +"OH","Other",-0.5,"40-49","HS",5192 +"OH","Other",-0.5,"40-49","Some college",10352 +"OH","Other",-0.5,"40-49","4-Year College",6537 +"OH","Other",-0.5,"40-49","Post-grad",5108 +"OH","Other",-0.5,"50-59","No HS",3261 +"OH","Other",-0.5,"50-59","HS",6088 +"OH","Other",-0.5,"50-59","Some college",7615 +"OH","Other",-0.5,"50-59","4-Year College",4766 +"OH","Other",-0.5,"50-59","Post-grad",3466 +"OH","Other",-0.5,"60-69","No HS",3485 +"OH","Other",-0.5,"60-69","HS",4416 +"OH","Other",-0.5,"60-69","Some college",5544 +"OH","Other",-0.5,"60-69","4-Year College",2680 +"OH","Other",-0.5,"60-69","Post-grad",2135 +"OH","Other",-0.5,"70+","No HS",4210 +"OH","Other",-0.5,"70+","HS",3404 +"OH","Other",-0.5,"70+","Some college",2277 +"OH","Other",-0.5,"70+","4-Year College",1702 +"OH","Other",-0.5,"70+","Post-grad",1291 +"OH","Other",0.5,"18-29","No HS",7912 +"OH","Other",0.5,"18-29","HS",19839 +"OH","Other",0.5,"18-29","Some college",20008 +"OH","Other",0.5,"18-29","4-Year College",7534 +"OH","Other",0.5,"18-29","Post-grad",1530 +"OH","Other",0.5,"30-39","No HS",3904 +"OH","Other",0.5,"30-39","HS",7955 +"OH","Other",0.5,"30-39","Some college",8819 +"OH","Other",0.5,"30-39","4-Year College",5825 +"OH","Other",0.5,"30-39","Post-grad",4580 +"OH","Other",0.5,"40-49","No HS",3896 +"OH","Other",0.5,"40-49","HS",7543 +"OH","Other",0.5,"40-49","Some college",7543 +"OH","Other",0.5,"40-49","4-Year College",5105 +"OH","Other",0.5,"40-49","Post-grad",5855 +"OH","Other",0.5,"50-59","No HS",2924 +"OH","Other",0.5,"50-59","HS",6110 +"OH","Other",0.5,"50-59","Some college",6013 +"OH","Other",0.5,"50-59","4-Year College",3018 +"OH","Other",0.5,"50-59","Post-grad",4648 +"OH","Other",0.5,"60-69","No HS",2215 +"OH","Other",0.5,"60-69","HS",3229 +"OH","Other",0.5,"60-69","Some college",3231 +"OH","Other",0.5,"60-69","4-Year College",2233 +"OH","Other",0.5,"60-69","Post-grad",3253 +"OH","Other",0.5,"70+","No HS",2281 +"OH","Other",0.5,"70+","HS",1434 +"OH","Other",0.5,"70+","Some college",1300 +"OH","Other",0.5,"70+","4-Year College",1682 +"OH","Other",0.5,"70+","Post-grad",2461 +"OH","Hispanic",-0.5,"18-29","No HS",2550 +"OH","Hispanic",-0.5,"18-29","HS",6493 +"OH","Hispanic",-0.5,"18-29","Some college",9406 +"OH","Hispanic",-0.5,"18-29","4-Year College",2645 +"OH","Hispanic",-0.5,"18-29","Post-grad",563 +"OH","Hispanic",-0.5,"30-39","No HS",1657 +"OH","Hispanic",-0.5,"30-39","HS",3207 +"OH","Hispanic",-0.5,"30-39","Some college",5601 +"OH","Hispanic",-0.5,"30-39","4-Year College",2390 +"OH","Hispanic",-0.5,"30-39","Post-grad",1714 +"OH","Hispanic",-0.5,"40-49","No HS",1250 +"OH","Hispanic",-0.5,"40-49","HS",3565 +"OH","Hispanic",-0.5,"40-49","Some college",3636 +"OH","Hispanic",-0.5,"40-49","4-Year College",2902 +"OH","Hispanic",-0.5,"40-49","Post-grad",1326 +"OH","Hispanic",-0.5,"50-59","No HS",1576 +"OH","Hispanic",-0.5,"50-59","HS",3154 +"OH","Hispanic",-0.5,"50-59","Some college",2950 +"OH","Hispanic",-0.5,"50-59","4-Year College",1755 +"OH","Hispanic",-0.5,"50-59","Post-grad",805 +"OH","Hispanic",-0.5,"60-69","No HS",1808 +"OH","Hispanic",-0.5,"60-69","HS",2484 +"OH","Hispanic",-0.5,"60-69","Some college",1884 +"OH","Hispanic",-0.5,"60-69","4-Year College",470 +"OH","Hispanic",-0.5,"60-69","Post-grad",843 +"OH","Hispanic",-0.5,"70+","No HS",2421 +"OH","Hispanic",-0.5,"70+","HS",2216 +"OH","Hispanic",-0.5,"70+","Some college",1195 +"OH","Hispanic",-0.5,"70+","4-Year College",519 +"OH","Hispanic",-0.5,"70+","Post-grad",326 +"OH","Hispanic",0.5,"18-29","No HS",3953 +"OH","Hispanic",0.5,"18-29","HS",7726 +"OH","Hispanic",0.5,"18-29","Some college",8530 +"OH","Hispanic",0.5,"18-29","4-Year College",2640 +"OH","Hispanic",0.5,"18-29","Post-grad",703 +"OH","Hispanic",0.5,"30-39","No HS",3151 +"OH","Hispanic",0.5,"30-39","HS",4634 +"OH","Hispanic",0.5,"30-39","Some college",4799 +"OH","Hispanic",0.5,"30-39","4-Year College",2144 +"OH","Hispanic",0.5,"30-39","Post-grad",1181 +"OH","Hispanic",0.5,"40-49","No HS",2029 +"OH","Hispanic",0.5,"40-49","HS",3341 +"OH","Hispanic",0.5,"40-49","Some college",3688 +"OH","Hispanic",0.5,"40-49","4-Year College",1819 +"OH","Hispanic",0.5,"40-49","Post-grad",1536 +"OH","Hispanic",0.5,"50-59","No HS",2216 +"OH","Hispanic",0.5,"50-59","HS",2847 +"OH","Hispanic",0.5,"50-59","Some college",2858 +"OH","Hispanic",0.5,"50-59","4-Year College",1029 +"OH","Hispanic",0.5,"50-59","Post-grad",1205 +"OH","Hispanic",0.5,"60-69","No HS",1504 +"OH","Hispanic",0.5,"60-69","HS",1890 +"OH","Hispanic",0.5,"60-69","Some college",1407 +"OH","Hispanic",0.5,"60-69","4-Year College",419 +"OH","Hispanic",0.5,"60-69","Post-grad",671 +"OH","Hispanic",0.5,"70+","No HS",1836 +"OH","Hispanic",0.5,"70+","HS",1474 +"OH","Hispanic",0.5,"70+","Some college",587 +"OH","Hispanic",0.5,"70+","4-Year College",306 +"OH","Hispanic",0.5,"70+","Post-grad",252 +"OK","White",-0.5,"18-29","No HS",17717 +"OK","White",-0.5,"18-29","HS",53228 +"OK","White",-0.5,"18-29","Some college",80936 +"OK","White",-0.5,"18-29","4-Year College",29952 +"OK","White",-0.5,"18-29","Post-grad",6790 +"OK","White",-0.5,"30-39","No HS",10723 +"OK","White",-0.5,"30-39","HS",36770 +"OK","White",-0.5,"30-39","Some college",57178 +"OK","White",-0.5,"30-39","4-Year College",38722 +"OK","White",-0.5,"30-39","Post-grad",16701 +"OK","White",-0.5,"40-49","No HS",10883 +"OK","White",-0.5,"40-49","HS",40327 +"OK","White",-0.5,"40-49","Some college",52713 +"OK","White",-0.5,"40-49","4-Year College",35090 +"OK","White",-0.5,"40-49","Post-grad",17409 +"OK","White",-0.5,"50-59","No HS",14840 +"OK","White",-0.5,"50-59","HS",56689 +"OK","White",-0.5,"50-59","Some college",62782 +"OK","White",-0.5,"50-59","4-Year College",34588 +"OK","White",-0.5,"50-59","Post-grad",16886 +"OK","White",-0.5,"60-69","No HS",13595 +"OK","White",-0.5,"60-69","HS",56501 +"OK","White",-0.5,"60-69","Some college",58331 +"OK","White",-0.5,"60-69","4-Year College",29058 +"OK","White",-0.5,"60-69","Post-grad",19093 +"OK","White",-0.5,"70+","No HS",28532 +"OK","White",-0.5,"70+","HS",75264 +"OK","White",-0.5,"70+","Some college",51355 +"OK","White",-0.5,"70+","4-Year College",20702 +"OK","White",-0.5,"70+","Post-grad",13139 +"OK","White",0.5,"18-29","No HS",22160 +"OK","White",0.5,"18-29","HS",69436 +"OK","White",0.5,"18-29","Some college",77432 +"OK","White",0.5,"18-29","4-Year College",24023 +"OK","White",0.5,"18-29","Post-grad",3975 +"OK","White",0.5,"30-39","No HS",14388 +"OK","White",0.5,"30-39","HS",51713 +"OK","White",0.5,"30-39","Some college",52142 +"OK","White",0.5,"30-39","4-Year College",31460 +"OK","White",0.5,"30-39","Post-grad",13471 +"OK","White",0.5,"40-49","No HS",13003 +"OK","White",0.5,"40-49","HS",51561 +"OK","White",0.5,"40-49","Some college",48113 +"OK","White",0.5,"40-49","4-Year College",29495 +"OK","White",0.5,"40-49","Post-grad",13854 +"OK","White",0.5,"50-59","No HS",17363 +"OK","White",0.5,"50-59","HS",64664 +"OK","White",0.5,"50-59","Some college",51700 +"OK","White",0.5,"50-59","4-Year College",29429 +"OK","White",0.5,"50-59","Post-grad",15648 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+"WY","Hispanic",-0.5,"60-69","No HS",155 +"WY","Hispanic",-0.5,"60-69","HS",500 +"WY","Hispanic",-0.5,"60-69","Some college",378 +"WY","Hispanic",-0.5,"60-69","4-Year College",157 +"WY","Hispanic",-0.5,"60-69","Post-grad",82 +"WY","Hispanic",-0.5,"70+","No HS",533 +"WY","Hispanic",-0.5,"70+","HS",175 +"WY","Hispanic",-0.5,"70+","Some college",187 +"WY","Hispanic",-0.5,"70+","4-Year College",41 +"WY","Hispanic",-0.5,"70+","Post-grad",0 +"WY","Hispanic",0.5,"18-29","No HS",809 +"WY","Hispanic",0.5,"18-29","HS",1557 +"WY","Hispanic",0.5,"18-29","Some college",1157 +"WY","Hispanic",0.5,"18-29","4-Year College",157 +"WY","Hispanic",0.5,"18-29","Post-grad",0 +"WY","Hispanic",0.5,"30-39","No HS",395 +"WY","Hispanic",0.5,"30-39","HS",1071 +"WY","Hispanic",0.5,"30-39","Some college",976 +"WY","Hispanic",0.5,"30-39","4-Year College",266 +"WY","Hispanic",0.5,"30-39","Post-grad",25 +"WY","Hispanic",0.5,"40-49","No HS",255 +"WY","Hispanic",0.5,"40-49","HS",744 +"WY","Hispanic",0.5,"40-49","Some 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+1,51 @@ +state,repvote,region +AL,0.643741436,South +AK,0.583856547,West +AZ,0.518900234,West +AR,0.642851377,South +CA,0.338717892,West +CO,0.473166666,West +CT,0.428584525,Northeast +DE,0.440013786,South +FL,0.506188355,South +GA,0.526611726,South +HI,0.325586625,West +ID,0.683101767,West +IL,0.409799486,North Central +IN,0.601173095,North Central +IA,0.550635478,North Central +KS,0.611114703,North Central +KY,0.65670629,South +LA,0.601716772,South +ME,0.484032089,Northeast +MD,0.359837503,South +MA,0.353487213,Northeast +MI,0.501176682,North Central +MN,0.491681431,North Central +MS,0.590898473,South +MO,0.59818561,North Central +MT,0.611096643,West +NE,0.635476741,North Central +NV,0.487062906,West +NH,0.498029716,Northeast +NJ,0.427158099,Northeast +NM,0.453492051,West +NY,0.382275815,Northeast +NC,0.519037458,South +ND,0.698092429,North Central +OH,0.542676846,North Central +OK,0.693047372,South +OR,0.438441611,West +PA,0.503755358,Northeast +RI,0.416892959,Northeast +SC,0.574602564,South +SD,0.659718581,North Central +TN,0.63624343,South +TX,0.547132256,South +UT,0.623836582,West +VT,0.348135737,Northeast +VA,0.471736237,South +WA,0.412130688,West +WV,0.721610523,South +WI,0.50407989,North Central +WY,0.757053196,West diff --git a/docs/notebooks/gallery.yml b/docs/notebooks/gallery.yml index 83cbaf3dc..930ce19af 100644 --- a/docs/notebooks/gallery.yml +++ b/docs/notebooks/gallery.yml @@ -80,6 +80,10 @@ subtitle: Model a percentile href: quantile_regression.ipynb thumbnail: thumbnails/quantile_regression.png + - title: MrP (Multilevel Regression and Post-stratification) + subtitle: Survey Data and Representative Sampling + href: mister_p.ipynb + thumbnail: thumbnails/mr_p_adjustment.png - category: More advanced models description: "" tiles: diff --git a/docs/notebooks/mister_p.ipynb b/docs/notebooks/mister_p.ipynb new file mode 100644 index 000000000..35b866896 --- /dev/null +++ b/docs/notebooks/mister_p.ipynb @@ -0,0 +1,8773 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multilevel Regression and Post-stratification\n", + "\n", + "## Regression and Effect Modification\n", + "\n", + "What are we even doing when we fit a regression model? Is a question that arises when first learning the tools of the trade and again when debugging strange results of your thousandth logistic regression model.\n", + "\n", + "This notebook is intended to showcase how regression can be seen as a method for automating the calculation of stratum specific conditional effects. Additionally, we'll see how we can enrich regression models by a post-stratification adjustment with knowledge of the appropriate stratum specific weights. This technique of multilevel regression and post stratification (MrP) is often used in the context of national surveys where we have knowledge of the population weights appropriate to different demographic groups. It can be used in a wide variety of areas ranging from political polling to online market research. We will demonstrate how to fit and and assess these models using Bambi. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "import arviz as az\n", + "import bambi as bmb\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pymc as pm\n", + "\n", + "warnings.simplefilter(action=\"ignore\", category=FutureWarning)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Risk Stratification\n", + "\n", + "First consider this example of heart transplant patients adapted from Hernan and Robins' excellent book *Causal Inference: What if*. Here we have a number of patients (anonymised with names for the Greek Gods). The data records the outcomes of a heart transplant program for those who were part of the program and those who were not. We also see the different risk levels of each patient assigned the treatment. \n", + "\n", + "What we want to show here is that a regression model fit to this data automatically accounts for the weighting appropriate to the different risk strata. The data is coded with 0-1 indicators for status. `Risk_Strata` is either 1 for higher risk or 0 for lower risk. `Outcome` is whether or not the patient died from the procedure, and `Treatment` is whether or not the patient received treatment. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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nameRisk_StrataTreatmentOutcomeTreatment_x_Risk_Strata
0Rheia0000
1Kronos0010
2Demeter0000
3Hades0000
4Hestia0100
5Poseidon0100
6Hera0100
7Zeus0110
8Artemis1010
9Apollo1010
10Leto1000
11Ares1111
12Athena1111
13Hephaestus1111
14Aphrodite1111
15Cyclope1111
16Persephone1111
17Hermes1101
18Hebe1101
19Dionysus1101
\n", + "
" + ], + "text/plain": [ + " name Risk_Strata Treatment Outcome Treatment_x_Risk_Strata\n", + "0 Rheia 0 0 0 0\n", + "1 Kronos 0 0 1 0\n", + "2 Demeter 0 0 0 0\n", + "3 Hades 0 0 0 0\n", + "4 Hestia 0 1 0 0\n", + "5 Poseidon 0 1 0 0\n", + "6 Hera 0 1 0 0\n", + "7 Zeus 0 1 1 0\n", + "8 Artemis 1 0 1 0\n", + "9 Apollo 1 0 1 0\n", + "10 Leto 1 0 0 0\n", + "11 Ares 1 1 1 1\n", + "12 Athena 1 1 1 1\n", + "13 Hephaestus 1 1 1 1\n", + "14 Aphrodite 1 1 1 1\n", + "15 Cyclope 1 1 1 1\n", + "16 Persephone 1 1 1 1\n", + "17 Hermes 1 1 0 1\n", + "18 Hebe 1 1 0 1\n", + "19 Dionysus 1 1 0 1" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(\n", + " {\n", + " \"name\": [\n", + " \"Rheia\",\n", + " \"Kronos\",\n", + " \"Demeter\",\n", + " \"Hades\",\n", + " \"Hestia\",\n", + " \"Poseidon\",\n", + " \"Hera\",\n", + " \"Zeus\",\n", + " \"Artemis\",\n", + " \"Apollo\",\n", + " \"Leto\",\n", + " \"Ares\",\n", + " \"Athena\",\n", + " \"Hephaestus\",\n", + " \"Aphrodite\",\n", + " \"Cyclope\",\n", + " \"Persephone\",\n", + " \"Hermes\",\n", + " \"Hebe\",\n", + " \"Dionysus\",\n", + " ],\n", + " \"Risk_Strata\": [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n", + " \"Treatment\": [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n", + " \"Outcome\": [0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n", + " }\n", + ")\n", + "\n", + "df[\"Treatment_x_Risk_Strata\"] = df.Treatment * df.Risk_Strata\n", + "\n", + "df" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If the treatment assignment procedure involved complete randomisation then we might expect a reasonable balance of strata effects across the treated and non-treated. In this sample we see (perhaps counter intuitively) that the treatment seems to induce a higher rate of death than the non-treated group." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Share
Treatment
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10.538462
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" + ], + "text/plain": [ + " Share\n", + "Treatment \n", + "0 0.428571\n", + "1 0.538462" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "simple_average = df.groupby(\"Treatment\")[[\"Outcome\"]].mean().rename({\"Outcome\": \"Share\"}, axis=1)\n", + "simple_average" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which suggests an alarming causal effect whereby the treatment seems to increase risk of death in the population. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Causal Risk Ratio: 1.2564102564102564\n" + ] + } + ], + "source": [ + "causal_risk_ratio = simple_average.iloc[1][\"Share\"] / simple_average.iloc[0][\"Share\"]\n", + "print(\"Causal Risk Ratio:\", causal_risk_ratio)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This finding we know on inspection is driven by the imbalance in the risk strata across the treatment groups." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Treatmentproportion
Risk_Strata
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" + ], + "text/plain": [ + " Treatment proportion\n", + "Risk_Strata \n", + "0 8 0.4\n", + "1 12 0.6" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby(\"Risk_Strata\")[[\"Treatment\"]].count().assign(\n", + " proportion=lambda x: x[\"Treatment\"] / len(df)\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can correct for this by weighting the results by the share each group represents across the `Risk_Strata`. In other words when we correct for the population size at the different levels of risk we get a better estimate of the effect. First we see what the expected outcome is for each strata. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Risk_Strata01
Treatment
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" + ], + "text/plain": [ + "Risk_Strata 0 1\n", + "Treatment \n", + "0 0.25 0.666667\n", + "1 0.25 0.666667" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "outcomes_controlled = (\n", + " df.groupby([\"Risk_Strata\", \"Treatment\"])[[\"Outcome\"]]\n", + " .mean()\n", + " .reset_index()\n", + " .pivot(index=\"Treatment\", columns=[\"Risk_Strata\"], values=\"Outcome\")\n", + ")\n", + "\n", + "outcomes_controlled" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note how the expected outcomes are equal across the stratified groups. We can now combine these estimate with the population weights (derived earlier) in each segment to get our weighted average." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Risk_Strata01formulaweighted_average
Treatment
00.250.6666670.4*0.25 + 0.6*0.660.5
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" + ], + "text/plain": [ + "Risk_Strata 0 1 formula weighted_average\n", + "Treatment \n", + "0 0.25 0.666667 0.4*0.25 + 0.6*0.66 0.5\n", + "1 0.25 0.666667 0.4*0.25 + 0.6*0.66 0.5" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weighted_avg = outcomes_controlled.assign(formula=\"0.4*0.25 + 0.6*0.66\").assign(\n", + " weighted_average=lambda x: x[0] * (df[df[\"Risk_Strata\"] == 0].shape[0] / len(df))\n", + " + x[1] * (df[df[\"Risk_Strata\"] == 1].shape[0] / len(df))\n", + ")\n", + "\n", + "weighted_avg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From which we can derive a more sensible treatment effect." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Causal Risk Ratio: 1.0\n" + ] + } + ], + "source": [ + "causal_risk_ratio = (\n", + " weighted_avg.iloc[1][\"weighted_average\"] / weighted_avg.iloc[0][\"weighted_average\"]\n", + ")\n", + "\n", + "print(\"Causal Risk Ratio:\", causal_risk_ratio)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Regression as Stratification" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So far, so good. But so what? \n", + "\n", + "The point here is that the above series of steps can be difficult to accomplish with more complex sets of groups and risk profiles. So it's useful to understand that regression can be used to automatically account for the variation in outcome effects across the different strata of our population. More prosaically, the example shows that it really matters what variables you put in your model. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [Outcome_sigma, Intercept, Treatment]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 1 seconds.\n", + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [Outcome_sigma, Intercept, Treatment, Risk_Strata, Treatment_x_Risk_Strata]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 1 seconds.\n" + ] + } + ], + "source": [ + "reg = bmb.Model(\"Outcome ~ 1 + Treatment\", df)\n", + "results = reg.fit()\n", + "\n", + "reg_strata = bmb.Model(\"Outcome ~ 1 + Treatment + Risk_Strata + Treatment_x_Risk_Strata\", df)\n", + "results_strata = reg_strata.fit()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now inspect the treatment effect and the implied causal risk ratio in each model. We can quickly recover that controlling for the __right__ variables in our regression model automatically adjusts the treatment effect downwards towards 0. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
Intercept0.4280.2030.0600.8230.0030.0024840.02982.01.0
Treatment0.1080.252-0.3570.5840.0040.0044258.02731.01.0
Outcome_sigma0.5420.0920.3880.7130.0010.0014073.02488.01.0
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" + ], + "text/plain": [ + " mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_bulk \\\n", + "Intercept 0.428 0.203 0.060 0.823 0.003 0.002 4840.0 \n", + "Treatment 0.108 0.252 -0.357 0.584 0.004 0.004 4258.0 \n", + "Outcome_sigma 0.542 0.092 0.388 0.713 0.001 0.001 4073.0 \n", + "\n", + " ess_tail r_hat \n", + "Intercept 2982.0 1.0 \n", + "Treatment 2731.0 1.0 \n", + "Outcome_sigma 2488.0 1.0 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "az.summary(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
Intercept0.2540.261-0.2330.7430.0050.0042710.02648.01.0
Treatment-0.0010.367-0.6530.7300.0080.0062312.02648.01.0
Risk_Strata0.4050.395-0.3491.1190.0080.0062274.02503.01.0
Treatment_x_Risk_Strata0.0100.496-0.9470.9390.0110.0091986.02113.01.0
Outcome_sigma0.5310.0980.3670.7140.0020.0012389.02533.01.0
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" + ], + "text/plain": [ + " mean sd hdi_3% hdi_97% mcse_mean mcse_sd \\\n", + "Intercept 0.254 0.261 -0.233 0.743 0.005 0.004 \n", + "Treatment -0.001 0.367 -0.653 0.730 0.008 0.006 \n", + "Risk_Strata 0.405 0.395 -0.349 1.119 0.008 0.006 \n", + "Treatment_x_Risk_Strata 0.010 0.496 -0.947 0.939 0.011 0.009 \n", + "Outcome_sigma 0.531 0.098 0.367 0.714 0.002 0.001 \n", + "\n", + " ess_bulk ess_tail r_hat \n", + "Intercept 2710.0 2648.0 1.0 \n", + "Treatment 2312.0 2648.0 1.0 \n", + "Risk_Strata 2274.0 2503.0 1.0 \n", + "Treatment_x_Risk_Strata 1986.0 2113.0 1.0 \n", + "Outcome_sigma 2389.0 2533.0 1.0 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "az.summary(results_strata)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = az.plot_forest(\n", + " [results, results_strata],\n", + " model_names=[\"naive_model\", \"stratified_model\"],\n", + " var_names=[\"Treatment\"],\n", + " kind=\"ridgeplot\",\n", + " ridgeplot_alpha=0.4,\n", + " combined=True,\n", + " figsize=(10, 6),\n", + ")\n", + "ax[0].axvline(0, color=\"black\", linestyle=\"--\")\n", + "ax[0].set_title(\"Treatment Effects under Stratification/Non-stratification\");" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can even see this in the predicted outcomes for the model. This is an important step. The regression model automatically adjusts for the risk profile within the appropriate strata in the data \"seen\" by the model." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected Outcome in the Treated\n" + ] + }, + { + "data": { + "text/plain": [ + "0.5068569705412103" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_df = df[[\"Risk_Strata\"]].assign(Treatment=1).assign(Treatment_x_Risk_Strata=1)\n", + "new_preds = reg_strata.predict(results_strata, kind=\"pps\", data=new_df, inplace=False)\n", + "print(\"Expected Outcome in the Treated\")\n", + "new_preds[\"posterior_predictive\"][\"Outcome\"].mean().item()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected Outcome in the Untreated\n" + ] + }, + { + "data": { + "text/plain": [ + "0.49944292437387866" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_df = df[[\"Risk_Strata\"]].assign(Treatment=0).assign(Treatment_x_Risk_Strata=0)\n", + "new_preds = reg_strata.predict(results_strata, kind=\"pps\", data=new_df, inplace=False)\n", + "print(\"Expected Outcome in the Untreated\")\n", + "\n", + "new_preds[\"posterior_predictive\"][\"Outcome\"].mean().item()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see these results more clearly using `bambi` model interpretation functions to see the predictions within a specific strata. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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5ubmmbdu2plGjRt75vufveezYsT7nnDFjhpFk0tPTi70uAJQW238BqFKcTqemT5+udevW+W3B5OH5pMrphRL/9Kc/KSIiwm/rqosuukhNmjTx3ne73WrRooV27Nhx1vH87W9/U1pamubPn69bb71VkZGRmjNnjhITE322fjqb9u3bq0WLFn7tX3/9ta6++mrVqlVLNptNDodDw4cPV15enn744Yegz18aq1ev1sGDBzVixAjl5uZ6b/n5+erfv7/Wrl2rY8eO6dixY1q7dq2uu+46ud1u7/FRUVG66qqrgr7esWPH5HA45HA4VLt2bd12220aMmSIHnnkEW+fjz76SLm5uRo+fLjPmNxut7p37+7dUm3r1q3KyMjQ4MGDfa7RpEkTdevWLeD1r776ap9P3/3000/6/vvvddNNN0mSz/Uuv/xypaena+vWrZKkiy++WB9++KHuv/9+rVy5UidOnPA5d2xsrJo3b64nnnhCM2fO1Ndffx1UzZ1PP/1UERERGjRokE+753v79O/lnj17Kioqyns/Li5OdevWDep7GQAAAPCoVauWVq1apbVr1+qxxx7TwIED9cMPP2jSpElq166d9u/fH/S5rr/++oDtc+bMUceOHeV2u2W32+VwOPTJJ59oy5YtZfU0ivX++++rRo0auuqqq3ze51900UWqV6+ed17h2RLYMyfwGDx4cInqwqSkpMjhcMjpdKpFixb68MMPtWjRIiUmJvqMqW3btrrooot8xtSvXz+f7aM/++wz7xiKuvHGG4u9/ul/B++//74sFov+/Oc/+1yrXr16uvDCC73XOu+881SzZk3dd999mjNnjjZv3ux37j/84Q/e8bz55pvavXv3WV+PY8eO6csvv9SgQYMUGRnpbbfZbBo2bJh27drlnWt5XH311T7327dvL0nMdQCUC0IVAFXODTfcoI4dO+rBBx/0qS3iceDAAdntdr9l4RaLRfXq1fPZkklSwC27XC6X3y/GixMXF6ebb75Zc+bM0aZNm/TZZ5/J6XTqr3/9a9DPKVBdlrS0NF1yySXavXu3nnnmGe+k5oUXXpCkoMdXWp6tuwYNGuQNOzy3xx9/XMYYHTx4UL/99pvy8/NVr149v3MEaitOWFiY1q5dq7Vr1+pf//qXevTooUWLFumxxx7zG9Mf/vAHvzEtXrzYO7nz/B3HxcX5XSdQm+T/d+C51sSJE/2uNXbsWEnyXu/ZZ5/Vfffdp6VLl6pnz56KjY3VNddcox9//FFSwffeJ598on79+mnGjBnq2LGj6tSpo7vuuuuM2wYcOHBA9erV86mRI0l169aV3W4v8+9lAAAAoKhOnTrpvvvu01tvvaVff/1V48eP1/bt20tUrD7QXGfmzJm67bbb1LlzZ73zzjv64osvtHbtWvXv379C3rvu2bNHhw4dktPp9Huvn5GR4TevOH1eY7fbA773Ls7gwYO1du1arV69WnPnzlVUVJRuuOEG73zBM6ZNmzb5jScqKkrGGJ8x2e12xcbG+lyjuHmOFHiuY4xRXFyc3/W++OIL77ViYmL02Wef6aKLLtIDDzygNm3aqEGDBpo8ebJ3Ln7ppZdq6dKl3g+/NWrUSG3btj3jhwx/++03GWMCfm80aNDA+zyLOv319mwzx1wHQHmgpgqAKsdisejxxx9Xnz59NG/ePL/Ha9WqpdzcXO3bt88nWDHGKCMjw/tJmvJy6aWXqm/fvlq6dKn27t2runXrnvWY039pLklLly7VsWPHtGTJEsXHx3vbixY2L0+1a9eWJD333HM+e/0WFRcXp5ycHFksFmVkZPg9HqitOFar1VuYXpL69OmjxMRETZ06VTfddJMaN27sHdPbb7/t85qczvOGO1BNl+LGdPrfgedakyZN0nXXXRfwmJYtW0qSIiIiNHXqVE2dOlV79uzxrlq56qqr9P3330sqKEb50ksvSZJ++OEHvfnmm5oyZYqys7M1Z86cYp/Hl19+KWOMz/j27t2r3Nxc7xgBAACA8uZwODR58mQ9/fTT+vbbb4M+LtBc59VXX1WPHj00e/Zsn/bfU6ekJDzFzpcvXx7wcc/qb8+8IiMjQw0bNvQ+npub6/dL/zOpU6eOd66TlJSk1q1bq3v37ho/frzef/9975jCwsK8tTMDjdkzptzcXB08eNAnWDnT3CvQXMdisWjVqlUBa2wWbWvXrp3eeOMNGWO0adMmLViwQNOmTVNYWJjuv/9+SdLAgQM1cOBAZWVl6YsvvlBycrKGDh2qpk2bemuRFlWzZk1ZrValp6f7PeYpPs9cB0AosVIFQJV02WWXqU+fPpo2bZqOHj3q85inePqrr77q0/7OO+/o2LFj3sd/rz179gTcwikvL08//vijwsPDVaNGDUml+xSN541v0Te0xhj94x//8OtbHqsRunXrpho1amjz5s3q1KlTwJvT6VRERIQuvvhiLVmyRCdPnvQef+TIEf3rX/8q9fVdLpdeeOEFnTx5UtOnT5ck9evXT3a7XT///HOxY5IKwo569er5bRGXlpam1atXB3X9li1b6vzzz9c333xT7LWKbrXlERcXp5EjR+rGG2/U1q1bdfz4cb8+LVq00EMPPaR27dppw4YNxY6hd+/eOnr0qJYuXerTvnDhQu/jAAAAQFkL9MtuSd6tuTyrCaTSzUUsFovfL/M3bdqkNWvW+LSV12qEK6+8UgcOHFBeXl7A9/meD0/16NFDkvTaa6/5HP/mm28qNze31Ne/5JJLNHz4cH3wwQfe53zllVfq559/Vq1atQKOqWnTppKk7t27S5IWL17sc8433ngj6OtfeeWVMsZo9+7dAa/Vrl07v2MsFosuvPBCPf3006pRo0bAeYzL5VL37t31+OOPSyrYzjqQiIgIde7cWUuWLPH5u83Pz9err76qRo0aBdweGwAqCitVAFRZjz/+uBITE7V37161adPG296nTx/169dP9913nzIzM9WtWzdt2rRJkydPVocOHTRs2LAyuf4rr7yiuXPnaujQofrDH/6gmJgY7dq1Sy+++KK+++47Pfzww3I6nZLkfVP6zDPPaMSIEXI4HGrZsmXAX8oXfR5Op1M33nij7r33Xp08eVKzZ8/Wb7/95te3Xbt2WrJkiWbPnq3ExES/VR+lERkZqeeee04jRozQwYMHNWjQINWtW1f79u3TN998o3379nk/Wfb3v/9d/fv3V58+fXT33XcrLy9Pjz/+uCIiInTw4MFSj6F79+66/PLL9fLLL+v+++9XQkKCpk2bpgcffFC//PKL+vfvr5o1a2rPnj366quvvCtGrFarpk6dqltvvVWDBg3SLbfcokOHDmnq1KmqX7++rNbgPnMwd+5cDRgwQP369dPIkSPVsGFDHTx4UFu2bNGGDRv01ltvSZI6d+6sK6+8Uu3bt1fNmjW1ZcsWvfLKK0pKSlJ4eLg2bdqkO+64Q3/60590/vnny+l06tNPP9WmTZu8n+4KZPjw4XrhhRc0YsQIbd++Xe3atdPnn3+uRx99VJdffrkuu+yyUr+2AAAAQHH69eunRo0a6aqrrlKrVq2Un5+vjRs36qmnnlJkZKTPVseelQyLFy9Ws2bN5Ha7A/5Svqgrr7xSf//73zV58mR1795dW7du1bRp05SQkOATVkRFRSk+Pl7//Oc/1bt3b8XGxqp27dregKG0brjhBr322mu6/PLL9de//lUXX3yxHA6Hdu3apRUrVmjgwIG69tpr1bp1a/35z3/WrFmz5HA4dNlll+nbb7/Vk08+qejo6N81hr///e9avHix/va3v+njjz/WuHHj9M477+jSSy/V+PHj1b59e+Xn5ystLU3//ve/dffdd6tz587q37+/unXrprvvvluZmZlKTEzUmjVrvB+8Cmau061bN/3lL3/RzTffrHXr1unSSy9VRESE0tPT9fnnn6tdu3a67bbb9P777yslJUXXXHONmjVrJmOMlixZokOHDqlPnz6SpIcffli7du1S79691ahRIx06dEjPPPOMHA6HNwAKJDk5WX369FHPnj01ceJEOZ1OpaSk6Ntvv9WiRYsCrnACgApTxoXvAaDCvfzyy0aSWbt2rd9jQ4cONZJMmzZtfNpPnDhh7rvvPhMfH28cDoepX7++ue2228xvv/3m0y8+Pt5cccUVfuft3r276d69+xnHtXnzZnP33XebTp06mTp16hi73W5q1qxpunfvbl555RW//pMmTTINGjQwVqvVSDIrVqw44xiMMeZf//qXufDCC43b7TYNGzY099xzj/nwww99jjfGmIMHD5pBgwaZGjVqGIvFYor++JdkJk+e7L2/bds2I8k88cQTPtfyvM7btm3zaf/ss8/MFVdcYWJjY43D4TANGzY0V1xxhXnrrbd8+r333numffv2xul0miZNmpjHHnvMTJ482QTzT9GIESNMREREwMf+97//GavVam6++WZv29KlS03Pnj1NdHS0cblcJj4+3gwaNMh8/PHHPsfOmzfPnHfeecbpdJoWLVqY+fPnm4EDB5oOHTqc9fXw+Oabb8zgwYNN3bp1jcPhMPXq1TO9evUyc+bM8fa5//77TadOnUzNmjWNy+UyzZo1M+PHjzf79+83xhizZ88eM3LkSNOqVSsTERFhIiMjTfv27c3TTz9tcnNzvecJ9H134MABM2bMGFO/fn1jt9tNfHy8mTRpkjl58qRPP0nm9ttv9xt/fHy8GTFiRMDnBgAAAASyePFiM3ToUHP++eebyMhI43A4TJMmTcywYcPM5s2bffpu377d9O3b10RFRRlJJj4+3hhjzIoVK4wkv3mDMcZkZWWZiRMnmoYNGxq32206duxoli5dakaMGOE93uPjjz82HTp0MC6Xy0jyvrcNNH/xzD/27dvnc45A77NzcnLMk08+6Z1vRUZGmlatWplbb73V/Pjjjz5jvfvuu03dunWN2+02Xbp0MWvWrAn6fXZx79ONMeaee+4xksxnn31mjDHm6NGj5qGHHjItW7Y0Tqf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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 2, figsize=(20, 6))\n", + "axs = axs.flatten()\n", + "bmb.interpret.plot_predictions(reg, results, covariates=[\"Treatment\"], ax=axs[0])\n", + "bmb.interpret.plot_predictions(reg_strata, results_strata, covariates=[\"Treatment\"], ax=axs[1])\n", + "axs[0].set_title(\"Non Stratified Regression \\n Model Predictions\")\n", + "axs[1].set_title(\"Stratified Regression \\n Model Predictions\");" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hernan and Robins expand on these foundational observations and elaborate the implications for causal inference and the bias of confounding variables. We won't go into these details, as we instead we want to draw out the connection with controlling for the risk of non-representative sampling. The usefulness of \"representative-ness\" as an idea is disputed in the statistical literature due to the vagueness of the term. To say a sample is representative is ussually akin to meaning that it was generated from a high-quality probability sampling design. This design is specified to avoid the creep of bias due to selection effects contaminating the results.\n", + "\n", + "We've seen how regression can automate stratification across the levels of covariates in the model conditional on the sample data. But what if the prevalence of the risk-profile in your data does not reflect the prevalance of risk in the wider population? Then the regression model will automatically adjust to the prevalence in the sample, but it is not adjusting to the correct weights. " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Need for Post-Stratification\n", + "\n", + "In the context of national survey design there is always a concern that the sample respondents may be more or less representative of the population across different key demographics e.g. it's unlikely we would put much faith in the survey's accuracy if it had 90% male respondents on a question about the lived experience of women. Given that we can know before hand that certain demographic splits are not relective of the census data, we can use this information to appropriately re-weight the regressions fit to non-representative survey data. \n", + "\n", + "We'll demonstrate the idea of multi-level regression and post-stratification adjustment by replicating some of the steps discussed in Martin, Philips and Gelmen's [\"Multilevel Regression and Poststratification Case Studies\"](https://bookdown.org/jl5522/MRP-case-studies/). \n", + "\n", + "They cite data from the Cooperative Congressional Election Study (Schaffner, Ansolabehere, and Luks (2018)), a US nationwide survey designed by a consortium of 60 research teams and administered by YouGov. The outcome of interest is a binary question: Should employers decline coverage of abortions in insurance plans?" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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caseidcommonweightcommonpostweightvvweightvvweight_posttookpostCCEStakebirthyrgendereduc...CL_partyCL_2018gvmCL_2018pepCL_2018pvmstarttimeendtimestarttime_postendtime_postDMAdmaname
01234642820.9405430.79360.7408580.64141221196424...11.01.0NaNNaN04oct2018 02:47:1009oct2018 04:16:3111nov2018 00:41:1311nov2018 01:21:53512.0BALTIMORE
11701692050.7697240.73880.4252360.41513421197122...13.0NaN6.02.002oct2018 06:55:2202oct2018 07:32:5112nov2018 00:49:5012nov2018 01:08:43531.0\"TRI-CITIES
21759960051.4916421.31051.7000941.60326421195823...13.05.0NaNNaN07oct2018 00:48:2307oct2018 01:38:4112nov2018 21:49:4112nov2018 22:19:28564.0CHARLESTON-HUNTINGTON
31768185565.1047094.63045.9467295.65884021194626...4.03.0NaN3.011oct2018 15:20:2611oct2018 16:18:4211nov2018 13:24:1611nov2018 14:00:14803.0LOS ANGELES
42021205330.4665260.37450.4124510.42232721197222...3.05.0NaNNaN08oct2018 02:31:2808oct2018 03:03:4815nov2018 01:04:1615nov2018 01:57:21529.0LOUISVILLE
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" + ], + "text/plain": [ + " caseid commonweight commonpostweight vvweight vvweight_post \\\n", + "0 123464282 0.940543 0.7936 0.740858 0.641412 \n", + "1 170169205 0.769724 0.7388 0.425236 0.415134 \n", + "2 175996005 1.491642 1.3105 1.700094 1.603264 \n", + "3 176818556 5.104709 4.6304 5.946729 5.658840 \n", + "4 202120533 0.466526 0.3745 0.412451 0.422327 \n", + "\n", + " tookpost CCEStake birthyr gender educ ... CL_party CL_2018gvm \\\n", + "0 2 1 1964 2 4 ... 11.0 1.0 \n", + "1 2 1 1971 2 2 ... 13.0 NaN \n", + "2 2 1 1958 2 3 ... 13.0 5.0 \n", + "3 2 1 1946 2 6 ... 4.0 3.0 \n", + "4 2 1 1972 2 2 ... 3.0 5.0 \n", + "\n", + " CL_2018pep CL_2018pvm starttime endtime \\\n", + "0 NaN NaN 04oct2018 02:47:10 09oct2018 04:16:31 \n", + "1 6.0 2.0 02oct2018 06:55:22 02oct2018 07:32:51 \n", + "2 NaN NaN 07oct2018 00:48:23 07oct2018 01:38:41 \n", + "3 NaN 3.0 11oct2018 15:20:26 11oct2018 16:18:42 \n", + "4 NaN NaN 08oct2018 02:31:28 08oct2018 03:03:48 \n", + "\n", + " starttime_post endtime_post DMA dmaname \n", + "0 11nov2018 00:41:13 11nov2018 01:21:53 512.0 BALTIMORE \n", + "1 12nov2018 00:49:50 12nov2018 01:08:43 531.0 \"TRI-CITIES \n", + "2 12nov2018 21:49:41 12nov2018 22:19:28 564.0 CHARLESTON-HUNTINGTON \n", + "3 11nov2018 13:24:16 11nov2018 14:00:14 803.0 LOS ANGELES \n", + "4 15nov2018 01:04:16 15nov2018 01:57:21 529.0 LOUISVILLE \n", + "\n", + "[5 rows x 526 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cces_all_df = pd.read_csv(\"data/mr_p_cces18_common_vv.csv.gz\", low_memory=False)\n", + "cces_all_df.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cleaning Census Data\n", + "\n", + "To prepare the census data for modelling we need to break the demographic data into appropriate stratum. We will break out these groupings as along broad categories familiar to audiences of election coverage news. Even these steps amount to a significant choice where we use our knowledge of pertinent demographics to decide upon the key strata we wish to represent in our model, as we seek to better predict and understand the voting outcome. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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abortionstateethmaleageeducaseid
01.0MSOther-0.550-59Some college123464282
11.0WAWhite-0.540-49HS170169205
21.0RIWhite-0.560-69Some college175996005
30.0COOther-0.570+Post-grad176818556
41.0MAWhite-0.540-49HS202120533
\n", + "
" + ], + "text/plain": [ + " abortion state eth male age edu caseid\n", + "0 1.0 MS Other -0.5 50-59 Some college 123464282\n", + "1 1.0 WA White -0.5 40-49 HS 170169205\n", + "2 1.0 RI White -0.5 60-69 Some college 175996005\n", + "3 0.0 CO Other -0.5 70+ Post-grad 176818556\n", + "4 1.0 MA White -0.5 40-49 HS 202120533" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states = [\n", + " \"AL\",\n", + " \"AK\",\n", + " \"AZ\",\n", + " \"AR\",\n", + " \"CA\",\n", + " \"CO\",\n", + " \"CT\",\n", + " \"DE\",\n", + " \"FL\",\n", + " \"GA\",\n", + " \"HI\",\n", + " \"ID\",\n", + " \"IL\",\n", + " \"IN\",\n", + " \"IA\",\n", + " \"KS\",\n", + " \"KY\",\n", + " \"LA\",\n", + " \"ME\",\n", + " \"MD\",\n", + " \"MA\",\n", + " \"MI\",\n", + " \"MN\",\n", + " \"MS\",\n", + " \"MO\",\n", + " \"MT\",\n", + " \"NE\",\n", + " \"NV\",\n", + " \"NH\",\n", + " \"NJ\",\n", + " \"NM\",\n", + " \"NY\",\n", + " \"NC\",\n", + " \"ND\",\n", + " \"OH\",\n", + " \"OK\",\n", + " \"OR\",\n", + " \"PA\",\n", + " \"RI\",\n", + " \"SC\",\n", + " \"SD\",\n", + " \"TN\",\n", + " \"TX\",\n", + " \"UT\",\n", + " \"VT\",\n", + " \"VA\",\n", + " \"WA\",\n", + " \"WV\",\n", + " \"WI\",\n", + " \"WY\",\n", + "]\n", + "\n", + "\n", + "numbers = list(range(1, 56, 1))\n", + "\n", + "lkup_states = dict(zip(numbers, states))\n", + "lkup_states\n", + "\n", + "\n", + "ethnicity = [\n", + " \"White\",\n", + " \"Black\",\n", + " \"Hispanic\",\n", + " \"Asian\",\n", + " \"Native American\",\n", + " \"Mixed\",\n", + " \"Other\",\n", + " \"Middle Eastern\",\n", + "]\n", + "numbers = list(range(1, 9, 1))\n", + "lkup_ethnicity = dict(zip(numbers, ethnicity))\n", + "lkup_ethnicity\n", + "\n", + "\n", + "edu = [\"No HS\", \"HS\", \"Some college\", \"Associates\", \"4-Year College\", \"Post-grad\"]\n", + "numbers = list(range(1, 7, 1))\n", + "lkup_edu = dict(zip(numbers, edu))\n", + "\n", + "\n", + "def clean_df(df):\n", + " ## 0 Oppose and 1 Support\n", + " df[\"abortion\"] = np.abs(df[\"CC18_321d\"] - 2)\n", + " df[\"state\"] = df[\"inputstate\"].map(lkup_states)\n", + " ## dichotomous (coded as -0.5 Female, +0.5 Male)\n", + " df[\"male\"] = np.abs(df[\"gender\"] - 2) - 0.5\n", + " df[\"eth\"] = df[\"race\"].map(lkup_ethnicity)\n", + " df[\"eth\"] = np.where(\n", + " df[\"eth\"].isin([\"Asian\", \"Other\", \"Middle Eastern\", \"Mixed\", \"Native American\"]),\n", + " \"Other\",\n", + " df[\"eth\"],\n", + " )\n", + " df[\"age\"] = 2018 - df[\"birthyr\"]\n", + " df[\"age\"] = pd.cut(\n", + " df[\"age\"].astype(int),\n", + " [0, 29, 39, 49, 59, 69, 120],\n", + " labels=[\"18-29\", \"30-39\", \"40-49\", \"50-59\", \"60-69\", \"70+\"],\n", + " ordered=True,\n", + " )\n", + " df[\"edu\"] = df[\"educ\"].map(lkup_edu)\n", + " df[\"edu\"] = np.where(df[\"edu\"].isin([\"Some college\", \"Associates\"]), \"Some college\", df[\"edu\"])\n", + "\n", + " df = df[[\"abortion\", \"state\", \"eth\", \"male\", \"age\", \"edu\", \"caseid\"]]\n", + " return df.dropna()\n", + "\n", + "\n", + "statelevel_predictors_df = pd.read_csv(\"data/mr_p_statelevel_predictors.csv\")\n", + "\n", + "cces_all_df = clean_df(cces_all_df)\n", + "cces_all_df.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now show how estimates drawn from sample data (biased for whatever reasons of chance and circumstance) can be improved by using a post-stratification adjustment based on known facts about the size of the population in each strata considered in the model. This additional step is simply another modelling choice - another way to invest our model with information. In this manner the technique comes naturally in the Bayesian perspective. " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Biased Sample\n", + "\n", + "Consider a deliberately biased sample. Biased away from the census data and in this manner we show how to better recover population level estimates by incorporating details about the census population size across each of the stratum." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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abortionstateethmaleageeducaseidrepvoteregionweight
351710.0KYWhite-0.560-69HS4152086360.656706South10.333531
51670.0NMWhite0.560-69No HS4122780200.453492West9.317460
523650.0OKHispanic-0.530-394-Year College4194674490.693047South4.465237
237621.0WVWhite-0.550-59Post-grad4137579030.721611South8.658053
481970.0RIWhite0.550-594-Year College4176193850.416893Northeast7.134465
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" + ], + "text/plain": [ + " abortion state eth male age edu caseid \\\n", + "35171 0.0 KY White -0.5 60-69 HS 415208636 \n", + "5167 0.0 NM White 0.5 60-69 No HS 412278020 \n", + "52365 0.0 OK Hispanic -0.5 30-39 4-Year College 419467449 \n", + "23762 1.0 WV White -0.5 50-59 Post-grad 413757903 \n", + "48197 0.0 RI White 0.5 50-59 4-Year College 417619385 \n", + "\n", + " repvote region weight \n", + "35171 0.656706 South 10.333531 \n", + "5167 0.453492 West 9.317460 \n", + "52365 0.693047 South 4.465237 \n", + "23762 0.721611 South 8.658053 \n", + "48197 0.416893 Northeast 7.134465 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cces_df = cces_all_df.merge(statelevel_predictors_df, left_on=\"state\", right_on=\"state\", how=\"left\")\n", + "cces_df[\"weight\"] = (\n", + " 5 * cces_df[\"repvote\"]\n", + " + (cces_df[\"age\"] == \"18-29\") * 0.5\n", + " + (cces_df[\"age\"] == \"30-39\") * 1\n", + " + (cces_df[\"age\"] == \"40-49\") * 2\n", + " + (cces_df[\"age\"] == \"50-59\") * 4\n", + " + (cces_df[\"age\"] == \"60-69\") * 6\n", + " + (cces_df[\"age\"] == \"70+\") * 8\n", + " + (cces_df[\"male\"] == 1) * 20\n", + " + (cces_df[\"eth\"] == \"White\") * 1.05\n", + ")\n", + "\n", + "cces_df = cces_df.sample(5000, weights=\"weight\", random_state=1000)\n", + "cces_df.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualise the Bias\n", + "\n", + "Now we plot the outcome of expected shares within each demographic bucket across both the biased sample and the census data. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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gYKAk6emnn9aKFSuKfAyHDx+utLQ0Pfjgg4qPj9fu3bttjrsoDz74oPbv3y9JuuuuuxQbG6tvv/1W33//vT777DPNmzdP3bt3d1g3KSlJQ4cOVXZ2turUqaOoqCht2bJFu3bt0muvvWad5fbKK69oxowZhfbj//7v/3TnnXfK19dXL7/8sr799lvt3LlTc+fOlbe3t8xms+6///5CZw4Xxmw268Ybb9T27dslSddcc40++OAD7d69W5988oluvvlmSdKBAwc0cOBA/fXXX9a6N998s/bu3au9e/da0x544AFr2t69e8t0/fbC/N///Z/1dv5r9EJFvQbS0tI0cOBAHT9+XCaTSffcc482btyo3bt3a9WqVbriiiskSbGxsRozZkyh/Tl+/LgiIiLk4eGhp556Sjt27NCOHTu0YMEC+fj4KC8vT4888ohiY2Md1i/P9/yjjz6qzZs366abblJsbKz27NmjuLg4DRkyRJGRkdq7d6+GDRtmfewKPl8XPp/FUZ59f/zxx/XOO+/o5ptvtun7DTfcIEn69ddf9fDDD9vV+/3333XPPfcoNzdXzZs31/z587Vp0yb98MMP2rFjh1avXq37779fTZs2LdGxlUTBz4MFCxYUeayV5a+//tKtt96qs2fPaubMmdq6dat27dqlN954Q35+frr22mut76GiZsHm57u5uWnkyJE2eWazWUOHDtWHH34ok8mkO+64w/pZsnPnTi1ZskStW7e29mfPnj029ceNGyfJssrGqlWrnPbh3LlzWr16tSQpLCxMrVq1KtHjUfB5mjRpUqk/O8t7zLrtttt07NgxzZgxQ19//bW+//57vfDCC/Lx8ZHZbNbYsWN15MgR3Xjjjfrnn3+0YMECbdu2Td99953mzZsnT09P5eTkWN8HhRk/frz27NmjESNGKC4uTrt379Y777yjq666SpJllYo777yzVI/L3r17de211yotLU3NmzfXnDlztHnzZiUmJmrjxo2aOHGiPDw8tGvXLg0bNkznzp0r8T5MJpN1VaA//vhDX3zxhdOyX3zxhU6cOCFJGjVqlN3KHcXx9ddfW2/feOONJa5/ob///lvXXHONtm/fLk9PT40fP14ff/yxfvjhByUkJGjBggVq0qSJfv/9dw0ZMsRmNYYLnTlzRjfddJNOnjypWbNmaevWrdq9e7eWL18uf39/SZbvGhs3brSru3TpUn3wwQeSpKuvvlorV65UQkKCEhMT9eWXX2rJkiX6z3/+I3d39zIfsyOfffaZXnjhBUnS5ZdfrqVLl2rr1q1KTEzU1q1b9eqrr+rWW28tdLl+AAAA1FCujvYDAACgcq1Zs8Y6++axxx4rc3v/93//Z7i5uRmSjKCgION///ufXZnPP//cWqZnz552+RfOuIuOjrYrk56ebp153axZM8NkMhkffvihXbkff/zRuq/8WfAFFZxdKcm4/vrrHc4ymz9/vrXM1KlT7fL/+OMPh8eaLycnx7juuuusM6nOnz9vV6bgLEE3NzenM2nzOZvZ/s8//xh16tQxJOcz1/OdPHnSLu2qq66yzkbcuHGjXf6ff/5pnTXv5uZm7Nu3z2nf9P9nlZ06dcquzOrVq61lFi9eXGg/nXn55ZetbYwePdrhrL/HHnvMWmbatGkO28nPL+2s4HzFmdl+9OhR62vXZDIZv/32mzWvJK+B4cOHFzpb8ezZs8a1115rLRMXF2dXJn/Wt2SZvb1//367Mvv27bP2t2XLlkZOTo5NfkW852fPnu30uA2j+LMSCx6jo5ntFdH3J5980q5MXl6eERYWZkgyPDw8jMzMTJv8N954w1rf0cz1fP/8849x5swZu/TymNluNpuNyy+/3NqOyWQyrrnmGuOJJ54wNm7cWOjnm7O+lMfMdskymzwpKclpW1OmTLF+ZjlbOSAvL8/w9/c3JBkDBw60y1+0aJEhyahTp47D94phWD77unTpYp09faH81VXatWvntK+xsbHW41q3bp3Tcs789ttvNiuF1K1b1wgPDzeio6ONnTt3GmfPni1WO+U9Znl5eTmcxbxhwwZrmWbNmhlNmzY1fv31V7tyr7zyirVcbGysXf6FK5E89dRTdmXOnTtnDB482Frms88+sytT2GdHXl6e9T1wxRVXGH/88YfDx6bgZ0JpZ4r//PPP1n7efvvtTssVnLl98ODBUu0r//tAy5YtS1X/QpMmTbKOGd9//73DMkePHjX8/PwMScZdd91ll1/w/X3RRRc5/B6RnJxseHt7G5KMm266yS4/NDTUkGT06tWr0NUBHH3PKY+Z7aNGjbK2cfr06RLtHwAAADUbwXYAAIBaZsmSJdYfEpcsWVLm9h544AFrezt37nRabty4cdZyu3btsskrGLzq1auX0zYK/lg7cuRIp+XyAyDdu3e3yysY8PHy8jLS0tIctmE2m42goCBDknHxxRcXO6BRUFJSknVfu3fvtssvGLi49957i2zPWbA9LS3N2s7HH39coj5+99131rrjx493Wm7btm3WcpGRkU77Jsn48ccfHbaRl5dnXQL9lltuKVE/83Xq1MmQZDRt2tTpssHnz583OnbsWOhzV9HB9ry8PCMzM9NYvXq1NeDn6HVb3NfA8ePHDXd3d0OSMXjwYKfljhw5Ynh4eBiS5USSCxUMti9atMhpO88884y1XP5y0PnK+z3fvn17h4G9gsor2F7efQ8ODna6zPMXX3zh9H25YMEC6+uzNMoj2G4YluDWZZddZhPULBh879q1qzFt2rRCg34VEWyfP39+of3es2ePtayjk7MMw/Z5evPNN23ycnNzrYHBhx9+uNB9xcXFWdtJTk62yVu1apU1b9u2bQ7r5y8D37RpU7sTV4rr008/NRo0aODwefLy8jJCQ0ONxYsXlznIV5Ix69FHH3XaTsHx4NVXX3VY5syZM9bAqqPnoODr6vLLL3d6CZPU1FRrcNnRZ15hnx2ffvppkeNWvttvv92QZPTt27fQcoXp2bOnIcnw9vZ2eELaqVOnrI9J7969S7WPrKws6zE5+g5UUn/88Ye1T0V9Z4yJiTEkywksf//9t01ewff3iy++6LSNkSNHOv1sbNeuXbHes46UR7A9/2SU0n5/AQAAQM3FMvIAAAC1zOnTp62369evX+b2Nm/eLEnq3Lmzevfu7bTcfffdZ1fHkQuX+i3o8ssvt97OX+rdkfzltA8fPuy0jGRZ0tfZkt5ubm7Wpbj/97//6Ycffii0rZycHKWkpGj//v3at2+f9u3bJ8MwrPlFLdFc2iVwJalJkyby9PSUJL399ts6f/58sesWfC7Gjh3rtFzfvn3VqVMnuzoX6tq1q83zVJDJZLIuiVzUc+PI8ePHdeDAAUnS7bffroYNGzos5+7urnvuuUdS8Z678tK2bVuZTCaZTCa5ubmpefPmuuuuu3Ts2DFJUu/evfXaa685rV/Ya2DLli0ym82SCn+e2rRpo+uuu06StHXrVmudC5lMpkKXmr/nnnusyxdf+HyX93t+xIgRFbbs74XKu+8RERFOl3kODg623r7w9e7n5yfJ8vr8+OOPi+74Be6++24ZlpPnNXfu3BLXz3fZZZcpKSlJTz/9tC699FKbPMMwtHfvXj377LPq3LmzHnnkkRJ9tpRFUZ+HV155pfXz6J133nFYJj/d29tb4eHhNnm7du1Senq6JMtnSWH69etnvb1z506bvOHDh+uiiy6SJIdLr//+++/6/PPPJVku75H/OV1SN954o/bv36+JEyeqcePGNnk5OTlKSEjQlClTdOmllxa6pP2F9coyZhVnrDaZTE4f37p166pdu3aSih4PxowZIzc3xz9f+fv7KywsTFLhn3mO5L/3OnTo4HTcypf/Ovj+++9LtI+C8j9zz549q3Xr1tnlf/DBBzp79qwkafTo0aXaR3l/x9u4caO1T8V9r5w7d87usgv5TCaTIiIinLaR/7n5v//9T6dOnbLJy//c/PTTT61L7Vem/P1/8803OnToUKXvHwAAAFUXwXYAAIBapmCA8u+//y5TWzk5OUpOTpYk9erVq9Cy3bt3V506dSRJ+/btc1quffv2TvPygxrFLVfwR2dH8q/36kzPnj2ttx31+e+//9bChQt1xRVXqH79+goMDFSXLl3UtWtXde3a1eZau0X9MFzUD/2F8fLysp58sG7dOl122WWaNm2a4uLilJWVVWjd/OPy9PR0ek33fPnPcXJystNr7Hbs2LHQNvIDRUU9N44UfA6Ker0VzC/s9VbR6tSpo6uuukqvvPKKEhIS5OPj47RsYa+B0hz7mTNnnAax2rZtW+j1wJs1a6Y2bdrY7bsi3vNlee2XREX0vbDXe8Gg6IWv95tuusn6OXXLLbdowIABeuGFF7Rnz55SB/JKq379+nr00Uf166+/6ueff9abb76phx56SD179rQGOM1msxYvXmy9TnlFatCggS655JIiy+UH5Hft2mV9XvPl5uZag5k33nijGjVqZJO/e/du6+2QkBDrSTKOtgYNGljLZmRk2LRTt25da+Dw/ffftxtTC578dO+99xZ5TIUJCAjQyy+/rN9//13fffedXnzxRd1zzz3WgLUknTp1SmPGjHF6zfXyHLOKMwY3bdpUF198cZHlymusLuwzz5H818HBgwcLfQ2YTCZNmjRJkuW19eeffxZ7HwWNHDnS5sS4C+WneXp6FnpCYWHK8zueZPte8fPzK/QxCgoKspa98L2Sr2nTpmrSpInT/RX2uZl/ssKvv/6qyy67TPfee6/effdd6wltFS3/BIiTJ08qKChII0eO1IoVK/Trr79Wyv4BAABQdRFsBwAAqGUKBth+//33MrX1v//9z3rb19e30LJ16tSx/sBa2A/V9erVc5pXcGZbccrl5eUV2qfmzZsXml/wmC7s89GjR9W1a1c99thj+r//+78iA2T//PNPofmFBSSK4+WXX9bQoUMlSb/99puee+453XDDDWrSpIl69uypRYsWKTs7265e/nE1btxYHh4ehe6jRYsWkiwzXgs+9wUV9rxIsgnelVTB56Co11t+Xy+sV5E2btyovXv3au/evfrpp5/022+/6fTp09q1a5ciIyOLfHwLew2U97EX9dovuJ+CbVTEe76sr/3icuXn1YWv9yZNmuiTTz5Rq1atZBiGtmzZoilTpqhHjx5q3Lixbr31Vn322WeF9rEidOjQQffcc4+WLFmi7777TikpKTYB9rfeekvbtm2r0D4UPKmqMAVnx144uz0uLs76fDuaJZ+ZmVmqvp05c8YuLX8VhNOnT2v9+vU2eflB76uuukpdu3Yt1T4v5OHhoZ49e+rBBx/Um2++qV9++UW7d+/W1VdfbS3zyCOP2AUqy3vMKs5rv7zGg7KM1YUpz9dBcTRu3Fg33HCDJMvs6N9++82a99tvvykhIUGSdMMNN9itYFBcPj4+1hOFyvodTyr/x6i4rwnJ/nVx77336rHHHpOHh4eysrK0YsUKRUREKCAgQJdddpmmTp1aqlVzimvgwIF6+eWXVbduXZ09e1Zr167Vvffeq3bt2snf318TJkwockUIAAAA1EwE2wEAAGqZ/CXWJZXr8trOllIuqOAStVVBUX0urL+jRo3SkSNHZDKZdO+992rTpk1KTU3V2bNnrcs7F/yhuKhjL+sy2j4+Pvrkk0/03Xff6ZFHHtGVV14pd3d3mc1mff/99/rvf/+rdu3a2S2DnK+6PX9lee4qSvv27RUUFKSgoCB17txZrVu3lpeXV7Hrl9dS6sU59vJ4vsvrNVNZS8gXVBVe76Ghofr111+1evVqRUREyN/fX5KUnZ2t2NhYDR06VP/5z39KHdgrD61atdLy5cttlgz/4IMPKnSfxX09tG3bVn369JFkH2zPv3/RRRfp+uuvt6tb8LN569at1pNkitoeeOABu7a6detmXfq64Izy7777Tvv375dU9lntRQkODtYXX3yhyy67TJLlxJILL39Q3mNWZaqoz/v84+3bt2+xXwN79+51evmZ4sifHW0YhtasWWNNX716tfU4SruEfL7873nHjx8vc8A9/zHy9PQs0WM0bNiwMu3XmQULFujXX3/VggULNGDAAGvw/tChQ3r++efVsWNHvfrqqxWyb0maOHGijh49qhdeeEHXX3+9ddWMtLQ0vfbaa+revbtmzZpVYfsHAABA1VT41AoAAADUOJ07d1bTpk114sQJJSQkKDs7u9ClrQtTcEaqsyVD850/f95mFnVVUNSP0AVndBXs888//2yd3TljxgwtWLDAYX1ns78rUs+ePa1L6p4+fVpbt27VihUr9OGHHyozM1O33nqrDh06pLp160r697hOnjyp8+fPFzr7Ov/xMplMlTYbuaCCz0FRr7eCz21Veb2VRcFj+P3339W6dWunZYtz7MUJwOS//gu2UZ3f81Wx797e3rrzzjuts68PHz6sDRs26OWXX9Yvv/yijRs3aubMmXrhhRcqrA/Fcd999+m9996TJLslkwvORC1sNZHyWNL6QnfddZd27Nhhnd3do0cPnT59Wp9++qkk6bbbbnN4nfSCy1h7enraLH9dGuPGjdOePXv09ddf6/Dhw7rkkkusgfe6devqjjvuKFP7xVG/fn3dcccdeuKJJyTZPk9Vecwqjt9//73QZeudjdVFadKkiX7//Xf98ccfZX4NFNcNN9xg/Q729ttv67HHHpNkCbbn9yl/9ntpXXPNNdbl3zds2FCmkz3y3yu5ublq0qSJ9brlrhQYGKjHHntMjz32mM6dO6ddu3bpgw8+0GuvvaazZ88qMjJSvXr1srksQnFXPCrO51Tz5s0VFRWlqKgo5eXlKSkpSbGxsXrllVd06tQpLViwQFdddVWFnXAAAACAqoeZ7QAAALWMyWTS3XffLcnyo+Lrr79e6ra8vLys14v97rvvCi2bmJioc+fOSVKl/ahdlO+//77Y+QX7/NNPP1lvF5zxeaGC1zp1hYYNG2ro0KGKjY3VQw89JElKT0+3WQY6/7hyc3OVmJhYaHu7du2SJLVr185hAKuiFXwOinq95ff1wnrVVWmOvV69emrbtq3DMkeOHNHJkyedtvHHH3/o6NGjdvt21Xu+ODPRi1IdPq8uueQSPfjgg/r++++tM93ff//9Stu/MwVn8hYMrku214guLFh78ODBcu/X7bffbl0yO382e2xsrM6ePSvJ8RLykmyCcJs2bSpzPyIiIlSvXj0ZhqG33npL//zzj/XkhPDwcLtrxlcUZ89TdRmznCnuWF2vXj1dcsklxW43/3Xwyy+/2CzpXpHq1KljfQ5+/vln7d69W99//71+/vlnSZbnJ/81XVr53/Ek6aWXXirVZVvylfd7pbzVqVNHffv2VXR0tPUzwDAMrVu3zqZc/ufUqVOnCm2vpJ9Tbm5uuvLKK/Xkk0/qyy+/tKZXhc9tAAAAVB6C7QAAALVQVFSUdenNxx9/3Pojb1Hy8vKss6/yDRo0SJK0f/9+ffvtt07rFgzq59dxtU2bNik9Pd1hXl5ent566y1JlhmxV155pTXv/Pnz1tuFLfFckUuZltTAgQOtt0+cOGG9XfC5eOONN5zW37lzp3VJZFc9fy1btlSnTp0kWZayvvCaxPnMZrNWrlwpyf65q6769+9vXV67sOcpJSVF8fHx1jrOViowDEOrVq1y2s7KlSutSxpf+Hy74j3v7e0tScrJySlTO9Xl88rHx0dXXXWVJNv3a3kqydLbBYOwF57AUfB+YcHaC5d6Lw9NmjTR4MGDJUnvvfee8vLyrEtz+/v7q1+/fg7rXX311dYZ0K+++qqys7PL1A8fHx/dfvvtkizXtV+3bp2ysrIkSWPHji1T2+XxPFXHMaugt99+2+njkJaWZg0CF/ycLI6bbrrJevvZZ58tWydLoOAy8W+//bbefvtth3mlFRQUZD22pKQkLVy4sNh1ExISdOTIEev9IUOGWIP/L7zwgs1rqapx9j1H+vf9cPr0aacB9dzcXK1fv77U+7/yyiutK6hU1Oc2AAAAqiaC7QAAALVQq1at9PLLL0uyzG6/5ppr9PXXXxdaZ//+/Ro8eLAWLVpkk/7AAw9YZ9Ddf//91gBDQZs2bbIGCHv27GkNIrlaTk6Oxo8f73DW19NPP629e/dKslxvt+C1t/Nnx0qyBuQvtHTpUn300Ufl22EnDh8+XOTzV3BGWsEgTMHn4/XXX7cGagvKysrS+PHjJVlmcTm6bnFlmThxoiTLzOsHH3zQYQBm3rx51hMD7rvvvhJdN72qatmypW655RZJ0saNG/Xmm2/alcnNzdW9995rnZE9adKkQtt84oknHAYdDhw4YF1m2s/Pz24pXFe85/OXLs7MzHR6kkVxVJXPq40bNzo90UeyvOfyVyhwtDrBypUrZTKZZDKZNHfu3FL1Yfny5br//vvtloW/0G+//aaZM2da71/4eggKCrIGrl9++WWHJ0S8++67ZQpiFSZ/9np6erreffddffXVV5Iss82drYjg7e2tqVOnSrJcUmDkyJGFLh99+vRp65jpzLhx4yRZHq9p06ZJsjx3/fv3L9HxXOiBBx7QU089Zb2sgTPx8fHW8ah+/fo2J4lUtTGrpJKSkvTcc8/ZpZ8/f1733XefcnNzJanEY9Ott95qPYFr6dKlhZ7IJEn79u2zXqKgLK666ip17txZkuUkkfxVEDp06GC9DExZvfbaa/L19ZUkzZ49W48//rj1cXLk77//1rx58zRw4ECbz8VWrVrpnnvukST9+OOPGj9+fKEB98zMzDKtmFSY1atXF7pvZ99zJMvS+vmef/55u7qGYWjy5Mk6fvy40/bXrl2rf/75x2n+7t27rat7OFtVBgAAADUT12wHAACope655x4dO3ZMjz/+uDIzM9W/f3+FhYVp2LBh6tSpky666CL9+eef+uWXX7RhwwZ98cUXMpvNuuKKK2za6dq1qx555BE999xz2rt3r6688ko9+uij6t69u86cOaNPP/1UL774osxmszw9PfXaa6+56Ijt9ejRQ59++qn69u2rhx9+WO3atVNmZqbeeust64/f/v7+mj17tk297t27KygoSPv27dPSpUt16tQp3XnnnfLz81NqaqpWr16tdevWqW/fvtq+fXuFH0dKSoquvfZade7cWbfccot69OihVq1aSZJSU1O1du1a65Km3bt3V69evWzqL1u2TL169VJubq5uuOEGPfjggxo6dKgaNGigxMREPf300zp8+LAkaerUqS5dln3ChAlas2aNdu7cqbfeeku//fabJk6cqEsuuUTp6el68803FRsbK0m69NJL7Z676uyFF17Ql19+qf/9738aN26ctm/frpEjR6px48b6+eeftWjRIiUlJUmyLK89ZMgQp23lv9Z79+6tRx991BoQ3Lp1q55++mlrsOWll16yu2SAK97zffr0kWRZcWLChAl68MEH1aRJE2sw9bLLLitWO1Xl8+rdd9/V0KFDdd111yksLMwasD59+rT27dunl19+WWlpaZJKHkAsrtzcXC1fvlzLly9Xnz59NHjwYAUHB8vX11dubm5KS0vTli1b9Prrr1tPcLjppptsZo9KkoeHh+6//349/fTT2rdvnwYMGKBp06apdevWysjI0AcffKC33npLISEh2rlzZ7kfx7Bhw9SwYUOdPn1akyZNsp485WwJ+XzTpk3Tl19+qS+//FKff/65OnfurAkTJigkJEQXXXSRdQbs1q1b9dFHH8nb27vQE1j69u2rTp066cCBA8rIyJBkGWfLegmEEydO6LXXXtO8efN0/fXXq3///urSpYuaNGmi8+fP69dff9Unn3yi999/33o96ieffFI+Pj7WNqramFVSPXr00KOPPqqkpCSNHj1azZs3V3JyshYvXmw9KWXo0KG68cYbS9Suu7u71q5dqz59+uivv/7SuHHj9MEHHygiIkIdOnRQnTp1lJmZqcTERH322WfasWOHHnnkEQ0dOrTMxzRq1CjNmDHD5nrz5TGrPV+LFi3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Ltt9333059dRTNyg/5ZRTcu+999ZqswAAAADDT7eeO0+OGpPmc65L8znX5clRYzZaj+Irau/xIrft6JoKuvfI1F1KpWTSJFNB96nrA7eivWd5vT9wjGwNDcm0acmJJ1bvDR0PbIqDPSNMzZLtO+64Y+65554Nyu+5557stNNOtdosAAAAwPCjh8+IU+Te493bbFRWN+aPc4/KH+celcrqxo3WGyqmgh6gIn/gAKA7B3tGmJol29/2trfl7W9/e+bOnZu2trbcfvvt+fjHP55//dd/zdvf/vZabRYAAABg+NHDZ8Qpcu/xorft6JoKetde2zcV9DMo8gcOAHrrOthPnNiz3MGeYahx01UG5gMf+EC23nrrfOITn8i5556bJNl1113z4Q9/OO985ztrtVkAAACA4aerh8+sWXr4DFBnZzWP2NFRTRK3tNT35eqr9/im6g2VrrYd7e19d4Qularr69m2o1xODpuR7H9edfnGG5JDp/sKbFS3D9K41auyZO7Rm6wHAHVVLiczZxbrBA4GoGY920ulUs4666wsX748jz32WB577LEsX748s2fPTmljrbQ34uKLL87uu++esWPH5sADD0zbM7TAXLRoUUql0ga33/zmN8/2XwIAAACona4ePhN27Vmuh88mtbYmL5jcmQ9PX5TvnHRVPjx9UV4wubOuU1QXuff4cBm9tfv2W6bWP55CK/IHDgA2pqEhmTYtOfHE6r2DPcNQzZLtXR566KHcc889+eUvf5mHH364339/9dVXZ86cOXnf+96Xu+++Oy0tLZkxY0aWLl36jH/329/+Nh0dHetuL3zhCwf6LwAAAAAMjXI5ue/e9cs33JgsXlycRHtnZ7JoUXLVVdX7Asz/3NqafP3Y1tze3pxFmZ6rclIWZXpub2/O149trVvCvegzAxi9dYQp+gcOAGCEqlmyfeXKlTn55JOz66675tWvfnWmTp2aXXfdNW984xvz2GOPbfbzXHTRRTn11FNz2mmnZd999828efMyadKkXHLJJc/4dzvttFN22WWXdbcGrWEAAACA4aD7NYypBRpKs7U1aW5Opk9PTjqpet/cnHp2H+/sTG58e2u+lVmZmJ7zVU9Me76VWbnp7a11aRMwHHqPl8vJkiXJwoXJlVdW74vUtoN+GA4fOACAEahmyfbTTjstP/nJT3L99dfnL3/5Sx577LFcd911+fnPf563ve1tm/UcTz/9dO66664cfvjhPcoPP/zw3Hnnnc/4ty95yUsyYcKEHHLIIVm4cOEz1l21alVWrlzZ4wYAAADA37W2VueTX94zoZ329mp5nRLubYs688FHZiepbHCRa6tUJyN//yNz0raoPj3wh0PvcaO3jiDD4QMHADDCNNbqia+//vrcfPPN+ad/+qd1ZUcccUS+8IUv5LWvfe1mPcef/vSndHZ2Zuedd+5RvvPOO+fBBx/s828mTJiQSy+9NAceeGBWrVqVr371qznkkEOyaNGiTJ06tc+/ueCCC3Leeedt5n8GAAAAsAXp7Exmz04qlQ3XVSrVXrNz5iQzZw55prZzUVsm/b1H+5OjxmTK2dckSe696NiMW70qW6WS3bIsv1/UlhwybUhj61IuV1+atrako6M6ZXZLgQYsYITxgQMAGFI1S7Zvv/32GT9+/Abl48ePz/Of//x+PVep19BHlUplg7Iue++9d/bee+91ywcddFCWLVuW//7v/95osv3cc8/N2WefvW555cqVmTRpUr9iBAAAABiR2to27NHeXaWSLFtWrTdt2pCFlSQT0rHu8bjVq7Jk7tGbrFcPXb3HYUj4wAEADJmaDSP//ve/P2effXY6Otb/mHnwwQfzrne9Kx/4wAc26zl22GGHNDQ0bNCL/aGHHtqgt/szeeUrX5nf//73G10/ZsyYbLPNNj1uAAAAAKTaO/bvnhw1Js3nXJfmc67Lk6PGbLTeUNl72oRBrQcAANAfNevZfskll+T+++/P5MmTs9tuuyVJli5dmjFjxuThhx/O5z//+XV1f/GLX/T5HKNHj86BBx6YW265Jf/yL/+yrvyWW27JzJkzNzuWu+++OxMm+FEFAAAA0G+be02lDtdeGqa15MntmzL2kfZ1c7R3tzalPLV9U8ZNaxny2AAAoCY6O9c/vq0tmT7VlDF1VLNk+zHHHDMoz3P22Wfn5JNPzste9rIcdNBBufTSS7N06dKcfvrpSapDwLe3t+crX/lKkmTevHlpbm7Oi170ojz99NP52te+lmuuuSbXXHPNoMQDAAAAsCXpPLgl/6+hKbt0tve5fm1K6Whoyi4Ht2TIL/E1NGTcpfNTOXZW1qbUI+G+NqWUkoy7dJ6LjwAAjAytrcnZ70pO+FR1+cgZyc47JvPnJ+VyfWPbQtUs2f6hD31oUJ7n+OOPzyOPPJLzzz8/HR0d2W+//XLDDTdk8uTJSZKOjo4sXbp0Xf2nn346//mf/5n29vY85znPyYte9KJcf/31OfLIIwclHgAAAIAtSdudDflU5/wsSDWh3V3X8pmd8/LOOxvqM010uZzSNQuS2bN7zC1fampKaf48Fx0BABgZWluTWbOSxtE9y9vbq+ULFjj3rYOaJdu73HXXXbnvvvtSKpUyZcqUvOQlL+n3c5xxxhk544wz+lx3xRVX9Fh+97vfnXe/+90DCRUAAACAXjo6km+nnFlZkPmrZ2fJ3KPXrVuaSZmTefl2ynn90E/Zvl65nNLMmUlbWzXgCRNSamnRox0AgJGhs7PauLSy4dRJqVSSUimZMyeZOdM58BCrWbL9oYceygknnJBFixZl2223TaVSyWOPPZbp06fnG9/4RnbcccdabRoAAACAQdI1Ffu3U87/ZGZa0pYJ6UhHJqQtLVn798Hj6zBle08NDalP13oAAKixtrYeozhtoFJJli2r1nNOPKS2qtUTn3nmmVm5cmV+/etf59FHH82f//zn/N///V9WrlyZd77znbXaLAAAAACDqKUlaWqqdpZZm4bcmmn5Rk7MrZmWtWlIqZRMmlStBwAA1EDH+mGkxq1elSVzj86SuUdn3OpVG63H0KhZsv2mm27KJZdckn333Xdd2ZQpU/LZz342N954Y602CwAAAMAgamhI5s+vPi71nLJ93fK8eUarBACAmtncYaTqPtzUlqdmyfa1a9dm1KhRG5SPGjUqa9eurdVmAQAAABhk5XKyYEEycWLP8qamanm5XJ+4ALYInZ3rH9/W1nMZgC1D9+Gm+mK4qbqpWbL9Na95TWbPnp0VK1asK2tvb89ZZ52VQw45pFabBQAAAKAGyuVkyZJk4cLkyiur94sXS7QD1FRra7LvlPXLR85Impur5QBsOQw3VVg1S7Z/5jOfyeOPP57m5ubsscce2XPPPbP77rvn8ccfz6c//elabRYAAACAGmloSKZNS048sXrvWh5ADbW2JrNmJSvae5a3t1fLJdwBtiyGmyqkxlo98aRJk/KLX/wit9xyS37zm9+kUqlkypQpOfTQQ2u1SQAAAIBhr/vowG23JYdOl9QG2OJ0diazZyeVyobrKpVqL8Y5c5KZMx0kALYk5XJ139/WlnR0VOdob2lxLKijmiTb16xZk7Fjx+aee+7JYYcdlsMOO6wWmwEAAAAYUVpbk9lnJw0nVJdnHJlM3Lk6YqSOKgBbkLa2ZPnyja+vVJJly6r1pk0bsrAAKICu4aYohJok2xsbGzN58uR0dm+KDQAAAMBGdY0WnMZkt27lXaMFGxkSYAvS0bHu4bjVq7Jk7tGbrAcADL2azdn+/ve/P+eee24effTRWm0CAAAAYEA6O5NFi5Krrqre17u/wKZGC06qowXXO04AhsiECYNbDwCoiZrN2f6pT30q999/f3bddddMnjw5z33uc3us/8UvflGrTQMAAAAF0NlZzKkEW1urie3uo/M2NdV3qHajBQPQQ0tL9eDU3t53S6xSqbq+pWXoYwMA1qlZsv2YY45JqVRKpa8TAWDkKOrVMwAA2EIU9ZS8iAntrrhmzdowb1Hvodq7jwJcWd2YP849apP1ABjBGhqqB81Zs6qJ9e4HrlKpej9vXjEO+gCwBRv0ZPuTTz6Zd73rXbn22muzevXqHHLIIfn0pz+dHXbYYbA3BdRbUa+eAQDAFqKop+RFTWhvaqj2Uqk6VPvMmUOfuzBaMPSh+7wJt7Ul06dKLLJlKZerB82+Dvbz5rn+BgAFMOhztn/oQx/KFVdckaOOOionnnhivv/97+ff/u3fBnszQL11XT3rPc5h19Wz1tb6xAUAAFuIop6SF3nu8f4M1T7UukYL7uqs2FuplEyaZLRgtiCtrcm+U9YvHzkjaW52vYEtT7mcLFmSLFyYXHll9X7xYol2ACiIQe/Z3trami996Us54YQTkiRveMMb8qpXvSqdnZ1p0PIURoYidwcBAIAaKNpQ7UU+JS/y3OPdh2AvjVqT3c6+OUmy9KIjUlnd2Ge9oWK0YOimqzVR4+ie5fUeHgPqpaFh6A+aAMBmGfSe7cuWLUtLt2bWL3/5y9PY2JgVK1YM9qaAeilydxAAABhkra3VzpTTpycnnVS9r3fnyiKfkvdOaE8+5/pMPuf6lEat2Wi9oVL0odq7RgueOLFneVOT3CJbkCIPjwEAAL0Mes/2zs7OjB7ds9VpY2Nj1qxZs5G/AIadblfFnhw1JlPOviZJcu9Fx2bc6lV91gMAgE0pWu/xpLhzjxe5h3aRE9pdQ7W3t/e9vlSqrq/nUO3lcnVEgqJ9F2DIFHl4DAAA6GXQk+2VSiVvectbMmbMmHVlTz31VE4//fQ897nPXVfWan4lGL6KfPUMAIBhqbW12pGxe36lqak6rHa9evMWeaj2Ip+SFzmh3Xuo9t5xJcUYqt1owWzRurUSGrd6VZbMPXqT9QAAoF4GfRj5N7/5zdlpp50yfvz4dbc3vvGN2XXXXXuUAcNY19Wz3lenupRKyaRJ9e0OAgBAnzo7k0WLkquuqt4XYRTert7jvTsydvUer1db7SIP1V7kU/KuhHZXHL3jSuqb0O4aqn3XXg0RDNUOBVHk1kQAANDLoPdsv/zyywf7KYGiGS7dQQAA6EHv8f4p8lDtRT8l70pozz67Z3lTUzWueie0y+XksBnJ/udVl2+8ITl0up8wUAjdh8fo6+BQhPkeAADg7wa9Zzuwhei6ejZh157luoMAABSS3uP9V/TOlUXvoV0uJ/fet375xhuSxYvrH1eX7on1lqkS7VAYRR8eAwAAupFshy5FHE+z6Mrl5L571y/fcGOxrp4BAJBk073Hk2rv8XqcAvfuPT75nOsz+ZzrUxq1ZqP1hkqRh2rvIqENjEhdrYkmTuxZXpTWRAAA8HeDPow8DEtFHE9zuOh+tWxqi6tnAMCQ6eys9nbu6Kj2LG4p0KlI0WLrT+/xadOGLKwkxe49XvSh2rtIaAMjUrlcnUOkSAdUAADoRbIdusbT7N3Np2s8TS2mAYAtWNGSxl2K3FayiLEVee7x7lPz9qXeU/MWfe5xgBGtoWHoW4EBAEA/GEaeLVuRx9MEAKiz1takuTmZPj056aTqfXNz/eb27h5XEeceT4ob23DoPZ4Ut/d40YdqBwAAAOpDsp0tW3/G0wQA2IIUNWlc5LaSRY6t6HOPd/Ue37VXsr9IU/Maqh0AAADoTbKdLVu3cTKfHDUmzedcl+ZzrsuTo8ZstB4AwEhX5KRxkdtKFjk2vccBAAAABp9kO1u2Io+nCQBsMTo7k0WLkquuqt7XewabIieNe889Pvmc6zP5nOtTGrVmo/WGSpFjS/QeBwAAABhsku1s2Yo+niYAMOIVcV70IieNi9xWssixddF7HAAAAGDwSLazZRsO42kCACNWUedFL3LSuHtbya2yfgiAf0pbtkpnXdtKDpd2nHqPAwAAAAwOyXboGk9zwq49y4s0niYAMOIUeV70IieNu9pK/kulNfdmyrrymzIjS9Kcf6m01q2tpHacAAAAAFsWyXZIqgn1++5dv3zDjcbTBABqqsjzohc9aVxOaxZkVnZNe4/yiWnPgsxKOfUbg7+rHWfTLj173e82sVM7TgAAAIARRrIdunS/Wjy1RZcjAKCmijwverI+abxrr6Hi6z74z9+HBCilssGPma1SqTYGqNeQAH9XTmvuLfXsdb84zXVtBMAI1/3zfltbXT//AAAAsCWRbAcAgDoo8rzoXcrl5N771i/feEMBBv8p8pAASdLamsyaldKKnr3uS+3tyaxZ1fUwmFpbk33XN+7IkTOS5mafNQAAABgCku0AAFAHRZ4Xvbvug/20TC3A4D/duvqPW70qS+YenSVzj8641as2Wm/I/L3XfSqVDdd1ldW51z0jzN8bd6RX445o3AEAAABDQrIdAADqoOjzohdWkYcEKHqve0YWjTsAAACg7iTbAQCgTgo7L3qRFXlIgCL3umfk0bjj2TPXPQAAAM+SZDsAANRRIedFL7IiDwlQ5F73jDwadzw75roHAABgEEi2AwBAnRVuXvSi6xoSYOLEnuX1HhKgyL3uGXk07hg4c90DAAAwSCTbAQCA4adcTpYsSRYuTK68snpf7yEBitzrnpFH446BMdc9AAAAg0iyHQAAGJ4aGpJp05ITT6zeFyGJXdRe94w8GncMjLnuAQAAGESN9Q4AAABgRCmXk5kzq8m6jo7qMN4tLZKeDL6uxh2zZ/dMIDc1VRPtGndsqI+57jdVDwAAADZGsh0AAGCwdfW6h1rTuKN/zHUPAADAIJJsBwAAoBi6z5N9W1syfaqk8ebQuGPzdc11397e97ztpVJ1vbnuAQAA2AzmbAegp87OZNGi5KqrqvfdL3oDDGN2b1Bwra3JvlPWLx85I2lurpbDYDHXPQAAAINIsh2A9Vpbqxe1p09PTjqpeu8iNzAC2L1BwbW2JrNmJSvae5a3t1fLi/Bl7d3rXoud4atrrvuJE3uWNzVVy811DwDUmnNLgBFDsh2Aqq6L3MuX9ywv0kVugAGwe4OC6+xMZs/ue0jvrrI5c+p7AVKv+5GnXE6WLEkWLkyuvLJ6v3ixRDsAUHvOLQFGFMl2AIbHRW5gPeOhbza7NxgG2to2bA3TXaWSLFtWrVcPw6HXPQPTNdf9iSdW7w0dDwDUmnNLgBFHsh2A4l/kBtYzHnq/2L3BMNDRse7huNWrsmTu0Vky9+iMW71qo/WGjBY7AAAMFueWACOSZDsAPS5ePzlqTJrPuS7N51yXJ0eN2Wg9oA6Mh95v3XdbpVFrMvmc6zP5nOtTGrVmo/WAITZhwuDWG0xa7AAAMFicWwKMSI31DgCAAijyRW6galMt4Eulagv4mTMNg9uN3RsMAy0tSVNTteFQX/u4Uqm6vqVl6GPro9f9puoBAECfnFsCjEh6tgOw/iJ3qdT3+lIpmTSpPhe5gSot4AfE7g2GgYaGZP786uPeX9au5Xnz6tOQSIsdAAAGi3NLgBFJsh2AYl/kBqqGyXQPnZ3JokXJVVdV7+s91ZzdGwwT5XKyYEEycWLP8qamanm5XJ+4tNgBAGCwOLcEGJEk2wGo6rrIPWHXnuX1vsgNVA2DFvCtrUlzczJ9enLSSdX75ub6TyXftXvbtddLY/cGBVMuJ0uWJAsXJldeWb1fvLi+X1ItdgAAGCzOLQFGJMl2ANYrl5P77l2/fMON9b/IDVQVvAV8a2sya9aGI923t1fLi5Bwv/e+9cs33mD3BoXU0JBMm5aceGL1vggXGova6x4AgOHHuSXAiNNY7wAAKJjuF7WnthTjIjewvgX8rFmFawHf2ZnMnl2dNr63SqUa3pw5ycyZ9d2ldN92y1S7N6AfyuXqTqytrTpdx4QJ1cZNdiQAAPSXc0uAEUWyHQCgD52dBfzd+/cW8JWz39WjuDKxKaX58+rWAr6tbcMe7d1VKsmyZdV606YNWVgAg6ur1z0AADxbzi0BRgzDyAMA9FLUuceTpDXlTKmsn+7htbkxzZXFaU39hprr6Fj/uDRqTSafc30mn3N9SqPWbLQeAAAAAMBwJ9kOANRNZ2eyaFFy1VXV+87OekdU7LnHu2Jb1rG+i/3tacmyFQ11jW3ChMGtBwAAAAAwHEi2AwB1UcTe45uaezypzj1ej0YBRY6tpSVpatpwKvkupVIyaVK1HgAAAADASCHZDgAMuaL2Hu/P3ONDrcixNTQk8+dXH/dOuHctz5tXgDnvAQAAAAAGkWQ7ADCkitxDu8hzj3ffZmV1Y/4496j8ce5Rqaxu3Gi9oVQuJwsWJLv2Giq+qalaXq7flPIAAAAAADXRuOkqQN11dla7KnZ0VCe8bWnRPRAKpshf06LF1p8e2tOmDVlYSYo993iRY+tSLieHzUj2P6+6fOMNyaHTi/NdAAAAAAAYTHq2Q9EVcVJjoIcif02LGFuRe48Xee7xIsfWXffEesvUgiXauw+XcFtbfYZPAAAAAABGDMl2KLKiTmoMddLZmSxalFx1VfW+CHmyIn9NixpbkXtoF3nu8SLHNiy0tib7Tlm/fOSM+rc8GS40UgAAAKAI/D4FCkiyHYqqyJMaQx0UsYd2kb+mRY6t6D20izz3eFdsEyf2LC9CbIXW1fJkRXvP8nq3PBkONFIAAACgCPw+BQpqWCTbL7744uy+++4ZO3ZsDjzwwLS1tW3W391xxx1pbGzMAQccUNsAoRb6M6kxjHBF7aFd5K9pkWMbDj20y+Xk3vvWL994Q7J4cTGS2eVysmRJsnBhcuWV1fuixFZIRW55UnQaKQAAAFAEfp8CBVb4ZPvVV1+dOXPm5H3ve1/uvvvutLS0ZMaMGVm6dOkz/t1jjz2WN73pTTnkkEOGKFIYZN0mK35y1Jg0n3Ndms+5Lk+OGrPRejASFTlPVuS5x4scW1Ls3uNdijz3eENDMm1acuKJ1fsixVY4RW55UmRF3vkCAACw5fD7FCi4wifbL7roopx66qk57bTTsu+++2bevHmZNGlSLrnkkmf8u3/913/NSSedlIMOOmiIIoVBVuRJjWEIFTlPVuSvaZFj61Lk3uOMIN1alIxbvSpL5h6dJXOPzrjVqzZajxR75wsAAMCWw+9ToOAa6x3AM3n66adz11135T3veU+P8sMPPzx33nnnRv/u8ssvzx/+8Id87Wtfy3/9139tcjurVq3KqlXrL7iuXLly4EHDYOma1Li9ve/1pVJ1fb0mNYYh0ruH9m5n35wkWXrREamsbuyz3lAp8te0yLF1V+Te44wQw6HlSRH10UhhU/UAAABg0Pl9ChRcoXu2/+lPf0pnZ2d23nnnHuU777xzHnzwwT7/5ve//33e85735Otf/3oaGzevLcEFF1yQ8ePHr7tNmjTpWccOz9pwmNQYhkCR82RF/poWOTYYUl0tT3p/EbqUSsmkSfVveVI0Rd75AgAAsOXw+xQouEIn27uUel0crVQqG5QlSWdnZ0466aScd9552WuvvTb7+c8999w89thj627Lli171jHDoOia1HjCrj3LizSpMdRY0fNkRZ57vMixwZDR8mRgir7zBQAAYMvg9ylQcIVOtu+www5paGjYoBf7Qw89tEFv9yR5/PHH8/Of/zz//u//nsbGxjQ2Nub888/PL3/5yzQ2NuaHP/xhn9sZM2ZMttlmmx43KIxyObnv3vXLN9xoUmO2KMMhT1bkuceLHBsMma6WJxMn9izX8mTjhsPOFwAAgJHP71Og4AqdbB89enQOPPDA3HLLLT3Kb7nllhx88MEb1N9mm23yq1/9Kvfcc8+62+mnn569994799xzT17xilcMVegwuLqfKExtceLAFmc49NAu8tzjRY4Nhky5nCxZkixcmFx5ZfVey5NnppECAAAAReD3KVBgmzepeR2dffbZOfnkk/Oyl70sBx10UC699NIsXbo0p59+epLqEPDt7e35yle+kq222ir77bdfj7/faaedMnbs2A3KARheyuXksBnJ/udVl2+8ITl0usQx0A8NDcm0afWOYngpl5OZM5O2tqSjozoHXouGfwAAAAwxv0+Bgip8sv3444/PI48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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mosaic = \"\"\"\n", + " ABCD\n", + " EEEE\n", + " \"\"\"\n", + "\n", + "fig = plt.figure(layout=\"constrained\", figsize=(20, 10))\n", + "ax_dict = fig.subplot_mosaic(mosaic)\n", + "\n", + "\n", + "def plot_var(var, ax):\n", + " a = (\n", + " cces_df.groupby(var, observed=False)[[\"abortion\"]]\n", + " .mean()\n", + " .rename({\"abortion\": \"share\"}, axis=1)\n", + " .reset_index()\n", + " )\n", + " b = (\n", + " cces_all_df.groupby(var, observed=False)[[\"abortion\"]]\n", + " .mean()\n", + " .rename({\"abortion\": \"share_census\"}, axis=1)\n", + " .reset_index()\n", + " )\n", + " a = a.merge(b).sort_values(\"share\")\n", + " ax_dict[ax].vlines(a[var], a.share, a.share_census)\n", + " ax_dict[ax].scatter(a[var], a.share, color=\"blue\", label=\"Sample\")\n", + " ax_dict[ax].scatter(a[var], a.share_census, color=\"red\", label=\"Census\")\n", + " ax_dict[ax].set_ylabel(\"Proportion\")\n", + "\n", + "\n", + "plot_var(\"age\", \"A\")\n", + "plot_var(\"edu\", \"B\")\n", + "plot_var(\"male\", \"C\")\n", + "plot_var(\"eth\", \"D\")\n", + "plot_var(\"state\", \"E\")\n", + "\n", + "ax_dict[\"E\"].legend()\n", + "\n", + "ax_dict[\"C\"].set_xticklabels([])\n", + "ax_dict[\"C\"].set_xlabel(\"Female / Male\")\n", + "plt.suptitle(\"Comparison of Proportions: Survey Sample V Census\", fontsize=20);" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see here how the proportions differ markedly across the census report and our biased sample in how they represent the preferential votes with each strata. We now try and quantify the overall differences between the biased sample and the census report. We calculate the expected proportions in each dataset and their standard error. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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meansedata
00.4340510.002113Full Data
10.4650000.007054Biased Data
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" + ], + "text/plain": [ + " mean se data\n", + "0 0.434051 0.002113 Full Data\n", + "1 0.465000 0.007054 Biased Data" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def get_se_bernoulli(p, n):\n", + " return np.sqrt(p * (1 - p) / n)\n", + "\n", + "\n", + "sample_cces_estimate = {\n", + " \"mean\": np.mean(cces_df[\"abortion\"].astype(float)),\n", + " \"se\": get_se_bernoulli(np.mean(cces_df[\"abortion\"].astype(float)), len(cces_df)),\n", + "}\n", + "sample_cces_estimate\n", + "\n", + "\n", + "sample_cces_all_estimate = {\n", + " \"mean\": np.mean(cces_all_df[\"abortion\"].astype(float)),\n", + " \"se\": get_se_bernoulli(np.mean(cces_all_df[\"abortion\"].astype(float)), len(cces_all_df)),\n", + "}\n", + "sample_cces_all_estimate\n", + "\n", + "summary = pd.DataFrame([sample_cces_all_estimate, sample_cces_estimate])\n", + "summary[\"data\"] = [\"Full Data\", \"Biased Data\"]\n", + "summary" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A 3 percent difference in a national survey is a substantial error in the case where the difference is due to preventable bias. " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modelling the Data\n", + "\n", + "To facilitate regression based stratification we first need a regression model. In our case we will ultimately fit a multi-level regression model with intercept terms for each for each of the groups in our demographic stratum. In this way we try to account for the appropriate set of variables (as in the example above) to better specify the effect modification due to membership within a particular demographic stratum. \n", + "\n", + "We will fit the model using `bambi` using the binomial link function on the biased sample data. But first we aggregate up by demographic strata and count the occurences within each strata. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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stateethmaleageedunabortionrepvoteregion
0IDWhite-0.570+HS32180.683102West
1IDWhite0.570+4-Year College20160.683102West
2WVWhite0.570+Some college17130.721611South
3WVWhite0.570+4-Year College15120.721611South
4IDWhite0.570+Post-grad17110.683102West
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" + ], + "text/plain": [ + " state eth male age edu n abortion repvote region\n", + "0 ID White -0.5 70+ HS 32 18 0.683102 West\n", + "1 ID White 0.5 70+ 4-Year College 20 16 0.683102 West\n", + "2 WV White 0.5 70+ Some college 17 13 0.721611 South\n", + "3 WV White 0.5 70+ 4-Year College 15 12 0.721611 South\n", + "4 ID White 0.5 70+ Post-grad 17 11 0.683102 West" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_df = (\n", + " cces_df.groupby([\"state\", \"eth\", \"male\", \"age\", \"edu\"], observed=False)\n", + " .agg({\"caseid\": \"nunique\", \"abortion\": \"sum\"})\n", + " .reset_index()\n", + " .sort_values(\"abortion\", ascending=False)\n", + " .rename({\"caseid\": \"n\"}, axis=1)\n", + " .merge(statelevel_predictors_df, left_on=\"state\", right_on=\"state\", how=\"left\")\n", + ")\n", + "model_df[\"abortion\"] = model_df[\"abortion\"].astype(int)\n", + "model_df[\"n\"] = model_df[\"n\"].astype(int)\n", + "model_df.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our `model_df` now has one row per Strata across all the demographic cuts. " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fit Base Model to Biased Sample\n", + "\n", + "Here we use some of bambi's latest functionality to assess the interaction effects between the variables. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [Intercept, C(state), C(eth), C(edu), male, repvote]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 816 seconds.\n" + ] + } + ], + "source": [ + "formula = \"\"\" p(abortion, n) ~ C(state) + C(eth) + C(edu) + male + repvote\"\"\"\n", + "\n", + "base_model = bmb.Model(formula, model_df, family=\"binomial\")\n", + "\n", + "result = base_model.fit(\n", + " random_seed=100,\n", + " target_accept=0.95,\n", + " # inference_method=\"nuts_numpyro\",\n", + " idata_kwargs={\"log_likelihood\": True},\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We plot the predicted outcomes within each group using the `plot_predictions` function. " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mosaic = \"\"\"\n", + " AABB\n", + " CCCC\n", + " \"\"\"\n", + "\n", + "fig = plt.figure(layout=\"constrained\", figsize=(20, 7))\n", + "axs = fig.subplot_mosaic(mosaic)\n", + "\n", + "bmb.interpret.plot_predictions(base_model, result, \"eth\", ax=axs[\"A\"])\n", + "bmb.interpret.plot_predictions(base_model, result, \"edu\", ax=axs[\"B\"])\n", + "bmb.interpret.plot_predictions(base_model, result, \"state\", ax=axs[\"C\"])\n", + "plt.suptitle(\"Plot Prediction per Class\", fontsize=20);" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More interesting we can use the comparison functionality to compare differences in `eth` conditional on `age` and `edu`. Where we can see that the differences between ethnicities are pretty stable across all age groups, slightly shifted by within the `Post-grad` level of education. " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = bmb.interpret.plot_comparisons(\n", + " model=base_model,\n", + " idata=result,\n", + " contrast={\"eth\": [\"Black\", \"White\"]},\n", + " conditional=[\"age\", \"edu\"],\n", + " comparison_type=\"diff\",\n", + " subplot_kwargs={\"main\": \"age\", \"group\": \"edu\"},\n", + " fig_kwargs={\"figsize\": (12, 5), \"sharey\": True},\n", + " legend=True,\n", + ")\n", + "ax[0].set_title(\"Comparison of Difference in Ethnicity \\n within Age and Educational Strata\");" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can pull these specific estimates out into a table for closer inspection to see that the differences in response expected between the extremes of educational attainment are moderated by state iand race." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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termestimate_typevalueethstatemalerepvoteestimatelower_3.0%upper_97.0%
0edudiff(Post-grad, No HS)BlackNY0.00.5301910.0931610.0001710.197388
1edudiff(Post-grad, No HS)BlackCA0.00.5301910.0781490.0000140.188560
2edudiff(Post-grad, No HS)BlackID0.00.5301910.0858100.0001160.194178
3edudiff(Post-grad, No HS)BlackVA0.00.5301910.1255380.0243550.220127
4edudiff(Post-grad, No HS)WhiteNY0.00.5301910.0936320.0005370.201009
5edudiff(Post-grad, No HS)WhiteCA0.00.5301910.0786560.0000370.193271
6edudiff(Post-grad, No HS)WhiteID0.00.5301910.0929980.0002690.198796
7edudiff(Post-grad, No HS)WhiteVA0.00.5301910.0996200.0024370.193426
\n", + "
" + ], + "text/plain": [ + " term estimate_type value eth state male repvote \\\n", + "0 edu diff (Post-grad, No HS) Black NY 0.0 0.530191 \n", + "1 edu diff (Post-grad, No HS) Black CA 0.0 0.530191 \n", + "2 edu diff (Post-grad, No HS) Black ID 0.0 0.530191 \n", + "3 edu diff (Post-grad, No HS) Black VA 0.0 0.530191 \n", + "4 edu diff (Post-grad, No HS) White NY 0.0 0.530191 \n", + "5 edu diff (Post-grad, No HS) White CA 0.0 0.530191 \n", + "6 edu diff (Post-grad, No HS) White ID 0.0 0.530191 \n", + "7 edu diff (Post-grad, No HS) White VA 0.0 0.530191 \n", + "\n", + " estimate lower_3.0% upper_97.0% \n", + "0 0.093161 0.000171 0.197388 \n", + "1 0.078149 0.000014 0.188560 \n", + "2 0.085810 0.000116 0.194178 \n", + "3 0.125538 0.024355 0.220127 \n", + "4 0.093632 0.000537 0.201009 \n", + "5 0.078656 0.000037 0.193271 \n", + "6 0.092998 0.000269 0.198796 \n", + "7 0.099620 0.002437 0.193426 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bmb.interpret.comparisons(\n", + " model=base_model,\n", + " idata=result,\n", + " contrast={\"edu\": [\"Post-grad\", \"No HS\"]},\n", + " conditional={\"eth\": [\"Black\", \"White\"], \"state\": [\"NY\", \"CA\", \"ID\", \"VA\"]},\n", + " comparison_type=\"diff\",\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With this in mind we want to fit our final model to incorporate the variation we see here across the different levels of our stratified data." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fit Final Model to Biased Sample\n", + "\n", + "We can specify these features of our model using a hierarchical structure as follows: \n", + "\n", + "$$ Pr(y_i = 1) = logit^{-1}(\n", + "\\alpha_{\\rm s[i]}^{\\rm state}\n", + "+ \\alpha_{\\rm a[i]}^{\\rm age}\n", + "+ \\alpha_{\\rm r[i]}^{\\rm eth}\n", + "+ \\alpha_{\\rm e[i]}^{\\rm edu} \n", + "+ \\beta^{\\rm male} \\cdot {\\rm Male}_{\\rm i}\n", + "+ \\alpha_{\\rm g[i], r[i]}^{\\rm male.eth}\n", + "+ \\alpha_{\\rm e[i], a[i]}^{\\rm edu.age}\n", + "+ \\alpha_{\\rm e[i], r[i]}^{\\rm edu.eth}\n", + ")\n", + "$$\n", + "\n", + "Here we have used the fact that we can add components to the $\\alpha$ intercept terms and interaction effects to express the stratum specific variation in the outcomes that we've seen in our exploratory work. Using the `bambi` formula syntax. We have:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "formula = \"\"\" p(abortion, n) ~ (1 | state) + (1 | eth) + (1 | edu) + male + repvote + (1 | male:eth) + (1 | edu:age) + (1 | edu:eth)\"\"\"\n", + "\n", + "model_hierarchical = bmb.Model(formula, model_df, family=\"binomial\")\n", + "\n", + "result = model_hierarchical.fit(\n", + " random_seed=100,\n", + " target_accept=0.99,\n", + " inference_method=\"nuts_numpyro\",\n", + " idata_kwargs={\"log_likelihood\": True},\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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+       "Dimensions:               (chain: 4, draw: 1000, state__factor_dim: 46,\n",
+       "                           eth__factor_dim: 4, edu__factor_dim: 5,\n",
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meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
Intercept0.4070.540-0.5481.3650.0160.0161587.01235.01.0
male0.2090.191-0.1660.5560.0060.0051459.01152.01.0
1|edu[4-Year College]-0.0430.189-0.4210.2940.0030.0033269.02748.01.0
1|edu[HS]0.0590.186-0.2850.4330.0030.0032936.02716.01.0
1|edu[No HS]0.1690.224-0.1810.6380.0050.0032432.03248.01.0
1|edu[Post-grad]-0.1980.221-0.6440.1270.0050.0032063.02871.01.0
1|edu[Some college]0.0320.188-0.3390.3860.0030.0033108.03001.01.0
1|eth[Black]-0.4370.486-1.3290.3320.0150.0141692.01144.01.0
1|eth[Hispanic]0.0590.455-0.6490.9530.0140.0132094.01166.01.0
1|eth[Other]0.0760.455-0.6141.0040.0140.0131979.01220.01.0
1|eth[White]0.1620.459-0.6220.9700.0150.0131687.01124.01.0
repvote-1.1920.529-2.200-0.1930.0130.0091749.02462.01.0
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" + ], + "text/plain": [ + " mean sd hdi_3% hdi_97% mcse_mean mcse_sd \\\n", + "Intercept 0.407 0.540 -0.548 1.365 0.016 0.016 \n", + "male 0.209 0.191 -0.166 0.556 0.006 0.005 \n", + "1|edu[4-Year College] -0.043 0.189 -0.421 0.294 0.003 0.003 \n", + "1|edu[HS] 0.059 0.186 -0.285 0.433 0.003 0.003 \n", + "1|edu[No HS] 0.169 0.224 -0.181 0.638 0.005 0.003 \n", + "1|edu[Post-grad] -0.198 0.221 -0.644 0.127 0.005 0.003 \n", + "1|edu[Some college] 0.032 0.188 -0.339 0.386 0.003 0.003 \n", + "1|eth[Black] -0.437 0.486 -1.329 0.332 0.015 0.014 \n", + "1|eth[Hispanic] 0.059 0.455 -0.649 0.953 0.014 0.013 \n", + "1|eth[Other] 0.076 0.455 -0.614 1.004 0.014 0.013 \n", + "1|eth[White] 0.162 0.459 -0.622 0.970 0.015 0.013 \n", + "repvote -1.192 0.529 -2.200 -0.193 0.013 0.009 \n", + "\n", + " ess_bulk ess_tail r_hat \n", + "Intercept 1587.0 1235.0 1.0 \n", + "male 1459.0 1152.0 1.0 \n", + "1|edu[4-Year College] 3269.0 2748.0 1.0 \n", + "1|edu[HS] 2936.0 2716.0 1.0 \n", + "1|edu[No HS] 2432.0 3248.0 1.0 \n", + "1|edu[Post-grad] 2063.0 2871.0 1.0 \n", + "1|edu[Some college] 3108.0 3001.0 1.0 \n", + "1|eth[Black] 1692.0 1144.0 1.0 \n", + "1|eth[Hispanic] 2094.0 1166.0 1.0 \n", + "1|eth[Other] 1979.0 1220.0 1.0 \n", + "1|eth[White] 1687.0 1124.0 1.0 \n", + "repvote 1749.0 2462.0 1.0 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "az.summary(result, var_names=[\"Intercept\", \"male\", \"1|edu\", \"1|eth\", \"repvote\"])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The terms in the model formula allow for specific intercept terms across the demographic splits of `eth`, `edu`, and `state`. These represent stratum specific adjustments of the intercept term in the model. Similarly we invoke intercepts for the interaction terms of `age:edu`, `male:eth` and `edu:eth`. Each of these cohorts represents a share of the data in our sample.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "clusterstate__factor_dim (46)\n", + "\n", + "state__factor_dim (46)\n", + "\n", + "\n", + "clustereth__factor_dim (4)\n", + "\n", + "eth__factor_dim (4)\n", + "\n", + "\n", + "clusteredu__factor_dim (5)\n", + "\n", + "edu__factor_dim (5)\n", + "\n", + "\n", + "clustermale:eth__factor_dim (8)\n", + "\n", + "male:eth__factor_dim (8)\n", + "\n", + "\n", + "clusteredu:age__factor_dim (30)\n", + "\n", + "edu:age__factor_dim (30)\n", + "\n", + "\n", + "clusteredu:eth__factor_dim (20)\n", + "\n", + "edu:eth__factor_dim (20)\n", + "\n", + "\n", + "clusterp(abortion, n)_obs (11040)\n", + "\n", + "p(abortion, n)_obs (11040)\n", + "\n", + "\n", + "\n", + "Intercept\n", + "\n", + "Intercept\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "p(abortion, n)\n", + "\n", + "p(abortion, n)\n", + "~\n", + "Binomial\n", + "\n", + "\n", + "\n", + "Intercept->p(abortion, n)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "repvote\n", + "\n", + "repvote\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "repvote->p(abortion, n)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1|eth_sigma\n", + "\n", + "1|eth_sigma\n", + "~\n", + "HalfNormal\n", + "\n", + "\n", + "\n", + "1|eth\n", + "\n", + "1|eth\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "1|eth_sigma->1|eth\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1|male&eth_sigma\n", + "\n", + "1|male:eth_sigma\n", + "~\n", + "HalfNormal\n", + "\n", + "\n", + "\n", + "1|male&eth\n", + "\n", + "1|male:eth\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "1|male&eth_sigma->1|male&eth\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1|edu&age_sigma\n", + "\n", + "1|edu:age_sigma\n", + "~\n", + "HalfNormal\n", + "\n", + "\n", + "\n", + "1|edu&age\n", + "\n", + "1|edu:age\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "1|edu&age_sigma->1|edu&age\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1|edu&eth_sigma\n", + "\n", + "1|edu:eth_sigma\n", + "~\n", + "HalfNormal\n", + "\n", + "\n", + "\n", + "1|edu&eth\n", + "\n", + "1|edu:eth\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "1|edu&eth_sigma->1|edu&eth\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "male\n", + "\n", + "male\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "male->p(abortion, n)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1|edu_sigma\n", + "\n", + "1|edu_sigma\n", + "~\n", + "HalfNormal\n", + "\n", + "\n", + "\n", + "1|edu\n", + "\n", + "1|edu\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "1|edu_sigma->1|edu\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1|state_sigma\n", + "\n", + "1|state_sigma\n", + "~\n", + "HalfNormal\n", + "\n", + "\n", + "\n", + "1|state\n", + "\n", + "1|state\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "1|state_sigma->1|state\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1|state_offset\n", + "\n", + "1|state_offset\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "1|state_offset->1|state\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1|state->p(abortion, n)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1|eth->p(abortion, n)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1|eth_offset\n", + "\n", + "1|eth_offset\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "1|eth_offset->1|eth\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1|edu_offset\n", + "\n", + "1|edu_offset\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "1|edu_offset->1|edu\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1|edu->p(abortion, n)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1|male&eth->p(abortion, n)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1|male&eth_offset\n", + "\n", + "1|male:eth_offset\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "1|male&eth_offset->1|male&eth\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1|edu&age_offset\n", + "\n", + "1|edu:age_offset\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "1|edu&age_offset->1|edu&age\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1|edu&age->p(abortion, n)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1|edu&eth_offset\n", + "\n", + "1|edu:eth_offset\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "1|edu&eth_offset->1|edu&eth\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1|edu&eth->p(abortion, n)\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_hierarchical.graph()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then predict the outcomes implied by the biased sample. These predictions are to be adjusted by what we take to be the share of that demographic cohort in population. We can plot the posterior predictive distribution against the observed data from our biased sample to see that we have generally good fit to the distribution. " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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c7eztt9/GDz/8gG7dumHatGlo1KgRsrKycODAAXTu3BktW7bUa/vDDz+Era0t3nzzTTx48ACTJ08GADRu3BidO3dGYGAgKleujPPnz+Pbb7+V9QcRUYUm6yVVRGQ07VX2x44dK3S6rKwsMXXqVOHj4yOUSqVwd3cXb7zxhkhJSZGmiYmJET169BA+Pj5CpVIJR0dH0aJFC7Flyxadtn799VdRr149oVKpBAAxdOhQadz169fFq6++KqpUqSKUSqVwdnYWwcHB4t1335Wm0V7dvn79er04819lr7V582bRuHFjYWFhIaytrUXr1q3F77//rjON9mr4e/fuFbHUio7DmHa//vpr0bhxY2FtbS0sLS2Fv7+/GDJkiDh+/Lg0TW5urpg3b57w8vIS5ubmIjAwUGzdulW0aNGiyKvshRDi3Llzok+fPsLR0VGYm5sLb29vMWzYMJGdnS1Ns2TJEuHn5ydMTU112sh/lX1ewcHBAoAYNGiQwfE5OTli0aJFok6dOsLCwkLY2NiIGjVqiJEjR4rLly8Xuty0Dhw4IHr37i3c3d2FUqkUdnZ2omnTpuKDDz7QuaIegBg9erRefR8fH51tTIjibWdCPL7Dwbhx44S3t7dQKpXCxcVFdOrUSVy4cEFqB/9eZZ/XBx98oHNnhWnTpokGDRqIypUrC5VKJapWrSrGjx8vkpKSirUMiMg4CiEMHFchIiIiInpKeA4pEREREcmKCSkRERERyYoJKRERERHJigkpEREREcmKCSkRlanbt29jzJgx8Pf3h4WFBSpXroywsDCsWbPG4L0qn0RUVBQUCoXOy9nZGWFhYdi2bZve9AqFApGRkaUaQ2m5ceMGFAoFoqKiipz2/PnzGDx4MKpWrQoLCws4OTmhfv36GDNmDNLS0so+2CcQGRlp9IMNiKji4X1IiajM/P777+jcuTNsbGwwefJkBAYGIjU1FT/++CNeeeUVbN26FWvXrtV5Zn1pWLVqFWrUqCE9merTTz9Fly5dsGXLFnTp0kWaLiYmptQf0fm0nTp1Cs2aNUNAQADeeecd+Pr6IikpCX/++Se+//57TJo0CXZ2dnKHSURUKCakRFQm7t+/j549e8Le3h5Hjx6Fq6urNK5bt24IDAzEtGnTULduXUybNq1U+65Vq5bOo0Hbt2+PypUrY926dToJaZMmTUq1XzksWbIEJiYm2L9/v86jPXv37o25c+eW+l5oIqKywEP2RFQmvvzyS9y9exfz58/XSUa1pkyZgho1auCDDz5ATk4OAGD//v1QKBRYt24dZs6cCQ8PD9jZ2aFNmza4ePFiiWOxsLCAubk5lEqlTnn+Q/b37t3Dm2++iZdeegk2NjZwcXFBq1atcOjQIb02ly9fjjp16sDGxga2traoUaMGZsyYoTNNQkICRo4cCU9PT5ibm8PPzw+zZ8+GWq3WmS4uLg59+/aFra0t7O3t0a9fPyQkJBRr3pKTk2FnZwcbGxuD4/MeDo+Ojka3bt3g6ekJCwsLvPDCCxg5cqTeI0q1h9H/+usv9OnTB/b29nBwcMCECROgVqtx8eJFtG/fHra2tvD19cXChQt16mvX43fffYcJEybAzc1NeiTqqVOnijVfP/zwA5o2bQpra2vY2NigXbt2xa5LROUPE1IiKhPR0dEwNTXV2SOZl0KhQNeuXfHPP//gxIkTOuNmzJiBmzdv4ssvv8Tnn3+Oy5cvo0uXLtBoNMXqW6PRQK1WIycnB3fu3MHbb7+NjIwMDBw4sNB6//zzDwAgIiIC27dvx6pVq1C1alWEhYVh//790nTff/893nzzTbRo0QKbNm3C5s2bMX78eGRkZEjTJCQkoFGjRvjll1/wzjvvYOfOnXjttdcwb948jBgxQpouKysLbdq0we7duzFv3jysX78ebm5ueo/1LEjTpk0RHx+PQYMG4cCBA4U+2/7q1ato2rQpli9fjt27d+Odd97B0aNH0bx5c+lHQV59+/ZFnTp1sHHjRowYMQIfffQRxo8fj+7du6NTp07YtGkTWrVqhalTp+Knn37Sqz9jxgxcu3YNX375Jb788kvExcUhLCwM165dK3Se3n//fQwYMAAvvfQSfvzxR3z77bdIT09HSEgIzp07V6zlQkTljKzPiSKiCqtGjRrCzc2t0GmWL18uAIgffvhBCPHfoz07duyoM92PP/4oAIiYmJhC29M+VjX/S6VSic8++0xvegAiIiKiwPbUarXIyckRrVu3Fj169JDKx4wZIypVqlRoLCNHjhQ2Njbi5s2bOuWLFi0SAMTff/+tswx+/vlnnelGjBhh8JGi+WVnZ4vu3btL82pqairq1asnZs6cKe7evVtgvdzcXJGTkyNu3ryp17/20akffvihTp26desKAOKnn36SynJycoSzs7Po2bOnVKZdj/Xr1xe5ublS+Y0bN4RSqRTDhw/X60vr1q1bwszMTLz11ls6faenpws3NzfRt2/fQpcHEZVP3ENKRLIR/57fmP8q665du+q8DwwMBADcvHmzWO2uXr0ax44dw7Fjx7Bz504MHToUo0ePxqefflpk3RUrVqB+/fqwsLCAmZkZlEol9uzZg/Pnz0vTNGrUCPfv38eAAQPw888/6x3yBoBt27ahZcuW8PDwgFqtll4dOnQAABw4cAAAsG/fPtja2urNc1F7c7VUKhU2bdqEc+fO4aOPPkL//v1x7949vPfeewgICNA51eHu3bsYNWoUvLy8pHnz8fEBAJ350+rcubPO+4CAACgUCmkeAMDMzAwvvPCCwXUzcOBAnXXr4+OD4OBg7Nu3r8D5+eWXX6BWqzFkyBCd5WZhYYEWLVro7KkmooqDFzURUZnw9vbG5cuXkZGRAWtra4PT3LhxAwDg5eWlU+7o6KjzXqVSAUChh6PzCggI0Luo6ebNm5gyZQpeeeUVVKpUyWC9xYsXY+LEiRg1ahTmzp0LJycnmJqaYtasWToJ2+DBg6FWq/HFF1+gV69eyM3NRcOGDfHuu+/i5ZdfBgAkJiZi69ateuetammT2OTkZIPn2Lq5uRVrXvPOc0BAAIDHif6SJUswYcIEzJo1Cz/++CNyc3PRtm1bxMXFYdasWahduzasra2Rm5uLJk2aGFy2Dg4OOu/Nzc1hZWUFCwsLvXJDt5cyNA9ubm74888/C5yPxMREAEDDhg0Nji/tOzIQ0bOBCSkRlYmXX34Zu3fvxtatW9G/f3+98UIIbNmyBQ4ODggKCirzeAIDA/HLL7/g0qVLaNSokcFpvvvuO4SFhWH58uU65enp6XrThoeHIzw8HBkZGTh48CAiIiLQuXNnXLp0CT4+PnByckJgYCDee+89g315eHgAeJx8//HHH3rji3tRkyEKhQLjx4/HnDlzcPbsWQDA2bNn8eeffyIqKgpDhw6Vpr1y5UqJ+ymKoXlISEjQ+8GRl5OTEwBgw4YN0t5bIqr4+FOTiMrE8OHD4eLigunTp+Pu3bt64xcuXIgLFy5gypQpBe5FLE2nT58GADg7Oxc4jUKhkPbGav3111+IiYkpsI61tTU6dOiAmTNn4tGjR/j7778BPD7cffbsWfj7+6NBgwZ6L21C2rJlS6Snp2PLli067a5du7ZY8xUfH2+wPC4uDmlpaVI/2kPn+edv5cqVxeqnJNatW6dz26mbN2/i8OHDCAsLK7BOu3btYGZmhqtXrxpcbnn3fBNRxcE9pERUJipVqoSffvoJnTt3RlBQECZPnow6deogLS0NP/zwA9asWYN+/fph8uTJpd732bNnpVsrJScn46effkJ0dDR69OgBPz+/Aut17twZc+fORUREBFq0aIGLFy9izpw58PPz07lV04gRI2BpaYlmzZrB3d0dCQkJmDdvHuzt7aVDzXPmzEF0dDSCg4MxduxYVK9eHdnZ2bhx4wZ27NiBFStWwNPTE0OGDMFHH32EIUOG4L333kO1atWwY8cO/PLLL8Wa19dffx33799Hr169UKtWLZiamuLChQv46KOPYGJigqlTpwIAatSoAX9/f0ybNg1CCDg4OGDr1q2Ijo4u6WIu0t27d9GjRw+MGDECqampiIiIgIWFBaZPn15gHV9fX8yZMwczZ87EtWvXpHvIJiYm4o8//oC1tTVmz55dZjETkTyYkBJRmWnWrBn++usvLFiwAB9//DHu3LkDS0tL1KlTB999953eRS+lJTw8XBq2t7eHn58fFi9ejDfffLPQejNnzkRmZia++uorLFy4EC+99BJWrFiBTZs26VxMExISgqioKPz4449ISUmBk5MTmjdvjtWrV0t7YN3d3XH8+HHMnTsXH3zwAe7cuQNbW1v4+flJSRYAWFlZYe/evRg3bhymTZsGhUKBtm3b4vvvv0dwcHCR8/rWW2/hhx9+wBdffIHY2FhkZGTA2dkZTZs2xerVq6Wb/yuVSmzduhXjxo3DyJEjYWZmhjZt2uDXX3+Ft7e3sYu4WN5//30cO3YM4eHhSEtLQ6NGjfD999/D39+/0HrTp0/HSy+9hI8//hjr1q3Dw4cP4ebmhoYNG2LUqFFlEisRyUshBB/jQUREpWf//v1o2bIl1q9fj969e8sdDhGVAzyHlIiIiIhkxYSUiIiIiGTFQ/ZEREREJCvuISUiIiIiWTEhJSIiIiJZMSElIiIiIlkxISUiIiIiWZWLG+Pn5uYiLi4Otra2ZXITbSIiIiJ6MkIIpKenw8PDAyYmxu3zLBcJaVxcHLy8vOQOg4iIiIiKcPv2bXh6ehpVp1wkpLa2tgAez6CdnZ3M0RARERFRfmlpafDy8pLyNmOUi4RUe5jezs6OCSkRERHRM6wkp1fyoiYiIiIikhUTUiIiIiKSFRNSIiIiIpIVE1IiIiIikhUTUiIiIiKSFRNSIiIiIpIVE1IiIiIikhUTUiIiIiKSFRNSIiIiIpIVE1IiIiIikpXRCenBgwfRpUsXeHh4QKFQYPPmzUXWOXDgAIKCgmBhYYGqVatixYoVJYmViIiIiCogoxPSjIwM1KlTB59++mmxpr9+/To6duyIkJAQnDp1CjNmzMDYsWOxceNGo4MlIiIioorHzNgKHTp0QIcOHYo9/YoVK+Dt7Y0lS5YAAAICAnD8+HEsWrQIvXr1MrZ7oudTTjaQq/7vvbk1oFDoTqNRA+ps49tW2eiXpcYCDxKMa8eiEuDor1uWkQzEnQJyMgGIx2Xi37/a94bKXGsBztX1+7i4C3iYlmf6ItoBAO8mgENV/bb+/AHQPCw4nrvnAMvKj5e1VmB/wNZVt51HGcAfX+i3X5SGrwEqW92yB3eBU98Z31bz8frbQ/JV4O9NxrVjbg00eUO/PP4v4PIvhusIw8WwcQGChuqX3/gduPm7cXE5VAVq99Yvv7gLiD9tXFvudYDqBr7DzmwAki4bqFDQDALwbQ74heqXH/sKSDfy81O9A1Clvn75b0uAh+nGtRXYD3B+Ub98z1wYnB9RyDw2GgHYeeiWZacBvy02LiYAaD4BsLDTLUuNBf5YaXxbL8/RL7t7ATi9poAKBcyjyg5oMUW//PYx4Nxm42KyqwI0fVO//MqvwJW9xrXlUgOoP0S//OxPwJ1j+uUFrcNavQCvhsb1LROjE1JjxcTEoG3btjpl7dq1w1dffYWcnBwolUq9Og8fPsTDhw+l92lpaWUdJj0HmkxbjeamZ42q81AosTU3WK/cVxGPBiaXAABeirtok7YVvybaF9rWjtzGOJVbTa/8bbMNsIbhRFIJNTRp93AhSQNr88cJhxDAV5r20Gg/vv/+I3JXJKGTyVGpbv5/T3n/X2kHH8EM36lb6/VbX3EJgSbXIfJUKqq9W8IFB3LrSCMt8RD2SX+ikqUpqqn+MTh/Bfk71xdXRBW98lYmp2CryAQAZKuBzRfUqO9uohOfNi7t+zu5TkiFtd4/7GqKOzBBrsHlkr8N7fs74n08hFIniTVFLrwU96RlVVQbAsD9bIG0h+NhYqW7zZhBA3tk6JQVkipI/hGRAHQTUiXUsEGWUW0JKJAq9L+czaGGZb5ttKi2NDDFAzFar1yFHKjwyKi21DBFljA32JYSap2y4rT1UOh/76jwCKbINbItM+QIU4NtKVDwZ8dwWxFQC/2Dlirk6Ly3VSlwb7Kt3nR5vboH2Jurn9zesFhUjEh0ddrjiL+Fn06ZM1JwzOIjo9tq+OsLuIfKOmW1FNewTfWx0W357mmsV9ba5AS+Mv/EqHYSRGU02VlTr3yA6R7MU35lVFt/5lZFt5999MrfNtuAt81+MqqtGLNGiDhgoK3MdeiYs7tYbWw3b4dPzj+AjcXv2PhmM6P6l0OZJ6QJCQlwddXdo+Dq6gq1Wo2kpCS4u7vr1Zk3bx5mz55d1qHRM8Zn6jYY+tcdbHIWXU1ijGrrpnDBck036f2juEtQHv0Sf2kM7QEp2COYITF3p/4IxT8wV9wBAOxMF3j7Xi6AopKu6wZLZxoVkdYWvZI0ABeNbucRgK16pQf+fRkn9t/XYw8A3Pt3+LjRbV3596Vrm4EpT8TnGijN667B0nNGx2S4LTUMRVo8uZmpOu8f4b9lZhz9PWgP/30ZR+DxmtOV/e/LOBogX3L9ZG1l6ZWWvC2NwbaMp/73VRptGY4r/zo0VxcnvaXyTggg18C/NmPWflX1dXz0YCpMMhXA1XmAf6tSi68slHlCCgCKfIeStHsS8pdrTZ8+HRMmTJDep6WlwcvLq+wCpGLJzc3FR5sOIi09U29cjkKFHBMLvXILzQOYGPgnm9/ms0m4910kshL1k7ZbAL4vUcT6v25vG92GBsApvdKb/76ethpOj/egXBMeENIescd/LRXZ8FIkSdPm/3jlfasdlwsTXBReelO5KlLgrEjVmz5/O3nHpQpr3ISbXlu2D27AxiQHLzoW/5T168Idd4SzXnmQySVY5fm6zxXApeRcdK1uphOb9n+LAsDB3Nq4qt3bmmdGBptGw0KhLnD+tGV532/QhOIeKulMaaV4hFdNdxZYx/B7BT7P6YDUtAdQOnpKfbsqUhBuuktvvgv4VymZpx6A/GvmBUUc+pruN6qtB8ISH6t76k1Tx+QqOpscMaqteOGAr9Qd9SYKMfkLYSZ/GtXWpVxPfK9ppbfxdTI5Ih2pKG5bx3OrYZsmWG+iAaZ7UOPfH5nFbWufpg725dbV+y4baboVVRTJxW4HAH7WBON4bnW9CaeYfQ87/Pc/V6m/Q5aeEeamClSx09+Tb6s2BYr63ZyPq505OtVw0Sv3vmpZ0G9sPQG5l5ALE9yp1BTwfPYP2yuEKOzkkSIqKxTYtGkTunfvXuA0oaGhqFevHj7++L9d8ps2bULfvn2RmZlp8JB9fmlpabC3t0dqairs7OyKnJ7KgCYHuyLawjbuDzx4pD96kyYYmzQheuURytXwV8QX2fzRWA0i9hu/P6ckBtQq/u+wh1Bil6aRXrm3IhH1TP7bLyYABLmbIryuEqdyXzDY1ip1OxwUdfTKPzb7FHYK/SQ/Lz/zfwAzS+zMfRzLx+peyMn3e9JbkWgwASnMQ6HEUk1PnP1fC9jY5DmXNP4v4O55o9qCfZXH59Tld+mXxz/3tedLSl+4ebPAfGWVvPTPWwOAhLOA+qFe9cLb8gasHPTbSjwHiNzCY8rVPD6PVMvaGTDL94WTqwEeJOq3byi2vGxcAJN82YUmB8hMNjx9YWxc9TMe9UMgO9Xw9AVRmADWTvrlOVnAQ/09p//VMzCPJqa6y07rUcbjc6KNYWoGWBg4JeZRxn/bQ3GZqXTPC9Z6+ED3PO1itWUBKPV/iONh+n/bVrHbstTftrRtGcvM8vEy02vrQdGZsV5bFvrbaW5uyc5XV1rq95+bC2gMfKkU2ZaB5Z6ba/w6BAwv91yN8esQCsPLPTcXxu3b/Ff+5Q4Ufr5vQYxd50/gSfK1Mk9Ip06diq1bt+Lcuf8OkL3xxhs4ffo0YmKKdxiWCekz4OZhTB/UEvN/L8E/DiO9EuyBnZX6Se+bmPyNrqbGHbK/leuCFZquOmVWKlO85F8FO94OK35DJqaAq/75Rcj8B0jV3Zti4uhn+EuTiIjoOfAk+ZrRh+wfPHiAK1f+2zN0/fp1nD59Gg4ODvD29sb06dMRGxuL1atXAwBGjRqFTz/9FBMmTMCIESMQExODr776CuvWrTO2ayqGpVtisPSwcReQFEdLkxOIvWvsr0XjBTiZoGZIe3w7vwRXcObzbinEUyAbp8cvIiIiemJGJ6THjx9Hy5Ytpffacz2HDh2KqKgoxMfH49atW9J4Pz8/7NixA+PHj8eyZcvg4eGBTz75hLd8KgNtp23HubS7aH1vHbqYHjaq7t+5flim6a5X3t9kL0JN/4QSueh++fGhkI7VzNCtYxju5P6XkP0p/HEiV/82I11ND8MJxT9smGTlh2nvl2kqSURERM+YJzpk/7TwkH3xeI37HrGfDYVQl+1h9V4BZtiwLRqoGlam/RAREVH58ST5Gp9lX4Fo0u6WeTKqNAE6VrcAXAycV0lERERUAk/ltk/09M14JQReLsX/dXJVeDy+rUo+7U3+QFCe26poTC2wxTQEr9ro35KHiIiIqCSYkFZQq8z64Mo8A48ALIThRxHoPq7PVKHAaN4Ij4iIiEoRE9IKzMqcq5eIiIiefcxYnrJPthzD4sPFfMyC0XILGCYiIiJ6djEhfZqEQPbvy/C/G7/g4aMcvdG5UGB2zhC98hqKWxhgtq/I5q8mpOOjf4cXK1cAGPeEARMRERGVPSakT9O9C0j+bR2mHi7sSnj9G8L//u/LGE/vQWFERERET4a3fXqa0uNxJaXsD6U3qmKCR076N6knIiIiehZxD6lMOrxghln96uCuqCSVCSgwLuctvWlrK65ihNn2Yrd9B25ICuheClESERERlT0mpDIxUQAbzbrhd9TXKfdQ6k+bjBcxH8Xf49k9yBVTOgc9aYhERERETwUTUhktGtAQqN5e7jCIiIiIZMVzSImIiIhIVkxIiYiIiEhWTEifJlsP3M59/Az4WOEI2HvKHBARERGR/JiQPk0uNfC38AMAXBTegFstmQMiIiIikh8TUiIiIiKSFRNSIiIiIpIVE1IiIiIikhUTUiIiIiKSFRPSpyn+L9Q3uQQACFRcBWJPyBwQERERkfyYkD5NmUlwUdwHADgp0oAH9+SNh4iIiOgZwISUiIiIiGTFhJSIiIiIZMWElIiIiIhkxYSUiIiIiGTFhJSIiIiIZMWElIiIiIhkZSZ3AM+irw5eRszV5FJv96Ws86XeJhEREVF5x4Q0n79uJWHujktl0na2SXqZtEtERERUnjEhzafLsiNI2fMFsm+fkcrc8Q+UCrVR7fwj7PAAFjplO5CNlLvGtUNERERU0TEhzUedmoj0E1t0ym6VqKUMvZKUPMNOVooStUpERERU0TAhzU+dIw1a1QiBqXVldDf9DQ6KB0Y1cyi3Ni7nVtErH2i2B07mORgZZA7Yez5xuERERETlHRPSQtg16AZVlRoYokyFnyLBqLpZ6k5Izm2kVz7K/DZUyMG3mmaY4VqztEIlIiIiKreYkObT0OQcfv53uJHJefyJGhieM7nU2u/+aK40PEPBw/ZERERETEjz6W+6X0pI+5vuh7XP0FLvw8rcDJM7vFTq7RIRERGVR0xIi7D2jRC5QyAiIiKq0PikJiIiIiKSFRNSIiIiIpIVE1IiIiIikhUTUiIiIiKSFRNSIiIiIpIVE1IiIiIikhUTUiIiIiKSFRNSIiIiIpIVE1IiIiIikhUTUiIiIiKSFRPSfOKEkzQcKxxljISIiIjo+cCENJ8P1X0NDhMRERFR2WBCSkRERESyYkJKRERERLJiQkpEREREsmJCSkRERESyYkKaTxuT4/8Nm56QMRIiIiKi5wMT0nw6mP4hDXc0+aOQKYmIiIioNDAhJSIiIiJZMSElIiIiIlkxISUiIiIiWTEhJSIiIiJZMSElIiIiIlkxISUiIiIiWTEhJSIiIiJZMSElIiIiIlkxISUiIiIiWTEhJSIiIiJZMSElIiIiIlkxIc3nvPDOM+wlYyREREREzwcmpPl8qe4sDX+RZ5iIiIiIygYTUiIiIiKSFRNSIiIiIpIVE1IiIiIikhUTUiIiIiKSFRPSfHqZHpCGe5oelDESIiIioucDE9J8gk3+loab5xkmIiIiorLBhJSIiIiIZMWElIiIiIhkxYSUiIiIiGTFhJSIiIiIZMWElIiIiIhkxYSUiIiIiGTFhJSIiIiIZMWElIiIiIhkxYSUiIiIiGTFhJSIiIiIZMWElIiIiIhkxYQ0nz9ya0jDR0V1GSMhIiIiej4wIc3nB02r/4bVrQqZkoiIiIhKAxNSIiIiIpIVE1IiIiIikhUTUiIiIiKSFRNSIiIiIpIVE9J8hpntlIbD8wwTERERUdlgQppPbcX1PMM35AuEiIiI6DnBhJSIiIiIZMWElIiIiIhkxYSUiIiIiGTFhJSIiIiIZMWElIiIiIhkxYSUiIiIiGTFhJSIiIiIZMWElIiIiIhkxYSUiIiIiGTFhJSIiIiIZMWElIiIiIhkxYQ0n+jcIGl4d259GSMhIiIiej4wIc1nl6axwWEiIiIiKhtMSImIiIhIVkxIiYiIiEhWTEiJiIiISFZMSImIiIhIVkxI8xlrtkEaHme2UcZIiIiIiJ4PTEjz8VHcNThMRERERGWDCSkRERERyYoJKRERERHJigkpEREREcmKCSkRERERyYoJKRERERHJigkpEREREcmKCSkRERERyYoJKRERERHJigkpEREREcmKCSkRERERyYoJKRERERHJiglpPhs1IdLwhjzDRERERFQ2mJDmczi3dp7hWjJGQkRERPR8YEJKRERERLJiQkpEREREsmJCSkRERESyYkJKRERERLJiQprPLOXq/4bNVhcyJRERERGVBiak+VTCA2m4siJDxkiIiIiIng9mcgdARERPl0ajQU5OjtxhEFE5YWZmBlNTUygUirLro8xaJiKiZ4oQAgkJCbh//77coRBROWNqagoXFxfY29uXSWLKhJSI6DmhTUZdXFxgZWVVpns7iKhiEEJArVYjLS0N8fHxyMrKgru7e6n3w4SUiOg5oNFopGTU0dFR7nCIqJyxtbWFSqVCUlISXFxcYGpqWqrt86ImIqLngPacUSsrK5kjIaLyytraGkKIMjkHnQkpEdFzhIfpiaikyvL/BxNSIiIiIpIVE1IiIqowjhw5gj59+sDd3R3m5uZwc3ND7969ERMTozNdZGQkFAoFkpKSZIpUHjdu3IBCoUBUVJTcoRDpYEJKREQVwtKlS9GsWTPcunULCxcuxK+//opFixYhNjYWzZs3x6effip3iERUAF5lT0RE5d7vv/+Ot99+G+3bt8fPP/8MM7P/vt769++Pbt26Ydy4cahXrx6aNWsmY6S61Go1hBBQKpVyh0IkK+4hJSKicm/evHlQKBRYsWKFTjIKPH7KzPLly6FQKDB//nydcbdv30a3bt1ga2uLypUrY/jw4UhLS9OZZv369WjUqBHs7OxgY2ODatWqYfz48TrTxMXFYcSIEfDw8IC5uTn8/f0xb9485ObmStNoD5cvWLAAkZGR8PHxgUqlwunTp2Fubo5Zs2bpzdfZs2ehUCiwcuVKo/rSTte3b1/Y2trC3t4e/fr1Q0JCgnELlugp4R7SfL5Wtwdw5vGwph0GyBsOEREVQaPRYN++fQgKCoKXl5fBaby9vREUFIS9e/dCo9FI5T179kT//v0xZswYnDhxApGRkbh27Rr27NkDhUKBw4cPo1+/fujXrx9mz54NlUqF69ev4/jx41Ib8fHxaNSoEZRKJWbPng0/Pz8cOnQIERERiIuLw9KlS3Vi+eSTT/DSSy9hyZIlMDc3h4+PDzp16oRvvvkGs2fPhonJf/uKVq1aBUtLS/Tv39+ovrKystCmTRvExcVh3rx5qFatGrZt24Z+/fqV2nInKk1MSPP5W1SVhs/mVi1kSiKi8i0rKwsXL16UOwwd1atXh6WlpVF1kpKSkJmZiapVC/+f7efnhz/++APJyclSWe/evTFv3jwAwMsvvwwrKyuMGzcOu3fvRrt27RATEwMhBFasWAF7e3up3muvvSYNR0ZGIjU1FX///Te8vb0BAG3atIGJiQnmzJmDSZMmwcfHR5reysoKO3fu1NmTGx4ejs2bN2Pv3r1o06YNgMeH89esWYMePXpIfRe3r2+++Qbnz5/Hzz//jK5duwIA2rVrh8zMTHz99ddGLV+ip4EJKRHRc+rixYuoV6+e3GHoOHXqFOrWrVsmbQshAOjeS1G751Fr4MCBGDduHPbt24d27dqhYcOGAIB+/frh9ddfR3BwMNzc3HTqbN++HS1btoSHhwfUarVU3qFDB0RGRuLgwYMYPHiwVN6lSxe90wo6duwIV1dXrFq1SkpId+zYgcTERISHhxvd1759+2Brayslo1qvvPIKE1J6JvEcUiIiKtecnJxgZWWF69evFzrdjRs3YGVlBQcHB6ksf3Lp5OQEMzMzaS9qaGgoNm3ahIcPH2LAgAFwd3dH/fr1sXXrVqlOYmIitm7dCqVSqfNq3LgxAOjdWsrQc8DNzMzwyiuvYNOmTUhNTQUAREVFwdvbG61atTK6r+TkZLi6uur1UxbPICcqDdxDSkT0nKpevTpOnToldxg6qlevbnQdU1NTtGzZErt27cKdO3fg6empN82dO3dw4sQJdOjQQecZ3AkJCahSpYr0PikpCWq1Go6OjlJZ9+7d0b17dzx8+BC///47IiMj0bNnT5w5cwY1atSAk5MTAgMD8d577xmML/95rQU97SY8PBwffvghfvjhB/Ts2RPbtm3DtGnTdM4pLW5fjo6O+OOPP/TGx8fHG6xHJDcmpEREzylLS8syOzz+tE2fPh07d+7EG2+8gc2bN+sknRqNBm+88QaEEJg+fbpOve+//x5BQUHS+7Vr1wIAwsLC9PpQqVRo1aoVhBBo06YNzp8/jxo1aqBjx47YtWsX/P39Ubly5RLPQ82aNdGwYUNERUUhKysLarUaQ4cO1ZmmuH21bNkSP/74I7Zs2aJz2P67774rcXxEZYkJaT4fKFdgkDS8HMBEOcMhIqJiaNasGZYsWYK3334bzZs3x+jRo+Hj44Nbt25h2bJlOHr0KJYsWYLg4GCdeuvXr4eZmRlatWqFkydPIiIiAi1atEC7du0AAO+88w7i4uLQqlUrVKlSBcnJyfjggw9QqVIlNG3aFAAwd+5cREdHo1mzZhg7diyqV6+OzMxMXLt2Ddu3b8e6deuKnaiGh4fjzTffxO3btxEaGgp/f3+d8cXta8iQIfjoo48wZMgQvPfee9JV9r/++mspLG2i0leic0g/++wz+Pn5wcLCAkFBQTh06FCB0+7fvx8KhULvdeHChRIHXZZM8N993EwLmY6IiJ4tb731Fn7//Xd4enpi0qRJaNWqFSZMmAB3d3f89ttveOutt/Tq/PTTTzhz5gx69OiBefPmYcCAAdi8ebN0WL1x48aIjY3F5MmT0bZtW7z55ptwdXXF3r17pfNPPTw8cPz4cbRu3RoLFixAu3btMHToUHz77bdo1KgRrK2tiz0PAwYMgIWFBe7cuYNhw4bpjS9uX1ZWVtIV+9OmTUPv3r0RFxeH77//vgRLlqjsKYT2ssNi+uGHHzB48GB89tlnaNasGVauXIkvv/wS586dk25Bkdf+/fvRsmVLXLx4EXZ2dlK5s7OzziGVwqSlpcHe3h6pqak6bZSF798IxIAVj+9DujbcHwO+vlKm/RERPQ3Z2dm4fv26tDOBiMhYRf0feZJ8zeg9pIsXL8Zrr72G4cOHIyAgAEuWLIGXlxeWL19eaD0XFxe4ublJr+Imo0RERERUsRmVkD569AgnTpxA27Ztdcrbtm2Lw4cPF1q3Xr16cHd3R+vWrbFv375Cp3348CHS0tJ0XkRERERUMRmVkCYlJUGj0ejd28zV1bXA5+O6u7vj888/x8aNG/HTTz+hevXqaN26NQ4ePFhgP/PmzYO9vb30KuhRcERERERU/pXoKvv891ATQhR4X7Xq1avr3FeuadOmuH37NhYtWoTQ0FCDdaZPn44JEyZI79PS0piUEhEREVVQRu0hdXJygqmpqd7e0Lt37xp8IkRBmjRpgsuXLxc4XqVSwc7OTudFRERERBWTUQmpubk5goKCEB0drVMeHR2td2+3wpw6dYqPLyMiIiIiACU4ZD9hwgQMHjwYDRo0QNOmTfH555/j1q1bGDVqFIDHh9tjY2OxevVqAMCSJUvg6+uLmjVr4tGjR/juu++wceNGbNy4sXTnhIiIiIjKJaMT0n79+iE5ORlz5sxBfHw8atWqhR07dsDHxwfA4+fk3rp1S5r+0aNHmDRpEmJjY2FpaYmaNWti+/bt6NixY+nNBRERERGVWyW6qOnNN9/Em2++aXBcVFSUzvspU6ZgypQpJemGiIiIiJ4DJXp0KBERERFRaWFCms8SdS9p+CN1TxkjISIiIno+MCHN57ZwNThMRETPpqioKCgUCullZmYGT09PhIeHIzY2ttT7i4uLQ2RkJE6fPl3qbQNAZGRkgff2rogMza+vry+GDRtmVDuFrZdhw4bB19e35EFSmSvROaRERETPmlWrVqFGjRrIysrCwYMHMW/ePBw4cABnzpyBtbV1qfUTFxeH2bNnw9fXF3Xr1i21drWGDx+O9u3bl3q75cmmTZuMvgd5Yetl1qxZGDduXClGSKWNCSkREVUItWrVQoMGDQAALVu2hEajwdy5c7F582YMGjRI5uiKlpmZCSsrK3h6esLT07NU2hRCIDs7G5aWlqXSXl7Z2dkwNzeHiUnpH2ytV69eqbbn7+9fqu1R6eMheyIiqpCaNGkCALh58yYAICsrC1OnToWvry/Mzc3h5eWFcePGIT09Xafe3r17ERYWBgcHB1haWsLX1xf9+/cHAOzfvx8NGzYEAISHh0unCeS9w8wvv/yCli1bwtbWFlZWVggNDcWhQ4d0+tAepj5+/Di6d+8Oe3t7NGvWTGdcXsWN3dfXF507d8aPP/6IwMBAWFhYYOXKlQUuo7CwMNSqVQsHDx5Eo0aNYGFhAS8vL8yfPx9CCGm6/fv3Q6FQYPXq1Rg3bhzc3d1hZWWFtLS0Ys8zAGzfvh1169aFSqWCn58fFi1aZDAuQ4fsY2Nj8frrr8PLywvm5ubw8PBA7969kZiYWOR6yXvIPicnBy4uLhg8eLBev8nJyVCpVJg+fbpUlpqaigkTJsDPz09a9pMnT0Z2dnaBy5WMxz2k+VgjM89wloyREBGVLSEEsnI0coehw1JpWmrnT169ehUA4OzsDCEEunbtigMHDmDmzJkIDg7GyZMnERkZiaNHj+LQoUNQKpW4fv06OnXqhJCQEERFRcHe3h63b9/Gtm3bAAD169fHqlWrEB4ejv/973/o1KkTgP/2wH3zzTcIDw9Hjx498O2338LU1BSffvop2rRpg/3796Np06Y6Mfbu3RuvvPIKxowZg0ePHhmcj+LGrnXixAlcvHgRs2bNgqenJxwdHQtdTgkJCRg4cCBmzJgBf39/bNiwAdOnT0d2djYiIyN1pp0+fTpCQ0Px5ZdfSnteizvPe/bsQbdu3dC0aVN8//33UKvVWLBgAe7du1fkuoyNjUXDhg2hVqsxY8YM1K5dG8nJydi1axdSUlKKXC95KZVKDBo0CCtXrsSyZct0Tg1Yu3YtHj16JCXDGRkZCAkJQXx8PGbOnInatWvj9OnTiIyMxIULF7B169YiY6fiYUKazxxlFAb8OzxXGQXgfzJGQ0RUdrJyNHjpnV/kDkPHuTntYGVesq8mjUYDtVqN7OxsHDp0CO+++y5sbW3RtWtX/PLLL/j111/x0Ucf4e233wYAvPzyy3B2dsZrr72G9evXY+DAgTh58iSys7PxwQcfoE6dOlLbr7zyCgDAzs4OtWrVAvA42dHuhQUeH3IfP348OnfurPM0wvbt26NmzZr43//+hz179ujEHB4ejoiIiELnq7ixayUlJeG3334r9mHq5ORkbNu2TUri2rVrh+TkZCxcuBBvv/02KlWqJE374osvYt26dSWa55kzZ8LV1RXR0dGwsLAAALRt2xZ+fn5FxvjOO+8gKSkJZ86cQfXq1aXyvn37SsMFrRdDwsPDsWTJEvz4448YPny4VB4VFYXg4GCpj08++QR///03jh49Kp0O0rp1azg5OWHYsGE4dOgQQkJCioyfisZD9kREVCE0adIESqUStra26NixI1xdXbFz5064urpi3759AP5LLLUGDx4MU1NT7N27FwBQt25dmJubY+TIkVizZo3OkweLcvjwYaSkpGDo0KFQq9XSS6FQoG3btvjtt9+g0ejuke7Ro0eR7RY3dq3atWsbdc6kra2tlIxqDRw4EFlZWThy5Eih8RZ3njMyMnDs2DH07NlTSkYBwN7eHl26dCkyxp07d6Jly5Y6yeiTCAwMlPaqap05cwYnT55EeHi4VLZ9+3bUqlULdevW1Zm/Dh06AHh8KgOVDu4hJSJ6TlkqTXFuTju5w9BhqTQtcd3Vq1cjICAAZmZmcHV1hbu7uzQuOTkZZmZmcHJy0qmjVCrh5OSE5ORkAI/3rkVHR2PBggV4/fXXkZmZierVq2Pq1Kk6iYohiYmJAB4fhi9IWloaKleuLL3PG2NBihu7MW3m5eqqf4tDNzc3qe/C2i7uPGdkZCA3N1dq19h47927V2oXemmFh4fjrbfewqVLl/Diiy9i1apVsLKy0tnrmpiYiCtXruicEpFXUlJSqcb0PGNCSkT0nFIoFCU+PP4sCggIkA6r5ufo6Ai1Wo2kpCSdxC4nJwdJSUk651mGhoYiNDQUGo0Gx44dw4IFC/Dqq6/Cy8sLbdq0KbB/bbtLly4t8JCxra2tzvvinC9rTOzFbTMvbVKZV0JCgtR3YW0Xd57Nzc2hUCikdvOKj48vMkZnZ2fcuXOnyOmMMXDgQEyaNAlRUVGYM2cO1qxZg169eumcU+rk5ARLS0t8/fXXBttwcXEp1ZieZzxkT0REFV6rVq0AAN99951O+Zo1a6DRaKTxeZmamqJJkybSleBnzpwBAKhUKgCPr3zPq1mzZrC3t8e5c+fQoEEDgy8zM+N/AJQkdmOkp6dj+/btOmVr166FhYVFkediFneera2t0ahRI/z00086V6enpaUV68KgDh06YN++fbh48WKB0xS0Xgri4OCArl274ttvv8WWLVtw9+5dvb3gHTt2xNWrV+Ho6Ghw3ry9vYvVFxWt4vw0JiIiKkDbtm3Rpk0bTJ06FWlpaQgODsapU6cQERGBxo0bo0+fPgCAFStWYP/+/ejYsSO8vb3x4MEDLF++HObm5lLi5+/vD0tLS6xbtw41a9aElZUV/Pz84OjoiE8++QTh4eFISUlB79694ezsjLt37+LUqVPIysrC4sWLyyz2knJ0dMTIkSMxc+ZMVK1aFRs2bMCmTZswa9YsnQuaDLGxsSn2PM+dOxft27fHyy+/jIkTJ0KtVmP+/PmwsbFBSkpKof3MmTMHO3fuRGhoqHSVfUpKCnbt2oWJEyeiRo0aha6XgoSHh2P9+vUYN24cfH19ERYWpjN+/Pjx2LhxI0JDQzFhwgTUrl0bGo0GN2/exK5duzB79mzUrl27WMuZiiDKgdTUVAFApKamlnlf60bVFgAEALE23L/M+yMiehqysrLEuXPnRFZWltyhlLpVq1YJAOLYsWOFTpeRkSEmT54svL29hZmZmahSpYp46623dL5bYmJiRPfu3YWXl5dQqVTCyclJtGnTRuzdu1enre+++05Uq1ZNmJmZCQBi1apV0rh9+/aJDh06iMqVKwtzc3Ph6ekpunbtKn7++WdpmoiICAFA3Lt3Ty9O7ThjYxdCCB8fH9GpU6cil5lWixYtRM2aNcXevXtFUFCQUKlUokqVKuK9994Tubm5OvMEQKxfv95gO8WZZyGE2LJliwgMDBTm5ubC29tbzJ8/3+D8+vj4iKFDh+qU3b59W7z66qvCzc1NKJVK4eHhIfr27SsSExOlaQpaL0OHDhU+Pj56cavVauHh4SEAiIiICIPzlp6eLmbMmCFefPFFYW5uLuzt7UWdOnXEpEmTRHJycgFLtmIq6v/Ik+RrCiHy3Pn2GZWWlgZ7e3ukpqYa/SgxY33/RiAGrHh8WGZtuD8GfH2lTPsjInoasrOzcf36dfj5+elc5UzPt7CwMCQlJeHs2bNyh0LlQFH/R54kX+M5pEREREQkKyakRERERCQrXtRERET0nOKN3elZwT2kRERERCQrJqREREREJCsmpPnMyRksDc/OM0xEREREZYMJaT6psM0zbCNjJERERETPByakRERERCQrJqREREREJCsmpEREREQkKyak+Xgo7uUZTpIxEiIiKo6oqCgoFArpZWZmBk9PT4SHhyM2NrbU+4uLi0NkZCROnz5d6m0DQGRkJBQKRZm0/SwyNL++vr4YNmyYUe0Utl6GDRsGX1/fkgdJZY4JaT4TzdZLw5PyDBMR0bNt1apViImJQXR0NEaMGIF169YhJCQEGRkZpdpPXFwcZs+eXWYJ6fDhwxETE1MmbZcXmzZtwqxZs4yqU9h6mTVrFjZt2lRK0VFZ4JOaiIioQqhVqxYaNGgAAGjZsiU0Gg3mzp2LzZs3Y9CgQTJHV7TMzExYWVnB09MTnp6epdKmEALZ2dmwtLQslfbyys7Ohrm5OUxMSn/fVr169Uq1PX9//1Jtj0of95ASEVGF1KRJEwDAzZs3AQBZWVmYOnUqfH19YW5uDi8vL4wbNw7p6ek69fbu3YuwsDA4ODjA0tISvr6+6N+/P4DHj9ps2LAhACA8PFw6TSAqKkqq/8svv6Bly5awtbWFlZUVQkNDcejQIZ0+tIepjx8/ju7du8Pe3h7NmjXTGZdXcWP39fVF586d8eOPPyIwMBAWFhZYuXJlgcsoLCwMtWrVwsGDB9GoUSNYWFjAy8sL8+fPhxBCmm7//v1QKBRYvXo1xo0bB3d3d1hZWSEtLa3Y8wwA27dvR926daFSqeDn54dFixYZjMvQIfvY2Fi8/vrr8PLygrm5OTw8PNC7d28kJiYWuV7yHrLPycmBi4sLBg/Wv9d4cnIyVCoVpk+fLpWlpqZiwoQJ8PPzk5b95MmTkZ2dXeByzb98f//9dzRp0gSWlpbw8fHBl19+CQDYtm0b6tevDysrK9SuXRu7d+/Wa+PixYvo378/XFxcoFKpULNmTam+VnZ2NiZMmIDAwEDY2dnByckJLVq00Hs07I0bN6BQKLBo0SJ8+OGH8PPzg42NDZo2bYojR44UOT9liXtIiYied1+0Nr5O9+WA84u6ZRlJwNp+xrc1aD1g5WB8vSJcvXoVAODs7AwhBLp27YoDBw5g5syZCA4OxsmTJxEZGYmjR4/i0KFDUCqVuH79Ojp16oSQkBBERUXB3t4et2/fxrZt2wAA9evXx6pVqxAeHo7//e9/6NSpE4D/9sB98803CA8PR48ePfDtt9/C1NQUn376Kdq0aYP9+/ejadOmOjH27t0br7zyCsaMGYNHjx4ZnI/ixq514sQJXLx4EbNmzYKnpyccHR0LXU4JCQkYOHAgZsyYAX9/f2zYsAHTp09HdnY2IiMjdaadPn06QkND8eWXX0p7Xos7z3v27EG3bt3QtGlTfP/991Cr1ViwYAHu3btnICpdsbGxaNiwIdRqNWbMmIHatWsjOTkZu3btQkpKSpHrJS+lUolBgwZh5cqVWLZsGezs7KRxa9euxaNHj6RkOCMjAyEhIYiPj8fMmTNRu3ZtnD59GpGRkbhw4QK2bt1aZOwJCQkYPnw4pkyZgipVqmDp0qUYMWIEYmNjsXHjRsyYMQN2dnaYPXs2unXrhmvXrsHd3R0AcPbsWTRr1gy+vr5YvHgxXF1dsXXrVrz++utITU3FxIkTAQAPHz5ESkoKJk+eDE9PT2RlZeHnn39Gq1at8Ouvv6JVq1Y6MS1btgw1atTAkiVLADw+paFjx464fv067O3ti5ynMiHKgdTUVAFApKamlnlf60bVFgAEALE23L/M+yMiehqysrLEuXPnRFZWlv7ICDvjX7Gn9NtJjStZW2kJTzRvq1atEgDEkSNHRE5OjkhPTxc7duwQLi4uwtbWViQkJIidO3cKAOKjjz7SqfvVV18JAGLNmjVCCCE2bNggAIjTp08X2N+xY8cEALFq1Sqd8oyMDFG5cmXRpUsXnfJHjx6JatWqiVatWkllERERAoCIjIzUa187Tqu4sQshhI+PjzAzMxNXrlwpMP68WrRoIQCIbdu26ZT36NFDWFpaipSUFCGEEPv27RMARFhYWInnuXHjxsLDw0NnG7x//76oXLmyyJ+O+Pj4iKFDh0rvX331VaFUKsWFCxcKnJeC1osQQgwdOlT4+PhI7//8808BQHzxxRc609WvX18EBwdL799//31hYmIijh07pjNdVFSUACAOHjxYYDxC/Ld8T5w4IZUlJSUJU1NTYWlpKWJjY6XykydPCgBi6dKlUlnbtm2Fp6enXv4zbNgwYWtrKx48eGCwX7VaLXJyckSbNm1E9+7dpfLr168LAKJ27dpCrVZL5UePHhUAxLp16wqdn0L/j4gny9d4yJ6IiCqEJk2aQKlUwtbWFh07doSrqyt27twJV1dX7Nu3DwDwyiuv6NQZPHgwTE1NsXfvXgBA3bp1YW5ujpEjR2LNmjW4detWsfs/fPgwUlJSMHToUKjVaumlUCjQtm1b/Pbbb9BoNDp1evToUWS7xY1dq3bt2kadM2lrayvtUdQaOHAgsrKy9A7j5o+3uPOckZGBY8eOoWfPnrCwsJDq29vbo0uXLkXGuHPnTrRs2RLVq1cv9nwVJjAwUNqrqnXmzBmcPHkS4eHhUtn27dtRq1Yt1K1bV2f+OnToAAB6h8QNcXd3R/369aX3jo6OcHFxQd26deHh4SGVv/TSSwD+O8UkOzsbe/fuRY8ePWBlZaXTf8eOHZGeno6TJ09K9Tdu3IjmzZvD0dERZmZmUCqV+PXXX3HhwgW9mDp16gRTU1Od5ZG3bznwkD0R0fOuSgPj6yit9MtMlSVry1RZ9DTFsHr1agQEBMDMzAyurq7SYU/g8bmBZmZmcHJy0qmjVCrh5OSE5ORkAI8P8UZHR2PBggV4/fXXkZmZierVq2Pq1Kk6iYohiYmJAB4fhi9IWloaKleuLL3PG2NBihu7MW3m5erqqlfm5uYm9V1Y28Wd54yMDOTm5krtGhvvvXv3Su1CL63w8HC89dZbuHTpEl588UWsWrUKVlZW6Nu3rzRNYmIirly5onNKRF5JSUXfHtLBQf90FHNzc71ylUoF4PH5wsDjZa9Wq7F06VIsXbq00P7Xr1+Pvn37ol+/fpgyZQpcXV1hamqKWbNm4fz583r18p/Gof2RoO1bDkxIiYiedyP2lE471k6l11YJBAQESFfZ5+fo6Ai1Wo2kpCSdxC4nJwdJSUk6X9ChoaEIDQ2FRqPBsWPHsGDBArz66qvw8vJCmzZtCuxf2+7SpUulC6rys7W11XlfnPuNGhN7cdvMS5tU5pWQkCD1XVjbxZ1nc3NzKBQKqd284uPji4zR2dkZd+7cKXI6YwwcOBCTJk1CVFQU5syZgzVr1qBXr14655Q6OTnB0tISX3/9tcE2XFxcSjWmvCpXrgwTExMMGTIEo0ePNjiNdk/42rVr4efnh3Xr1umsowcPHpRZfKWNh+yJiKjC017U8d133+mUr1mzBhqNRu+iDwAwNTVFkyZNpCvBz5w5A0B/T5ZWs2bNYG9vj3PnzqFBgwYGX2Zmxu8HKknsxkhPT8f27dt1ytauXQsLC4sCk0yt4s6ztbU1GjVqhJ9++knn6vS0tLRiXRjUoUMH7Nu3DxcvXixwmoLWS0EcHBzQtWtXfPvtt9iyZQvu3r2rtxe8Y8eOuHr1KhwdHQ3Om7e3d7H6KgkrKyuEhYXh1KlTCAwMNNi/dm+7QqGQkn6tv//+u1zdz5Z7SImIqMJr27Yt2rRpg6lTpyItLQ3BwcE4deoUIiIi0LhxY/Tp0wcAsGLFCuzfvx8dO3aEt7c3Hjx4gOXLl8Pc3FxK/Pz9/WFpaYl169ahZs2asLKygp+fHxwdHfHJJ58gPDwcKSkp6N27N5ydnXH37l2cOnUKWVlZWLx4cZnFXlKOjo4YOXIkZs6ciapVq2LDhg3SjekrVapUaF0bG5tiz/PcuXPRvn17vPzyy5g4cSLUajXmz58PGxsbpKSkFNrPnDlzsHPnToSGhkpX2aekpGDXrl2YOHEiatSoUeh6KUh4eDjWr1+PcePGwdfXF2FhYTrjx48fj40bNyI0NBQTJkxA7dq1odFocPPmTezatQuzZ89G7dq1i7WcS+Ljjz9G8+bNERISgjfeeAO+vr5IS0vDpUuXsGfPHuzcuRPA43NCN23ahNGjR6Nnz564du0aIiMj4ePjo3fe8jPL6MugZMCr7ImInkxRV8eWZ9qr7PNfCZ1fRkaGmDx5svD29hZmZmaiSpUq4q233tL5bomJiRHdu3cXXl5eQqVSCScnJ9GmTRuxd+9enba+++47Ua1aNWFmZqZ3Zfe+fftEhw4dROXKlYW5ubnw9PQUXbt2FT///LM0jfZK+nv37unFmf8q++LGLsTjq9M7depU5DLTatGihahZs6bYu3evCAoKEiqVSlSpUkW89957Ijc3V2eeAIj169cbbKc48yyEEFu2bBGBgYHC3NxceHt7i/nz5xuc3/xX2QshxO3bt8Wrr74q3NzchFKpFB4eHqJv374iMTFRmqag9ZL/KnsttVotPDw8BAARERFhcN7S09PFjBkzxIsvvijMzc2Fvb29qFOnjpg0aZJITk4uYMk+pl2++RW0ngCI0aNH65RdvXpVDBs2THh4eAgzMzPh4uIimjdvLhYtWiRNk5ubK959913h7e0tVCqVqFOnjtiwYYPefGuvsv/ggw8M9l3QMtAqy6vsFf8G8UxLS0uDvb09UlNTdc7tKAvfvxGIASseH5ZZG+6PAV9fKdP+iIiehuzsbFy/fh1+fn46VznT8y0sLAxJSUk4e/as3KFQOVDU/5Enydd4DikRERERyYoJaT4Tc0ZJwxNyRsoYCREREdHzgRc16TEpYJiIiKhiKc6N3YmeBmZcRERERCQrJqREREREJCsmpEREz5FycGMVInpGleX/Dyak+QQobkrDNfIMExGVZ9onBKnVapkjIaLyKicnB8Djp5iVNiak+Qw3++/xaa+b7ZAxEiKi0mNqagpTU1OkpaXJHQoRlUNCCKSmpkKlUkGpVJZ6+7zKnojoOaBQKODi4oL4+HioVCpYW1vrPPeaiMgQIQRycnKQmpqKBw8eoEqVKmXSDxNSIqLnhL29PbKyspCUlIR79+7JHQ4RlSMqlQpVqlQpsydmMiElInpOKBQKuLu7w8XFRToXjIioKKampmVymD4vJqRERM8Z7fmkRETPCl7URERERESyYkJKRERERLJiQkpEREREsmJCSkRERESyYkJKRERERLJiQkpEREREsmJCSkRERESyYkKaTzqspOE0YSljJERERETPByak+UTmDPtvWD2swOmIiIiIqHQwISUiIiIiWTEhJSIiIiJZMSElIiIiIlkxISUiIiIiWTEhzaeRyTlpuLHJeRkjISIiIno+MCHNp5/pfoPDRERERFQ2mJASERERkayYkBIRERGRrJiQEhEREZGsmJASERERkayYkBIRERGRrJiQEhEREZGsmJASERERkayYkBIRERGRrJiQEhEREZGsmJASERERkayYkBIRERGRrJiQ5hMnnKThWOEoYyREREREzwcmpPl8qO5rcJiIiIiIygYTUiIiIiKSFRNSIiIiIpIVE1IiIiIikhUTUiIiIiKSFRPSfF42Of7fsOnxQqYkIiIiotLAhDSf9qZ/SMMdTI7JGAkRERHR84EJKRERERHJigkpEREREcmKCSkRERERyYoJKRERERHJigkpEREREcmKCSkRERERyYoJKRERERHJigkpEREREcmKCSkRERERyYoJKRERERHJigkpEREREcmKCWk+54WPNHxOeMkYCREREdHzgQlpPl+qO+UZ7ixjJERERETPByakRERERCQrJqREREREJCsmpEREREQkKyakRERERCQrJqT59DI9kGf4oIyREBERET0fmJDmE2zytzTcLM8wEREREZUNJqREREREJCsmpEREREQkKyakRERERCQrJqREREREJCsmpEREREQkKyakRERERCQrJqREREREJCsmpEREREQkKyakRERERCQrJqREREREJCsmpEREREQkKyak+RzJDTA4TERERERlgwlpPus1LaXhHzVh8gVCRERE9JxgQkpEREREsmJCSkRERESyYkJKRERERLJiQkpEREREsmJCmk+42U6Dw0RERERUNpiQ5lNLcV0arq24IV8gRERERM8JJqREREREJCsmpEREREQkKyakRERERCQrJqREREREJCsmpEREREQkKyakRERERCQrJqREREREJCsmpEREREQkKyakRERERCQrJqREREREJCsmpEREREQkKyak+ezObZBnOEjGSIiIiIieD0xI8/lF00ga3pVnmIiIiIjKBhNSIiIiIpIVE1IiIiIikhUTUiIiIiKSFRNSIiIiIpIVE9J83jbbYHCYiIiIiMoGE9J8vBR3pWFvxT0ZIyEiIiJ6PjAhJSIiIiJZMSElIiIiIlkxISUiIiIiWTEhJSIiIiJZMSElIiIiIlkxISUiIiIiWTEhJSIiIiJZlSgh/eyzz+Dn5wcLCwsEBQXh0KFDhU5/4MABBAUFwcLCAlWrVsWKFStKFCwRERERVTxGJ6Q//PAD3n77bcycOROnTp1CSEgIOnTogFu3bhmc/vr16+jYsSNCQkJw6tQpzJgxA2PHjsXGjRufOHgiIiIiKv+MTkgXL16M1157DcOHD0dAQACWLFkCLy8vLF++3OD0K1asgLe3N5YsWYKAgAAMHz4cr776KhYtWvTEwRMRERFR+WdUQvro0SOcOHECbdu21Slv27YtDh8+bLBOTEyM3vTt2rXD8ePHkZOTY7DOw4cPkZaWpvMiIiIioorJqIQ0KSkJGo0Grq6uOuWurq5ISEgwWCchIcHg9Gq1GklJSQbrzJs3D/b29tLLy8vLmDCfyAZNC2l4vSb0qfVLRERE9Lwq0UVNCoVC570QQq+sqOkNlWtNnz4dqamp0uv27dslCbNEjpg3gk3d9rCp2x5HLZs9tX6JiIiInldmxkzs5OQEU1NTvb2hd+/e1dsLquXm5mZwejMzMzg6Ohqso1KpoFKpjAmt1Nz5dDCAwbL0TURERPQ8MmoPqbm5OYKCghAdHa1THh0djeDgYIN1mjZtqjf97t270aBBAyiVSiPDJSIiIqKKxuhD9hMmTMCXX36Jr7/+GufPn8f48eNx69YtjBo1CsDjw+1DhgyRph81ahRu3ryJCRMm4Pz58/j666/x1VdfYdKkSaU3F0RERERUbhl1yB4A+vXrh+TkZMyZMwfx8fGoVasWduzYAR8fHwBAfHy8zj1J/fz8sGPHDowfPx7Lli2Dh4cHPvnkE/Tq1av05oKIiIiIyi2F0F5h9AxLS0uDvb09UlNTYWdnJ3c4RERERJTPk+RrfJY9EREREcmKCSkRERERyYoJKRERERHJigkpEREREcmKCSkRERERyYoJKRERERHJigkpEREREcmKCSkRERERyYoJKRERERHJigkpEREREcmKCSkRERERyYoJKRERERHJigkpEREREcmKCSkRERERyYoJKRERERHJigkpEREREcmKCSkRERERyYoJKRERERHJigkpEREREcmKCSkRERERyYoJKRERERHJigmpAQqFQnqVt/bLa9tl3X6lSpWgUChQqVKlUm8bALy8vKBQKODl5VXqbY8dOxYKhQJjx44t9bYB4PTp0wgLC8Pp06fLVdsAsHz5clhYWGD58uWl3nZ0dDR8fX0RHR1d6m0DQHx8PCIjIxEfH1+u2i7vyvOyKc+xExWFCSk9F1JTU3X+lrbY2Fidv6Vpz549On9L299//40DBw7g77//LldtA8DevXvx8OFD7N27t9TbPnr0KG7evImjR4+WetvA4+Ri9uzZZZaQllXb5V15XjblOXaiojAhJSIiIiJZMSElIiIiIlkxISUiIiIiWTEhJSIiIiJZMSElIiIiIlkxISUiIiIiWTEhpeeCvb29zt/SVqVKFZ2/pal169Y6f0tbzZo10aJFC9SsWbNctQ0ArVq1gkqlQqtWrUq97caNG8PHxweNGzcu9bYBwN3dHREREXB3dy9XbZd35XnZlOfYiYqiEEIIuYMoSlpaGuzt7ZGamgo7Ozu5wyEiIiKifJ4kX+MeUiIiIiKSFRNSIiIiIpIVE1IiIiIikhUTUiIiIiKSFRNSIiIiIpIVE1IiIiIikhUTUiIiIiKSFRNSIiIiIpIVE1IiIiIikhUTUiIiIiKSFRNSIiIiIpKVmdwBFIcQAsDjZ6QSERER0bNHm6dp8zZjlIuEND09HQDg5eUlcyREREREVJj09HTY29sbVUchSpLGPmW5ubmIi4uDra0tFApFmfeXlpYGLy8v3L59G3Z2dmXeH5U9rtOKieu14uE6rZi4XiseQ+tUCIH09HR4eHjAxMS4s0LLxR5SExMTeHp6PvV+7ezs+MGpYLhOKyau14qH67Ri4nqtePKvU2P3jGrxoiYiIiIikhUTUiIiIiKSFRNSA1QqFSIiIqBSqeQOhUoJ12nFxPVa8XCdVkxcrxVPaa/TcnFRExERERFVXNxDSkRERESyYkJKRERERLJiQkpEREREsmJCms9nn30GPz8/WFhYICgoCIcOHZI7JHoCkZGRUCgUOi83Nze5wyIjHTx4EF26dIGHhwcUCgU2b96sM14IgcjISHh4eMDS0hJhYWH4+++/5QmWiqWodTps2DC9z26TJk3kCZaKZd68eWjYsCFsbW3h4uKC7t274+LFizrT8LNavhRnnZbWZ5UJaR4//PAD3n77bcycOROnTp1CSEgIOnTogFu3bskdGj2BmjVrIj4+XnqdOXNG7pDISBkZGahTpw4+/fRTg+MXLlyIxYsX49NPP8WxY8fg5uaGl19+WXrsMD17ilqnANC+fXudz+6OHTueYoRkrAMHDmD06NE4cuQIoqOjoVar0bZtW2RkZEjT8LNavhRnnQKl9FkVJGnUqJEYNWqUTlmNGjXEtGnTZIqInlRERISoU6eO3GFQKQIgNm3aJL3Pzc0Vbm5uYv78+VJZdna2sLe3FytWrJAhQjJW/nUqhBBDhw4V3bp1kyUeKh13794VAMSBAweEEPysVgT516kQpfdZ5R7Sfz169AgnTpxA27Ztdcrbtm2Lw4cPyxQVlYbLly/Dw8MDfn5+6N+/P65duyZ3SFSKrl+/joSEBJ3PrkqlQosWLfjZLef2798PFxcXvPjiixgxYgTu3r0rd0hkhNTUVACAg4MDAH5WK4L861SrND6rTEj/lZSUBI1GA1dXV51yV1dXJCQkyBQVPanGjRtj9erV+OWXX/DFF18gISEBwcHBSE5Oljs0KiXazyc/uxVLhw4dsGbNGuzduxcffvghjh07hlatWuHhw4dyh0bFIITAhAkT0Lx5c9SqVQsAP6vlnaF1CpTeZ9WstAMu7xQKhc57IYReGZUfHTp0kIZr166Npk2bwt/fH9988w0mTJggY2RU2vjZrVj69esnDdeqVQsNGjSAj48Ptm/fjp49e8oYGRXHmDFj8Ndff+G3337TG8fPavlU0Dotrc8q95D+y8nJCaampnq/0u7evav3a47KL2tra9SuXRuXL1+WOxQqJdq7JvCzW7G5u7vDx8eHn91y4K233sKWLVuwb98+eHp6SuX8rJZfBa1TQ0r6WWVC+i9zc3MEBQUhOjpapzw6OhrBwcEyRUWl7eHDhzh//jzc3d3lDoVKiZ+fH9zc3HQ+u48ePcKBAwf42a1AkpOTcfv2bX52n2FCCIwZMwY//fQT9u7dCz8/P53x/KyWP0WtU0NK+lnlIfs8JkyYgMGDB6NBgwZo2rQpPv/8c9y6dQujRo2SOzQqoUmTJqFLly7w9vbG3bt38e677yItLQ1Dhw6VOzQywoMHD3DlyhXp/fXr13H69Gk4ODjA29sbb7/9Nt5//31Uq1YN1apVw/vvvw8rKysMHDhQxqipMIWtUwcHB0RGRqJXr15wd3fHjRs3MGPGDDg5OaFHjx4yRk2FGT16NNauXYuff/4Ztra20p5Qe3t7WFpaQqFQ8LNazhS1Th88eFB6n9Unvk6/glm2bJnw8fER5ubmon79+jq3NqDyp1+/fsLd3V0olUrh4eEhevbsKf7++2+5wyIj7du3TwDQew0dOlQI8fh2MhEREcLNzU2oVCoRGhoqzpw5I2/QVKjC1mlmZqZo27atcHZ2FkqlUnh7e4uhQ4eKW7duyR02FcLQ+gQgVq1aJU3Dz2r5UtQ6Lc3PquLfDomIiIiIZMFzSImIiIhIVkxIiYiIiEhWTEiJiIiISFZMSImIiIhIVkxIiYiIiEhWTEiJiIiISFZMSImIiIhIVkxIiYiIiEhWTEiJ8tFoNAgMDISNjQ0SExNLpc0bN25AoVBAoVCUSntyiIqKgkKhQFhYmNyhyGrLli1QKBQYPXq03KE8kVu3bsHKygoNGzYstTYrwjYybNgwKBQKREZGyh2KrCZMmACFQoH169fLHQo9J5iQEuWzcuVKnDlzBmPHjoWrq6vc4TwVUVFRiIyMxOnTp+UO5ZnXtWtXNGnSBCtXrsTZs2dLpU1fX1+YmJhIz4l+GiZNmoSsrCy89957T61PuUVGRiIyMhL379+XO5Rn3owZM2Bra4vJkycjOztb7nDoOcCElCiPtLQ0vPPOO7C1tcWUKVPkDuepiYqKwuzZswtNSO3t7VG9enV4e3s/vcCeUXPmzIFGo8HEiROfuK2TJ0/i5s2baNq0Kdzc3EohuqIdPnwY69evR/PmzdG2bdun0uezYPbs2Zg9e3ahCam7uzuqV68OJyenpxfYM8jJyQlvvfUWbt68iY8++kjucOg5wISUKI+oqCgkJyejb9++qFSpktzhPFN69OiBCxcuYPXq1XKHIrs2bdrAz88Pu3fvxpkzZ56orU2bNgF4vHyflkWLFgEARo4c+dT6LC/mzZuHCxcuYMyYMXKHIrsRI0ZAoVDgk08+QU5OjtzhUAXHhJQoj+XLlwN4fB4ZUUEUCgWGDBkCAFi2bNkTtfXTTz8BeHoJ6Z07d7BlyxbY2tqiZ8+eT6VPKp98fX0RGhqKhIQEbNy4Ue5wqIJjQkoViq+vLxQKBfbv349Lly5h8ODB8PDwgEqlgr+/P6ZMmYLU1FSDdf/44w9cuHABzs7OaN68ucFpkpKS8OWXX6JXr16oXr067OzsYGlpiWrVqmHkyJG4dOlSseLcvXs32rZtC0dHR1hZWaF+/fpYtmwZNBpNgXXi4+MxceJEBAQEwMrKCra2tqhbt26h58SFhYVBoVAgKioKiYmJGDt2LF544QVYWFjA19dXugjlwIEDAIDw8HDp4qv8F6cUdcFKdnY2Fi9ejMaNG8Pe3h4WFhbw9/fHyJEjceXKFYN1IiMjoVAoMGzYMGg0Gnz88ceoW7curK2tYW9vjzZt2mDfvn3FWqbGyD8v3377LZo2bQpbW1vY2NggODgYGzZsKLQNbTK3du1aqNXqEsVx+fJlnDt3DoGBgfD39y9WnfwXyBm7La1duxYajQbt27eHlZVVgXEtXLhQ2hNsaWkJOzs71KtXr9jnYAoh8NlnnyEoKAg2NjaoVKkS2rRpg19++aXQeidPnsQrr7wCLy8vqFQqODg4ICwsDF999VWB86RdHjdu3MCJEyfQv39/eHh4wMzMDMOGDZMuVNLy8/PT2c7zXsBU1EVNT/o5TElJwcSJE1G1alWoVCq4urpi0KBBuHbtWqHLpSTyzkt2djZmz56NgIAAWFpawtHREd26dSvyvHHtds4jI1TmBFEF4uPjIwCIhQsXCmtra2Fubi7q1asnatSoIQAIAKJatWoiNjZWr+77778vAIiuXbsW2P7SpUsFAKFUKoWnp6cICgoSAQEBwsrKSgAQ1tbWYs+ePXr1rl+/LvW/bNkyoVAoRKVKlUSDBg1ElSpVpHHdunUTOTk5evUPHz4sHBwcBABhZmYm6tatK2rWrCkUCoUAIHx8fMTFixf16rVo0UIAELNmzRIuLi7CxMRE1KxZU9SrV08EBASIHTt2iGbNmgk7Oztp2TRr1kx6jRkzRmpr1apVAoBo0aKFXj8JCQmiTp06Osu4fv36wsLCQgAQFhYWYvPmzXr1IiIiBAAxaNAg0aFDBwFA+Pn5ibp160rL1NTUVGzdurXAdVISeedlxIgRAoCoUqWKCAoKkpYFAPHZZ58V2IZGoxG2trYCgIiJiSlRHPPnzxcARERERLHrPOm21LZtWwFALF68uMA+evXqJQAIKysrUbVqVdGwYUPh7+8vzMzMBABRtWpVg5+hvMu1X79+0nJt0KCBqFSpkhTbwoULDfb72WefCRMTEwFA2NnZiQYNGkifaQCiTZs2IiMjQ6+edvzixYuFmZmZsLKyEvXr1xc1a9YU4eHh4r333hPNmjWTpmvQoIHOdv7VV19JbQ0dOrTAdfKkn8N3331X+Pr6ClNTU1GrVi0REBAgTE1NBQDh7Owsbt26VeA6KQntvEyYMEHUr19fKBQKUb16dVG7dm1hbm4urePjx48X2MYff/whAAgbGxuD2xNRaWFCShWK9stLqVSK9u3bi7t370rjTp8+Lfz8/AQA8fLLL+vVbdeunfSlUZCjR4+Kbdu2iczMTJ3yzMxM8dFHHwkTExPh5uYmsrKydMbnTSKUSqWYNm2aePjwoTT+xx9/lJK39957T6ducnKycHNzEwBE69atRXx8vDTu4sWLolatWgKAqFmzpnj06JFOXe0XoampqQgLC9P5wss7D9rpVq1aVeC8F5aQvvzyywKA8Pb21vlyS0lJEX369JG++C5fvqxTT5uQKpVK4e3tLY4ePSqNS09PF127dpWS1Nzc3AJjM5Z2XpRKpahcubLYsWOHNO7Ro0di5MiRAoCwtbUV6enpBbbTunVrAUDMmzevRHE0btxYABCnT58udp0n2ZbUarWwtrYWAMRvv/1WYB+bN28WMTExQqPR6JTHx8eLYcOGCQCie/fuevXyLlcLCwvx448/SuMePnwopk2bJgAIhUIhDh06pFP30KFDUjI6ceJEnc/Qzp07pR8Kr7/+ul6/2uVhamoqxo4dKx48eCCNy7uda6e7fv16gfNeUEJaGp9DpVIpWrduLe7cuSONu3z5sqhWrZoAIIYNG1ZgXCWhnRelUinq1KkjLly4II1LTEwUTZo0EQBEWFhYgW3k5OQIlUr1RD+8iIqDCSlVKNqEtFKlSiI1NVVv/MGDB6Uvpfz/XLVfCoUlZUUZMGCAACA2bNigU543iWjVqpXBurNnzxYAROXKlXX2Ar333nsCgLC3txcpKSl69c6fPy99ka9Zs0ZnnPaL0NHR0WDd/NOVJCH9/fffpXnbt2+fXr1Hjx5J62XEiBE647QJKQCxf/9+vbpxcXFCqVQKAOKvv/4qMDZjaecFgIiKitIbn5mZKZydnQUAsWXLlgLbGTJkSIFJUlHu3LkjFAqFqFq1qlH1nmRbio2NLVZSVpjc3Fzh4eEhFAqFzg8+IXSX65w5cwzWb9mypQAg2rVrp1Ou/VFj6AePEEKsXLlSSjrz70nU9tm6detCY3+ShLQ0PocODg4iOTlZr+769eul8aVJOy/m5ubi0qVLeuOPHTsm/UC4f/9+ge14e3sLAGLt2rWlGh9RXjyHlCqk4cOHw87OTq88JCQEDRo0AABs27ZNZ9zdu3cBAI6OjoW2nZOTg02bNuGtt95Cp06dEBoaiubNm6N58+b47bffAADHjx8vsP6ECRMMlo8dOxZmZmZISUnB4cOHpXJtnMOHDzd45X+NGjXQtWtXAMD27dsNtt2nT58yu2uANr769esbPL9UqVRi/PjxhcZXu3ZttGjRQq/c3d0dfn5+AB6f11ja7OzspIuT8rK0tES9evWK7Fe7rWi3HWNs3rwZQognupjJ2G0pb5xFbef379/H559/jvDwcLRr1w4hISFo3rw5QkJC8ODBAwghcPLkSYN1zczMMHbsWIPjtLfK2rNnD7KysgAAmZmZ0rnCkyZNMlgvPDwczs7O0Gg02LVrl8FpRowYUeg8PYnS+BwOGjQIDg4OeuXNmjUDAPzzzz/4559/Sini/7Rr1w7VqlXTKw8KCoJKpYIQAlevXi2w/pNs50TFZSZ3AERloVatWgWOq1mzJo4fP45z587plGu/HC0sLAqse+3aNXTp0kWvbn5JSUlGx1apUiVUqVIFN2/exLlz59CmTRsAwIULFwA8TtoKEhgYiM2bN0vTFrfP0qDtMzAwsMBptOPi4uKQnp4OW1tbnfHVq1cvsK6rqysuXbqEBw8elEK0ul588cUCn56lfShCYf1qtxXttmOM0rjdk7HbUt44C9vOd+zYgcGDBxeZHBW0nXt6esLe3t7guJo1awIA1Go1Ll++jMDAQFy5ckW6MKyg7VypVCIgIAD37t2TdTt/ks9hQdt53gdwPHjwwGDS+iQK6lehUMDFxQW3b98us+2cqLi4h5QqpMJuMK4dl56erlOuvRF2QV/CQgj07NlTuip648aNuHXrFrKzsyEen/6CWbNmAUCh9+wzNra0tLQi67m7u+tMm5+1tXWBdZ+UMfHlnT6vwuIzMXn8byo3N7ekIRboSfvVbivG3kQ9JSUFBw4cgKurK5o2bWpU3byM3ZbyxlnQdn7nzh306dMH//zzD3r06IF9+/YhMTERjx49krbzkJAQAAVv58WJK29sebeJirCdGxufdlsDKtZ2TmQM7iGlCqmwZ9Brx+XfS+fm5oa4uDgkJycbrHf8+HH8+eefsLCwwK+//gpnZ2e9aQrbM5q3/4KedmQoNjs7O6SkpBT6WMn4+Hhp2qdN22dx4ss7fUWg3VaMfcTsli1boFar0a1bN51kxFjGbkt5k6nk5GSDca9btw6ZmZlo2LAhNmzYYDC+orbz4nz+8saWd5tISEiAj4+Pwbpyb+fP8uewLJV0OycyBveQUoVU2DPGteNeeuklnXLtuaUF1dWeYxUQEGAwGQWA33//vcSxpaam4s6dO3qx1ahRAwAKfSKQdlxAQECR/RtS0GHr4jAmvipVquj9ECjPtPPVsGFDo+ppD9c/6Y3pjd2WbG1t8eKLLxZaV7udN2/evMBktKj77d6+fbvA+/1q+zUzM5POa3zhhRdgZvZ4/0hB21FOTo50KLyk2/mTeBqfw2dRQkKC9ANE+z+SqCwwIaUK6csvv9Q7JA8Av/32m3TBUadOnXTGtWzZEgBw5MgRg21aWloCePwP2tDhre3bt+Ovv/4qMraCngu9dOlSqNVqVKpUCcHBwVJ5586dATyeJ0M33r506RK2bNkCQH+eikt7g/SSnCOmje/EiRPYv3+/3vicnBxpnksa37Pon3/+kRKzgh4WYEhmZiZ2794Ne3t7aZsrKWO3JaD423lsbKzB8QsWLCj0AQ7A4/NDP/3000JjbtWqldSXlZWVFJf2sab5ffPNN7h79y7MzMzQrl27QvsvSGls52X5OXwWaS+Kq1Gjhs6pN0SljQkpVUgPHjxA//79dQ4tnjlzRnokaOvWrfXO3Xv55ZehUqlw5swZg+fXBQcHw9zcHPHx8ZgyZYrO+XPbtm3DoEGDpC/Ywhw4cACzZs3Sqb9x40a8//77AB5fhZz3CTqjRo2Cm5sbUlNT0bdvX51DnpcvX0afPn2g0WhQq1Yt9OnTp8j+DXnhhRcAAPv374cQwqi6wcHBaNu2LYDHT4bJe+V1amoqhgwZghs3bsDKygpTpkwpUXwF0T6ZS45HvWqXVVBQUKHnFea3a9cuZGVloVOnTjA3N3+iGIzdloD/EitDPx6A/5LrjRs3Yv369VJ5Tk4O5s+fjw8//LDI7VypVOK9996THouqrf+///0Pe/bsgUKhwMyZM3XqvPPOOzAxMcGBAwcwefJkPHz4UBq3e/du6er71157DV5eXoX2XxDtdr53716j6z6Nz6EheZ/MFRUVVWrtFpd2O6lISTY9o+S63xRRWdDe73LBggXCyspKmJubi/r164uAgADpHoT+/v46N6bO65VXXhEAxNKlSw2Onzt3rtSOg4ODztNxgoKCxKRJkwQAMXToUJ16ee8dqX3aU6VKlUTDhg2Fp6enNK5z584FPqmpcuXK0k2u69atK2rVqiXd97CoJ8QUdW/Vo0ePSm15enqK5s2bixYtWohx48ZJ0xjzpKbq1auLoKAgYWlpKYDHT2ratGmTXj3tfUjzL6/izoN2fRdW35DC5kWrsCf2CCFE9+7dBQCdp/wUx6BBgwQAsX79eqPqaT3ptqTRaKTldubMGYPjtfcE1W4PDRo0EPb29gKAGDVqVIHrJO9y1T4QwdPTUzRs2FDnSU0FPUhg2bJlOk9qatiwofD19dW5z2hhT2oq6t6qixYtkqYNCAgQoaGhokWLFjrzUdSTmsrqc1jQPORd38beI7mobViI/z5Dhu4hnJOTI5ydnYVCodB7qAVRaeMeUqqQGjVqhOPHj6Nnz56Ii4vD1atX4evri4kTJ+LYsWOoUqWKwXpjxowBgAL3RPzvf//Dd999h4YNGyIrKwvnz59H5cqVMWfOHPz222/Fusp3zJgx2L59O+rXr4/Lly8jKSkJderUwSeffILNmzdL59Ll1bRpU5w9exbjx49H1apVceHCBVy7dg21atXCO++8g9OnT0vnBpZEo0aNsHnzZoSFheHBgwc4fPgwDhw4UORzrrVcXV0RExODRYsWoWHDhoiLi8OZM2fg6uqKESNG4K+//kL37t1LHJ8harVa2gNu7DmcTyopKQnbt2+Hg4MDBgw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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model_hierarchical.predict(result, kind=\"pps\")\n", + "ax = az.plot_ppc(result, figsize=(8, 5), kind=\"cumulative\", observed_rug=True)\n", + "ax.set_title(\"Posterior Predictive Checks \\n On Biased Sample\");" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply the Post-stratification Weighting\n", + "\n", + "We now use the fitted model to predict the voting shares on the data where we use the genuine state numbers per strata. To do so we load data from the national census and augment our data set so as to be able to apply the appropriate weights." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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stateethmaleageedunrepvotestate_totalstate_percent
0IDWhite-0.570+HS315030.68310211938850.026387
1IDWhite0.570+4-Year College118090.68310211938850.009891
2IDWhite0.570+Post-grad98730.68310211938850.008270
3IDWhite0.550-59Some college304560.68310211938850.025510
4IDWhite0.570+HS198980.68310211938850.016667
\n", + "
" + ], + "text/plain": [ + " state eth male age edu n repvote state_total \\\n", + "0 ID White -0.5 70+ HS 31503 0.683102 1193885 \n", + "1 ID White 0.5 70+ 4-Year College 11809 0.683102 1193885 \n", + "2 ID White 0.5 70+ Post-grad 9873 0.683102 1193885 \n", + "3 ID White 0.5 50-59 Some college 30456 0.683102 1193885 \n", + "4 ID White 0.5 70+ HS 19898 0.683102 1193885 \n", + "\n", + " state_percent \n", + "0 0.026387 \n", + "1 0.009891 \n", + "2 0.008270 \n", + "3 0.025510 \n", + "4 0.016667 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "poststrat_df = pd.read_csv(\"data/mr_p_poststrat_df.csv\")\n", + "\n", + "new_data = poststrat_df.merge(\n", + " statelevel_predictors_df, left_on=\"state\", right_on=\"state\", how=\"left\"\n", + ")\n", + "new_data.rename({\"educ\": \"edu\"}, axis=1, inplace=True)\n", + "new_data = model_df.merge(\n", + " new_data,\n", + " how=\"left\",\n", + " left_on=[\"state\", \"eth\", \"male\", \"age\", \"edu\"],\n", + " right_on=[\"state\", \"eth\", \"male\", \"age\", \"edu\"],\n", + ").rename({\"n_y\": \"n\", \"repvote_y\": \"repvote\"}, axis=1)[\n", + " [\"state\", \"eth\", \"male\", \"age\", \"edu\", \"n\", \"repvote\"]\n", + "]\n", + "\n", + "\n", + "new_data = new_data.merge(\n", + " new_data.groupby(\"state\").agg({\"n\": \"sum\"}).reset_index().rename({\"n\": \"state_total\"}, axis=1)\n", + ")\n", + "new_data[\"state_percent\"] = new_data[\"n\"] / new_data[\"state_total\"]\n", + "new_data.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This dataset is exactly the same structure and length as our input data to the fitted model. We have simply switched the observed counts across the demographic strata with the counts that reflect their proportion in the national survey. Additionally we have calculated the state totals and the share of each strata within the state. This will be important for later when we use this `state_percent` variable to calculate an adjusted MrP estimate of the predictions at a state level. We now use this data set with our fitted model to generate posterior predictive distribution. " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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+       "Dimensions:               (chain: 4, draw: 1000, state__factor_dim: 46,\n",
+       "                           eth__factor_dim: 4, edu__factor_dim: 5,\n",
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+       "Data variables: (12/16)\n",
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statebase_expectedbase_lbbase_ubmrp_adjustedmrp_ubmrp_lbcensus_share
9OK0.4233500.2091440.6605330.3262910.4139120.2454310.321553
34MS0.4391450.2155650.6837800.3815750.4937990.2784980.374640
2CO0.4759610.2512500.6984780.3971010.4826990.3155350.354857
24ME0.4386380.2360100.6696740.4189640.5371560.2963730.403636
25MO0.5132910.2253260.7485390.4207350.5254250.3211950.302954
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" + ], + "text/plain": [ + " state base_expected base_lb base_ub mrp_adjusted mrp_ub mrp_lb \\\n", + "9 OK 0.423350 0.209144 0.660533 0.326291 0.413912 0.245431 \n", + "34 MS 0.439145 0.215565 0.683780 0.381575 0.493799 0.278498 \n", + "2 CO 0.475961 0.251250 0.698478 0.397101 0.482699 0.315535 \n", + "24 ME 0.438638 0.236010 0.669674 0.418964 0.537156 0.296373 \n", + "25 MO 0.513291 0.225326 0.748539 0.420735 0.525425 0.321195 \n", + "\n", + " census_share \n", + "9 0.321553 \n", + "34 0.374640 \n", + "2 0.354857 \n", + "24 0.403636 \n", + "25 0.302954 " + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "estimates = []\n", + "abortion_posterior_base = az.extract(result, num_samples=2000)[\"p(abortion, n)_mean\"]\n", + "abortion_posterior_mrp = az.extract(result_adjust, num_samples=2000)[\"p(abortion, n)_mean\"]\n", + "\n", + "for s in new_data[\"state\"].unique():\n", + " idx = new_data.index[new_data[\"state\"] == s].tolist()\n", + " predicted_mrp = (\n", + " ((abortion_posterior_mrp[idx].mean(dim=\"sample\") * new_data.iloc[idx][\"state_percent\"]))\n", + " .sum()\n", + " .item()\n", + " )\n", + " predicted_mrp_lb = (\n", + " (\n", + " (\n", + " abortion_posterior_mrp[idx].quantile(0.025, dim=\"sample\")\n", + " * new_data.iloc[idx][\"state_percent\"]\n", + " )\n", + " )\n", + " .sum()\n", + " .item()\n", + " )\n", + " predicted_mrp_ub = (\n", + " (\n", + " (\n", + " abortion_posterior_mrp[idx].quantile(0.975, dim=\"sample\")\n", + " * new_data.iloc[idx][\"state_percent\"]\n", + " )\n", + " )\n", + " .sum()\n", + " .item()\n", + " )\n", + " predicted = abortion_posterior_base[idx].mean().item()\n", + " base_lb = abortion_posterior_base[idx].quantile(0.025).item()\n", + " base_ub = abortion_posterior_base[idx].quantile(0.975).item()\n", + "\n", + " estimates.append(\n", + " [s, predicted, base_lb, base_ub, predicted_mrp, predicted_mrp_ub, predicted_mrp_lb]\n", + " )\n", + "\n", + "\n", + "state_predicted = pd.DataFrame(\n", + " estimates,\n", + " columns=[\"state\", \"base_expected\", \"base_lb\", \"base_ub\", \"mrp_adjusted\", \"mrp_ub\", \"mrp_lb\"],\n", + ")\n", + "\n", + "state_predicted = (\n", + " state_predicted.merge(cces_all_df.groupby(\"state\")[[\"abortion\"]].mean().reset_index())\n", + " .sort_values(\"mrp_adjusted\")\n", + " .rename({\"abortion\": \"census_share\"}, axis=1)\n", + ")\n", + "state_predicted.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was the crucial step and we'll need to unpack it a little. We have taken (state by state) each demographic strata and reweighted the expected posterior predictive value by the share that strata represents in the national census within that state. We have then aggregated this score within the state to generate a state specific value. This value can now be compared to the expected value derived from our biased data and, more interestingly, the value reported in the national census. " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the Effect of Adjustment\n", + "\n", + "These adjusted estimates can be plotted against the shares ascribed at the state level in the census. These adjustments provide a far better reflection of the national picture than the ones derived from model fitted to the biased sample. " + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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4uDidOXNGb7zxhqeaRUm1Zo10IJ9bGA1D2r8/az4ABeM7BaCEMqssgpklESTzA62mlkXwNI6JAACgGAoqeJbCiY2N1ebNm7Vy5Upt375dhmGoYcOG6tChg6eaREmW81a4os4HlHR8pwAUUznr3NZvEeHW2+1dKYtQt2GZQrdjLYmQX1vurD0rZQVa68U10MOjdknKCrTWb1HVM+UKZFJZBDNwTERxkHOQvi6dyMADgBLOY5m6Vh07dtTIkSP1wAMPENCF5+QsBl7U+YCSju8U4Bc8OdCXN9rxdJ1bybyyCN6oPWtt18qnA61m4pgIb2OQPgCAA27N1H399dc1bNgwhYWF6fXXX8933gceeMCdTaOka906a1THgwcdP2+xZD3furW5/QJ8Fd8pwOelbEzV0nd2SMq6fX/G5D0Kr3RcvQbFuPUWeDPbmTl5j4LT06WgyyUJrHVuh46WW9ozsyxCVkkEaek7O3Qs23ho0eWC1dPN758N2X6u45gIb7IO0hcQaj/dOkifL49qDwAoErcGdV977TUNGDBAYWFheu211/Kcz2KxENSFewUGSlOmZJ3Y5GT5XwbK5MlctADO4jsFeIynywdI5gVAzWrH2Tq3TRKjivxeml0WwdSSCMnJ0qiHpTaLsx7f3l2qWiFrf09QKG8cE+EtBQ3SZ7FkDdLXtSvbH1Cc+eEPqqGhAZr6XmLWgxln858ZHuPW8gt79uxR+fLlbf/P62/37t3ubBbI0r171i/VOW99i4nhF2ygMPhOoYQxo4SAGeUDzBroy6x2JNfq3BaVN8oimFISwZrtl7PuqzXbj9u488cxEd7AIH2A76N8CjzIYzV1x48fr/Pnz+ea/s8//2j8+PGeahYlXffu0ubNlx8vTZb27OFEGygsvlPwMn+q1WrNaj11Mt1uujWr1V1tmRUANTPQaladW6ussghxioyyv6ktulywho6O80xZBE8qKNtPysr2y55JhNw4JsJsDNIH+DZ+UIWHeSyo+9xzz+ns2dwn8efPn9dzzz3nqWYB+9sYrrve529rALyO75TPYFCswrfj6WCrmVmtZgVAzQy0mlnn1iqhebQef7GB7fGw0XEaP6Wx7wV0JbL93IljIszEIH0ogay39U99L1GhoR4LWXkeP6jCBB77hhiGIYsl961jP/30k8qVK+epZgEAKJHMDID6S6BVMi/YamZWq1kBUDMDrdY6t/lxZ51bK1PKIpjB37P9ctYq5AIZ/sI6SJ+D62pJWdNjYxmkDyiO+EEVJnB7UDc6OlrlypWTxWJR3bp1Va5cOdtfZGSkOnbsqN69e7u7WQAASiyzAqD+FmiVzAu2mpnValYA1MxA6+U6t4ZkZNo/aWRKMtxe59av+HO2H7UK4c+sg/Q5wiB9QPHm7z+oolhwe1B38uTJmjRpkgzD0HPPPafXXnvN9vfWW29p7dq1mjp1qrubBQDAJf5SqoBBsYrGrGCrmVmtZg30ZfaAYgn7V2nomkcVef643fTo80c1dM2jSti/yi3t+CV/zfajViGKA09nijNIH+Cbsn1nQzMuXC4pkXEhz/kAVwUVPItr7rzzTqWnZ2XwdOjQQTEx+Z/sAwBgtpSNqVr6zg5JWQG0GZP3KLzScfUaFOPWeplmtONKALRuwzLFvh3JP2u1WrNa83sP3Vk+IGugL2npOzt0LFvsO7pcsHq6cftLaB6todd+r6VfXdKxoOqX27nwt3q2M5TQvGk+r3bB/+rSJRw4oHqHNujhPmslScO+GaP6R39UgAxp9E6pa1cy1hyxZvv17Jn7OV/N9iuoVqHFklWrkG0CnpScLI16WGqzOOvx7d2lqhWyvm/uDLZ27y61vkEavTvr8dJkqUsntm2gOLP+oHrwoONjlcWS9byv/aCKYsUjNXWDgoI0fPhwZVDPCgDgAjOyZ/2tVAGDYhWNWSUEzM5qlUwa6Cs5WQmjuujxT/tcbuebMRr/0S1KGNXFfZmS2erSBejyfqHOsZ8VYGRSl84Z/pbtR61CeJvZmeIM0gf4FsqnwAQeGyitRYsW2rJli6cWDwDwM2YMwOWPpQoYFKtozAy2ZmXPxikyyv5GqehywRo6Os69wdb/8ehAX9kyJXMFWjP/98O+u0Z1pi6de3TvLm3efPnx0mRpzx7fC+hKbBPwLka1R34YvBFW1h9Uq1Wzn+6rP6ii2PFYUHf48OF66KGH9Oabb2rDhg36+eef7f4AALAyK6vVrFqtZtaE9e9BsfLm1lqtJgZbTcmeNYuZmZL+PNCX2fwl249tAt5EpjjywuCNyKl7d2nb5cQVLVvmuz+ootjxWFC3T58+2rNnjx544AFdd911SkhI0NVXX237FyWQP/5i6Y/rBOTgLwN9Sf5ZqsBvB8UyOas1oXm0Hh9f1/Z4WIdzGj+pge9lz5rJzExJfx3oC4XHNgFvIlMcjjB4I/KS/QfUNm189wdVFDseC+ru2bMn19/u3btt/6KE8cdfLP1xnVBkZtSENbMtM0oimJnV6o+lCiTzAqBeCbSaldWanKyAZom2h3Xu66qA2nHs0/NjZqakt+rS8eNt8UWtQngTmeLIiZIcALzAY0HdmjVr5vuHEsQff7H0x3XyY2YFWs0IgJrZlr8N9CX9r4RAqXTJyHQ8g5Gp6FLpPlWqwMqsAKjZ5QNMyWpln144ZmdKmj3QFz/eFp2ng+L+NvgbfAeZ4siJkhwAvMBjQV1J+uOPPzRy5Eh16NBBHTt21AMPPKA//vjDk02iuPHHXyz9cZ38mFmB1qwA6G6dSrUPPJ48nqaZk3f7XLDVHwf6kqQAI1O9fvy/rAc5A7v/e9xz078VkFfQ19l2TC5VkL1dK0/e1u835QMk9ulF4Y1MSbMG+iLQX3RmBcX9afA3+A5/zxTnLgXXUZIDgBd4LKj7xRdfqGHDhvrhhx/UpEkTNW7cWN9//70aNWqklStXeqpZFDf++IulP66Tt3j4hNGsTNPMTEOL3t6Z9dnnzNiwBEiGocVv73RLANSsYKs/DvQlSVqzRgk/LdbQNY8q8vxx+zbOH9XQNY8qIWWRW76/ZpcqkMRFWGGwTy8ab2RKenqgLwL9RWd2UNxfBn+Db/HXTHHuUigcSnIA8AKPBXUff/xxPfjgg/r+++81adIkvfbaa/r+++81evRoPfbYY55qFsWNP/5i6Y/r5A3Jycps2sz28I+hzyqzVm23nTCamWm6a+tpnfwnKCuA64glQKn/BGnX1tNFb8ukYKs/DvQlyfa9TDiwSo8v72+bPOybMRr/8W1KOLDKbr6iMrsmLBdhhcA+vej8LVOSQH/REBRHSeJv+z/uUig8SnIA8AKPBXW3bdumpKSkXNOHDBmirVu3eqpZFDf++IulP66T2ZKTlTJ6ql6+6t+2STPaTtK4xLeUMnqqW04Yzcw0Pf3dL26dL99lmBRs9deBvrJ/LwN0OeBQ59jP9iUX3Pj9pSZsMcc+3T38KVOSQH/REBRHSeMv+z9+kCkafy/JAaBY8lhQt2LFikpJSck1PSUlRZUqVfJUsyhu/PEXS39cJzNlZChl/Puaef0rOlW6vN1TJ0tX1MzrX1HK8x8U+YTRzEzTsv8cc+t8+S7DpGCr3w705Y/fXy7CisYftwkUDYH+oiEoDvgmfpApOn8tySFR4gsopjwW1B06dKiGDRumV155RWvWrNHatWv18ssv65577tGwYcM81SxclX1n/O237t85++Mvlv64TtlkL0fwx+9n3FKewG7536zRolpDsh7kLFfwv8eLaw5W5jdFO2E0M9M0vlGUos79lXvgLSsjU9Hnjii+UVTR2zIp2Oq3A3354/eXi7Ci8cdtAkVDoL9oCIoDvokfZNzD30pySJT4gmOejiXBKR4L6o4bN05PP/203njjDbVt21Zt2rTRm2++qWeffVZPPvmkp5qFK5KTpQaXs+LUpYvnRiX2t18s/XGdlDWw2MtPbrM9njF5j8aN+tVtA4pJ0q7fTupkeOX868+GV9Gu304WqR0zM00D2rZWr72zsx7kDOz+73HPP+cqoG3RAwBmBlu9MtCXGfzt+8tFWNH52zaBoiHQXzQExQHf5K0fZPwxA9TEkhyhoQGa+l6ipr6XqNBQD4R3KPEFR8yKJaFAHgvqWiwWPfjggzpw4IBOnTqlU6dO6cCBAxo1apQseZ3kwTzWnfOhQ/bTPbVz9sdfLE1eJ09n0KZsTNXMyXt06mS63fSTJy5p5uQ9bgvsni5Vwa3z5cXUTNPAQCU83U9D1z6myPPH7Z6KPn9UQ9c+poRxfd12QmdmsNXUgb7M5E/7JG9chPnjBZg/bRMoOgL9hUdQHPBN3vhBhgzQ4o0SX3DE7FgS8uWxoK7V0aNHlZKSop9++kl///23p5uDM7y1c/aXQQSyycyWbfpHhavsHruTpzNoMzMNLZqfz+3bkhbPP+CWQHLZa69063z5MTXTtHt3JUweocd/etg2adg3YzR+83AlTB7h9gCAmcFWUwb68gZ/2SeZfRHmzxdg/rJNwD0I9BceQXHA95j9g4zJGaAez2j1R5T4Qk4E+osdj+3NTp8+rYEDB6patWq28gvVqlXTHXfcoVOnTnmqWTiDnbNbmFGqwNqOpzNod20/q5Mn8h8wLPXEJe3afrbIbcU3LKuoUun5158tla74hmWL3JZkcqZp9+4K2Pyj7WGdmc8qYM8fHrt49dtgK1xj5kWYN27B88esYPgOAv2FR1AcjrBPL97M+kHGzwNDfhNA9vcSX+yPXEcsqdjx2B7m7rvv1vfff6/PPvtMJ0+e1KlTp/Tpp5/qxx9/1NChQz3VLJzh7ztnE5hVqsCsDNrTJ/MP6Lo6X34CAizqdc8VWcEmR/VnLRb1vOcKtwYoTQ1+EgCAN5hxEeaNCzB/zgoGSgKOiciOfbpvMOMHGQJDvsGfB75kf1Q4xJKKHY8FdT/77DPNnj1bnTt3VtmyZVWmTBl17txZM2fO1GeffeapZuEMf945m8DMUgVmZdCWjcp/QDFX5yt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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(2, 1, figsize=(17, 10))\n", + "axs = axs.flatten()\n", + "ax = axs[0]\n", + "ax1 = axs[1]\n", + "ax.scatter(\n", + " state_predicted[\"state\"], state_predicted[\"base_expected\"], color=\"red\", label=\"Biased Sample\"\n", + ")\n", + "ax.scatter(\n", + " state_predicted[\"state\"],\n", + " state_predicted[\"mrp_adjusted\"],\n", + " color=\"slateblue\",\n", + " label=\"Mr P Adjusted\",\n", + ")\n", + "ax.scatter(\n", + " state_predicted[\"state\"],\n", + " state_predicted[\"census_share\"],\n", + " color=\"darkgreen\",\n", + " label=\"Census Aggregates\",\n", + ")\n", + "ax.legend()\n", + "ax.vlines(\n", + " state_predicted[\"state\"],\n", + " state_predicted[\"mrp_adjusted\"],\n", + " state_predicted[\"census_share\"],\n", + " color=\"black\",\n", + " linestyles=\"--\",\n", + ")\n", + "\n", + "\n", + "ax1.scatter(\n", + " state_predicted[\"state\"], state_predicted[\"base_expected\"], color=\"red\", label=\"Biased Sample\"\n", + ")\n", + "ax1.scatter(\n", + " state_predicted[\"state\"],\n", + " state_predicted[\"mrp_adjusted\"],\n", + " color=\"slateblue\",\n", + " label=\"Mr P Adjusted\",\n", + ")\n", + "ax1.legend()\n", + "\n", + "ax1.vlines(\n", + " state_predicted[\"state\"], state_predicted[\"base_ub\"], state_predicted[\"base_lb\"], color=\"red\"\n", + ")\n", + "ax1.vlines(\n", + " state_predicted[\"state\"],\n", + " state_predicted[\"mrp_ub\"],\n", + " state_predicted[\"mrp_lb\"],\n", + " color=\"slateblue\",\n", + ")\n", + "ax.set_xlabel(\"State\")\n", + "ax.set_ylabel(\"Proportion\")\n", + "ax1.set_title(\n", + " \"Comparison of Uncertainty in Biased Predictions and Post-stratified Adjustment\", fontsize=15\n", + ")\n", + "ax.set_title(\"Comparison of Post-stratified Adjustment and Census Report\", fontsize=15)\n", + "ax1.set_ylabel(\"Proportion\");" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the top plot here we see the state specific MrP estimates for the proportion voting yes, compared to the estimate inferred from the biased sample and estimates from the national census. We can see how the MrP estimates are much closer to those drawn from the national census. \n", + "\n", + "In the below plot we've shown the estimates from the MrP model and the estimates drawn from the biased sample, but here we've shown the uncertainty in the estimation on a state level. Clearly, the MrP adjustments also shrinks the uncertainty in our estimate of vote-share. \n", + "\n", + "MrP is in this sense a corrective procedure for the avoidance of bias in sample data, where we have strong evidence for adjusting the weight accorded to any stratum of data in our population. " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "In this notebook we have seen how to use `bambi` to concisely and quickly apply the technique of multilevel regression and post-stratification. We've seen how this technique is a natural and compelling extension to regression modelling in general, that incorporates prior knowledge in an interesting and flexible manner. \n", + "\n", + "The problems of representation in data are serious. Policy gets made and changed on the basis of anticipated policy effects. Without the ability to control and adjust for non-representative samples, politicians and policy makers risk prioritising initiatives for a vocal majority among the represented in the sample.\n", + "The question of whether a given sample is \"good\" or \"bad\" cannot (at the time) ever be known, so some care needs to be taken when choosing to adjust your model of the data. \n", + "\n", + "Predictions made from sample data are consequential. It's not even an exaggeration to say that the fates of entire nations can hang on decisions made from poorly understood sampling procedures. Multilevel regression and post-stratification is an apt tool for making the adjustments required and guiding decisions makers in crucial policy choices, but it should be used carefully. \n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/thumbnails/mr_p_adjustment.png b/docs/notebooks/thumbnails/mr_p_adjustment.png new file mode 100644 index 000000000..899c23674 Binary files /dev/null and b/docs/notebooks/thumbnails/mr_p_adjustment.png differ