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plot(cars)library(tidyverse)
library(psych)
library(lme4)
library(haven)X04275_0001_Data <- read_dta("ICPSR_04275-V1/ICPSR_04275/DS0001/04275-0001-Data.dta")
glimpse(X04275_0001_Data)Rows: 15,362
Columns: 907
$ STU_ID <dbl> 101101, 101102, 101104, 101105, 101106, 101107,...
$ SCH_ID <dbl> 1011, 1011, 1011, 1011, 1011, 1011, 1011, 1011,...
$ STRAT_ID <dbl> 101, 101, 101, 101, 101, 101, 101, 101, 101, 10...
$ PSU <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYSTUWT <dbl> 178.9513, 28.2951, 589.7248, 235.7822, 178.9513...
$ SEX <dbl+lbl> 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1...
$ RACE <dbl+lbl> 5, 2, 7, 3, 4, 4, 4, 7, 4, 3, 3, 4, 3, 2, 2...
$ STLANG <dbl+lbl> 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1...
$ HOMELANG <dbl+lbl> 1, 4, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1...
$ DOBIRTHP <dbl+lbl> 198512, 198605, 198601, 198607, 198511, 198...
$ PARACE <dbl+lbl> 7, 2, 7, -9, 4, 5, 4, 7, 4, 3, 3,...
$ PARLANG <dbl+lbl> 1, 4, 1, -9, 2, 1, -9, 1, 2, 1, 1,...
$ BYFCOMP <dbl+lbl> 3, 1, 1, 5, 1, 5, 1, 1, 1, 1, 3, 5, 1, 2, 1...
$ PARED <dbl+lbl> 5, 5, 2, 2, 1, 2, 6, 2, 2, 1, 6, 4, 4, 2, 7...
$ MOTHED <dbl+lbl> 1, 5, 2, 2, 1, 2, 6, 2, 2, 1, 6, 4, 3, 2, 7...
$ FATHED <dbl+lbl> 5, 5, 2, 2, 1, 1, 3, 2, 1, 1, 4, 2, 4, 2, 3...
$ OCCUMOTH <dbl+lbl> 8, 0, 5, 4, 8, 5, 14, 1, 5, 9, 15,...
$ OCCUFATH <dbl+lbl> 6, 9, 5, 6, 5, 8, 15, 12, 2, 5, 7,...
$ INCOME <dbl+lbl> 10, 11, 10, 2, 6, 9, 10, 10, 8, 3, 8,...
$ SES1 <dbl> -0.25, 0.58, -0.85, -0.80, -1.41, -1.07, 0.27, ...
$ SES1QU <dbl+lbl> 2, 4, 1, 1, 1, 1, 3, 2, 1, 1, 2, 2, 1, 2, 4...
$ SES2 <dbl> -0.23, 0.69, -0.68, -0.89, -1.28, -0.93, 0.36, ...
$ SES2QU <dbl+lbl> 2, 4, 1, 1, 1, 1, 3, 2, 1, 1, 2, 3, 1, 1, 4...
$ STEXPECT <dbl+lbl> 3, 7, -1, 5, 5, 4, -1, 6, 7, 6, -1,...
$ PARASPIR <dbl+lbl> 5, 7, 7, 6, 2, 3, 5, 5, 5, 2, 5, 3, 6, 5, 6...
$ BYOCCHS <dbl+lbl> 7, -3, -1, 15, 15, -3, -3, -1, -3, 9, -1,...
$ BYOCC30 <dbl+lbl> -1, 9, 10, 10, 16, 11, 9, -1, 10, -1, 9,...
$ SCHPROG <dbl+lbl> 2, 2, 2, 2, 3, 1, 2, 2, 2, 2, 2, 3, 3, 2, 2...
$ BYSQSTAT <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYQXDATP <dbl> 200204, 200204, 200204, 200204, 200204, 200204,...
$ BYTXSTAT <dbl+lbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3...
$ BYTEQFLG <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYPQSTAT <dbl+lbl> 2, 1, 2, 0, 2, 2, 4, 2, 2, 3, 1, 1, 1, 2, 0...
$ BYTXPAFG <dbl+lbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0...
$ BYADMFLG <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYLMCFLG <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYIEPFLG <dbl+lbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYTXACC <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYTXCSTD <dbl> 56.21, 57.66, 66.50, 46.46, 36.17, 30.72, 45.46...
$ BYTXCQU <dbl+lbl> 3, 4, 4, 2, 1, 1, 2, 4, 1, 2, 4, 2, 1, 3, 4...
$ BYNELS2M <dbl> 47.84, 55.30, 66.24, 35.33, 29.97, 24.28, 45.16...
$ BYNELS2R <dbl> 39.04, 36.35, 42.68, 27.86, 13.07, 11.70, 19.66...
$ BYNELS0M <dbl> 41.25, 47.30, 54.15, 30.17, 25.26, 20.02, 38.95...
$ BYPISARE <dbl> 616.89, 591.84, 654.43, 511.11, 379.17, 358.38,...
$ BYTXMIRR <dbl> 39.940, 47.361, 56.717, 29.603, 24.673, 19.458,...
$ BYTXMSTD <dbl> 52.11, 57.65, 66.44, 44.68, 40.57, 35.04, 50.71...
$ BYTXMQU <dbl+lbl> 3, 4, 4, 2, 1, 1, 3, 4, 1, 2, 4, 2, 1, 4, 4...
$ BYTX1MPP <dbl> 0.998, 1.000, 1.000, 0.972, 0.906, 0.630, 0.997...
$ BYTX2MPP <dbl> 0.991, 1.000, 1.000, 0.298, 0.029, 0.001, 0.971...
$ BYTX3MPP <dbl> 0.729, 0.997, 1.000, 0.002, 0.000, 0.000, 0.345...
$ BYTX4MPP <dbl> 0.029, 0.287, 0.974, 0.001, 0.000, 0.000, 0.012...
$ BYTX5MPP <dbl> 0.000, 0.000, 0.020, 0.000, 0.000, 0.000, 0.000...
$ BYTXRIRR <dbl> 39.806, 37.121, 43.536, 27.854, 13.732, 11.698,...
$ BYTXRSTD <dbl> 59.53, 56.70, 64.46, 48.69, 33.53, 28.85, 40.80...
$ BYTXRQU <dbl+lbl> 4, 3, 4, 2, 1, 1, 1, 4, 2, 1, 3, 1, 1, 1, 4...
$ BYTX1RPP <dbl> 1.000, 1.000, 1.000, 0.999, 0.098, 0.021, 0.930...
$ BYTX2RPP <dbl> 0.961, 0.890, 0.992, 0.276, 0.000, 0.000, 0.017...
$ BYTX3RPP <dbl> 0.079, 0.021, 0.406, 0.000, 0.000, 0.000, 0.000...
$ BYSF1RCE <dbl+lbl> 4, 2, 7, 3, 4, 4, 4, 3, 4, 7, 7,...
$ BYSF2RCE <dbl+lbl> 4, 2, 7, 3, 4, 3, 7, 3, -3, 4, 7,...
$ BYSF3RCE <dbl+lbl> 4, 3, 7, -9, 4, 3, 7, 7, -3, 3, 3,...
$ BYBASEBL <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,...
$ BYSOFTBL <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,...
$ BYBSKTBL <dbl+lbl> 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2...
$ BYFOOTBL <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 4,...
$ BYSOCCER <dbl+lbl> 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,...
$ BYTEAMSP <dbl+lbl> 2, 2, 1, 2, 2, 4, 4, 3, 2, 2, 4,...
$ BYSOLOSP <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 4,...
$ BYCHRDRL <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,...
$ BYWORKSY <dbl+lbl> 1, 0, 0, 0, 1, 0, 1, 0, -9, 0, 0,...
$ BYERACE <dbl+lbl> 3, 3, 7, 7, 7, 7, 7, 7, 6, 6, 7,...
$ BYTEHDEG <dbl+lbl> 3, 3, 3, 3, 3, 3, 5, 3, 3, 3, 3,...
$ BYMRACE <dbl+lbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,...
$ BYTMHDEG <dbl+lbl> 3, 3, 5, 3, 5, 3, 4, 5, 3, 3, 5,...
$ BYG10EP <dbl+lbl> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6...
$ BYSCENP <dbl+lbl> -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9,...
$ BYSCTRL <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYURBAN <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYREGION <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYSPANP <dbl+lbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3...
$ BY10FLP <dbl+lbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5...
$ SEXIM <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ STLANGIM <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ FAMCMPIM <dbl+lbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...
$ MOTHEDIM <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...
$ FATHEDIM <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...
$ OCCMOMIM <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0...
$ OCCFTHIM <dbl+lbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ INCOMEIM <dbl+lbl> 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1...
$ STEXPTIM <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ PARASPIM <dbl+lbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...
$ SCHPRGIM <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYTESTIM <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYMATHIM <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYREADIM <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYS14 <dbl+lbl> 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1...
$ BYS15 <dbl+lbl> 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0...
$ BYS20A <dbl+lbl> 2, 2, 3, -9, 2, 2, 3, 1, 3, 2, 2,...
$ BYS20B <dbl+lbl> 3, 3, 3, 2, 2, 3, 3, 2, 3, 2, 3, 3, 3, 3, 3...
$ BYS20C <dbl+lbl> 1, 2, 3, -9, 2, 2, 1, 2, 3, 2, 1,...
$ BYS20D <dbl+lbl> 2, 2, 2, -9, 2, 1, 2, 2, 2, 2, 2,...
$ BYS20E <dbl+lbl> 2, 3, 3, 1, 3, 1, 2, 2, 3, 1, 1, 2, 3, 1, 3...
$ BYS20F <dbl+lbl> 2, 2, 2, 2, 2, 1, 2, 2, 3, 2, 1, 2, 3, 2, 4...
$ BYS20G <dbl+lbl> 3, 2, 3, 2, 3, 2, 2, 2, 3, 1, 2, 2, 3, 2, 2...
$ BYS20H <dbl+lbl> 1, 4, 3, -9, 3, 3, 4, 4, 3, 3, 3,...
$ BYS20I <dbl+lbl> 3, 4, 3, -9, 3, 3, 4, 3, 3, 4, 3,...
$ BYS20J <dbl+lbl> 3, 3, -9, -9, 3, 3, 3, 3, 2, 2, 3,...
$ BYS20K <dbl+lbl> 3, 3, 2, 2, 2, 1, 2, 2, 2, 4, 2, 2, 1, 2, 3...
$ BYS20L <dbl+lbl> 2, 2, 3, -9, 1, 4, 3, 2, 3, 1, 3,...
$ BYS20M <dbl+lbl> 2, 3, 2, 2, 1, 1, 2, 2, 2, 1, 3,...
$ BYS20N <dbl+lbl> 1, 4, 2, -9, 1, 2, 2, -9, 2, 1, 3,...
$ BYS21A <dbl+lbl> 2, 2, 2, 1, 1, 1, 3, 3, 2, 3, 2, 2, 1, 3, 2...
$ BYS21B <dbl+lbl> 3, 3, 3, -9, 4, 2, 3, 3, 3, 2, 3,...
$ BYS21C <dbl+lbl> 4, 2, 2, 2, 3, 3, 2, 3, 3, 2, 3, 2, 2, 1, 3...
$ BYS21D <dbl+lbl> 3, 2, 2, 2, 3, 2, 3, 3, 2, 3, 2,...
$ BYS21E <dbl+lbl> 2, 1, 2, -9, 2, 1, 2, 3, 2, 2, 3,...
$ BYS22A <dbl+lbl> 1, 2, 2, 2, 1, 1, 1, 1, 1, 3, 2, 1, 2, 2, 3...
$ BYS22B <dbl+lbl> 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYS22C <dbl+lbl> 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2...
$ BYS22D <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1...
$ BYS22E <dbl+lbl> 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 3, 1, 2, 2, 1...
$ BYS22F <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYS22G <dbl+lbl> 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYS22H <dbl+lbl> 1, 1, 2, 1, 3, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2...
$ BYS23A <dbl+lbl> 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1...
$ BYS23B <dbl+lbl> 0, 0, 1, -9, 0, 0, 1, 1, 1, 0, 0,...
$ BYS23C <dbl+lbl> 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1...
$ BYS23D <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0...
$ BYS23E <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0...
$ BYS23F <dbl+lbl> 0, 0, 0, -9, 0, 0, 0, 0, 0, 0, 0,...
$ BYS24A <dbl+lbl> 3, 3, 2, 2, 2, 5, 3, 1, 2, 1, 2, 1, 4, 2, 2...
$ BYS24B <dbl+lbl> 3, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYS24C <dbl+lbl> 3, 2, 2, 3, 2, 1, 1, 1, 2, 3, 1, 1, 4, 1, 1...
$ BYS24D <dbl+lbl> 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 1, 2...
$ BYS24E <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1...
$ BYS24F <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1...
$ BYS24G <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYS25AA <dbl+lbl> 2, 2, 2, -9, 2, 1, 2, 1, 1, 2, 1,...
$ BYS25BA <dbl+lbl> 1, 0, 0, -9, 1, 1, 1, 0, 1, 0, 0,...
$ BYS25DA <dbl+lbl> 10, 10, 10, 10, 10, 10, 10, 10, 9, 10, 10,...
$ BYS25EA <dbl+lbl> 2, 3, 2, 3, 3, 2, 2, 3, 2, 3, 3,...
$ BYS25FA <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,...
$ BYS25GA <dbl+lbl> 0, 1, 1, -9, 1, 0, 1, 0, 1, 0, 0,...
$ BYS25AB <dbl+lbl> 1, 2, 2, 2, 1, 1, 1, 1, -3, 1, 1,...
$ BYS25BB <dbl+lbl> 1, 0, 0, -9, 1, 0, 0, 0, -3, 1, 0,...
$ BYS25DB <dbl+lbl> 10, 10, 10, 10, 10, 10, 10, 10, -3, 10, 10,...
$ BYS25EB <dbl+lbl> 2, 3, 3, 3, 3, 2, 2, -9, -3, 2, 3,...
$ BYS25FB <dbl+lbl> 0, 1, 1, 0, 1, 1, 1, 0, -3, 1, 1,...
$ BYS25GB <dbl+lbl> 0, 1, 1, -9, -9, 1, 1, -9, -3, 1, 1,...
$ BYS25AC <dbl+lbl> 2, 2, 1, -9, 1, 1, 1, 1, -3, 1, 1,...
$ BYS25BC <dbl+lbl> 1, 0, 0, 0, 1, 0, 0, 0, -3, 0, 0,...
$ BYS25DC <dbl+lbl> 12, 10, 10, 10, 10, 9, -9, 10, -3, 10, 10,...
$ BYS25EC <dbl+lbl> 3, 2, 2, 3, 3, 2, 2, 3, -3, 2, 3,...
$ BYS25FC <dbl+lbl> 1, 0, 1, 1, 0, 1, 1, 1, -3, 0, 1,...
$ BYS25GC <dbl+lbl> 1, 0, 1, 1, 0, 1, 1, 1, -3, 0, 0,...
$ BYS26 <dbl+lbl> 2, 2, 2, 2, 3, 1, 2, 2, 2, 2, 2, 3, 3, 2, 2...
$ BYS27A <dbl+lbl> 2, 2, 3, 1, 1, 2, 2, 2, 3, 2, 1, 2, 2, 2, 4...
$ BYS27B <dbl+lbl> 2, 2, 3, 1, 1, 1, 3, 2, 3, 2, 2, 2, 2, 2, 4...
$ BYS27C <dbl+lbl> 3, 2, 3, 4, 1, 3, 3, 2, 3, 4, 2, 3, 3, 3, 2...
$ BYS27D <dbl+lbl> 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1...
$ BYS27E <dbl+lbl> 2, 1, 3, 2, 1, 4, 2, 1, 2, 4, 2, 3, 4, 3, 2...
$ BYS27F <dbl+lbl> 3, 1, 2, 1, 2, 2, 2, 1, 3, 3, 1, 3, 4, 3, 2...
$ BYS27G <dbl+lbl> 3, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 4...
$ BYS27H <dbl+lbl> 2, 2, -9, 1, 1, 1, 1, 2, 2, 2, 2,...
$ BYS27I <dbl+lbl> 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 2, 1...
$ BYS28 <dbl+lbl> 2, 2, 1, 3, 3, 3, 2, 2, 2, 3, 2,...
$ BYS29A <dbl+lbl> 2, 4, 4, 5, 5, 5, 3, 2, 5, 4, 4, 5, 3, 3, 4...
$ BYS29B <dbl+lbl> 2, 5, 4, 2, 5, 2, 2, 5, 4, 5, 5, 5, 2, 5, 3...
$ BYS29C <dbl+lbl> 2, 5, 5, -9, 5, -9, 5, 5, 5, 4, 4,...
$ BYS29D <dbl+lbl> 1, 2, -9, 4, 5, 3, 3, 1, 2, 3, 1,...
$ BYS29E <dbl+lbl> 2, 3, 5, 3, 4, 4, 4, 4, 5, 4, 5, 5, 5, 5, 3...
$ BYS29F <dbl+lbl> 5, 3, 3, -9, 2, 2, 3, 4, 5, 2, 5,...
$ BYS29G <dbl+lbl> 1, 1, 4, 2, 1, 1, 2, 4, 2, 2, 5, 1, 1, 3, 5...
$ BYS29H <dbl+lbl> 1, 1, 1, 3, 2, 1, 1, 1, 2, 3, 5, 5, 2, 4, 1...
$ BYS29I <dbl+lbl> 1, 2, 1, 2, 4, 5, 2, 1, 4, 1, 2, 3, 2, 3, 1...
$ BYS29J <dbl+lbl> 5, 2, 1, -9, 5, 2, 1, 1, 2, 5, 5,...
$ BYS30 <dbl+lbl> 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0,...
$ BYS31A <dbl+lbl> -3, -3, -3, -3, 1, -3, 1, -3, -3, 2, -3,...
$ BYS31B <dbl+lbl> -3, -3, -3, -3, 3, -3, 4, -3, -3, 3, -3,...
$ BYS31C <dbl+lbl> -3, -3, -3, -3, 1, -3, 1, -3, -3, 2, -3,...
$ BYS31D <dbl+lbl> -3, -3, -3, -3, 3, -3, 4, -3, -3, 3, -3,...
$ BYS31E <dbl+lbl> -3, -3, -3, -3, 1, -3, 1, -3, -3, 5, -3,...
$ BYS31F <dbl+lbl> -3, -3, -3, -3, 1, -3, 4, -3, -3, 2, -3,...
$ BYS31G <dbl+lbl> -3, -3, -3, -3, 1, -3, 1, -3, -3, 4, -3,...
$ BYS31H <dbl+lbl> -3, -3, -3, -3, 2, -3, 1, -3, -3, 3, -3,...
$ BYS32AA <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ BYS32BA <dbl+lbl> 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,...
$ BYS32CA <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,...
$ BYS32DA <dbl+lbl> 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1,...
$ BYS32EA <dbl+lbl> 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0,...
$ BYS32FA <dbl+lbl> 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0,...
$ BYS32GA <dbl+lbl> 1, 1, -9, 1, -3, -9, 0, 1, 1, 1, 1,...
$ BYS32HA <dbl+lbl> 1, 1, 1, 1, -3, -9, 0, 1, 1, 1, 1,...
$ BYS32AB <dbl+lbl> 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,...
$ BYS32BB <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ BYS32CB <dbl+lbl> 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1,...
$ BYS32DB <dbl+lbl> 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1,...
$ BYS32EB <dbl+lbl> 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0,...
$ BYS32FB <dbl+lbl> 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0,...
$ BYS32GB <dbl+lbl> 1, 1, 1, 0, -3, 1, 0, 1, 1, 0, 1,...
$ BYS32HB <dbl+lbl> 1, 1, 1, 1, -3, 1, 0, 1, 1, 1, 1,...
$ BYS33A <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYS33B <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYS33C <dbl+lbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0...
$ BYS33D <dbl+lbl> 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYS33E <dbl+lbl> 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0...
$ BYS33F <dbl+lbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0...
$ BYS33G <dbl+lbl> 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0...
$ BYS33H <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYS33I <dbl+lbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYS33J <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS33K <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0...
$ BYS33L <dbl+lbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0...
$ BYS34A <dbl+lbl> 1, 1, -9, 4, 8, 7, 1, 2, -9, 7, 4,...
$ BYS34B <dbl+lbl> 6, 4, 18, 7, 2, 6, 11, 10, -9, 16, 5,...
$ BYS35A <dbl+lbl> 0, 1, 5, 4, 1, 7, 1, 1, -9, 5, 2,...
$ BYS35B <dbl+lbl> 2, 0, 4, 5, 3, 5, 1, 4, -9, 8, 2,...
$ BYS36A <dbl+lbl> 0, 1, 1, 2, 1, 0, 1, 1, -9, 3, 3,...
$ BYS36B <dbl+lbl> 4, 2, 5, 3, 3, 0, 1, 2, -9, 8, 2,...
$ BYS37 <dbl+lbl> 3, 4, 4, 4, 4, 3, 2, 4, 4, 4, 3, 4, 3, 4, 4...
$ BYS38A <dbl+lbl> 2, 2, 1, 1, 4, 2, 3, 2, 4, 1, 2,...
$ BYS38B <dbl+lbl> 3, 1, 1, -9, 4, 1, 4, 2, 4, 1, 2,...
$ BYS38C <dbl+lbl> 2, 1, 2, 2, 4, 2, 3, 2, 4, 1, 2,...
$ BYS39A <dbl+lbl> -9, 2, 2, 2, 3, 2, 2, 1, 2, 1, 2,...
$ BYS39B <dbl+lbl> 3, 2, 2, 2, 3, 2, 2, 1, 2, 1, 2, 3, 2, 3, 1...
$ BYS39C <dbl+lbl> 3, 2, 2, 3, 3, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1...
$ BYS39D <dbl+lbl> 3, 2, 2, 2, 3, 2, 2, 1, 2, 1, 2, 2, 2, 3, 1...
$ BYS39E <dbl+lbl> -9, 2, 2, 2, 3, 2, 2, 1, 2, 1, 2,...
$ BYS39F <dbl+lbl> 3, 2, 3, 3, 3, 3, 3, 3, 2, 1, 2, 2, 3, 3, 2...
$ BYS39G <dbl+lbl> 3, 2, 2, -9, 3, 2, 2, 1, 2, 2, 2,...
$ BYS39H <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 3, 3, 3, 1...
$ BYS40AA <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40AB <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ BYS40AC <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40AD <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40AE <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40BA <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40BB <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ BYS40BC <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40BD <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40BE <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40CA <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYS40CB <dbl+lbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1...
$ BYS40CC <dbl+lbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYS40CD <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0...
$ BYS40CE <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYS40DA <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40DB <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,...
$ BYS40DC <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,...
$ BYS40DD <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,...
$ BYS40DE <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40EA <dbl+lbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40EB <dbl+lbl> 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ BYS40EC <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40ED <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40EE <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40FA <dbl+lbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40FB <dbl+lbl> 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0,...
$ BYS40FC <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,...
$ BYS40FD <dbl+lbl> 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,...
$ BYS40FE <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40GA <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40GB <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0,...
$ BYS40GC <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,...
$ BYS40GD <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,...
$ BYS40GE <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40HA <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40HB <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ BYS40HC <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40HD <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS40HE <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS41A <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS41B <dbl+lbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYS41C <dbl+lbl> 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0...
$ BYS41D <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYS41E <dbl+lbl> 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0...
$ BYS41F <dbl+lbl> 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...
$ BYS41G <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0...
$ BYS41H <dbl+lbl> 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,...
$ BYS41I <dbl+lbl> 0, 0, 0, -9, 0, 0, 0, 0, 0, 0, 0,...
$ BYS42 <dbl+lbl> 0, 2, 2, 14, 0, 8, 5, 18, -9, 2, 18,...
$ BYS43 <dbl+lbl> 2, 2, 3, 5, 2, 12, -9, 0, -9, 2, 0,...
$ BYS44A <dbl+lbl> 4, 2, 3, 3, 2, 4, 4, 2, 2, 2, 2, 3, 3, 2, 4...
$ BYS44B <dbl+lbl> 4, 2, 2, 1, 2, 1, 3, 3, 3, 3, 4,...
$ BYS44C <dbl+lbl> 1, 3, 1, 1, 1, 1, 4, 1, 1, 1, 1,...
$ BYS44D <dbl+lbl> 4, 3, 3, 3, 4, 1, 2, 1, 2, 4, 1, 3, 3, 1, 2...
$ BYS44E <dbl+lbl> 4, 4, 1, 4, 1, 4, 3, -9, 3, 3, 1,...
$ BYS44F <dbl+lbl> 3, 1, 3, 1, 3, 1, 1, 1, 1, 1, 1,...
$ BYS44G <dbl+lbl> 1, 1, 1, 1, 1, 4, 2, -9, 1, 2, 1,...
$ BYS44H <dbl+lbl> 1, 1, 3, 1, -9, 1, 3, -9, 3, 2, 1,...
$ BYS45A <dbl+lbl> 4, 5, 5, 5, 5, 3, 5, 5, 5, 3, 5, 5, 2, 3, 5...
$ BYS45B <dbl+lbl> 2, 4, 4, 4, 4, 2, 4, 4, 5, 4, 4, 4, 3, 4, 5...
$ BYS45C <dbl+lbl> 4, 4, 4, 5, 5, 1, 4, 4, 5, 5, 5, 5, 2, 4, 4...
$ BYS46A <dbl+lbl> 1, 1, 1, 2, 3, 0, 1, 2, -9, 2, 3,...
$ BYS46B <dbl+lbl> 3, 5, 1, 4, 3, 0, 3, 6, -9, 3, 6,...
$ BYS47A <dbl+lbl> 4, 5, 5, 5, 5, 3, 5, 5, 5, 4, 5, 5, 3, 5, 5...
$ BYS47B <dbl+lbl> 3, 2, 3, 3, 3, 2, 3, 5, 5, 3, 3, 4, 3, 5, 3...
$ BYS47C <dbl+lbl> 2, 2, 2, 2, 2, 2, 3, 2, 5, 2, 2, 2, 2, 4, 2...
$ BYS47D <dbl+lbl> 2, 3, 2, 2, 2, 2, 3, 3, 2, 2, 2, 3, 3, 3, 3...
$ BYS47E <dbl+lbl> 3, 2, 2, -9, 2, 2, 3, 2, 2, 2, 2,...
$ BYS48A <dbl+lbl> 6, 2, 1, 6, 4, 3, 1, 2, -9, 3, 6,...
$ BYS48B <dbl+lbl> 6, 4, 6, 6, 4, 6, 1, 6, -9, 2, 6,...
$ BYS49A <dbl+lbl> 0, 2, 0, 1, 0, 0, 1, 1, -9, 0, 6,...
$ BYS49B <dbl+lbl> 1, 3, 2, 3, 0, 2, 1, 5, -9, 2, 6,...
$ BYS50 <dbl+lbl> 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYS51A <dbl+lbl> -3, 4, 2, 3, 1, 1, 3, 2, 3, 2, 3,...
$ BYS51B <dbl+lbl> -3, 2, 2, 3, 2, 2, 3, 2, 4, 3, 2,...
$ BYS51C <dbl+lbl> -3, 1, 1, 3, 2, 1, 3, 2, 2, 3, 1,...
$ BYS51D <dbl+lbl> -3, 4, 4, 4, 2, 1, 4, 3, 3, 4, 3,...
$ BYS51E <dbl+lbl> -3, 1, 1, 4, 1, 1, 2, 1, 2, 2, 1,...
$ BYS51F <dbl+lbl> -3, 1, 1, 4, 1, 1, 1, 1, 1, 2, 1,...
$ BYS51G <dbl+lbl> -3, 2, 1, -9, 1, 1, 1, 1, 3, 1, 1,...
$ BYS51H <dbl+lbl> -3, 1, 1, -9, 1, 1, 1, 1, 3, 1, 1,...
$ BYS51I <dbl+lbl> -3, 1, 1, -9, 2, 1, 2, 2, 3, 2, 2,...
$ BYS52 <dbl+lbl> -3, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2,...
$ BYS53A <dbl+lbl> -3, 3, 2, 1, 2, 2, 1, 2, 3, 3, 2,...
$ BYS53B <dbl+lbl> -3, 4, 2, 2, 2, 2, 2, 4, 3, 3, 2,...
$ BYS53C <dbl+lbl> -3, 4, 4, 1, 2, 2, 2, 4, 3, 2, 2,...
$ BYS54A <dbl+lbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3...
$ BYS54B <dbl+lbl> 1, 3, 3, 3, 3, 3, 2, 3, 3, 1, 3, 3, 3, 3, 3...
$ BYS54C <dbl+lbl> 1, 2, 2, 3, 2, 3, 2, 3, 3, 2, 3, 2, 3, 3, 2...
$ BYS54D <dbl+lbl> 3, 3, 3, 3, 3, 2, 3, 3, 2, 1, 3, 3, 3, 3, 3...
$ BYS54E <dbl+lbl> 3, 3, 3, 3, 3, 3, 2, -9, 3, 3, 3,...
$ BYS54F <dbl+lbl> 3, 2, 2, 3, 3, 2, 2, 2, 1, 3, 2, 2, 3, 3, 2...
$ BYS54G <dbl+lbl> 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3...
$ BYS54H <dbl+lbl> 1, 2, 2, 3, 3, 2, 3, 2, 2, 1, 2, 2, 3, 3, 2...
$ BYS54I <dbl+lbl> 3, 2, 2, 1, 3, 1, 1, 3, 2, 3, 2, 3, 3, 2, 1...
$ BYS54J <dbl+lbl> 2, 2, 2, 3, 3, 2, 2, 2, 1, 2, 2, 2, 3, 3, 1...
$ BYS54K <dbl+lbl> 1, 2, 2, 3, 3, 1, 1, 3, 2, 1, 2, 3, 1, 3, 2...
$ BYS54L <dbl+lbl> 3, 3, 3, 3, 3, 3, 2, 3, 2, 3, 2, 3, 3, 3, 3...
$ BYS54N <dbl+lbl> 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2...
$ BYS54O <dbl+lbl> 3, 3, 3, 3, 3, 3, 2, -9, 3, 3, 3,...
$ BYS55A <dbl+lbl> 1, 4, 4, 4, 4, 4, 3, 3, 2, 4, 4, 4, 4, 1, 3...
$ BYS55B <dbl+lbl> 5, 4, 5, 1, 4, 1, 5, 4, 5, 4, 5, 4, 2, 1, 4...
$ BYS55C <dbl+lbl> 1, 4, 1, 1, 4, 2, 2, 4, 2, 4, 4, 1, 2, 1, 4...
$ BYS55D <dbl+lbl> 4, 1, 1, 1, 3, 2, 2, 1, 2, 4, 5, 1, 3, 1, 2...
$ BYS56 <dbl+lbl> 3, 7, -1, 5, 5, 4, -1, 6, 7, 6, -1,...
$ BYS57 <dbl+lbl> 3, 1, 1, 1, 4, 1, 2, 1, 1, 1, 1,...
$ BYS58 <dbl+lbl> 3, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1,...
$ BYS59A <dbl+lbl> 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1,...
$ BYS59B <dbl+lbl> 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1,...
$ BYS59C <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,...
$ BYS59D <dbl+lbl> 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0,...
$ BYS59E <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,...
$ BYS59F <dbl+lbl> 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,...
$ BYS59G <dbl+lbl> 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,...
$ BYS59H <dbl+lbl> 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1,...
$ BYS59I <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,...
$ BYS59J <dbl+lbl> 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1,...
$ BYS59K <dbl+lbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,...
$ BYS60 <dbl+lbl> 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1,...
$ BYS61 <dbl+lbl> -3, -3, -3, 1, 0, 1, 1, 1, -3, 1, 1,...
$ BYS62A <dbl+lbl> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3,...
$ BYS62B <dbl+lbl> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3,...
$ BYS62C <dbl+lbl> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3,...
$ BYS62D <dbl+lbl> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3,...
$ BYS62E <dbl+lbl> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3,...
$ BYS62F <dbl+lbl> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3,...
$ BYS62G <dbl+lbl> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3,...
$ BYS62H <dbl+lbl> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3,...
$ BYS65A <dbl+lbl> -9, 7, 5, 3, 1, -9, -1, 5, 7, 7, -9,...
$ BYS65B <dbl+lbl> 3, 7, 5, 4, 1, -9, -1, -9, 7, -9, 5,...
$ BYS66A <dbl+lbl> 4, 1, 1, 1, 1, 6, 1, 1, 1, 4, -9,...
$ BYS66B <dbl+lbl> 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ BYS66C <dbl+lbl> 3, -1, -1, 6, 1, -3, -1, 6, 1, 6, 1,...
$ BYS66D <dbl+lbl> 3, 1, -1, 1, 1, -9, 1, 1, 1, -1, 1,...
$ BYS66E <dbl+lbl> 1, -1, -1, 1, -9, -3, 1, 1, 1, 1, 1,...
$ BYS66F <dbl+lbl> 1, 1, -1, 1, 1, -3, 1, 1, 1, 1, 1,...
$ BYS66G <dbl+lbl> -3, -3, -1, 1, -3, -9, 1, 1, -3, -1, 1,...
$ BYS67 <dbl+lbl> 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1...
$ BYS69A <dbl+lbl> -3, 4, -3, -3, 4, 2, -3, -3, -3, -3, -3,...
$ BYS69B <dbl+lbl> -3, 2, -3, -3, 4, 2, -3, -3, -3, -3, -3,...
$ BYS69C <dbl+lbl> -3, 2, -3, -3, 4, 2, -3, -3, -3, -3, -3,...
$ BYS69D <dbl+lbl> -3, 1, -3, -3, 2, 2, -3, -3, -3, -3, -3,...
$ BYS70A <dbl+lbl> -3, 1, -3, -3, 1, 1, -3, -3, -3, -3, -3,...
$ BYS70B <dbl+lbl> -3, 1, -3, -3, 1, 1, -3, -3, -3, -3, -3,...
$ BYS70C <dbl+lbl> -3, 1, -3, -3, 1, 1, -3, -3, -3, -3, -3,...
$ BYS70D <dbl+lbl> -3, 1, -3, -3, 1, 1, -3, -3, -3, -3, -3,...
$ BYS71A <dbl+lbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,...
$ BYS71B <dbl+lbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,...
$ BYS71C <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,...
$ BYS71D <dbl+lbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,...
$ BYS71E <dbl+lbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYS71F <dbl+lbl> 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,...
$ BYS71G <dbl+lbl> 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1,...
$ BYS72 <dbl+lbl> 2, 1, 1, 1, 2, 1, 2, 1, 3, 3, 1, 1, 2, 1, 1...
$ BYS73 <dbl+lbl> -3, -3, -3, -3, -3, ...
$ BYS74 <dbl+lbl> 200203, -3, -3, -3, 200105, ...
$ BYS75 <dbl+lbl> 27, -3, -3, -3, 14, -3, 5, -3, -9, -9, -3,...
$ BYS76 <dbl+lbl> 17, -3, -3, -3, 15, -3, 0, -3, -9, -9, -3,...
$ BYS77 <dbl+lbl> 1, -3, -3, -3, 4, -3, -9, -3, -6, 9, -3,...
$ BYS79 <dbl+lbl> 3, -3, -3, -3, 3, -3, 1, -3, -6, 1, -3,...
$ BYS80 <dbl+lbl> 3, -3, -3, -3, 2, -3, 3, -3, 3, 3, -3,...
$ BYS83A <dbl+lbl> 2, 5, 2, 2, 1, -9, -1, -1, -1, 2, -1,...
$ BYS83B <dbl+lbl> 4, 7, 4, 2, 1, -9, -1, -1, -1, 2, -1,...
$ BYS84A <dbl+lbl> 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0...
$ BYS84B <dbl+lbl> 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1...
$ BYS84C <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYS84D <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYS84E <dbl+lbl> 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1...
$ BYS84F <dbl+lbl> 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1...
$ BYS84G <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1...
$ BYS84H <dbl+lbl> 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1...
$ BYS84I <dbl+lbl> 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1...
$ BYS84J <dbl+lbl> 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1...
$ BYS85A <dbl+lbl> 4, 4, 4, 4, 4, 4, 2, 4, 4, 4, 2,...
$ BYS85B <dbl+lbl> 3, 4, 2, 4, 4, 4, 3, 2, 4, 3, 2,...
$ BYS85C <dbl+lbl> 3, 4, 3, 4, 4, 4, 4, 2, 4, 4, 4,...
$ BYS85D <dbl+lbl> 3, 4, -9, -9, 2, 4, 4, 2, 4, 2, 3,...
$ BYS85E <dbl+lbl> 3, 2, 3, -9, 4, 4, 4, 3, 4, 3, 4,...
$ BYS85F <dbl+lbl> 2, 2, 3, 2, 2, 1, 3, 2, 3, 1, 3,...
$ BYS85G <dbl+lbl> 3, 3, 4, 2, 3, 3, 4, 2, 4, 4, 3,...
$ BYS86A <dbl+lbl> 2, 3, 1, 3, 2, 1, 3, 2, 3, 3, 2,...
$ BYS86B <dbl+lbl> 2, 2, 2, 3, 2, 1, 3, 3, 3, 2, 2,...
$ BYS86C <dbl+lbl> 2, 3, 2, 3, 2, -9, 3, 3, 3, 2, 2,...
$ BYS86D <dbl+lbl> 3, 2, 2, 3, 3, -9, 3, 3, 3, 3, 2,...
$ BYS86E <dbl+lbl> 1, 1, 1, 1, 2, -9, 1, 1, 2, 2, 1,...
$ BYS86F <dbl+lbl> 1, 3, 1, 1, 2, -9, 2, 3, 3, 2, 2,...
$ BYS86G <dbl+lbl> 2, 3, 2, 3, 2, 3, 3, 3, 3, 3, 2,...
$ BYS86H <dbl+lbl> 2, 2, 2, 2, 2, 1, 1, 3, 1, 1, 1,...
$ BYS86I <dbl+lbl> 2, 1, 2, 3, 2, 1, 2, 2, 3, 3, -9,...
$ BYS87A <dbl+lbl> 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4...
$ BYS87B <dbl+lbl> 3, 2, 1, 2, 2, 2, 3, 3, 2, 3, 3, 2, 3, 3, 4...
$ BYS87C <dbl+lbl> 3, 2, 3, 2, 2, 2, 3, 3, 3, 3, 3, 2, 3, 2, 4...
$ BYS87D <dbl+lbl> 3, 2, 1, 2, 2, 3, 3, 3, 2, 2, -9,...
$ BYS87E <dbl+lbl> 2, 2, 1, 2, 2, -9, 2, 2, 2, 2, 1,...
$ BYS87F <dbl+lbl> 3, 2, 3, 1, 2, 1, 3, 1, 2, 1, 2, 2, 2, 2, 4...
$ BYS88A <dbl+lbl> 3, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1...
$ BYS88B <dbl+lbl> 2, 3, 3, 3, 2, 4, 4, 3, 3, 4, 3, 3, 4, 3, 4...
$ BYS89A <dbl+lbl> 2, 4, 3, 4, 2, 2, 2, 4, 3, 4, 1, 2, 2, 2, 4...
$ BYS89B <dbl+lbl> 1, 3, 2, 3, 2, 2, 1, 3, 3, 3, 2, 2, 2, 2, 3...
$ BYS89C <dbl+lbl> 2, 3, 2, 3, 2, 4, 1, 3, 3, 3, 2, 2, 2, 2, 2...
$ BYS89D <dbl+lbl> 1, 3, 2, 4, 3, -9, 2, 2, 3, 4, 2,...
$ BYS89E <dbl+lbl> 1, 3, 3, 4, 3, 3, 3, 3, 3, 4, 2, 1, 4, 4, 3...
$ BYS89F <dbl+lbl> 1, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 2, 3, 3, 1...
$ BYS89G <dbl+lbl> 2, 3, 3, 4, 3, 3, 4, 2, 3, 3, 3, 1, 4, 4, 2...
$ BYS89H <dbl+lbl> 1, 3, 2, 4, 3, 3, 3, 2, 3, 4, 2, 1, 4, 3, 4...
$ BYS89I <dbl+lbl> 3, 3, 2, 4, 3, 3, 3, 3, 3, 4, 3, 1, 4, 2, 4...
$ BYS89J <dbl+lbl> 1, 4, 3, 4, 3, 3, 3, 2, 3, 4, 3, 1, 4, 4, 3...
$ BYS89K <dbl+lbl> 3, 3, 2, 4, 3, 3, 2, 3, 3, 4, 3, 1, 4, 2, 3...
$ BYS89L <dbl+lbl> 2, 4, 2, 3, 3, 3, 2, 3, 3, 3, 2, 1, 2, 3, 3...
$ BYS89M <dbl+lbl> 2, 3, 2, 4, 3, 3, 2, 3, 3, 3, 3, 1, 4, 2, 2...
$ BYS89N <dbl+lbl> 3, 3, 3, 4, 3, 3, 3, 4, 3, 3, 2, 1, 4, 3, 3...
$ BYS89O <dbl+lbl> 2, 4, 3, 3, 3, 3, 3, 4, 3, 3, 2,...
$ BYS89P <dbl+lbl> 2, 4, 3, 4, 3, 3, 4, 2, 3, 4, 3,...
$ BYS89Q <dbl+lbl> 1, 3, 2, 1, 3, 3, 3, 4, 3, 3, 2,...
$ BYS89R <dbl+lbl> 2, 4, 3, 3, 3, 3, 3, 3, 3, 3, 2,...
$ BYS89S <dbl+lbl> 2, 3, 3, 4, 3, 3, 4, 3, 3, 3, 2,...
$ BYS89T <dbl+lbl> 2, 3, 3, -9, 3, -9, 3, 4, 3, 3, 2,...
$ BYS89U <dbl+lbl> 1, 4, 2, 4, 3, -9, 3, 3, 3, 3, 1,...
$ BYS89V <dbl+lbl> 2, 3, 3, 4, 3, -9, 4, 2, 3, 4, 3,...
$ BYS90A <dbl+lbl> 2, 3, 2, 3, 3, 3, 3, 2, 3, 2, 2, 3, 3, 3, 2...
$ BYS90B <dbl+lbl> 2, 3, 2, 3, 3, 3, 2, 2, 3, 2, 2, 2, 3, 3, 2...
$ BYS90C <dbl+lbl> 3, 1, 1, 2, 3, 3, 2, 3, 1, 2, 2, 3, 2, 2, 2...
$ BYS90D <dbl+lbl> 2, 2, 2, 3, 3, 3, 3, 2, 3, 3, 2, 3, 2, 3, 2...
$ BYS90E <dbl+lbl> 2, 3, 1, 3, 3, 3, 2, 3, 1, 1, 2, 2, 1, 1, 3...
$ BYS90F <dbl+lbl> 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 2,...
$ BYS90G <dbl+lbl> 2, 3, 1, 3, 1, 1, 2, 2, 1, 1, 2, 3, 2, 3, 1...
$ BYS90H <dbl+lbl> 2, 3, 2, 2, 3, 3, 3, 2, 3, 3, 2, 3, 3, 2, 3...
$ BYS90J <dbl+lbl> 1, 2, 2, 2, 3, 3, 2, 2, 1, 1, 2, 2, 2, 2, 1...
$ BYS90K <dbl+lbl> 3, 2, 2, 3, 3, 3, 2, 2, 1, 2, 2, 3, 3, 2, 1...
$ BYS90L <dbl+lbl> 3, 3, 2, 3, 2, 3, 3, 3, 2, 2, 2, 3, 3, 2, 3...
$ BYS90M <dbl+lbl> 3, 3, 2, 3, 1, -9, 3, 3, 1, 1, 2,...
$ BYS90Q <dbl+lbl> 3, 3, 2, 3, 2, 3, 2, 3, 1, 2, 2, 3, 3, 3, 2...
$ BYS91 <dbl+lbl> 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1...
$ BYS92A <dbl+lbl> 1, 2, 1, 1, 3, 1, 2, 1, 2, 2, 2, 1, 1, 2, 3...
$ BYS92B <dbl+lbl> 4, 3, 2, 2, 3, 1, 2, 3, 2, 3, 2, 3, 4, 3, 2...
$ BYS92C <dbl+lbl> 1, 2, 2, 1, 3, 1, 1, 1, 2, 2, 2, 2, 3, 2, 2...
$ BYS92D <dbl+lbl> 1, 2, -9, 3, 3, 3, 3, 4, 2, 2, 1,...
$ BYS94 <dbl+lbl> 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1...
$ BYS96 <dbl+lbl> 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1,...
$ BYS97A <dbl+lbl> -3, 1, -3, 1, 0, 1, 1, 1, -3, -3, 1,...
$ BYS97B <dbl+lbl> -3, 0, -3, 0, 1, 0, 0, 0, -3, -3, 0,...
$ BYS97C <dbl+lbl> -3, 0, -3, 0, 0, 0, 0, 0, -3, -3, 0,...
$ BYS97D <dbl+lbl> -3, 0, -3, 0, 0, 0, 0, 0, -3, -3, 0,...
$ BYS97E <dbl+lbl> -3, 0, -3, 0, 0, 0, 0, 0, -3, -3, 0,...
$ BYP01 <dbl+lbl> 2, 2, 1, -4, 1, 1, 1, 1, 2, 1, 2,...
$ BYP02 <dbl+lbl> -3, -3, -3, -4, -3, -3, -3, -3, -3, -3, -3,...
$ BYP03 <dbl+lbl> 1, -9, 1, -4, 1, -9, 1, 1, 1, 1, 1,...
$ BYP04 <dbl+lbl> 5, 1, 2, -4, 2, -9, 2, 2, 1, 2, 5,...
$ BYP05 <dbl+lbl> 1, 1, 1, -4, 1, 1, 1, 1, 1, 1, 1,...
$ BYP06 <dbl+lbl> 1, 8, 2, -4, 3, 3, -7, 4, 2, -7, 3,...
$ BYP07A <dbl+lbl> 0, -9, 1, -4, 1, 1, -7, 2, 0, -7, 1,...
$ BYP07B <dbl+lbl> 0, -9, 0, -4, 0, 0, -7, 0, 0, -7, 0,...
$ BYP07C <dbl+lbl> 0, -9, 0, -4, 0, 0, -7, 0, 0, -7, 0,...
$ BYP07D <dbl+lbl> 0, -9, 0, -4, 2, 1, -7, 1, 1, -7, 1,...
$ BYP07E <dbl+lbl> 0, -9, 0, -4, 0, 0, -7, 0, 0, -7, 0,...
$ BYP07F <dbl+lbl> 0, -9, 0, -4, 0, 0, -7, 0, 0, -7, 0,...
$ BYP07G <dbl+lbl> 0, -9, 0, -4, 0, 0, -7, 0, 2, -7, 0,...
$ BYP07H <dbl+lbl> 0, -9, 0, -4, 0, 0, -7, 0, 0, -7, 0,...
$ BYP07I <dbl+lbl> 0, -9, 0, -4, 0, 0, -7, 0, 0, -7, 0,...
$ BYP07J <dbl+lbl> 0, -9, 0, -4, 0, 0, -7, 0, 0, -7, 1,...
$ BYP07K <dbl+lbl> 0, -9, 0, -4, 0, 0, -7, 0, 0, -7, 0,...
$ BYP07L <dbl+lbl> 0, -9, 0, -4, 0, 0, -7, 0, 0, -7, 0,...
$ BYP08 <dbl+lbl> 6, 5, 1, -4, 5, 2, -7, 3, 2, -7, 2,...
$ BYP09 <dbl+lbl> 3, 0, -1, -4, 0, 0, -7, 0, 0, -7, 0,...
$ BYP10 <dbl+lbl> 1, 1, 1, -4, 1, 2, 1, 1, 1, 1, 1,...
$ BYP11 <dbl+lbl> 1959, 1953, 1965, -4, 1964, 1970, 1962, 1...
$ BYP12 <dbl+lbl> 1945, 1964, 1963, -4, 1955, 1966, 1960, 1...
$ BYP13 <dbl+lbl> 0, 0, 0, -4, 1, 1, 1, 0, 1, 0, 0,...
$ BYP17 <dbl+lbl> 2, 3, 1, -4, 2, 1, 2, 1, 2, 1, -1,...
$ BYP18 <dbl+lbl> 25, 13, -3, -4, 30, -3, -7, -3, 21, -3, -3,...
$ BYP20 <dbl+lbl> 1, 3, 1, -4, 2, 1, 1, 1, 2, 1, -1,...
$ BYP21 <dbl+lbl> -3, 13, -3, -4, 40, -3, -3, -3, 43, -3, -3,...
$ BYP23 <dbl+lbl> 1, 3, 1, -4, 1, 1, 1, 1, 1, 1, 3,...
$ BYP24 <dbl+lbl> -3, 2, -3, -4, -3, -3, -3, -3, -3, -3, 11,...
$ BYP25 <dbl+lbl> 0, 0, 0, -4, 0, 0, -7, 0, 0, -7, 0,...
$ BYP26A <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP26B <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP26C <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP26D <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP26E <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP26F <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP26G <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP26H <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP26I <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP26J <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP26K <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP26L <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP27 <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP28 <dbl+lbl> 1, 0, 1, -4, 0, 1, 0, 1, 0, 1, 1,...
$ BYP30A <dbl+lbl> -3, 3, -3, -4, 4, -3, -7, -3, 4, -3, -3,...
$ BYP30B <dbl+lbl> -3, 1, -3, -4, 4, -3, -7, -3, 2, -3, -3,...
$ BYP30C <dbl+lbl> -3, 2, -3, -4, 4, -3, -7, -3, 4, -3, -3,...
$ BYP30D <dbl+lbl> -3, 2, -3, -4, 4, -3, -7, -3, 4, -3, -3,...
$ BYP31A <dbl+lbl> -3, 1, -3, -4, 2, -3, -7, -3, 1, -3, -3,...
$ BYP31B <dbl+lbl> -3, 1, -3, -4, 2, -3, -7, -3, 1, -3, -3,...
$ BYP31C <dbl+lbl> -3, 1, -3, -4, 2, -3, -7, -3, 3, -3, -3,...
$ BYP31D <dbl+lbl> -3, 1, -3, -4, 2, -3, -7, -3, 4, -3, -3,...
$ BYP32A <dbl+lbl> -3, 0, -3, -4, 0, -3, -7, -3, 1, -3, -3,...
$ BYP32B <dbl+lbl> -3, 0, -3, -4, 0, -3, -7, -3, 1, -3, -3,...
$ BYP32C <dbl+lbl> -3, 0, -3, -4, 0, -3, -7, -3, 0, -3, -3,...
$ BYP32D <dbl+lbl> -3, 0, -3, -4, 0, -3, -7, -3, 0, -3, -3,...
$ BYP32E <dbl+lbl> -3, 0, -3, -4, 0, -3, -7, -3, 0, -3, -3,...
$ BYP33 <dbl+lbl> 6, 8, -1, -4, 1, 2, -7, 1, 1, -7, 2,...
$ BYP34A <dbl+lbl> 5, 5, 2, -4, 1, 2, 6, 2, 1, 1, 4,...
$ BYP34B <dbl+lbl> 1, -9, 2, -4, 1, 1, 3, 2, 2, 1, 6,...
$ BYP35A <dbl+lbl> 1, 1, 1, -4, -1, -2, -7, 2, 1, -7, -1,...
$ BYP35B <dbl+lbl> 2, 7, 1, -4, 2, -2, -7, 2, 1, -7, -1,...
$ BYP35C <dbl+lbl> -1, 1, 2, -4, -1, -2, -7, 2, -1, -7, -1,...
$ BYP35D <dbl+lbl> -1, 8, 2, -4, -1, -2, -7, 2, -1, -7, -1,...
$ BYP36 <dbl+lbl> 1, 1, 1, -4, 3, 3, 1, 3, 1, 1, 1,...
$ BYP37 <dbl+lbl> -3, -3, -3, -4, 2, 2, -3, 1, -3, -3, -3,...
$ BYP38 <dbl+lbl> 1, 1, 1, -4, 1, 1, 1, 1, 1, 1, 1,...
$ BYP39C <dbl+lbl> 6, 9, 5, -4, 8, 5, 14, 1, -9, 9, 7,...
$ BYP40 <dbl+lbl> 1, 3, 1, -4, 1, 3, 1, 1, 1, 3, -3,...
$ BYP41 <dbl+lbl> -3, -9, -3, -4, -3, 1, -3, -3, -3, 4, -3,...
$ BYP42 <dbl+lbl> 1, 0, 1, -4, 1, 1, 1, 1, 1, 1, -3,...
$ BYP43C <dbl+lbl> 8, -3, 5, -4, 5, 8, 15, 12, 5, 5, -3,...
$ BYP44A <dbl+lbl> 0, 1, 0, -4, 0, 0, -7, 1, 0, -7, 0,...
$ BYP44B <dbl+lbl> 1, 1, 1, -4, 0, 0, -7, 1, 1, -7, 1,...
$ BYP44C <dbl+lbl> 0, 0, 0, -4, 1, 0, -7, 0, 0, -7, 0,...
$ BYP44D <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 1, 1, -7, 1,...
$ BYP45 <dbl+lbl> 4, 0, 2, -4, 0, 0, -7, 2, 0, -7, 4,...
$ BYP46 <dbl+lbl> 0, 0, 0, -4, 0, 0, -7, 0, 0, -7, 0,...
$ BYP47A <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP47B <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP47C <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP48A <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP48B <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP48C <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP48D <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP48E <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP48F <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP48G <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP48H <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP48I <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP48J <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP48K <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP49 <dbl+lbl> 0, 0, 0, -4, 0, 0, -7, 0, 0, -7, 0,...
$ BYP50A <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP50B <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP50C <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP50D <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP50E <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP50F <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP50G <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP50H <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP51 <dbl+lbl> 0, 0, 0, -4, 0, 0, -7, 0, 0, -7, 0,...
$ BYP52A <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 2, 2, -7, 1,...
$ BYP52B <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 3, 1, -7, 3,...
$ BYP52C <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 1, 1, -7, 2,...
$ BYP52D <dbl+lbl> 2, 1, 1, -4, 1, 1, -7, 1, 1, -7, 2,...
$ BYP52E <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 1, 1, -7, 1,...
$ BYP52F <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 1, 1, -7, 1,...
$ BYP52G <dbl+lbl> 1, 1, -1, -4, 1, 1, -7, 1, 4, -7, 1,...
$ BYP52H <dbl+lbl> 1, 2, 1, -4, 1, 1, -7, 1, 1, -7, 1,...
$ BYP52I <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 1, 1, -7, 1,...
$ BYP52J <dbl+lbl> 1, 1, 2, -4, 2, 1, -7, 1, 1, -7, 2,...
$ BYP53A <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 2, 1, -7, 1,...
$ BYP53B <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 2, 1, -7, 3,...
$ BYP53C <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 2, 1, -7, 2,...
$ BYP53D <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 2, 1, -7, 2,...
$ BYP53E <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 2, 1, -7, 1,...
$ BYP53F <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 2, 2, -7, 1,...
$ BYP53G <dbl+lbl> 1, 2, 1, -4, 1, 1, -7, 2, 1, -7, 1,...
$ BYP53H <dbl+lbl> 1, 2, 1, -4, 1, 1, -7, 2, 1, -7, 1,...
$ BYP53I <dbl+lbl> 1, 2, 1, -4, 1, 1, -7, 2, 1, -7, 1,...
$ BYP53J <dbl+lbl> 1, 1, 2, -4, 1, 1, -7, 2, 1, -7, 2,...
$ BYP54A <dbl+lbl> 0, 0, 0, -4, 0, 0, -7, 0, 0, -7, 0,...
$ BYP54B <dbl+lbl> 0, 1, 0, -4, 1, 0, -7, 0, 0, -7, 0,...
$ BYP54C <dbl+lbl> 0, 1, 0, -4, 0, 0, -7, 0, 0, -7, 0,...
$ BYP54D <dbl+lbl> 0, 1, 0, -4, 0, 0, -7, 0, 0, -7, 0,...
$ BYP54E <dbl+lbl> 1, 0, 0, -4, 0, 0, -7, 0, 0, -7, 0,...
$ BYP55A <dbl+lbl> 3, 2, 4, -4, 4, 2, -7, 4, 4, -7, 2,...
$ BYP55B <dbl+lbl> 4, 2, 4, -4, 4, 4, -7, 4, 4, -7, 4,...
$ BYP55C <dbl+lbl> 4, 3, 4, -4, 4, 4, -7, 4, 4, -7, 3,...
$ BYP55D <dbl+lbl> 4, 1, 4, -4, 4, 4, -7, 4, 4, -7, 4,...
$ BYP56A <dbl+lbl> 2, 3, -1, -4, 3, 3, -7, 3, 3, -7, 1,...
$ BYP56B <dbl+lbl> 2, 3, 2, -4, 3, 3, -7, 3, 3, -7, 1,...
$ BYP56C <dbl+lbl> 2, 2, -1, -4, 3, 3, -7, 2, 3, -7, 2,...
$ BYP56D <dbl+lbl> 3, 3, 1, -4, 3, 3, -7, 2, 1, -7, 2,...
$ BYP56E <dbl+lbl> 2, 3, 1, -4, 1, 3, -7, 2, 2, -7, 1,...
$ BYP56F <dbl+lbl> 2, 2, 3, -4, 1, 3, -7, 2, 1, -7, 1,...
$ BYP57A <dbl+lbl> 2, 4, 1, -4, 4, 4, -7, 3, 3, -7, 3,...
$ BYP57B <dbl+lbl> 2, 4, 1, -4, 4, 4, -7, 3, 4, -7, 3,...
$ BYP57C <dbl+lbl> 1, 4, 3, -4, 4, 4, -7, 1, 3, -7, 3,...
$ BYP57D <dbl+lbl> 4, 3, 1, -4, 2, 4, -7, 3, 2, -7, 1,...
$ BYP57E <dbl+lbl> 4, 3, 1, -4, 4, 4, -7, 3, 4, -7, 1,...
$ BYP57F <dbl+lbl> 4, 4, 4, -4, 4, 4, -7, 3, 2, -7, 1,...
$ BYP57G <dbl+lbl> 3, 2, 4, -4, 4, 4, -7, 2, 2, -7, 3,...
$ BYP57H <dbl+lbl> 3, 2, 1, -4, 2, 4, -7, 1, 4, -7, 1,...
$ BYP57I <dbl+lbl> 4, 4, 4, -4, 4, 4, -7, 3, 3, -7, 1,...
$ BYP57J <dbl+lbl> 4, 3, 3, -4, 4, 4, -7, 3, 3, -7, 1,...
$ BYP57K <dbl+lbl> 4, 3, 4, -4, 4, 4, -7, 4, 4, -7, 1,...
$ BYP57L <dbl+lbl> 3, 3, 3, -4, 4, 4, -7, 4, 3, -7, 1,...
$ BYP58A <dbl+lbl> 1, 2, 2, -4, 2, 2, -7, 2, 1, -7, 2,...
$ BYP58B <dbl+lbl> 4, 3, 2, -4, 2, 3, -7, 3, 2, -7, 3,...
$ BYP59BA <dbl+lbl> 0, 1, 1, -4, 0, 1, -7, 1, 1, -7, 1,...
$ BYP59CA <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 1, 1, -7, 1,...
$ BYP59DA <dbl+lbl> 1, 1, 1, -4, 1, 0, -7, 1, 1, -7, 0,...
$ BYP59EA <dbl+lbl> 1, 1, 1, -4, 1, 0, -7, 1, 1, -7, 0,...
$ BYP59BB <dbl+lbl> 1, 1, 1, -4, -3, 1, -7, 1, -3, -7, 1,...
$ BYP59CB <dbl+lbl> 1, 1, 1, -4, -3, 1, -7, 1, -3, -7, 1,...
$ BYP59DB <dbl+lbl> 1, 1, 1, -4, -3, 1, -7, 1, -3, -7, 0,...
$ BYP59EB <dbl+lbl> 0, 1, 1, -4, -3, 1, -7, 1, -3, -7, 0,...
$ BYP59BC <dbl+lbl> 0, -3, 1, -4, -3, 1, -7, 1, -3, -7, 1,...
$ BYP59CC <dbl+lbl> 1, -3, 1, -4, -3, 1, -7, 1, -3, -7, 0,...
$ BYP59DC <dbl+lbl> 1, -3, 1, -4, -3, 0, -7, 0, -3, -7, 0,...
$ BYP59EC <dbl+lbl> 1, -3, 1, -4, -3, 1, -7, 0, -3, -7, 0,...
$ BYP60A <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 1, 1, -7, 1,...
$ BYP60B <dbl+lbl> 4, 1, 1, -4, 1, 1, -7, 2, 1, -7, 1,...
$ BYP60C <dbl+lbl> 4, 2, 1, -4, 3, 1, -7, 2, 1, -7, 1,...
$ BYP60D <dbl+lbl> 1, 1, 2, -4, 3, 2, -7, 1, 1, -7, 2,...
$ BYP61 <dbl+lbl> 1, 0, 0, -4, 0, 0, -7, 0, 0, -7, 0,...
$ BYP62 <dbl+lbl> 1, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP63 <dbl+lbl> 3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP64A <dbl+lbl> 0, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP64B <dbl+lbl> 0, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP64C <dbl+lbl> 1, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP64D <dbl+lbl> 0, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3,...
$ BYP65 <dbl+lbl> 13, 6, 17, -4, 23, 0, -7, 14, 19, -7, 6,...
$ BYP66 <dbl+lbl> 1, 1, -1, -4, 1, 1, -7, 1, 2, -7, 2,...
$ BYP67 <dbl+lbl> 2, 3, 3, -4, 3, 3, -7, 3, 3, -7, 3,...
$ BYP68 <dbl+lbl> 2, 1, 1, -4, 3, 1, -7, 1, 1, -7, 2,...
$ BYP69A <dbl+lbl> 1, 1, 1, -4, 0, 1, -7, 1, 0, -7, 1,...
$ BYP69B <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 1, 1, -7, 1,...
$ BYP69C <dbl+lbl> 1, 0, -1, -4, 1, 1, -7, 1, 1, -7, 1,...
$ BYP69D <dbl+lbl> 1, 1, -1, -4, 0, 1, -7, 0, 1, -7, 1,...
$ BYP70 <dbl+lbl> 5, 2, 6, -4, 7, 5, 5, 4, 2, 7, 4,...
$ BYP71 <dbl+lbl> 1, 1, 1, -4, 1, 1, 1, 1, 1, 1, 1,...
$ BYP72 <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 1, 1, -7, 1,...
$ BYP73 <dbl+lbl> 3, -6, 3, -4, 1, 3, -7, 3, 3, -7, 3,...
$ BYP74A <dbl+lbl> -3, 1, -3, -4, 1, -3, -7, -3, -3, -7, -3,...
$ BYP74B <dbl+lbl> -3, 1, -3, -4, 3, -3, -7, -3, -3, -7, -3,...
$ BYP74C <dbl+lbl> -3, 1, -3, -4, 5, -3, -7, -3, -3, -7, -3,...
$ BYP74D <dbl+lbl> -3, 2, -3, -4, 3, -3, -7, -3, -3, -7, -3,...
$ BYP74E <dbl+lbl> -3, 2, -3, -4, 1, -3, -7, -3, -3, -7, -3,...
$ BYP75 <dbl+lbl> -1, 1, 1, -4, 0, 1, -7, 1, 1, -7, 0,...
$ BYP76 <dbl+lbl> -3, 2, 1, -4, -3, 1, -7, 1, 3, -7, -3,...
$ BYP77A <dbl+lbl> 2, 3, 4, -4, 2, 1, -7, 3, 2, -7, 3,...
$ BYP77B <dbl+lbl> 4, 2, 1, -4, 2, 1, -7, 2, 1, -7, 2,...
$ BYP77C <dbl+lbl> 3, 3, 1, -4, 2, 1, -7, 2, 1, -7, 2,...
$ BYP77D <dbl+lbl> 2, 2, 1, -4, 2, 3, -7, 2, 1, -7, 2,...
$ BYP77E <dbl+lbl> 2, 2, 1, -4, 3, 1, -7, 2, 1, -7, 2,...
$ BYP77F <dbl+lbl> 2, 2, 2, -4, 2, 2, -7, 2, 1, -7, 2,...
$ BYP77G <dbl+lbl> 2, 4, 2, -4, 2, 1, -7, 2, 1, -7, 2,...
$ BYP77H <dbl+lbl> 2, 4, 2, -4, 2, 1, -7, 2, 4, -7, 2,...
$ BYP77I <dbl+lbl> 2, 4, 2, -4, 2, 1, -7, 2, 1, -7, 2,...
$ BYP77J <dbl+lbl> 3, -1, -1, -4, 3, 3, -7, 3, 3, -7, 4,...
$ BYP77K <dbl+lbl> 3, -1, -1, -4, 2, 3, -7, -1, 3, -7, 4,...
$ BYP77L <dbl+lbl> 2, -1, 2, -4, 2, 3, -7, -1, 3, -7, 4,...
$ BYP77M <dbl+lbl> 2, -1, 2, -4, 2, 3, -7, 2, 3, -7, 2,...
$ BYP77N <dbl+lbl> 3, -1, 2, -4, 2, 3, -7, 3, 3, -7, 3,...
$ BYP77O <dbl+lbl> 3, 4, 3, -4, 2, 1, -7, 3, 3, -7, 2,...
$ BYP78 <dbl+lbl> 2, 2, 1, -4, 2, 2, 1, 1, 1, 2, 2,...
$ BYP79 <dbl+lbl> 5, 7, 7, -4, 2, 3, 5, 5, 5, 2, 5,...
$ BYP80A <dbl+lbl> 3, 2, 2, -4, -3, 1, -7, 2, 1, -3, 1,...
$ BYP80B <dbl+lbl> 2, 1, 2, -4, -3, 1, -7, 2, 1, -3, 1,...
$ BYP80C <dbl+lbl> 2, 1, 2, -4, -3, 1, -7, 2, 1, -3, 1,...
$ BYP80D <dbl+lbl> 3, 2, 3, -4, -3, 1, -7, 2, 1, -3, 2,...
$ BYP80E <dbl+lbl> 3, 3, 3, -4, -3, 2, -7, 2, 1, -3, 2,...
$ BYP80F <dbl+lbl> 2, 2, 2, -4, -3, 1, -7, 2, 1, -3, 2,...
$ BYP80G <dbl+lbl> 2, 2, 3, -4, -3, 2, -7, 2, 3, -3, 2,...
$ BYP80H <dbl+lbl> 2, 3, 3, -4, -3, 3, -7, 2, 1, -3, 3,...
$ BYP80I <dbl+lbl> 1, 2, 1, -4, -3, 1, -7, 2, 1, -3, 1,...
$ BYP80J <dbl+lbl> 1, 2, 2, -4, -3, 1, -7, 2, 1, -3, 2,...
$ BYP80K <dbl+lbl> 1, 2, 2, -4, -3, 1, -7, 2, 1, -3, 2,...
$ BYP80L <dbl+lbl> 1, 2, 2, -4, -3, 1, -7, 2, 1, -3, 2,...
$ BYP80M <dbl+lbl> 3, 2, 2, -4, -3, 1, -7, 2, 1, -3, 2,...
$ BYP80N <dbl+lbl> 3, 2, 3, -4, -3, 2, -7, 2, 1, -3, 3,...
$ BYP80O <dbl+lbl> 3, 2, 3, -4, -3, 2, -7, 2, 1, -3, 2,...
$ BYP81 <dbl+lbl> 2, 7, 6, -4, 5, 3, -7, 5, 5, -7, 5,...
$ BYP82 <dbl+lbl> -3, 1, 0, -4, 0, 0, -7, 0, 1, -7, 0,...
$ BYP83A <dbl+lbl> -3, 0, -3, -4, -3, -3, -7, -3, 1, -7, -3,...
$ BYP83B <dbl+lbl> -3, 1, -3, -4, -3, -3, -7, -3, 1, -7, -3,...
$ BYP83C <dbl+lbl> -3, 0, -3, -4, -3, -3, -7, -3, 0, -7, -3,...
$ BYP83D <dbl+lbl> -3, 1, -3, -4, -3, -3, -7, -3, 0, -7, -3,...
$ BYP83E <dbl+lbl> -3, 0, -3, -4, -3, -3, -7, -3, 0, -7, -3,...
$ BYP83F <dbl+lbl> -3, -9, -3, -4, -3, -3, -7, -3, 0, -7, -3,...
$ BYP83G <dbl+lbl> -3, 1, -3, -4, -3, -3, -7, -3, 0, -7, -3,...
$ BYP83H <dbl+lbl> -3, 0, -3, -4, -3, -3, -7, -3, 0, -7, -3,...
$ BYP83I <dbl+lbl> -3, 0, -3, -4, -3, -3, -7, -3, 0, -7, -3,...
$ BYP83J <dbl+lbl> -3, 0, -3, -4, -3, -3, -7, -3, 0, -7, -3,...
$ BYP83K <dbl+lbl> -3, 1, -3, -4, -3, -3, -7, -3, 0, -7, -3,...
$ BYP83L <dbl+lbl> -3, 0, -3, -4, -3, -3, -7, -3, 0, -7, -3,...
$ BYP83M <dbl+lbl> -3, 0, -3, -4, -3, -3, -7, -3, 0, -7, -3,...
$ BYP84 <dbl+lbl> -3, 4, -3, -4, -3, -3, -7, -3, 4, -7, -3,...
$ BYP85 <dbl+lbl> 10, 11, 10, -4, 6, -9, 10, 10, 8, -9, 8,...
$ BYP86 <dbl+lbl> 2, 1, 2, -4, 2, -2, -7, 1, 2, -7, 1,...
$ BYP97 <dbl+lbl> 0, 0, 0, -4, 0, 0, -9, 0, 0, -9, 1,...
$ BYP98A <dbl+lbl> -3, -3, -3, -4, -3, -3, -9, -3, -3, -9, 1,...
$ BYP98B <dbl+lbl> -3, -3, -3, -4, -3, -3, -9, -3, -3, -9, 0,...
$ BYP98C <dbl+lbl> -3, -3, -3, -4, -3, -3, -9, -3, -3, -9, 0,...
$ BYP98D <dbl+lbl> -3, -3, -3, -4, -3, -3, -9, -3, -3, -9, 0,...
$ BYP98E <dbl+lbl> -3, -3, -3, -4, -3, -3, -9, -3, -3, -9, 0,...
$ BYP99 <dbl+lbl> 200207, 200206, 200207, -4, 200207, 200...
$ BYTE01 <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ BYTE02 <dbl+lbl> 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1,...
$ BYTE03 <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ BYTE04 <dbl+lbl> 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,...
$ BYTE05 <dbl+lbl> 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0,...
$ BYTE06 <dbl+lbl> 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,...
$ BYTE07 <dbl+lbl> 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0,...
$ BYTE08A <dbl+lbl> 1, -3, 0, 0, 0, 0, 1, -3, 0, -3, 0,...
$ BYTE08B <dbl+lbl> -3, -3, 0, 0, 0, 0, 0, -3, -3, -3, 1,...
$ BYTE08C <dbl+lbl> 1, -3, 0, 0, 0, 0, 1, -3, 0, -3, 0,...
$ BYTE08D <dbl+lbl> 1, -3, 0, 0, 0, 0, 0, -3, -3, -3, 0,...
$ BYTE08E <dbl+lbl> 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0,...
$ BYTE09 <dbl+lbl> 2, 2, -1, 3, 1, 2, 2, 2, 2, 1, 1,...
$ BYTE10 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 2,...
$ BYTE11 <dbl+lbl> 1, 0, 0, 0, 0, 0, 0, 0, -1, 0, 1,...
$ BYTE12 <dbl+lbl> 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,...
$ BYTE12A <dbl+lbl> 0, -3, 0, -3, -3, -3, -3, -3, 0, -3, -3,...
$ BYTE12B <dbl+lbl> 0, -3, 0, -3, -3, -3, -3, -3, 0, -3, -3,...
$ BYTE12C <dbl+lbl> 0, -3, 0, -3, -3, -3, -3, -3, 0, -3, -3,...
$ BYTE12D <dbl+lbl> 1, -3, 1, -3, -3, -3, -3, -3, 1, -3, -3,...
$ BYTE12E <dbl+lbl> 1, -3, 0, -3, -3, -3, -3, -3, 1, -3, -3,...
$ BYTE13 <dbl+lbl> 3, 5, 3, 4, 4, -9, 4, 4, 3, 4, 4,...
$ BYTE14 <dbl+lbl> 2, 1, 2, 2, 2, 2, 2, 2, 3, 3, 2,...
$ BYTE15 <dbl+lbl> 1, 1, 1, 1, 1, 3, 1, 1, 3, 1, 2,...
$ BYTE16 <dbl+lbl> 4, 5, 3, 4, 4, 5, 4, 4, 2, 4, 3,...
$ BYTE17 <dbl+lbl> 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2,...
$ BYTE18A <dbl+lbl> 1, -3, 0, -3, -3, -3, 1, -3, 1, -3, 0,...
$ BYTE18B <dbl+lbl> -3, -3, 0, -3, -9, -3, 0, -3, -3, -3, 1,...
$ BYTE19 <dbl+lbl> -3, -3, 1, -3, 0, 0, 0, 1, 0, 0, 0,...
$ BYTE20 <dbl+lbl> 6, 6, 5, 3, 3, 3, 3, 5, 3, 5, 6,...
$ BYTE21A <dbl+lbl> 2, 2, 2, 3, 3, 3, 3, 2, 3, 3, 1,...
$ BYTE21B <dbl+lbl> 2, 2, 3, 3, 3, 4, 3, 2, 4, 3, 2,...
$ BYTE21C <dbl+lbl> 2, 2, 2, 3, 4, 4, 4, 3, 5, 4, 1,...
$ BYTE21D <dbl+lbl> 1, 1, 1, 4, 4, 4, 3, 2, 5, 5, 2,...
$ BYTE22 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,...
$ BYTE23 <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYTE25 <dbl+lbl> 1942, 1942, 1950, 1976, 1959, 1965, 1953, 1...
$ BYTE26A <dbl+lbl> 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 1,...
$ BYTE26B <dbl+lbl> 25, 25, 25, 3, 3, 4, 26, 5, 1, 1, 4,...
$ BYTE26C <dbl+lbl> 25, 25, 25, 3, 3, 5, 26, 7, 1, 1, 5,...
$ BYTE27 <dbl+lbl> 25, 25, 27, 2, 3, 3, 5, 5, 1, 1, 3,...
$ BYTE28 <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ BYTE29 <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1,...
$ BYTE30A <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYTE30B <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYTE30C <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ BYTE30D <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYTE30E <dbl+lbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,...
$ BYTE30F <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYTE30G <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYTE31A <dbl+lbl> 1, 1, 1, 2, 1, 2, -9, 2, 2, 2, 2,...
$ BYTE31B <dbl+lbl> 2, 2, 2, 1, -3, 1, -9, 1, 1, 1, 1,...
$ BYTE32A <dbl+lbl> -3, -3, -3, -3, -3, -3, 10, -3, -3, -3, -3,...
$ BYTE32B <dbl+lbl> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3,...
$ BYTE33A <dbl+lbl> 5, 5, -9, 5, 3, 5, 5, 5, 5, 5, 5,...
$ BYTE33B <dbl+lbl> 5, 5, 5, -3, -3, 2, 2, 2, -3, -3, 2,...
$ BYTE34 <dbl+lbl> 1, 1, 3, 1, 1, 1, 4, 1, 1, 1, 1,...
$ BYTE35A <dbl+lbl> 5, 5, 5, 4, 4, 5, 5, 4, 6, 6, 5,...
$ BYTE35B <dbl+lbl> 4, 4, 5, 4, 2, 4, 3, 3, 4, 4, 4,...
$ BYTE35C <dbl+lbl> 3, 3, 3, 3, 2, 4, 3, 2, 3, 3, 4,...
$ BYTE35D <dbl+lbl> 3, 3, 3, 3, 2, 3, 1, 2, 2, 2, 3,...
$ BYTE35E <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ BYTE35F <dbl+lbl> 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ BYTE35G <dbl+lbl> 2, 2, 1, 2, 1, 3, 1, 2, 2, 2, 3,...
$ BYTE35H <dbl+lbl> 2, 2, 3, 2, 1, 2, 1, 2, 3, 3, 2,...
$ BYTE35I <dbl+lbl> 3, 3, 6, 6, 1, 1, 1, 3, 6, 6, 1,...
$ BYTE35J <dbl+lbl> 2, 2, 3, 2, 1, 2, 1, 2, 2, 2, 2,...
$ BYTE35K <dbl+lbl> 6, 6, 6, 1, 3, 6, 4, 1, 6, 6, 6,...
$ BYTE35L <dbl+lbl> 4, 4, 3, 3, 3, 6, 2, 3, 4, 4, 6,...
$ BYTE35M <dbl+lbl> 3, 3, 3, 5, 1, 3, 2, 2, 3, 3, 3,...
$ BYTE35N <dbl+lbl> 1, 1, 1, 6, 1, 1, 1, 1, 1, 1, 1,...
$ BYTE36 <dbl+lbl> 24, 24, 6, 15, 40, 0, 0, 4, 0, 0, 0,...
$ BYTE37 <dbl+lbl> 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYTE38A <dbl+lbl> 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,...
$ BYTE38B <dbl+lbl> 1, 1, 1, 1, 1, 1, 0, -9, 1, 1, 1,...
$ BYTE38C <dbl+lbl> 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,...
$ BYTE38D <dbl+lbl> 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1,...
$ BYTE38E <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ BYTE38F <dbl+lbl> 1, 1, 1, 1, 0, 0, -9, 0, 1, 1, 0,...
$ BYTE39 <dbl+lbl> 10, 10, 5, 6, 2, 1, 3, 2, -9, -9, 1,...
$ BYTE40 <dbl+lbl> 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0,...
$ BYTE41 <dbl+lbl> -3, -3, -3, 1, -3, -3, -3, -3, -3, -3, -3,...
$ BYTE42 <dbl+lbl> 3, 3, 0, 3, 1, 0, 0, 1, 1, 1, 0,...
$ BYTE43 <dbl+lbl> 1, 1, -3, 0, 1, -3, -3, 1, 0, 0, -3,...
$ BYTE44A <dbl+lbl> 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2,...
$ BYTE44B <dbl+lbl> 2, 2, 2, 2, 3, 2, 2, 2, 3, 3, 2,...
$ BYTE44C <dbl+lbl> 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2,...
$ BYTE44D <dbl+lbl> 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1,...
$ BYTE44E <dbl+lbl> 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,...
$ BYTE44F <dbl+lbl> 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,...
$ BYTE47 <dbl+lbl> 200204, 200204, 200204, 200204, 200204, 200...
$ BYTM01 <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,...
$ BYTM02 <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, -3, -3, 1,...
$ BYTM03 <dbl+lbl> 1, 1, 1, -9, 1, -9, 1, 1, 1, 1, 0,...
$ BYTM04 <dbl+lbl> 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1,...
$ BYTM05 <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ BYTM06 <dbl+lbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,...
$ BYTM07 <dbl+lbl> 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1,...
$ BYTM08A <dbl+lbl> 0, 0, 0, 0, -3, 0, 1, 0, 0, 0, 0,...
$ BYTM08B <dbl+lbl> 0, 0, 0, -3, -3, -3, 1, 0, -3, -3, 0,...
$ BYTM08C <dbl+lbl> 0, 0, 0, -3, -3, 0, 1, 0, 1, 0, 0,...
$ BYTM08D <dbl+lbl> 0, 0, 0, -3, -3, -3, -3, 0, 0, -3, 0,...
$ BYTM08E <dbl+lbl> 1, 1, 0, 0, 0, 0, 1, 0, 0, -3, 0,...
$ BYTM09 <dbl+lbl> 2, 1, 2, -1, 2, -1, 1, 2, -1, -1, 2,...
$ BYTM10 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,...
$ BYTM11 <dbl+lbl> 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,...
$ BYTM12 <dbl+lbl> 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0,...
$ BYTM12A <dbl+lbl> 1, -3, -3, -3, -3, 0, 1, -3, -9, -3, -3,...
$ BYTM12B <dbl+lbl> 0, -3, -3, -3, -3, 0, 0, -3, -9, -3, -3,...
$ BYTM12C <dbl+lbl> 0, -3, -3, -3, -3, 0, 0, -3, -9, -3, -3,...
$ BYTM12D <dbl+lbl> 0, -3, -3, -3, -3, 1, 1, -3, -9, -3, -3,...
$ BYTM12E <dbl+lbl> 0, -3, -3, -3, -3, 0, 0, -3, -9, -3, -3,...
$ BYTM13 <dbl+lbl> 5, 5, 5, 4, 5, 3, 4, 5, 3, 3, 5,...
$ BYTM14 <dbl+lbl> 2, 2, 2, 2, 2, 3, 2, 2, 3, 3, 2,...
$ BYTM15 <dbl+lbl> 1, 1, 2, 1, 1, 2, 1, 2, 3, 2, 2,...
$ BYTM16 <dbl+lbl> 5, 5, 5, 5, 5, 4, 4, 5, 3, 3, 4,...
$ BYTM17 <dbl+lbl> 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1,...
$ BYTM18A <dbl+lbl> 1, -3, 0, -3, -3, -3, 1, 0, -3, -3, 0,...
$ BYTM18B <dbl+lbl> -3, -3, 0, -3, -3, -3, 1, 0, -3, -3, -9,...
$ BYTM19 <dbl+lbl> 0, 1, 1, -3, -3, 0, 1, 1, 0, 0, 1,...
$ BYTM20 <dbl+lbl> 3, 7, 5, 3, 2, 3, 5, 5, 2, 3, 5,...
$ BYTM22 <dbl+lbl> 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1,...
$ BYTM23 <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYTM25 <dbl+lbl> 1964, 1964, 1964, 1967, 1968, 1967, 1958, 1...
$ BYTM26A <dbl+lbl> 0, 0, 0, 0, 1, 0, 0, 0, -9, 0, 0,...
$ BYTM26B <dbl+lbl> 10, 10, 14, 13, 4, 13, 6, 14, -9, 7, 28,...
$ BYTM26C <dbl+lbl> -9, -9, 14, 13, 5, 13, 6, 14, 19, 7, 28,...
$ BYTM27 <dbl+lbl> 10, 10, 13, 13, 4, 13, 3, 13, 19, 4, 25,...
$ BYTM28 <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ BYTM29 <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ BYTM30A <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYTM30B <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,...
$ BYTM30C <dbl+lbl> 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,...
$ BYTM30D <dbl+lbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,...
$ BYTM30E <dbl+lbl> 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1,...
$ BYTM30F <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYTM30G <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYTM31A <dbl+lbl> -9, -9, 3, 1, 10, 1, 3, 3, -9, -9, 3,...
$ BYTM31B <dbl+lbl> -9, -9, 1, 3, -3, 3, 1, 1, -9, 10, 10,...
$ BYTM32A <dbl+lbl> -3, -3, 3, -3, 1, -3, -9, 3, -3, -3, -3,...
$ BYTM32B <dbl+lbl> -3, -3, -3, -3, -3, -3, -9, -3, -3, -3, -9,...
$ BYTM33C <dbl+lbl> -9, -9, 5, 5, -3, 5, 5, 5, 5, 5, 5,...
$ BYTM33D <dbl+lbl> -9, -9, 5, -3, -3, -3, 2, 5, 1, -3, 5,...
$ BYTM34 <dbl+lbl> 1, 1, 4, 1, 2, 1, 4, 4, 1, 1, 2,...
$ BYTM35A <dbl+lbl> 4, 4, 5, 2, 5, 2, 4, 5, 4, 3, 4,...
$ BYTM35B <dbl+lbl> 1, 1, 2, 3, 2, 3, 3, 2, -9, 2, 4,...
$ BYTM35C <dbl+lbl> 1, 1, 2, 1, 2, 1, 2, 2, 3, 1, 4,...
$ BYTM35D <dbl+lbl> 1, 1, 2, 1, 2, 1, 3, 2, 3, 1, 4,...
$ BYTM35E <dbl+lbl> 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 4,...
$ BYTM35F <dbl+lbl> 1, 1, 2, 1, 1, 1, 1, 2, 1, 3, 4,...
$ BYTM35G <dbl+lbl> 3, 3, 2, 1, 1, 1, 2, 2, 2, 2, 4,...
$ BYTM35H <dbl+lbl> 1, 1, 2, 1, 2, 1, 2, 2, 1, 2, 4,...
$ BYTM35I <dbl+lbl> 1, 1, 1, 1, 6, 1, 3, 1, 6, 1, 4,...
$ BYTM35J <dbl+lbl> 1, 1, 2, 1, 1, 1, 2, 2, 2, 2, 4,...
$ BYTM35K <dbl+lbl> 3, 3, 3, 4, 4, 4, 3, 3, 3, 2, 4,...
$ BYTM35L <dbl+lbl> 2, 2, 3, 3, 3, 3, 1, 3, 3, 3, 4,...
$ BYTM35M <dbl+lbl> 1, 1, 3, 1, 3, 1, 1, 3, 5, 4, 4,...
$ BYTM35N <dbl+lbl> 1, 1, 1, 1, 6, 1, 1, 1, 6, 5, 4,...
$ BYTM36 <dbl+lbl> 0, 0, 3, 30, 50, 30, 3, 3, 0, 22, 10,...
$ BYTM37 <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ BYTM38A <dbl+lbl> 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,...
$ BYTM38B <dbl+lbl> 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1,...
$ BYTM38C <dbl+lbl> 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1,...
$ BYTM38D <dbl+lbl> 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1,...
$ BYTM38E <dbl+lbl> 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,...
$ BYTM38F <dbl+lbl> 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1,...
$ BYTM39 <dbl+lbl> 10, 10, 2, 1, 1, 1, 2, 2, 40, 2, 0,...
$ BYTM40 <dbl+lbl> 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,...
$ BYTM41 <dbl+lbl> 0, 0, -3, -3, 0, -3, -3, -3, -3, -3, -3,...
$ BYTM42 <dbl+lbl> 1, 1, 0, 3, 0, 3, 3, 0, 0, 0, 0,...
$ BYTM43 <dbl+lbl> 0, 0, -3, 0, -3, 0, 1, -3, -3, -3, -3,...
$ BYTM44A <dbl+lbl> 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2,...
$ BYTM44B <dbl+lbl> 2, 2, 2, 2, 2, 2, 3, 2, 2, -9, 2,...
$ BYTM44C <dbl+lbl> 1, 1, 1, 1, 2, 1, 2, 1, 2, -9, 2,...
$ BYTM44D <dbl+lbl> 2, 2, 1, 2, 3, 2, 2, 1, 2, -9, 2,...
$ BYTM44E <dbl+lbl> 1, 1, 2, 2, 3, 2, 2, 2, 2, -9, 2,...
$ BYTM44F <dbl+lbl> 1, 1, 2, 1, 3, 1, 1, 2, 1, -9, 2,...
$ BYTM45A <dbl+lbl> 2, 2, 2, 2, 3, 2, 1, 2, 1, 2, 2,...
$ BYTM45B <dbl+lbl> 3, 3, 3, 3, 2, 3, 4, 3, 4, 2, 3,...
$ BYTM47 <dbl+lbl> 200205, 200205, 200208, 200204, 200204, 200...ELS.clean <- X04275_0001_DataELS.clean <- ELS.clean %>%
select(., BYS20J,
BYS21B,
BYS20G,
BYS20F,
BYS20H,
BYS20A,
BYS20I,
BYS20C,
BYS88B,
BYS88A,
BYS89R,
BYS89A,
BYS89B,
BYS89U,
BYS89L,
BYS29B,
BYS29E,
BYS29C,
BYS29J,
SCH_ID,
STU_ID,
SEX,
RACE)
glimpse(ELS.clean)Rows: 15,362
Columns: 23
$ BYS20J <dbl+lbl> 3, 3, -9, -9, 3, 3, 3, 3, 2, 2, 3, ...
$ BYS21B <dbl+lbl> 3, 3, 3, -9, 4, 2, 3, 3, 3, 2, 3, ...
$ BYS20G <dbl+lbl> 3, 2, 3, 2, 3, 2, 2, 2, 3, 1, 2, 2, 3, 2, 2, ...
$ BYS20F <dbl+lbl> 2, 2, 2, 2, 2, 1, 2, 2, 3, 2, 1, 2, 3, 2, 4, ...
$ BYS20H <dbl+lbl> 1, 4, 3, -9, 3, 3, 4, 4, 3, 3, 3, ...
$ BYS20A <dbl+lbl> 2, 2, 3, -9, 2, 2, 3, 1, 3, 2, 2, ...
$ BYS20I <dbl+lbl> 3, 4, 3, -9, 3, 3, 4, 3, 3, 4, 3, ...
$ BYS20C <dbl+lbl> 1, 2, 3, -9, 2, 2, 1, 2, 3, 2, 1, ...
$ BYS88B <dbl+lbl> 2, 3, 3, 3, 2, 4, 4, 3, 3, 4, 3, 3, 4, 3, 4, ...
$ BYS88A <dbl+lbl> 3, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, ...
$ BYS89R <dbl+lbl> 2, 4, 3, 3, 3, 3, 3, 3, 3, 3, 2, ...
$ BYS89A <dbl+lbl> 2, 4, 3, 4, 2, 2, 2, 4, 3, 4, 1, 2, 2, 2, 4, ...
$ BYS89B <dbl+lbl> 1, 3, 2, 3, 2, 2, 1, 3, 3, 3, 2, 2, 2, 2, 3, ...
$ BYS89U <dbl+lbl> 1, 4, 2, 4, 3, -9, 3, 3, 3, 3, 1, ...
$ BYS89L <dbl+lbl> 2, 4, 2, 3, 3, 3, 2, 3, 3, 3, 2, 1, 2, 3, 3, ...
$ BYS29B <dbl+lbl> 2, 5, 4, 2, 5, 2, 2, 5, 4, 5, 5, 5, 2, 5, 3, ...
$ BYS29E <dbl+lbl> 2, 3, 5, 3, 4, 4, 4, 4, 5, 4, 5, 5, 5, 5, 3, ...
$ BYS29C <dbl+lbl> 2, 5, 5, -9, 5, -9, 5, 5, 5, 4, 4, ...
$ BYS29J <dbl+lbl> 5, 2, 1, -9, 5, 2, 1, 1, 2, 5, 5, ...
$ SCH_ID <dbl> 1011, 1011, 1011, 1011, 1011, 1011, 1011, 1011, 1...
$ STU_ID <dbl> 101101, 101102, 101104, 101105, 101106, 101107, 1...
$ SEX <dbl+lbl> 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, ...
$ RACE <dbl+lbl> 5, 2, 7, 3, 4, 4, 4, 7, 4, 3, 3, 4, 3, 2, 2, ...ELS.clean.mu <- ELS.clean %>%
mutate(.,
sch.clim.sch1 = BYS20J,
sch.clim.sch2 = BYS21B,
sch.clim.tea1 = BYS20G,
sch.clim.tea2 = BYS20F,
sch.clim.tea3 = BYS20H,
sch.clim.tea4 = BYS20A,
sch.clim.st1 = BYS20I,
sch.clim.racial1 = BYS20C,
mindset.entity = BYS88B,
mindset.growth = BYS88A,
math.se1 = BYS89R,
math.se2 = BYS89A,
math.se3 = BYS89B,
math.se4 = BYS89U,
math.se5 = BYS89L,
math.engage1 = BYS29B,
math.engage2 = BYS29E,
math.engage3 = BYS29C,
math.engage4 = BYS29J,
SCH_ID.fac = as_factor(SCH_ID),
STU_ID.fac = as_factor(STU_ID),
sex.fac = as_factor(SEX),
race.fac = as_factor(RACE))
table(ELS.clean.mu$sch.clim.sch1)
-9 -7 -6 1 2 3 4
247 586 23 391 1191 6969 5955 ELS.clean.filter <- ELS.clean.mu %>%
filter(.,
!sch.clim.sch1 %in%
(-9),
!sch.clim.sch1 %in%
(-7),
!sch.clim.sch1 %in%
(-6),
!sch.clim.sch2 %in%
(-9),
!sch.clim.sch2 %in%
(-7),
!sch.clim.sch2 %in%
(-6),
!sch.clim.tea1 %in%
(-9),
!sch.clim.tea1 %in%
(-7),
!sch.clim.tea1 %in%
(-6),
!sch.clim.tea2 %in%
(-9),
!sch.clim.tea2 %in%
(-7),
!sch.clim.tea2 %in%
(-6),
!sch.clim.tea3 %in%
(-9),
!sch.clim.tea3 %in%
(-7),
!sch.clim.tea3 %in%
(-6),
!sch.clim.tea4 %in%
(-9),
!sch.clim.tea4 %in%
(-7),
!sch.clim.tea4 %in%
(-6),
!sch.clim.st1 %in%
(-9),
!sch.clim.st1 %in%
(-7),
!sch.clim.st1 %in%
(-6),
!sch.clim.racial1 %in%
(-9),
!sch.clim.racial1 %in%
(-7),
!sch.clim.racial1 %in%
(-6),
!mindset.entity %in%
(-9),
!mindset.entity %in%
(-7),
!mindset.entity %in%
(-6),
!mindset.growth %in%
(-9),
!mindset.growth %in%
(-7),
!mindset.growth %in%
(-6),
!math.se1 %in%
(-9),
!math.se1 %in%
(-7),
!math.se1 %in%
(-6),
!math.se2 %in%
(-9),
!math.se2 %in%
(-7),
!math.se2 %in%
(-6),
!math.se3 %in%
(-9),
!math.se3 %in%
(-7),
!math.se3 %in%
(-6),
!math.se4 %in%
(-9),
!math.se4 %in%
(-7),
!math.se4 %in%
(-6),
!math.se5 %in%
(-9),
!math.se5 %in%
(-7),
!math.se5 %in%
(-6),
!math.engage1 %in%
(-9),
!math.engage1 %in%
(-7),
!math.engage1 %in%
(-6),
!math.engage2 %in%
(-9),
!math.engage2 %in%
(-7),
!math.engage2 %in%
(-6),
!math.engage3 %in%
(-9),
!math.engage3 %in%
(-7),
!math.engage3 %in%
(-6),
!math.engage4 %in%
(-9),
!math.engage4 %in%
(-7),
!math.engage4 %in%
(-6))
ELS.clean.filter <- ELS.clean.filter %>%
mutate(.,
sch.clim.sch2.recode = case_when(
sch.clim.sch2 == 1 ~ 4,
sch.clim.sch2 == 2 ~ 3,
sch.clim.sch2 == 3 ~ 2,
sch.clim.sch2 == 4 ~ 1),
sch.clim.tea1.recode = case_when(
sch.clim.tea1 == 1 ~ 4,
sch.clim.tea1 == 2 ~ 3,
sch.clim.tea1 == 3 ~ 2,
sch.clim.tea1 == 4 ~ 1),
sch.clim.tea2.recode = case_when(
sch.clim.tea2 == 1 ~ 4,
sch.clim.tea2 == 2 ~ 3,
sch.clim.tea2 == 3 ~ 2,
sch.clim.tea2 == 4 ~ 1),
sch.clim.tea4.recode = case_when(
sch.clim.tea4 == 1 ~ 4,
sch.clim.tea4 == 2 ~ 3,
sch.clim.tea4 == 3 ~ 2,
sch.clim.tea4 == 4 ~ 1),
sch.clim.racial1.recode = case_when(
sch.clim.racial1 == 1 ~ 4,
sch.clim.racial1 == 2 ~ 3,
sch.clim.racial1 == 3 ~ 2,
sch.clim.racial1 == 4 ~ 1),
mindset.growth.recode = case_when(
mindset.growth == 1 ~ 4,
mindset.growth == 2 ~ 3,
mindset.growth == 3 ~ 2,
mindset.growth == 4 ~ 1))se_items <- ELS.clean.filter %>%
select(.,
math.se1,
math.se2,
math.se3,
math.se4,
math.se5)
alpha(se_items)
Reliability analysis
Call: alpha(x = se_items)
lower alpha upper 95% confidence boundaries
0.93 0.93 0.94
Reliability if an item is dropped:
Item statistics
Non missing response frequency for each item
1 2 3 4 miss
math.se1 0.10 0.38 0.30 0.22 0
math.se2 0.10 0.46 0.24 0.21 0
math.se3 0.17 0.43 0.26 0.14 0
math.se4 0.10 0.37 0.31 0.23 0
math.se5 0.15 0.39 0.27 0.18 0engage_items <- ELS.clean.filter %>%
select(.,
math.engage1,
math.engage3,
math.engage4)
alpha(engage_items)
Reliability analysis
Call: alpha(x = engage_items)
lower alpha upper 95% confidence boundaries
0.43 0.45 0.47
Reliability if an item is dropped:
Item statistics
Non missing response frequency for each item
1 2 3 4 5 miss
math.engage1 0.03 0.10 0.04 0.15 0.68 0
math.engage3 0.05 0.09 0.05 0.15 0.67 0
math.engage4 0.39 0.25 0.11 0.14 0.11 0my.keys.list <- list(school.climate.school = c("sch.clim.sch1", "sch.clim.sch2.recode"),
School.climate.teacher = c("sch.clim.tea1.recode", "sch.clim.tea2.recode", "sch.clim.tea3", "sch.clim.tea4.recode"),
math.se = c("math.se1", "math.se2", "math.se3", "math.se4", "math.se5"),
growth.mindset = c("mindset.growth.recode", "mindset.entity"))
my.scales <- scoreItems(my.keys.list, ELS.clean.filter, impute = "none")print(my.scales, short = FALSE)Call: scoreItems(keys = my.keys.list, items = ELS.clean.filter, impute = "none")
(Standardized) Alpha:
school.climate.school School.climate.teacher math.se
alpha 0.33 0.66 0.93
growth.mindset
alpha 0.49
Standard errors of unstandardized Alpha:
school.climate.school School.climate.teacher math.se
ASE 0.019 0.0094 0.0044
growth.mindset
ASE 0.018
Standardized Alpha of observed scales:
school.climate.school School.climate.teacher math.se
[1,] 0.33 0.66 0.93
growth.mindset
[1,] 0.49
Average item correlation:
school.climate.school School.climate.teacher math.se
average.r 0.2 0.33 0.74
growth.mindset
average.r 0.32
Median item correlation:
school.climate.school School.climate.teacher math.se
0.20 0.31 0.73
growth.mindset
0.33
Guttman 6* reliability:
school.climate.school School.climate.teacher math.se
Lambda.6 0.31 0.63 0.92
growth.mindset
Lambda.6 0.35
Signal/Noise based upon av.r :
school.climate.school School.climate.teacher math.se
Signal/Noise 0.49 2 14
growth.mindset
Signal/Noise 0.95
Scale intercorrelations corrected for attenuation
raw correlations below the diagonal, alpha on the diagonal
corrected correlations above the diagonal:
Note that these are the correlations of the complete scales based on the correlation matrix,
not the observed scales based on the raw items.
school.climate.school School.climate.teacher
school.climate.school 0.33 1.07
School.climate.teacher 0.50 0.66
math.se 0.16 0.23
growth.mindset 0.10 0.13
math.se growth.mindset
school.climate.school 0.30 0.25
School.climate.teacher 0.30 0.23
math.se 0.93 0.32
growth.mindset 0.22 0.49
Item by scale correlations:
corrected for item overlap and scale reliability
school.climate.school School.climate.teacher
sch.clim.sch1 0.41 0.45
sch.clim.sch2.recode 0.45 0.52
sch.clim.tea1.recode 0.55 0.57
sch.clim.tea2.recode 0.70 0.68
sch.clim.tea3 0.65 0.48
sch.clim.tea4.recode 0.64 0.50
math.se1 0.30 0.28
math.se2 0.26 0.27
math.se3 0.22 0.22
math.se4 0.31 0.28
math.se5 0.23 0.25
mindset.growth.recode 0.23 0.20
mindset.entity 0.08 0.07
math.se growth.mindset
sch.clim.sch1 0.12 0.10
sch.clim.sch2.recode 0.15 0.16
sch.clim.tea1.recode 0.18 0.18
sch.clim.tea2.recode 0.18 0.17
sch.clim.tea3 0.16 0.20
sch.clim.tea4.recode 0.16 0.06
math.se1 0.87 0.34
math.se2 0.83 0.34
math.se3 0.84 0.28
math.se4 0.86 0.34
math.se5 0.86 0.33
mindset.growth.recode 0.29 0.54
mindset.entity 0.10 0.44
Non missing response frequency for each item
1 2 3 4 miss
sch.clim.sch1 0.02 0.07 0.48 0.42 0
sch.clim.sch2.recode 0.10 0.35 0.49 0.07 0
sch.clim.tea1.recode 0.04 0.30 0.51 0.15 0
sch.clim.tea2.recode 0.03 0.20 0.61 0.15 0
sch.clim.tea3 0.03 0.10 0.58 0.29 0
sch.clim.tea4.recode 0.03 0.20 0.71 0.06 0
math.se1 0.10 0.38 0.30 0.22 0
math.se2 0.10 0.46 0.24 0.21 0
math.se3 0.17 0.43 0.26 0.14 0
math.se4 0.10 0.37 0.31 0.23 0
math.se5 0.15 0.39 0.27 0.18 0
mindset.growth.recode 0.03 0.19 0.60 0.19 0
mindset.entity 0.07 0.24 0.51 0.17 0school_items <- ELS.clean.filter %>%
select(.,
sch.clim.sch1,
sch.clim.sch2.recode)
alpha(school_items)
Reliability analysis
Call: alpha(x = school_items)
lower alpha upper 95% confidence boundaries
0.3 0.33 0.35
Reliability if an item is dropped:
Item statistics
Non missing response frequency for each item
1 2 3 4 miss
sch.clim.sch1 0.02 0.07 0.48 0.42 0
sch.clim.sch2.recode 0.10 0.35 0.49 0.07 0 ELS.clean.filter <- ELS.clean.filter %>%
group_by(SCH_ID.fac) %>%
mutate(.,
sch_sch.clim.sch1 = mean(sch.clim.sch1, na.rm = TRUE),
sch_sch.clim.sch2.recode = mean(sch.clim.sch2.recode, na.rm = TRUE)) %>%
ungroup()sch_school_items <- ELS.clean.filter %>%
select(.,
sch_sch.clim.sch1,
sch_sch.clim.sch2.recode)
alpha(sch_school_items)
Reliability analysis
Call: alpha(x = sch_school_items)
lower alpha upper 95% confidence boundaries
0.47 0.49 0.51
Reliability if an item is dropped:
Item statistics my.scores <- as_tibble(my.scales$scores)ELS.clean.filter.1 <-bind_cols(ELS.clean.filter, my.scores) ELS.clean.filter.1 <- ELS.clean.filter.1 %>%
group_by(SCH_ID.fac) %>%
mutate(.,
sch_level_school.climate = mean(sch.clim.sch1, na.rm = TRUE),
sch_level_school.climate.teacher = mean(School.climate.teacher, na.rm = TRUE),
sch_level_school.climate.student = mean(sch.clim.st1, na.rm = TRUE),
sch_level_school.climate.racial = mean(sch.clim.racial1, na.rm = TRUE)) %>%
ungroup()ELS.clean.filter.1 <- ELS.clean.filter.1 %>%
mutate(.,
mindset.entity.recode = case_when(
mindset.entity == 1 ~ 4,
mindset.entity == 2 ~ 3,
mindset.entity == 3 ~ 2,
mindset.entity == 4 ~ 1))ELS.final <- ELS.clean.filter.1 %>%
select(.,
sch_level_school.climate,
sch_level_school.climate.teacher,
sch_level_school.climate.student,
sch_level_school.climate.racial,
mindset.entity.recode,
mindset.growth.recode,
math.se,
sex.fac,
race.fac,
SCH_ID.fac,
STU_ID.fac,
SCH_ID)
model.null <- lmer(math.se ~ (1|SCH_ID.fac), REML = FALSE, data = ELS.final)
summary(model.null)Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: math.se ~ (1 | SCH_ID.fac)
Data: ELS.final
AIC BIC logLik deviance df.resid
22974.2 22995.6 -11484.1 22968.2 9317
Scaled residuals:
Min 1Q Median 3Q Max
-2.1065 -0.6845 -0.1471 0.6954 2.0416
Random effects:
Groups Name Variance Std.Dev.
SCH_ID.fac (Intercept) 0.02003 0.1415
Residual 0.67149 0.8194
Number of obs: 9320, groups: SCH_ID.fac, 744
Fixed effects:
Estimate Std. Error t value
(Intercept) 2.54703 0.01015 251ICC <- 0.02/(0.02 + 0.67)
ICC[1] 0.02898551model.1 <- lmer(math.se ~ race.fac + sex.fac + mindset.entity.recode + mindset.growth.recode + (1|SCH_ID.fac), REML = FALSE, data = ELS.final)
summary(model.1)Linear mixed model fit by maximum likelihood ['lmerMod']
Formula:
math.se ~ race.fac + sex.fac + mindset.entity.recode + mindset.growth.recode +
(1 | SCH_ID.fac)
Data: ELS.final
AIC BIC logLik deviance df.resid
22022.8 22108.5 -10999.4 21998.8 9308
Scaled residuals:
Min 1Q Median 3Q Max
-2.6652 -0.7424 -0.1377 0.6953 3.0764
Random effects:
Groups Name Variance Std.Dev.
SCH_ID.fac (Intercept) 0.02115 0.1454
Residual 0.60300 0.7765
Number of obs: 9320, groups: SCH_ID.fac, 744
Fixed effects:
Estimate Std. Error
(Intercept) 1.73844 0.11254
race.facAsian, Hawaii/Pac. Islander,non-Hispanic 0.10554 0.10362
race.facBlack or African American, non-Hispanic -0.06835 0.10338
race.facHispanic, no race specified -0.04652 0.10586
race.facHispanic, race specified -0.10863 0.10482
race.facMultiracial, non-Hispanic -0.03909 0.10653
race.facWhite, non-Hispanic 0.02246 0.10044
sex.facFemale -0.21329 0.01659
mindset.entity.recode -0.01519 0.01062
mindset.growth.recode 0.32169 0.01269
t value
(Intercept) 15.447
race.facAsian, Hawaii/Pac. Islander,non-Hispanic 1.018
race.facBlack or African American, non-Hispanic -0.661
race.facHispanic, no race specified -0.439
race.facHispanic, race specified -1.036
race.facMultiracial, non-Hispanic -0.367
race.facWhite, non-Hispanic 0.224
sex.facFemale -12.858
mindset.entity.recode -1.431
mindset.growth.recode 25.355
Correlation of Fixed Effects:
(Intr) r.A,HI r.oAAn r.Hnrs r.H,rs r.M,n- r.W,n- sx.fcF
r.A,H/P.I,- -0.848
rc.BoAA,n-H -0.852 0.931
rc.fcHs,nrs -0.834 0.910 0.911
rc.fcHsp,rs -0.840 0.917 0.919 0.900
rc.fcMl,n-H -0.832 0.904 0.905 0.883 0.891
rc.fcWh,n-H -0.883 0.957 0.960 0.936 0.945 0.931
sex.facFeml -0.123 -0.001 -0.008 0.004 -0.004 0.003 0.001
mndst.ntty. -0.311 -0.007 -0.002 -0.002 -0.010 -0.006 -0.012 0.057
mndst.grwt. -0.412 -0.016 -0.011 -0.006 -0.005 0.005 0.012 0.101
mndst.n.
r.A,H/P.I,-
rc.BoAA,n-H
rc.fcHs,nrs
rc.fcHsp,rs
rc.fcMl,n-H
rc.fcWh,n-H
sex.facFeml
mndst.ntty.
mndst.grwt. 0.319 mindset.entity isn’t significant, taking out of model
model.2 <- lmer(math.se ~ sex.fac + mindset.growth.recode + sch_level_school.climate +
sch_level_school.climate.teacher + sch_level_school.climate.student +
sch_level_school.climate.racial + (1|SCH_ID.fac), REML = FALSE, data = ELS.final)
summary(model.2)Linear mixed model fit by maximum likelihood ['lmerMod']
Formula:
math.se ~ sex.fac + mindset.growth.recode + sch_level_school.climate +
sch_level_school.climate.teacher + sch_level_school.climate.student +
sch_level_school.climate.racial + (1 | SCH_ID.fac)
Data: ELS.final
AIC BIC logLik deviance df.resid
21969.4 22033.7 -10975.7 21951.4 9311
Scaled residuals:
Min 1Q Median 3Q Max
-2.6958 -0.7340 -0.1352 0.7005 2.9215
Random effects:
Groups Name Variance Std.Dev.
SCH_ID.fac (Intercept) 0.01272 0.1128
Residual 0.60600 0.7785
Number of obs: 9320, groups: SCH_ID.fac, 744
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.12664 0.21471 0.590
sex.facFemale -0.21613 0.01649 -13.108
mindset.growth.recode 0.32449 0.01190 27.257
sch_level_school.climate 0.05221 0.03655 1.428
sch_level_school.climate.teacher 0.37118 0.05870 6.323
sch_level_school.climate.student 0.09239 0.04444 2.079
sch_level_school.climate.racial 0.02488 0.03877 0.642
Correlation of Fixed Effects:
(Intr) sx.fcF mnds.. sch__. sch_lvl_schl.clmt.t
sex.facFeml -0.043
mndst.grwt. -0.194 0.088
sch_lvl_sc. -0.141 0.020 0.056
sch_lvl_schl.clmt.t -0.480 -0.019 -0.028 -0.465
sch_lvl_schl.clmt.s -0.469 -0.022 0.002 -0.140 -0.176
sch_lvl_schl.clmt.r -0.638 0.017 0.049 0.080 0.205
sch_lvl_schl.clmt.s
sex.facFeml
mndst.grwt.
sch_lvl_sc.
sch_lvl_schl.clmt.t
sch_lvl_schl.clmt.s
sch_lvl_schl.clmt.r 0.153 sch_level_school.climate.racial not significant-might try with an interaction with race
sch_level_school.climate.student not significant sch_level_school.climate not significant
model.3 <- lmer(math.se ~ sex.fac + mindset.growth.recode + sch_level_school.climate.racial + sch_level_school.climate.teacher + race.fac + sch_level_school.climate.racial:race.fac + (1|SCH_ID.fac), REML = FALSE, data = ELS.final)
summary(model.3)Linear mixed model fit by maximum likelihood ['lmerMod']
Formula:
math.se ~ sex.fac + mindset.growth.recode + sch_level_school.climate.racial +
sch_level_school.climate.teacher + race.fac + sch_level_school.climate.racial:race.fac +
(1 | SCH_ID.fac)
Data: ELS.final
AIC BIC logLik deviance df.resid
21956.4 22092.1 -10959.2 21918.4 9301
Scaled residuals:
Min 1Q Median 3Q Max
-2.7077 -0.7350 -0.1296 0.7042 3.0810
Random effects:
Groups Name Variance Std.Dev.
SCH_ID.fac (Intercept) 0.0131 0.1145
Residual 0.6035 0.7769
Number of obs: 9320, groups: SCH_ID.fac, 744
Fixed effects:
Estimate
(Intercept) 0.83580
sex.facFemale -0.21510
mindset.growth.recode 0.32433
sch_level_school.climate.racial -0.21143
sch_level_school.climate.teacher 0.43010
race.facAsian, Hawaii/Pac. Islander,non-Hispanic -0.52377
race.facBlack or African American, non-Hispanic -0.57555
race.facHispanic, no race specified -0.41585
race.facHispanic, race specified -0.38271
race.facMultiracial, non-Hispanic -0.19381
race.facWhite, non-Hispanic -0.31036
sch_level_school.climate.racial:race.facAsian, Hawaii/Pac. Islander,non-Hispanic 0.35093
sch_level_school.climate.racial:race.facBlack or African American, non-Hispanic 0.29389
sch_level_school.climate.racial:race.facHispanic, no race specified 0.20437
sch_level_school.climate.racial:race.facHispanic, race specified 0.14705
sch_level_school.climate.racial:race.facMultiracial, non-Hispanic 0.07884
sch_level_school.climate.racial:race.facWhite, non-Hispanic 0.18106
Std. Error
(Intercept) 0.75794
sex.facFemale 0.01647
mindset.growth.recode 0.01200
sch_level_school.climate.racial 0.40593
sch_level_school.climate.teacher 0.05049
race.facAsian, Hawaii/Pac. Islander,non-Hispanic 0.77241
race.facBlack or African American, non-Hispanic 0.76450
race.facHispanic, no race specified 0.79338
race.facHispanic, race specified 0.77405
race.facMultiracial, non-Hispanic 0.78085
race.facWhite, non-Hispanic 0.74704
sch_level_school.climate.racial:race.facAsian, Hawaii/Pac. Islander,non-Hispanic 0.42256
sch_level_school.climate.racial:race.facBlack or African American, non-Hispanic 0.41783
sch_level_school.climate.racial:race.facHispanic, no race specified 0.43581
sch_level_school.climate.racial:race.facHispanic, race specified 0.42483
sch_level_school.climate.racial:race.facMultiracial, non-Hispanic 0.42818
sch_level_school.climate.racial:race.facWhite, non-Hispanic 0.40818
t value
(Intercept) 1.103
sex.facFemale -13.059
mindset.growth.recode 27.038
sch_level_school.climate.racial -0.521
sch_level_school.climate.teacher 8.518
race.facAsian, Hawaii/Pac. Islander,non-Hispanic -0.678
race.facBlack or African American, non-Hispanic -0.753
race.facHispanic, no race specified -0.524
race.facHispanic, race specified -0.494
race.facMultiracial, non-Hispanic -0.248
race.facWhite, non-Hispanic -0.415
sch_level_school.climate.racial:race.facAsian, Hawaii/Pac. Islander,non-Hispanic 0.830
sch_level_school.climate.racial:race.facBlack or African American, non-Hispanic 0.703
sch_level_school.climate.racial:race.facHispanic, no race specified 0.469
sch_level_school.climate.racial:race.facHispanic, race specified 0.346
sch_level_school.climate.racial:race.facMultiracial, non-Hispanic 0.184
sch_level_school.climate.racial:race.facWhite, non-Hispanic 0.444
Correlation matrix not shown by default, as p = 17 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need itinterplot::interplot(model.interaction, var1 = "race.fac", var2 = "sch_level_school.climate.racial")Error in quantile.default(m.sims@fixef[, match(var1[j + 1], unlist(dimnames(m@pp$X)[2]))] + :
missing values and NaN's not allowed if 'na.rm' is FALSEmodel.4 <- lmer(math.se ~ sex.fac + mindset.growth.recode +
sch_level_school.climate.teacher + (race.fac|SCH_ID.fac), REML = FALSE, data = ELS.final)boundary (singular) fit: see ?isSingularsummary(model.4)Linear mixed model fit by maximum likelihood ['lmerMod']
Formula:
math.se ~ sex.fac + mindset.growth.recode + sch_level_school.climate.teacher +
(race.fac | SCH_ID.fac)
Data: ELS.final
AIC BIC logLik deviance df.resid
22007.7 22243.3 -10970.8 21941.7 9287
Scaled residuals:
Min 1Q Median 3Q Max
-2.6851 -0.7376 -0.1324 0.6987 2.7319
Random effects:
Groups Name
SCH_ID.fac (Intercept)
race.facAsian, Hawaii/Pac. Islander,non-Hispanic
race.facBlack or African American, non-Hispanic
race.facHispanic, no race specified
race.facHispanic, race specified
race.facMultiracial, non-Hispanic
race.facWhite, non-Hispanic
Residual
Variance Std.Dev. Corr
0.15805 0.3976
0.11418 0.3379 -0.84
0.11409 0.3378 -0.99 0.88
0.12975 0.3602 -1.00 0.84 1.00
0.06795 0.2607 -0.94 0.62 0.91 0.94
0.28076 0.5299 -0.98 0.83 0.99 0.99 0.92
0.14211 0.3770 -0.95 0.96 0.97 0.95 0.79 0.95
0.59975 0.7744
Number of obs: 9320, groups: SCH_ID.fac, 744
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.42255 0.13946 3.030
sex.facFemale -0.21538 0.01645 -13.094
mindset.growth.recode 0.32310 0.01186 27.247
sch_level_school.climate.teacher 0.44175 0.04660 9.479
Correlation of Fixed Effects:
(Intr) sx.fcF mnds..
sex.facFeml -0.062
mndst.grwt. -0.243 0.088
sch_lvl_s.. -0.962 -0.023 -0.014
convergence code: 0
boundary (singular) fit: see ?isSingularmodel.5 <- lmer(math.se ~ sex.fac + mindset.growth.recode +
sch_level_school.climate.teacher + (1|SCH_ID.fac), REML = FALSE, data = ELS.final)
summary(model.5)Linear mixed model fit by maximum likelihood ['lmerMod']
Formula:
math.se ~ sex.fac + mindset.growth.recode + sch_level_school.climate.teacher +
(1 | SCH_ID.fac)
Data: ELS.final
AIC BIC logLik deviance df.resid
21970.7 22013.5 -10979.3 21958.7 9314
Scaled residuals:
Min 1Q Median 3Q Max
-2.6855 -0.7361 -0.1346 0.6971 2.9337
Random effects:
Groups Name Variance Std.Dev.
SCH_ID.fac (Intercept) 0.01347 0.1161
Residual 0.60590 0.7784
Number of obs: 9320, groups: SCH_ID.fac, 744
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.43234 0.14146 3.056
sex.facFemale -0.21601 0.01649 -13.101
mindset.growth.recode 0.32325 0.01188 27.213
sch_level_school.climate.teacher 0.44002 0.04731 9.300
Correlation of Fixed Effects:
(Intr) sx.fcF mnds..
sex.facFeml -0.059
mndst.grwt. -0.239 0.086
sch_lvl_s.. -0.963 -0.025 -0.015model.6 <- lmer(math.se ~ sex.fac + mindset.growth.recode +
sch_level_school.climate.teacher + (mindset.growth.recode|SCH_ID.fac), REML = FALSE, data = ELS.final)Model failed to converge with max|grad| = 0.0154513 (tol = 0.002, component 1)summary(model.6)Linear mixed model fit by maximum likelihood ['lmerMod']
Formula:
math.se ~ sex.fac + mindset.growth.recode + sch_level_school.climate.teacher +
(mindset.growth.recode | SCH_ID.fac)
Data: ELS.final
AIC BIC logLik deviance df.resid
21970.7 22027.8 -10977.4 21954.7 9312
Scaled residuals:
Min 1Q Median 3Q Max
-2.6825 -0.7375 -0.1375 0.6956 2.9319
Random effects:
Groups Name Variance Std.Dev. Corr
SCH_ID.fac (Intercept) 0.081043 0.28468
mindset.growth.recode 0.004467 0.06683 -0.95
Residual 0.603788 0.77704
Number of obs: 9320, groups: SCH_ID.fac, 744
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.42783 0.14200 3.013
sex.facFemale -0.21502 0.01648 -13.044
mindset.growth.recode 0.32349 0.01221 26.490
sch_level_school.climate.teacher 0.44102 0.04732 9.320
Correlation of Fixed Effects:
(Intr) sx.fcF mnds..
sex.facFeml -0.061
mndst.grwt. -0.252 0.085
sch_lvl_s.. -0.960 -0.023 -0.012
convergence code: 0
Model failed to converge with max|grad| = 0.0154513 (tol = 0.002, component 1)anova(model.null, model.1)Data: ELS.final
Models:
model.null: math.se ~ (1 | SCH_ID.fac)
model.1: math.se ~ race.fac + sex.fac + mindset.entity.recode + mindset.growth.recode +
model.1: (1 | SCH_ID.fac)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
model.null 3 22974 22996 -11484 22968
model.1 12 22023 22109 -10999 21999 969.35 9 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1anova(model.1, model.2)Data: ELS.final
Models:
model.2: math.se ~ sex.fac + mindset.growth.recode + sch_level_school.climate +
model.2: sch_level_school.climate.teacher + sch_level_school.climate.student +
model.2: sch_level_school.climate.racial + (1 | SCH_ID.fac)
model.1: math.se ~ race.fac + sex.fac + mindset.entity.recode + mindset.growth.recode +
model.1: (1 | SCH_ID.fac)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
model.2 9 21969 22034 -10976 21951
model.1 12 22023 22109 -10999 21999 0 3 1anova(model.2, model.3)Data: ELS.final
Models:
model.2: math.se ~ sex.fac + mindset.growth.recode + sch_level_school.climate +
model.2: sch_level_school.climate.teacher + sch_level_school.climate.student +
model.2: sch_level_school.climate.racial + (1 | SCH_ID.fac)
model.3: math.se ~ sex.fac + mindset.growth.recode + sch_level_school.climate.racial +
model.3: sch_level_school.climate.teacher + race.fac + sch_level_school.climate.racial:race.fac +
model.3: (1 | SCH_ID.fac)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
model.2 9 21969 22034 -10976 21951
model.3 19 21956 22092 -10959 21918 32.965 10 0.000276 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1library(lmerTest)
lmerTest::rand(model.4)ANOVA-like table for random-effects: Single term deletions
Model:
math.se ~ sex.fac + mindset.growth.recode + sch_level_school.climate.teacher +
(race.fac | SCH_ID.fac)
npar logLik AIC LRT Df
<none> 33 -10971 22008
race.fac in (race.fac | SCH_ID.fac) 6 -10979 21971 17.03 27
Pr(>Chisq)
<none>
race.fac in (race.fac | SCH_ID.fac) 0.9304anova(model.3, model.5)Data: ELS.final
Models:
model.5: math.se ~ sex.fac + mindset.growth.recode + sch_level_school.climate.teacher +
model.5: (1 | SCH_ID.fac)
model.3: math.se ~ sex.fac + mindset.growth.recode + sch_level_school.climate.racial +
model.3: sch_level_school.climate.teacher + race.fac + sch_level_school.climate.racial:race.fac +
model.3: (1 | SCH_ID.fac)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
model.5 6 21971 22014 -10979 21959
model.3 19 21956 22092 -10959 21918 40.265 13 0.0001253 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1model.1.1 <- lmer(math.se ~ sex.fac + mindset.growth.recode + (1|SCH_ID.fac), REML = FALSE, data = ELS.final)
summary(model.1.1)Linear mixed model fit by maximum likelihood . t-tests use
Satterthwaite's method [lmerModLmerTest]
Formula:
math.se ~ sex.fac + mindset.growth.recode + (1 | SCH_ID.fac)
Data: ELS.final
AIC BIC logLik deviance df.resid
22049.7 22085.4 -11019.8 22039.7 9315
Scaled residuals:
Min 1Q Median 3Q Max
-2.6368 -0.7402 -0.1359 0.6951 2.9676
Random effects:
Groups Name Variance Std.Dev.
SCH_ID.fac (Intercept) 0.02114 0.1454
Residual 0.60572 0.7783
Number of obs: 9320, groups: SCH_ID.fac, 744
Fixed effects:
Estimate Std. Error df t value
(Intercept) 1.69888 0.03830 8932.96579 44.36
sex.facFemale -0.21351 0.01659 9245.00750 -12.87
mindset.growth.recode 0.32532 0.01192 9300.96955 27.29
Pr(>|t|)
(Intercept) <2e-16 ***
sex.facFemale <2e-16 ***
mindset.growth.recode <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) sx.fcF
sex.facFeml -0.307
mndst.grwt. -0.939 0.085model.2.2 <- lmer(math.se ~ sex.fac + mindset.growth.recode +
sch_level_school.climate.teacher + (1|SCH_ID.fac), REML = FALSE, data = ELS.final)
summary(model.2.2)Linear mixed model fit by maximum likelihood . t-tests use
Satterthwaite's method [lmerModLmerTest]
Formula:
math.se ~ sex.fac + mindset.growth.recode + sch_level_school.climate.teacher +
(1 | SCH_ID.fac)
Data: ELS.final
AIC BIC logLik deviance df.resid
21970.7 22013.5 -10979.3 21958.7 9314
Scaled residuals:
Min 1Q Median 3Q Max
-2.6855 -0.7361 -0.1346 0.6971 2.9337
Random effects:
Groups Name Variance Std.Dev.
SCH_ID.fac (Intercept) 0.01347 0.1161
Residual 0.60590 0.7784
Number of obs: 9320, groups: SCH_ID.fac, 744
Fixed effects:
Estimate Std. Error df
(Intercept) 0.43234 0.14146 936.94088
sex.facFemale -0.21601 0.01649 9154.31098
mindset.growth.recode 0.32325 0.01188 9317.02478
sch_level_school.climate.teacher 0.44002 0.04731 843.49556
t value Pr(>|t|)
(Intercept) 3.056 0.0023 **
sex.facFemale -13.101 <2e-16 ***
mindset.growth.recode 27.213 <2e-16 ***
sch_level_school.climate.teacher 9.300 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) sx.fcF mnds..
sex.facFeml -0.059
mndst.grwt. -0.239 0.086
sch_lvl_s.. -0.963 -0.025 -0.015anova(model.null, model.1.1)Data: ELS.final
Models:
model.null: math.se ~ (1 | SCH_ID.fac)
model.1.1: math.se ~ sex.fac + mindset.growth.recode + (1 | SCH_ID.fac)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
model.null 3 22974 22996 -11484 22968
model.1.1 5 22050 22085 -11020 22040 928.49 2 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1anova(model.1.1, model.2.2)Data: ELS.final
Models:
model.1.1: math.se ~ sex.fac + mindset.growth.recode + (1 | SCH_ID.fac)
model.2.2: math.se ~ sex.fac + mindset.growth.recode + sch_level_school.climate.teacher +
model.2.2: (1 | SCH_ID.fac)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
model.1.1 5 22050 22085 -11020 22040
model.2.2 6 21971 22014 -10979 21959 80.972 1 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1anova(model.2.2, model.5)Data: ELS.final
Models:
model.2.2: math.se ~ sex.fac + mindset.growth.recode + sch_level_school.climate.teacher +
model.2.2: (1 | SCH_ID.fac)
model.5: math.se ~ sex.fac + mindset.growth.recode + sch_level_school.climate.teacher +
model.5: (1 | SCH_ID.fac)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
model.2.2 6 21971 22014 -10979 21959
model.5 6 21971 22014 -10979 21959 0 0 1library(modelsummary)
Attaching package: 㤼㸱modelsummary㤼㸲
The following object is masked from 㤼㸱package:psych㤼㸲:
SDlibrary(broom.mixed)Registered S3 method overwritten by 'broom.mixed':
method from
tidy.gamlss broomlibrary(tables)models <- list(model.1, model.2, model.3, model.4, model.5)
modelsummary(models, output = "markdown")| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
|---|---|---|---|---|---|
| (Intercept) | 1.738 | 0.127 | 0.836 | 0.423 | 0.432 |
| (0.113) | (0.215) | (0.758) | (0.139) | (0.141) | |
| race.facAsian, Hawaii/Pac. Islander,non-Hispanic | 0.106 | -0.524 | |||
| (0.104) | (0.772) | ||||
| race.facBlack or African American, non-Hispanic | -0.068 | -0.576 | |||
| (0.103) | (0.764) | ||||
| race.facHispanic, no race specified | -0.047 | -0.416 | |||
| (0.106) | (0.793) | ||||
| race.facHispanic, race specified | -0.109 | -0.383 | |||
| (0.105) | (0.774) | ||||
| race.facMultiracial, non-Hispanic | -0.039 | -0.194 | |||
| (0.107) | (0.781) | ||||
| race.facWhite, non-Hispanic | 0.022 | -0.310 | |||
| (0.100) | (0.747) | ||||
| sex.facFemale | -0.213 | -0.216 | -0.215 | -0.215 | -0.216 |
| (0.017) | (0.016) | (0.016) | (0.016) | (0.016) | |
| mindset.entity.recode | -0.015 | ||||
| (0.011) | |||||
| mindset.growth.recode | 0.322 | 0.324 | 0.324 | 0.323 | 0.323 |
| (0.013) | (0.012) | (0.012) | (0.012) | (0.012) | |
| sd__(Intercept) | 0.145 | 0.113 | 0.114 | 0.398 | 0.116 |
| sd__Observation | 0.777 | 0.778 | 0.777 | 0.774 | 0.778 |
| sch_level_school.climate | 0.052 | ||||
| (0.037) | |||||
| sch_level_school.climate.teacher | 0.371 | 0.430 | 0.442 | 0.440 | |
| (0.059) | (0.050) | (0.047) | (0.047) | ||
| sch_level_school.climate.student | 0.092 | ||||
| (0.044) | |||||
| sch_level_school.climate.racial | 0.025 | -0.211 | |||
| (0.039) | (0.406) | ||||
| sch_level_school.climate.racial × race.facAsian, Hawaii/Pac. Islander,non-Hispanic | 0.351 | ||||
| (0.423) | |||||
| sch_level_school.climate.racial × race.facBlack or African American, non-Hispanic | 0.294 | ||||
| (0.418) | |||||
| sch_level_school.climate.racial × race.facHispanic, no race specified | 0.204 | ||||
| (0.436) | |||||
| sch_level_school.climate.racial × race.facHispanic, race specified | 0.147 | ||||
| (0.425) | |||||
| sch_level_school.climate.racial × race.facMultiracial, non-Hispanic | 0.079 | ||||
| (0.428) | |||||
| sch_level_school.climate.racial × race.facWhite, non-Hispanic | 0.181 | ||||
| (0.408) | |||||
| cor__(Intercept).race.facAsian, Hawaii/Pac. Islander,non-Hispanic | -0.842 | ||||
| cor__(Intercept).race.facBlack or African American, non-Hispanic | -0.994 | ||||
| cor__(Intercept).race.facHispanic, no race specified | -0.996 | ||||
| cor__(Intercept).race.facHispanic, race specified | -0.943 | ||||
| cor__(Intercept).race.facMultiracial, non-Hispanic | -0.980 | ||||
| cor__(Intercept).race.facWhite, non-Hispanic | -0.947 | ||||
| sd__race.facAsian, Hawaii/Pac. Islander,non-Hispanic | 0.338 | ||||
| cor__race.facAsian, Hawaii/Pac. Islander,non-Hispanic.race.facBlack or African American, non-Hispanic | 0.881 | ||||
| cor__race.facAsian, Hawaii/Pac. Islander,non-Hispanic.race.facHispanic, no race specified | 0.842 | ||||
| cor__race.facAsian, Hawaii/Pac. Islander,non-Hispanic.race.facHispanic, race specified | 0.615 | ||||
| cor__race.facAsian, Hawaii/Pac. Islander,non-Hispanic.race.facMultiracial, non-Hispanic | 0.834 | ||||
| cor__race.facAsian, Hawaii/Pac. Islander,non-Hispanic.race.facWhite, non-Hispanic | 0.963 | ||||
| sd__race.facBlack or African American, non-Hispanic | 0.338 | ||||
| cor__race.facBlack or African American, non-Hispanic.race.facHispanic, no race specified | 0.997 | ||||
| cor__race.facBlack or African American, non-Hispanic.race.facHispanic, race specified | 0.911 | ||||
| cor__race.facBlack or African American, non-Hispanic.race.facMultiracial, non-Hispanic | 0.990 | ||||
| cor__race.facBlack or African American, non-Hispanic.race.facWhite, non-Hispanic | 0.973 | ||||
| sd__race.facHispanic, no race specified | 0.360 | ||||
| cor__race.facHispanic, no race specified.race.facHispanic, race specified | 0.938 | ||||
| cor__race.facHispanic, no race specified.race.facMultiracial, non-Hispanic | 0.994 | ||||
| cor__race.facHispanic, no race specified.race.facWhite, non-Hispanic | 0.953 | ||||
| sd__race.facHispanic, race specified | 0.261 | ||||
| cor__race.facHispanic, race specified.race.facMultiracial, non-Hispanic | 0.919 | ||||
| cor__race.facHispanic, race specified.race.facWhite, non-Hispanic | 0.792 | ||||
| sd__race.facMultiracial, non-Hispanic | 0.530 | ||||
| cor__race.facMultiracial, non-Hispanic.race.facWhite, non-Hispanic | 0.952 | ||||
| sd__race.facWhite, non-Hispanic | 0.377 | ||||
| AIC | 22022.8 | 21969.4 | 21956.4 | 22007.7 | 21970.7 |
| BIC | 22108.5 | 22033.7 | 22092.1 | 22243.3 | 22013.5 |
| Log.Lik. | -10999.404 | -10975.698 | -10959.215 | -10970.833 | -10979.347 |
describe(ELS.final, fast = TRUE)no non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -Infdiagnostics <- augment(model.5)ggplot(data = diagnostics, mapping = aes(x = .resid)) +
geom_histogram(binwidth = .25) + theme_classic() +
labs(title = "Histogram of Residuals for Education Longitudinal Study Model",
x = "Residual Value") +
geom_vline(xintercept = c(-2.5, 2.5), linetype = "dotted")
Assess Normality of Residuals Visually, with a Histogram: A little skewed
shapiro.test(diagnostics$.resid)Error in shapiro.test(diagnostics$.resid) :
sample size must be between 3 and 5000too many observaitons to check
ggplot(data = diagnostics, mapping = aes(x = .fitted, y = .resid)) +
geom_point() + labs(title = "RVF Plot for Education Longitudinal Study Model",
x = "Predicted Value, math self-efficacy",
y = "Residual Value") + theme_classic()
Use Residuals vs. Fitted (RVF) Plot to Assess Homoskedasticity of Errors: Looks like there is a pattern
ggplot(data = diagnostics, mapping = aes(x = .fitted, y = .cooksd, label = SCH_ID.fac)) +
geom_point() + geom_text(nudge_x = .25) + theme_classic() +
labs(title = "Cook's Distance Plot for School Education Longitudinal Study Model",
x = "Predicted Value, math self-efficacy",
y = "Cook's Distance") +
geom_hline(yintercept = 4/816, linetype = "dotted")
prod.trimmed <- diagnostics %>%
filter(., .cooksd < .30)
model.trimmed <- lmer(math.se ~ sex.fac + mindset.growth.recode +
sch_level_school.climate.teacher + (1|SCH_ID.fac), REML = FALSE, data = prod.trimmed)
summary(model.trimmed)Linear mixed model fit by maximum likelihood ['lmerMod']
Formula:
math.se ~ sex.fac + mindset.growth.recode + sch_level_school.climate.teacher +
(1 | SCH_ID.fac)
Data: prod.trimmed
AIC BIC logLik deviance df.resid
21970.7 22013.5 -10979.3 21958.7 9314
Scaled residuals:
Min 1Q Median 3Q Max
-2.6855 -0.7361 -0.1346 0.6971 2.9337
Random effects:
Groups Name Variance Std.Dev.
SCH_ID.fac (Intercept) 0.01347 0.1161
Residual 0.60590 0.7784
Number of obs: 9320, groups: SCH_ID.fac, 744
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.43234 0.14146 3.056
sex.facFemale -0.21601 0.01649 -13.101
mindset.growth.recode 0.32325 0.01188 27.213
sch_level_school.climate.teacher 0.44002 0.04731 9.300
Correlation of Fixed Effects:
(Intr) sx.fcF mnds..
sex.facFeml -0.059
mndst.grwt. -0.239 0.086
sch_lvl_s.. -0.963 -0.025 -0.015models <- list(model.1, model.2, model.3, model.4, model.5)
modelsummary(models, output = "html")| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
|---|---|---|---|---|---|
| (Intercept) | 1.738 | 0.127 | 0.836 | 0.423 | 0.432 |
| (0.113) | (0.215) | (0.758) | (0.139) | (0.141) | |
| race.facAsian, Hawaii/Pac. Islander,non-Hispanic | 0.106 | -0.524 | |||
| (0.104) | (0.772) | ||||
| race.facBlack or African American, non-Hispanic | -0.068 | -0.576 | |||
| (0.103) | (0.764) | ||||
| race.facHispanic, no race specified | -0.047 | -0.416 | |||
| (0.106) | (0.793) | ||||
| race.facHispanic, race specified | -0.109 | -0.383 | |||
| (0.105) | (0.774) | ||||
| race.facMultiracial, non-Hispanic | -0.039 | -0.194 | |||
| (0.107) | (0.781) | ||||
| race.facWhite, non-Hispanic | 0.022 | -0.310 | |||
| (0.100) | (0.747) | ||||
| sex.facFemale | -0.213 | -0.216 | -0.215 | -0.215 | -0.216 |
| (0.017) | (0.016) | (0.016) | (0.016) | (0.016) | |
| mindset.entity.recode | -0.015 | ||||
| (0.011) | |||||
| mindset.growth.recode | 0.322 | 0.324 | 0.324 | 0.323 | 0.323 |
| (0.013) | (0.012) | (0.012) | (0.012) | (0.012) | |
| sd__(Intercept) | 0.145 | 0.113 | 0.114 | 0.398 | 0.116 |
| sd__Observation | 0.777 | 0.778 | 0.777 | 0.774 | 0.778 |
| sch_level_school.climate | 0.052 | ||||
| (0.037) | |||||
| sch_level_school.climate.teacher | 0.371 | 0.430 | 0.442 | 0.440 | |
| (0.059) | (0.050) | (0.047) | (0.047) | ||
| sch_level_school.climate.student | 0.092 | ||||
| (0.044) | |||||
| sch_level_school.climate.racial | 0.025 | -0.211 | |||
| (0.039) | (0.406) | ||||
| sch_level_school.climate.racial × race.facAsian, Hawaii/Pac. Islander,non-Hispanic | 0.351 | ||||
| (0.423) | |||||
| sch_level_school.climate.racial × race.facBlack or African American, non-Hispanic | 0.294 | ||||
| (0.418) | |||||
| sch_level_school.climate.racial × race.facHispanic, no race specified | 0.204 | ||||
| (0.436) | |||||
| sch_level_school.climate.racial × race.facHispanic, race specified | 0.147 | ||||
| (0.425) | |||||
| sch_level_school.climate.racial × race.facMultiracial, non-Hispanic | 0.079 | ||||
| (0.428) | |||||
| sch_level_school.climate.racial × race.facWhite, non-Hispanic | 0.181 | ||||
| (0.408) | |||||
| cor__(Intercept).race.facAsian, Hawaii/Pac. Islander,non-Hispanic | -0.842 | ||||
| cor__(Intercept).race.facBlack or African American, non-Hispanic | -0.994 | ||||
| cor__(Intercept).race.facHispanic, no race specified | -0.996 | ||||
| cor__(Intercept).race.facHispanic, race specified | -0.943 | ||||
| cor__(Intercept).race.facMultiracial, non-Hispanic | -0.980 | ||||
| cor__(Intercept).race.facWhite, non-Hispanic | -0.947 | ||||
| sd__race.facAsian, Hawaii/Pac. Islander,non-Hispanic | 0.338 | ||||
| cor__race.facAsian, Hawaii/Pac. Islander,non-Hispanic.race.facBlack or African American, non-Hispanic | 0.881 | ||||
| cor__race.facAsian, Hawaii/Pac. Islander,non-Hispanic.race.facHispanic, no race specified | 0.842 | ||||
| cor__race.facAsian, Hawaii/Pac. Islander,non-Hispanic.race.facHispanic, race specified | 0.615 | ||||
| cor__race.facAsian, Hawaii/Pac. Islander,non-Hispanic.race.facMultiracial, non-Hispanic | 0.834 | ||||
| cor__race.facAsian, Hawaii/Pac. Islander,non-Hispanic.race.facWhite, non-Hispanic | 0.963 | ||||
| sd__race.facBlack or African American, non-Hispanic | 0.338 | ||||
| cor__race.facBlack or African American, non-Hispanic.race.facHispanic, no race specified | 0.997 | ||||
| cor__race.facBlack or African American, non-Hispanic.race.facHispanic, race specified | 0.911 | ||||
| cor__race.facBlack or African American, non-Hispanic.race.facMultiracial, non-Hispanic | 0.990 | ||||
| cor__race.facBlack or African American, non-Hispanic.race.facWhite, non-Hispanic | 0.973 | ||||
| sd__race.facHispanic, no race specified | 0.360 | ||||
| cor__race.facHispanic, no race specified.race.facHispanic, race specified | 0.938 | ||||
| cor__race.facHispanic, no race specified.race.facMultiracial, non-Hispanic | 0.994 | ||||
| cor__race.facHispanic, no race specified.race.facWhite, non-Hispanic | 0.953 | ||||
| sd__race.facHispanic, race specified | 0.261 | ||||
| cor__race.facHispanic, race specified.race.facMultiracial, non-Hispanic | 0.919 | ||||
| cor__race.facHispanic, race specified.race.facWhite, non-Hispanic | 0.792 | ||||
| sd__race.facMultiracial, non-Hispanic | 0.530 | ||||
| cor__race.facMultiracial, non-Hispanic.race.facWhite, non-Hispanic | 0.952 | ||||
| sd__race.facWhite, non-Hispanic | 0.377 | ||||
| AIC | 22022.8 | 21969.4 | 21956.4 | 22007.7 | 21970.7 |
| BIC | 22108.5 | 22033.7 | 22092.1 | 22243.3 | 22013.5 |
| Log.Lik. | -10999.404 | -10975.698 | -10959.215 | -10970.833 | -10979.347 |
`
models.1 <- list(model.1, model.2, model.3, model.5)
modelsummary(models.1, output = "html")| Model 1 | Model 2 | Model 3 | Model 4 | |
|---|---|---|---|---|
| (Intercept) | 1.738 | 0.127 | 0.836 | 0.432 |
| (0.113) | (0.215) | (0.758) | (0.141) | |
| race.facAsian, Hawaii/Pac. Islander,non-Hispanic | 0.106 | -0.524 | ||
| (0.104) | (0.772) | |||
| race.facBlack or African American, non-Hispanic | -0.068 | -0.576 | ||
| (0.103) | (0.764) | |||
| race.facHispanic, no race specified | -0.047 | -0.416 | ||
| (0.106) | (0.793) | |||
| race.facHispanic, race specified | -0.109 | -0.383 | ||
| (0.105) | (0.774) | |||
| race.facMultiracial, non-Hispanic | -0.039 | -0.194 | ||
| (0.107) | (0.781) | |||
| race.facWhite, non-Hispanic | 0.022 | -0.310 | ||
| (0.100) | (0.747) | |||
| sex.facFemale | -0.213 | -0.216 | -0.215 | -0.216 |
| (0.017) | (0.016) | (0.016) | (0.016) | |
| mindset.entity.recode | -0.015 | |||
| (0.011) | ||||
| mindset.growth.recode | 0.322 | 0.324 | 0.324 | 0.323 |
| (0.013) | (0.012) | (0.012) | (0.012) | |
| sd__(Intercept) | 0.145 | 0.113 | 0.114 | 0.116 |
| sd__Observation | 0.777 | 0.778 | 0.777 | 0.778 |
| sch_level_school.climate | 0.052 | |||
| (0.037) | ||||
| sch_level_school.climate.teacher | 0.371 | 0.430 | 0.440 | |
| (0.059) | (0.050) | (0.047) | ||
| sch_level_school.climate.student | 0.092 | |||
| (0.044) | ||||
| sch_level_school.climate.racial | 0.025 | -0.211 | ||
| (0.039) | (0.406) | |||
| sch_level_school.climate.racial × race.facAsian, Hawaii/Pac. Islander,non-Hispanic | 0.351 | |||
| (0.423) | ||||
| sch_level_school.climate.racial × race.facBlack or African American, non-Hispanic | 0.294 | |||
| (0.418) | ||||
| sch_level_school.climate.racial × race.facHispanic, no race specified | 0.204 | |||
| (0.436) | ||||
| sch_level_school.climate.racial × race.facHispanic, race specified | 0.147 | |||
| (0.425) | ||||
| sch_level_school.climate.racial × race.facMultiracial, non-Hispanic | 0.079 | |||
| (0.428) | ||||
| sch_level_school.climate.racial × race.facWhite, non-Hispanic | 0.181 | |||
| (0.408) | ||||
| AIC | 22022.8 | 21969.4 | 21956.4 | 21970.7 |
| BIC | 22108.5 | 22033.7 | 22092.1 | 22013.5 |
| Log.Lik. | -10999.404 | -10975.698 | -10959.215 | -10979.347 |
models.2 <- list(model.5, model.trimmed)
modelsummary(models.2, output = "html")
| Model 1 | Model 2 | |
|---|---|---|
| (Intercept) | 0.432 | 0.432 |
| (0.141) | (0.141) | |
| sex.facFemale | -0.216 | -0.216 |
| (0.016) | (0.016) | |
| mindset.growth.recode | 0.323 | 0.323 |
| (0.012) | (0.012) | |
| sch_level_school.climate.teacher | 0.440 | 0.440 |
| (0.047) | (0.047) | |
| sd__(Intercept) | 0.116 | 0.116 |
| sd__Observation | 0.778 | 0.778 |
| AIC | 21970.7 | 21970.7 |
| BIC | 22013.5 | 22013.5 |
| Log.Lik. | -10979.347 | -10979.347 |
NAtable(ELS.final$sex.fac)
Male Female
4403 4917 table(ELS.final$race.fac)
Amer. Indian/Alaska Native, non-Hispanic
66
Asian, Hawaii/Pac. Islander,non-Hispanic
896
Black or African American, non-Hispanic
944
Hispanic, no race specified
535
Hispanic, race specified
636
Multiracial, non-Hispanic
451
White, non-Hispanic
5792 models.1 <- list(model.null, model.1.1, model.2.2)
modelsummary(models.1, output = "models.1.html")[WARNING] This document format requires a nonempty <title> element.
Please specify either 'title' or 'pagetitle' in the metadata,
e.g. by using --metadata pagetitle="..." on the command line.
Falling back to 'models.1'---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. 

```{r}
plot(cars)
```

```{r}
library(tidyverse)
library(psych)
library(lme4)
library(haven)
```



```{r}
X04275_0001_Data <- read_dta("ICPSR_04275-V1/ICPSR_04275/DS0001/04275-0001-Data.dta")
glimpse(X04275_0001_Data)
```
```{r}
ELS.clean <- X04275_0001_Data
```

```{r}
ELS.clean <- ELS.clean %>%
  select(., BYS20J,
         BYS21B,
         BYS20G,
         BYS20F,
         BYS20H,
         BYS20A,
         BYS20I,
         BYS20C,
         BYS88B,
         BYS88A,
         BYS89R,
         BYS89A,
         BYS89B,
         BYS89U,
         BYS89L,
         BYS29B,
         BYS29E,
         BYS29C,
         BYS29J,
         SCH_ID,
         STU_ID,
         SEX,
         RACE)
glimpse(ELS.clean)
```
```{r}
ELS.clean.mu <- ELS.clean %>%
mutate(.,
       sch.clim.sch1 = BYS20J,
       sch.clim.sch2 = BYS21B,
       sch.clim.tea1 = BYS20G,
       sch.clim.tea2 = BYS20F,
       sch.clim.tea3 = BYS20H,
       sch.clim.tea4 = BYS20A,
       sch.clim.st1 = BYS20I,
       sch.clim.racial1 = BYS20C,
       mindset.entity = BYS88B,
       mindset.growth = BYS88A,
       math.se1 = BYS89R,
       math.se2 = BYS89A,
       math.se3 = BYS89B,
       math.se4 = BYS89U,
       math.se5 = BYS89L,
       math.engage1 = BYS29B,
       math.engage2 = BYS29E,
       math.engage3 = BYS29C,
       math.engage4 = BYS29J,
       SCH_ID.fac = as_factor(SCH_ID),
       STU_ID.fac = as_factor(STU_ID),
       sex.fac = as_factor(SEX),
       race.fac = as_factor(RACE))

```

```{r}
table(ELS.clean.mu$sch.clim.sch1)
```


```{r}
ELS.clean.filter <- ELS.clean.mu %>%
  filter(.,
         !sch.clim.sch1 %in% 
             (-9), 
         !sch.clim.sch1 %in% 
             (-7),
         !sch.clim.sch1 %in% 
             (-6),
         !sch.clim.sch2 %in% 
             (-9), 
         !sch.clim.sch2 %in% 
             (-7),
         !sch.clim.sch2 %in% 
             (-6),
         !sch.clim.tea1 %in% 
             (-9), 
         !sch.clim.tea1 %in% 
             (-7),
         !sch.clim.tea1 %in% 
             (-6),
         !sch.clim.tea2 %in% 
             (-9), 
         !sch.clim.tea2 %in% 
             (-7),
         !sch.clim.tea2 %in% 
             (-6),
         !sch.clim.tea3 %in% 
             (-9), 
         !sch.clim.tea3 %in% 
             (-7),
         !sch.clim.tea3 %in% 
             (-6),
         !sch.clim.tea4 %in% 
             (-9), 
         !sch.clim.tea4 %in% 
             (-7),
         !sch.clim.tea4 %in% 
             (-6),
         !sch.clim.st1 %in% 
             (-9), 
         !sch.clim.st1 %in% 
             (-7),
         !sch.clim.st1 %in% 
             (-6),
         !sch.clim.racial1 %in% 
             (-9), 
         !sch.clim.racial1 %in% 
             (-7),
         !sch.clim.racial1 %in% 
             (-6),
         !mindset.entity %in% 
             (-9), 
         !mindset.entity %in% 
             (-7),
         !mindset.entity %in% 
             (-6),
         !mindset.growth %in% 
             (-9), 
         !mindset.growth %in% 
             (-7),
         !mindset.growth %in% 
             (-6),
         !math.se1 %in% 
             (-9), 
         !math.se1 %in% 
             (-7),
         !math.se1 %in% 
             (-6),
        !math.se2 %in% 
             (-9), 
         !math.se2 %in% 
             (-7),
         !math.se2 %in% 
             (-6),
         !math.se3 %in% 
             (-9), 
         !math.se3 %in% 
             (-7),
         !math.se3 %in% 
             (-6),
         !math.se4 %in% 
             (-9), 
         !math.se4 %in% 
             (-7),
         !math.se4 %in% 
             (-6),
         !math.se5 %in% 
             (-9), 
         !math.se5 %in% 
             (-7),
         !math.se5 %in% 
             (-6),
         !math.engage1 %in% 
             (-9), 
         !math.engage1 %in% 
             (-7),
         !math.engage1 %in% 
             (-6),
        !math.engage2 %in% 
             (-9), 
         !math.engage2 %in% 
             (-7),
         !math.engage2 %in% 
             (-6),
        !math.engage3 %in% 
             (-9), 
         !math.engage3 %in% 
             (-7),
         !math.engage3 %in% 
             (-6),
        !math.engage4 %in% 
             (-9), 
         !math.engage4 %in% 
             (-7),
         !math.engage4 %in% 
             (-6))
        
```


```{r}
ELS.clean.filter <- ELS.clean.filter %>%
  mutate(.,
         sch.clim.sch2.recode = case_when(
           sch.clim.sch2 == 1 ~ 4,
           sch.clim.sch2 == 2 ~ 3,
           sch.clim.sch2 == 3 ~ 2,
           sch.clim.sch2 == 4 ~ 1),
         sch.clim.tea1.recode = case_when(
           sch.clim.tea1 == 1 ~ 4,
           sch.clim.tea1 == 2 ~ 3,
           sch.clim.tea1 == 3 ~ 2,
           sch.clim.tea1 == 4 ~ 1),
         sch.clim.tea2.recode = case_when(
           sch.clim.tea2 == 1 ~ 4,
           sch.clim.tea2 == 2 ~ 3,
           sch.clim.tea2 == 3 ~ 2,
           sch.clim.tea2 == 4 ~ 1),
        sch.clim.tea4.recode = case_when(
           sch.clim.tea4 == 1 ~ 4,
           sch.clim.tea4 == 2 ~ 3,
           sch.clim.tea4 == 3 ~ 2,
           sch.clim.tea4 == 4 ~ 1),
        sch.clim.racial1.recode = case_when(
           sch.clim.racial1 == 1 ~ 4,
           sch.clim.racial1 == 2 ~ 3,
           sch.clim.racial1 == 3 ~ 2,
           sch.clim.racial1 == 4 ~ 1),
        mindset.growth.recode = case_when(
           mindset.growth == 1 ~ 4,
           mindset.growth == 2 ~ 3,
           mindset.growth == 3 ~ 2,
           mindset.growth == 4 ~ 1))
```

```{r}
se_items <- ELS.clean.filter %>%
  select(.,
         math.se1,
       math.se2,
       math.se3,
       math.se4,
       math.se5)
alpha(se_items)
```
```{r}
engage_items <- ELS.clean.filter %>%
  select(.,
       math.engage1,
       math.engage3,
       math.engage4)
alpha(engage_items)
```





```{r}
my.keys.list <- list(school.climate.school = c("sch.clim.sch1", "sch.clim.sch2.recode"),
                     School.climate.teacher = c("sch.clim.tea1.recode", "sch.clim.tea2.recode", "sch.clim.tea3", "sch.clim.tea4.recode"),
                     math.se = c("math.se1", "math.se2", "math.se3", "math.se4", "math.se5"),
                     growth.mindset = c("mindset.growth.recode", "mindset.entity"))
                     
my.scales <- scoreItems(my.keys.list, ELS.clean.filter, impute = "none")
```

```{r}
print(my.scales, short = FALSE)
```

```{r}
school_items <- ELS.clean.filter %>%
  select(.,
         sch.clim.sch1,
         sch.clim.sch2.recode)
alpha(school_items)
```

```{r}
 ELS.clean.filter <- ELS.clean.filter %>%
  group_by(SCH_ID.fac) %>% 
  mutate(.,
            sch_sch.clim.sch1 = mean(sch.clim.sch1, na.rm = TRUE),
         sch_sch.clim.sch2.recode = mean(sch.clim.sch2.recode, na.rm = TRUE)) %>%
  ungroup()
```

```{r}
sch_school_items <- ELS.clean.filter %>%
  select(.,
         sch_sch.clim.sch1,
         sch_sch.clim.sch2.recode)

```


```{r}
alpha(sch_school_items)
```
```{r}
my.scores <- as_tibble(my.scales$scores)
```

```{r}
ELS.clean.filter.1 <-bind_cols(ELS.clean.filter, my.scores)
```




```{r}
 ELS.clean.filter.1 <- ELS.clean.filter.1 %>%
  group_by(SCH_ID.fac) %>% 
  mutate(.,
            sch_level_school.climate = mean(sch.clim.sch1, na.rm = TRUE),
         sch_level_school.climate.teacher = mean(School.climate.teacher, na.rm = TRUE),
         sch_level_school.climate.student = mean(sch.clim.st1, na.rm = TRUE),
         sch_level_school.climate.racial = mean(sch.clim.racial1, na.rm = TRUE)) %>%
  ungroup()
```

```{r}
ELS.clean.filter.1 <- ELS.clean.filter.1 %>%       
  mutate(.,
         mindset.entity.recode = case_when(
           mindset.entity == 1 ~ 4,
           mindset.entity == 2 ~ 3,
           mindset.entity == 3 ~ 2,
           mindset.entity == 4 ~ 1))
```


```{r}
ELS.final <- ELS.clean.filter.1 %>%
  select(.,
         sch_level_school.climate,
         sch_level_school.climate.teacher,
         sch_level_school.climate.student,
         sch_level_school.climate.racial,
         mindset.entity.recode,
         mindset.growth.recode,
         math.se,
         sex.fac,
         race.fac,
         SCH_ID.fac,
         STU_ID.fac,
         SCH_ID)

```

```{r}
model.null <- lmer(math.se ~ (1|SCH_ID.fac), REML = FALSE, data = ELS.final)
summary(model.null)
```
```{r}
ICC <- 0.02/(0.02 + 0.67)
ICC
```

```{r}
model.1 <- lmer(math.se ~ race.fac + sex.fac + mindset.entity.recode + mindset.growth.recode + (1|SCH_ID.fac), REML = FALSE, data = ELS.final)
summary(model.1)
```
mindset.entity isn't significant, taking out of model

```{r}
model.2 <- lmer(math.se ~ sex.fac + mindset.growth.recode + sch_level_school.climate +
         sch_level_school.climate.teacher + sch_level_school.climate.student +
         sch_level_school.climate.racial + (1|SCH_ID.fac), REML = FALSE, data = ELS.final)
summary(model.2)
```
sch_level_school.climate.racial not significant-might try with an interaction with race

sch_level_school.climate.student not significant
sch_level_school.climate not significant
```{r}
model.3 <- lmer(math.se ~ sex.fac + mindset.growth.recode + sch_level_school.climate.racial + sch_level_school.climate.teacher + race.fac + sch_level_school.climate.racial:race.fac + (1|SCH_ID.fac), REML = FALSE, data = ELS.final)
summary(model.3)
```
```{r}
interplot::interplot(model.interaction, var1 = "race.fac", var2 = "sch_level_school.climate.racial")
```





```{r}
model.4 <- lmer(math.se ~ sex.fac + mindset.growth.recode + 
         sch_level_school.climate.teacher + (race.fac|SCH_ID.fac), REML = FALSE, data = ELS.final)
summary(model.4)
```
```{r}
model.5 <- lmer(math.se ~ sex.fac + mindset.growth.recode + 
         sch_level_school.climate.teacher + (1|SCH_ID.fac), REML = FALSE, data = ELS.final)
summary(model.5)
```

```{r}
model.6 <- lmer(math.se ~ sex.fac + mindset.growth.recode + 
         sch_level_school.climate.teacher + (mindset.growth.recode|SCH_ID.fac), REML = FALSE, data = ELS.final)
summary(model.6)
```

```{r}
anova(model.null, model.1)
```

```{r}
anova(model.1, model.2)
```

```{r}
anova(model.2, model.3)
```

```{r}
library(lmerTest)
lmerTest::rand(model.4)
```

```{r}
anova(model.3, model.5)
```

```{r}
model.1.1 <- lmer(math.se ~ sex.fac + mindset.growth.recode + (1|SCH_ID.fac), REML = FALSE, data = ELS.final)
summary(model.1.1)
```

```{r}
model.2.2 <- lmer(math.se ~ sex.fac + mindset.growth.recode + 
         sch_level_school.climate.teacher +  (1|SCH_ID.fac), REML = FALSE, data = ELS.final)
summary(model.2.2)
```

```{r}
anova(model.null, model.1.1)
```

```{r}
anova(model.1.1, model.2.2)
```

```{r}
anova(model.2.2, model.5)
```




```{r}
library(modelsummary)
library(broom.mixed)
library(tables)
```
```{r}
models <- list(model.1, model.2, model.3, model.4, model.5)
modelsummary(models, output = "markdown")
```

```{r}
describe(ELS.final, fast = TRUE)
```

```{r}
diagnostics <- augment(model.5)
```



```{r}
ggplot(data = diagnostics, mapping = aes(x = .resid)) +
  geom_histogram(binwidth = .25) + theme_classic() + 
  labs(title = "Histogram of Residuals for Education Longitudinal Study Model",
                      x = "Residual Value") +
  geom_vline(xintercept = c(-2.5, 2.5), linetype = "dotted")
```


Assess Normality of Residuals
Visually, with a Histogram: A little skewed
```{r}
shapiro.test(diagnostics$.resid)
```
too many observaitons to check
```{r}
ggplot(data = diagnostics, mapping = aes(x = .fitted, y = .resid)) +
  geom_point() + labs(title = "RVF Plot for Education Longitudinal Study Model",
                      x = "Predicted Value, math self-efficacy",
                      y = "Residual Value") + theme_classic()
```
Use Residuals vs. Fitted (RVF) Plot to Assess Homoskedasticity of Errors: Looks like there is a pattern
```{r}
ggplot(data = diagnostics, mapping = aes(x = .fitted, y = .cooksd, label = SCH_ID.fac)) +
  geom_point() + geom_text(nudge_x = .25) + theme_classic() + 
  labs(title = "Cook's Distance Plot for School Education Longitudinal Study Model",
                      x = "Predicted Value, math self-efficacy",
                      y = "Cook's Distance") + 
  geom_hline(yintercept = 4/816, linetype = "dotted")
```
```{r}
prod.trimmed <- diagnostics %>%
  filter(., .cooksd < .30)
model.trimmed <- lmer(math.se ~ sex.fac + mindset.growth.recode + 
         sch_level_school.climate.teacher + (1|SCH_ID.fac), REML = FALSE, data = prod.trimmed)
summary(model.trimmed)
```

```{r}
models <- list(model.1, model.2, model.3, model.4, model.5)
modelsummary(models, output = "html")
```

`
```{r}
models.1 <- list(model.1, model.2, model.3, model.5)
modelsummary(models.1, output = "html")
```


```{r}
models.2 <- list(model.5, model.trimmed)
modelsummary(models.2, output = "html")

```

```{r}
table(ELS.final$sex.fac)
```
```{r}
table(ELS.final$race.fac)
```
```{r}
models.1 <- list(model.null, model.1.1, model.2.2)
modelsummary(models.1, output = "models.1.html")
```


