R Notebook
This is an R Markdown 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.
plot(cars)library(tidyverse)Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
-- Attaching packages --------------------------------------- tidyverse 1.3.0 --
v ggplot2 3.3.2 v purrr 0.3.4
v tibble 3.0.3 v dplyr 1.0.2
v tidyr 1.1.1 v stringr 1.4.0
v readr 1.3.1 v forcats 0.5.0
-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()library(psych)
Attaching package: 㤼㸱psych㤼㸲
The following objects are masked from 㤼㸱package:ggplot2㤼㸲:
%+%, alphalibrary(lme4)Loading required package: Matrix
Attaching package: 㤼㸱Matrix㤼㸲
The following objects are masked from 㤼㸱package:tidyr㤼㸲:
expand, pack, unpackX04275_0001_Data <- read_dta("EDUS 651/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, 101108...
$ SCH_ID <dbl> 1011, 1011, 1011, 1011, 1011, 1011, 1011, 1011, 1011, ...
$ STRAT_ID <dbl> 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101,...
$ PSU <dbl+lbl> 1, 1, 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, 256.9...
$ SEX <dbl+lbl> 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2,...
$ RACE <dbl+lbl> 5, 2, 7, 3, 4, 4, 4, 7, 4, 3, 3, 4, 3, 2, 2, 3, 3,...
$ STLANG <dbl+lbl> 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,...
$ HOMELANG <dbl+lbl> 1, 4, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1,...
$ DOBIRTHP <dbl+lbl> 198512, 198605, 198601, 198607, 198511, 198510, 19...
$ PARACE <dbl+lbl> 7, 2, 7, -9, 4, 5, 4, 7, 4, 3, 3, 4, 3...
$ PARLANG <dbl+lbl> 1, 4, 1, -9, 2, 1, -9, 1, 2, 1, 1, 1, 1...
$ BYFCOMP <dbl+lbl> 3, 1, 1, 5, 1, 5, 1, 1, 1, 1, 3, 5, 1, 2, 1, 5, 5,...
$ PARED <dbl+lbl> 5, 5, 2, 2, 1, 2, 6, 2, 2, 1, 6, 4, 4, 2, 7, 2, 7,...
$ MOTHED <dbl+lbl> 1, 5, 2, 2, 1, 2, 6, 2, 2, 1, 6, 4, 3, 2, 7, 2, 2,...
$ FATHED <dbl+lbl> 5, 5, 2, 2, 1, 1, 3, 2, 1, 1, 4, 2, 4, 2, 3, 2, 7,...
$ OCCUMOTH <dbl+lbl> 8, 0, 5, 4, 8, 5, 14, 1, 5, 9, 15, 14, 15...
$ OCCUFATH <dbl+lbl> 6, 9, 5, 6, 5, 8, 15, 12, 2, 5, 7, 12, 15...
$ INCOME <dbl+lbl> 10, 11, 10, 2, 6, 9, 10, 10, 8, 3, 8, 8, 5...
$ SES1 <dbl> -0.25, 0.58, -0.85, -0.80, -1.41, -1.07, 0.27, -0.16, ...
$ SES1QU <dbl+lbl> 2, 4, 1, 1, 1, 1, 3, 2, 1, 1, 2, 2, 1, 2, 4, 3, 3,...
$ SES2 <dbl> -0.23, 0.69, -0.68, -0.89, -1.28, -0.93, 0.36, -0.24, ...
$ SES2QU <dbl+lbl> 2, 4, 1, 1, 1, 1, 3, 2, 1, 1, 2, 3, 1, 1, 4, 3, 3,...
$ STEXPECT <dbl+lbl> 3, 7, -1, 5, 5, 4, -1, 6, 7, 6, -1, 6, 6...
$ PARASPIR <dbl+lbl> 5, 7, 7, 6, 2, 3, 5, 5, 5, 2, 5, 3, 6, 5, 6, 3, 5,...
$ BYOCCHS <dbl+lbl> 7, -3, -1, 15, 15, -3, -3, -1, -3, 9, -1, 16, 15...
$ BYOCC30 <dbl+lbl> -1, 9, 10, 10, 16, 11, 9, -1, 10, -1, 9, 16, 11...
$ SCHPROG <dbl+lbl> 2, 2, 2, 2, 3, 1, 2, 2, 2, 2, 2, 3, 3, 2, 2, 1, 1,...
$ BYSQSTAT <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ BYQXDATP <dbl> 200204, 200204, 200204, 200204, 200204, 200204, 200204...
$ BYTXSTAT <dbl+lbl> 3, 3, 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, 1, 0,...
$ BYPQSTAT <dbl+lbl> 2, 1, 2, 0, 2, 2, 4, 2, 2, 3, 1, 1, 1, 2, 0, 2, 2,...
$ BYTXPAFG <dbl+lbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,...
$ BYADMFLG <dbl+lbl> 1, 1, 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, 1, 1,...
$ BYIEPFLG <dbl+lbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,...
$ BYTXACC <dbl+lbl> 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, 68.39...
$ BYTXCQU <dbl+lbl> 3, 4, 4, 2, 1, 1, 2, 4, 1, 2, 4, 2, 1, 3, 4, 1, 1,...
$ BYNELS2M <dbl> 47.84, 55.30, 66.24, 35.33, 29.97, 24.28, 45.16, 66.01...
$ BYNELS2R <dbl> 39.04, 36.35, 42.68, 27.86, 13.07, 11.70, 19.66, 45.37...
$ BYNELS0M <dbl> 41.25, 47.30, 54.15, 30.17, 25.26, 20.02, 38.95, 54.05...
$ BYPISARE <dbl> 616.89, 591.84, 654.43, 511.11, 379.17, 358.38, 435.98...
$ BYTXMIRR <dbl> 39.940, 47.361, 56.717, 29.603, 24.673, 19.458, 37.965...
$ BYTXMSTD <dbl> 52.11, 57.65, 66.44, 44.68, 40.57, 35.04, 50.71, 66.17...
$ BYTXMQU <dbl+lbl> 3, 4, 4, 2, 1, 1, 3, 4, 1, 2, 4, 2, 1, 4, 4, 1, 1,...
$ BYTX1MPP <dbl> 0.998, 1.000, 1.000, 0.972, 0.906, 0.630, 0.997, 1.000...
$ BYTX2MPP <dbl> 0.991, 1.000, 1.000, 0.298, 0.029, 0.001, 0.971, 1.000...
$ BYTX3MPP <dbl> 0.729, 0.997, 1.000, 0.002, 0.000, 0.000, 0.345, 1.000...
$ BYTX4MPP <dbl> 0.029, 0.287, 0.974, 0.001, 0.000, 0.000, 0.012, 0.972...
$ BYTX5MPP <dbl> 0.000, 0.000, 0.020, 0.000, 0.000, 0.000, 0.000, 0.018...
$ BYTXRIRR <dbl> 39.806, 37.121, 43.536, 27.854, 13.732, 11.698, 19.084...
$ BYTXRSTD <dbl> 59.53, 56.70, 64.46, 48.69, 33.53, 28.85, 40.80, 68.28...
$ BYTXRQU <dbl+lbl> 4, 3, 4, 2, 1, 1, 1, 4, 2, 1, 3, 1, 1, 1, 4, 1, 1,...
$ BYTX1RPP <dbl> 1.000, 1.000, 1.000, 0.999, 0.098, 0.021, 0.930, 1.000...
$ BYTX2RPP <dbl> 0.961, 0.890, 0.992, 0.276, 0.000, 0.000, 0.017, 0.998...
$ BYTX3RPP <dbl> 0.079, 0.021, 0.406, 0.000, 0.000, 0.000, 0.000, 0.812...
$ BYSF1RCE <dbl+lbl> 4, 2, 7, 3, 4, 4, 4, 3, 4, 7, 7, 4, 3...
$ BYSF2RCE <dbl+lbl> 4, 2, 7, 3, 4, 3, 7, 3, -3, 4, 7, 7, 3...
$ BYSF3RCE <dbl+lbl> 4, 3, 7, -9, 4, 3, 7, 7, -3, 3, 3, 4, 3...
$ BYBASEBL <dbl+lbl> 2, 2, 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, 3, 2...
$ BYBSKTBL <dbl+lbl> 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2...
$ BYFOOTBL <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 4, 2, 2...
$ BYSOCCER <dbl+lbl> 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2...
$ BYTEAMSP <dbl+lbl> 2, 2, 1, 2, 2, 4, 4, 3, 2, 2, 4, 2, 1...
$ BYSOLOSP <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 4, 2, 2...
$ BYCHRDRL <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 1...
$ BYWORKSY <dbl+lbl> 1, 0, 0, 0, 1, 0, 1, 0, -9, 0, 0, 0, 1...
$ BYERACE <dbl+lbl> 3, 3, 7, 7, 7, 7, 7, 7, 6, 6, 7, 7, 3...
$ BYTEHDEG <dbl+lbl> 3, 3, 3, 3, 3, 3, 5, 3, 3, 3, 3, 3, 3...
$ BYMRACE <dbl+lbl> 7, 7, 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, 3, 4...
$ BYG10EP <dbl+lbl> 6, 6, 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, -9, -9...
$ BYSCTRL <dbl+lbl> 1, 1, 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, 1, 1,...
$ BYREGION <dbl+lbl> 1, 1, 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, 3, 3,...
$ BY10FLP <dbl+lbl> 5, 5, 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, 0, 0,...
$ STLANGIM <dbl+lbl> 0, 0, 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, 0, 0,...
$ MOTHEDIM <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,...
$ FATHEDIM <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,...
$ OCCMOMIM <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,...
$ OCCFTHIM <dbl+lbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,...
$ INCOMEIM <dbl+lbl> 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0,...
$ STEXPTIM <dbl+lbl> 0, 0, 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, 0, 0,...
$ SCHPRGIM <dbl+lbl> 0, 0, 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, 0, 0,...
$ BYMATHIM <dbl+lbl> 0, 0, 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, 0, 0,...
$ BYS14 <dbl+lbl> 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2,...
$ BYS15 <dbl+lbl> 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,...
$ BYS20A <dbl+lbl> 2, 2, 3, -9, 2, 2, 3, 1, 3, 2, 2, 2, 3...
$ BYS20B <dbl+lbl> 3, 3, 3, 2, 2, 3, 3, 2, 3, 2, 3, 3, 3...
$ BYS20C <dbl+lbl> 1, 2, 3, -9, 2, 2, 1, 2, 3, 2, 1, 3, 2...
$ BYS20D <dbl+lbl> 2, 2, 2, -9, 2, 1, 2, 2, 2, 2, 2, 2, 1...
$ BYS20E <dbl+lbl> 2, 3, 3, 1, 3, 1, 2, 2, 3, 1, 1, 2, 3...
$ BYS20F <dbl+lbl> 2, 2, 2, 2, 2, 1, 2, 2, 3, 2, 1, 2, 3...
$ BYS20G <dbl+lbl> 3, 2, 3, 2, 3, 2, 2, 2, 3, 1, 2, 2, 3...
$ BYS20H <dbl+lbl> 1, 4, 3, -9, 3, 3, 4, 4, 3, 3, 3, 3, 3...
$ BYS20I <dbl+lbl> 3, 4, 3, -9, 3, 3, 4, 3, 3, 4, 3, 3, 4...
$ BYS20J <dbl+lbl> 3, 3, -9, -9, 3, 3, 3, 3, 2, 2, 3, 3, 4...
$ BYS20K <dbl+lbl> 3, 3, 2, 2, 2, 1, 2, 2, 2, 4, 2, 2, 1...
$ BYS20L <dbl+lbl> 2, 2, 3, -9, 1, 4, 3, 2, 3, 1, 3, 3, 2...
$ BYS20M <dbl+lbl> 2, 3, 2, 2, 1, 1, 2, 2, 2, 1, 3, 2, 1...
$ BYS20N <dbl+lbl> 1, 4, 2, -9, 1, 2, 2, -9, 2, 1, 3, 2, 1...
$ BYS21A <dbl+lbl> 2, 2, 2, 1, 1, 1, 3, 3, 2, 3, 2, 2, 1...
$ BYS21B <dbl+lbl> 3, 3, 3, -9, 4, 2, 3, 3, 3, 2, 3, 2, 4...
$ BYS21C <dbl+lbl> 4, 2, 2, 2, 3, 3, 2, 3, 3, 2, 3, 2, 2...
$ BYS21D <dbl+lbl> 3, 2, 2, 2, 3, 2, 3, 3, 2, 3, 2, 2, 3...
$ BYS21E <dbl+lbl> 2, 1, 2, -9, 2, 1, 2, 3, 2, 2, 3, 2, 2...
$ BYS22A <dbl+lbl> 1, 2, 2, 2, 1, 1, 1, 1, 1, 3, 2, 1, 2...
$ BYS22B <dbl+lbl> 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYS22C <dbl+lbl> 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1...
$ BYS22D <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2...
$ BYS22E <dbl+lbl> 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 3, 1, 2...
$ BYS22F <dbl+lbl> 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...
$ BYS22H <dbl+lbl> 1, 1, 2, 1, 3, 1, 1, 1, 1, 1, 1, 2, 1...
$ BYS23A <dbl+lbl> 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1...
$ BYS23B <dbl+lbl> 0, 0, 1, -9, 0, 0, 1, 1, 1, 0, 0, 1, 0...
$ BYS23C <dbl+lbl> 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1...
$ BYS23D <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0...
$ BYS23E <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...
$ BYS23F <dbl+lbl> 0, 0, 0, -9, 0, 0, 0, 0, 0, 0, 0, 0, 1...
$ BYS24A <dbl+lbl> 3, 3, 2, 2, 2, 5, 3, 1, 2, 1, 2, 1, 4...
$ BYS24B <dbl+lbl> 3, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1...
$ BYS24C <dbl+lbl> 3, 2, 2, 3, 2, 1, 1, 1, 2, 3, 1, 1, 4...
$ BYS24D <dbl+lbl> 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2...
$ BYS24E <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2...
$ BYS24F <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2...
$ BYS24G <dbl+lbl> 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, 1, 2...
$ BYS25BA <dbl+lbl> 1, 0, 0, -9, 1, 1, 1, 0, 1, 0, 0, 1, 0...
$ BYS25DA <dbl+lbl> 10, 10, 10, 10, 10, 10, 10, 10, 9, 10, 10, 9, 10...
$ BYS25EA <dbl+lbl> 2, 3, 2, 3, 3, 2, 2, 3, 2, 3, 3, 3, 2...
$ BYS25FA <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1...
$ BYS25GA <dbl+lbl> 0, 1, 1, -9, 1, 0, 1, 0, 1, 0, 0, 1, 1...
$ BYS25AB <dbl+lbl> 1, 2, 2, 2, 1, 1, 1, 1, -3, 1, 1, 2, 2...
$ BYS25BB <dbl+lbl> 1, 0, 0, -9, 1, 0, 0, 0, -3, 1, 0, 0, 0...
$ BYS25DB <dbl+lbl> 10, 10, 10, 10, 10, 10, 10, 10, -3, 10, 10, 10, 10...
$ BYS25EB <dbl+lbl> 2, 3, 3, 3, 3, 2, 2, -9, -3, 2, 3, 3, 2...
$ BYS25FB <dbl+lbl> 0, 1, 1, 0, 1, 1, 1, 0, -3, 1, 1, 1, 1...
$ BYS25GB <dbl+lbl> 0, 1, 1, -9, -9, 1, 1, -9, -3, 1, 1, 1, 1...
$ BYS25AC <dbl+lbl> 2, 2, 1, -9, 1, 1, 1, 1, -3, 1, 1, 2, 2...
$ BYS25BC <dbl+lbl> 1, 0, 0, 0, 1, 0, 0, 0, -3, 0, 0, 1, 0...
$ BYS25DC <dbl+lbl> 12, 10, 10, 10, 10, 9, -9, 10, -3, 10, 10, 10, 10...
$ BYS25EC <dbl+lbl> 3, 2, 2, 3, 3, 2, 2, 3, -3, 2, 3, 3, 3...
$ BYS25FC <dbl+lbl> 1, 0, 1, 1, 0, 1, 1, 1, -3, 0, 1, 1, 1...
$ BYS25GC <dbl+lbl> 1, 0, 1, 1, 0, 1, 1, 1, -3, 0, 0, 1, 1...
$ BYS26 <dbl+lbl> 2, 2, 2, 2, 3, 1, 2, 2, 2, 2, 2, 3, 3...
$ BYS27A <dbl+lbl> 2, 2, 3, 1, 1, 2, 2, 2, 3, 2, 1, 2, 2...
$ BYS27B <dbl+lbl> 2, 2, 3, 1, 1, 1, 3, 2, 3, 2, 2, 2, 2...
$ BYS27C <dbl+lbl> 3, 2, 3, 4, 1, 3, 3, 2, 3, 4, 2, 3, 3...
$ BYS27D <dbl+lbl> 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1...
$ BYS27E <dbl+lbl> 2, 1, 3, 2, 1, 4, 2, 1, 2, 4, 2, 3, 4...
$ BYS27F <dbl+lbl> 3, 1, 2, 1, 2, 2, 2, 1, 3, 3, 1, 3, 4...
$ BYS27G <dbl+lbl> 3, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1...
$ BYS27H <dbl+lbl> 2, 2, -9, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3...
$ BYS27I <dbl+lbl> 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1...
$ BYS28 <dbl+lbl> 2, 2, 1, 3, 3, 3, 2, 2, 2, 3, 2, 3, 2...
$ BYS29A <dbl+lbl> 2, 4, 4, 5, 5, 5, 3, 2, 5, 4, 4, 5, 3...
$ BYS29B <dbl+lbl> 2, 5, 4, 2, 5, 2, 2, 5, 4, 5, 5, 5, 2...
$ BYS29C <dbl+lbl> 2, 5, 5, -9, 5, -9, 5, 5, 5, 4, 4, 5, 5...
$ BYS29D <dbl+lbl> 1, 2, -9, 4, 5, 3, 3, 1, 2, 3, 1, 2, 5...
$ BYS29E <dbl+lbl> 2, 3, 5, 3, 4, 4, 4, 4, 5, 4, 5, 5, 5...
$ BYS29F <dbl+lbl> 5, 3, 3, -9, 2, 2, 3, 4, 5, 2, 5, 5, 5...
$ BYS29G <dbl+lbl> 1, 1, 4, 2, 1, 1, 2, 4, 2, 2, 5, 1, 1...
$ BYS29H <dbl+lbl> 1, 1, 1, 3, 2, 1, 1, 1, 2, 3, 5, 5, 2...
$ BYS29I <dbl+lbl> 1, 2, 1, 2, 4, 5, 2, 1, 4, 1, 2, 3, 2...
$ BYS29J <dbl+lbl> 5, 2, 1, -9, 5, 2, 1, 1, 2, 5, 5, 4, 3...
$ BYS30 <dbl+lbl> 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1...
$ BYS31A <dbl+lbl> -3, -3, -3, -3, 1, -3, 1, -3, -3, 2, -3, -3, 1...
$ BYS31B <dbl+lbl> -3, -3, -3, -3, 3, -3, 4, -3, -3, 3, -3, -3, 2...
$ BYS31C <dbl+lbl> -3, -3, -3, -3, 1, -3, 1, -3, -3, 2, -3, -3, 2...
$ BYS31D <dbl+lbl> -3, -3, -3, -3, 3, -3, 4, -3, -3, 3, -3, -3, 2...
$ BYS31E <dbl+lbl> -3, -3, -3, -3, 1, -3, 1, -3, -3, 5, -3, -3, 2...
$ BYS31F <dbl+lbl> -3, -3, -3, -3, 1, -3, 4, -3, -3, 2, -3, -3, 1...
$ BYS31G <dbl+lbl> -3, -3, -3, -3, 1, -3, 1, -3, -3, 4, -3, -3, 1...
$ BYS31H <dbl+lbl> -3, -3, -3, -3, 2, -3, 1, -3, -3, 3, -3, -3, 3...
$ BYS32AA <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1...
$ BYS32BA <dbl+lbl> 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1...
$ BYS32CA <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0...
$ BYS32DA <dbl+lbl> 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0...
$ BYS32EA <dbl+lbl> 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0...
$ BYS32FA <dbl+lbl> 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0...
$ BYS32GA <dbl+lbl> 1, 1, -9, 1, -3, -9, 0, 1, 1, 1, 1, 1, 0...
$ BYS32HA <dbl+lbl> 1, 1, 1, 1, -3, -9, 0, 1, 1, 1, 1, 1, 0...
$ BYS32AB <dbl+lbl> 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1...
$ BYS32BB <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0...
$ BYS32CB <dbl+lbl> 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0...
$ BYS32DB <dbl+lbl> 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0...
$ BYS32EB <dbl+lbl> 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1...
$ BYS32FB <dbl+lbl> 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0...
$ BYS32GB <dbl+lbl> 1, 1, 1, 0, -3, 1, 0, 1, 1, 0, 1, 1, 1...
$ BYS32HB <dbl+lbl> 1, 1, 1, 1, -3, 1, 0, 1, 1, 1, 1, 1, 1...
$ BYS33A <dbl+lbl> 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...
$ BYS33C <dbl+lbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1...
$ BYS33D <dbl+lbl> 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYS33E <dbl+lbl> 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYS33F <dbl+lbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYS33G <dbl+lbl> 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0...
$ BYS33H <dbl+lbl> 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...
$ BYS33J <dbl+lbl> 0, 0, 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...
$ BYS33L <dbl+lbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1...
$ BYS34A <dbl+lbl> 1, 1, -9, 4, 8, 7, 1, 2, -9, 7, 4, 1, 5...
$ BYS34B <dbl+lbl> 6, 4, 18, 7, 2, 6, 11, 10, -9, 16, 5, 3, 2...
$ BYS35A <dbl+lbl> 0, 1, 5, 4, 1, 7, 1, 1, -9, 5, 2, 1, 1...
$ BYS35B <dbl+lbl> 2, 0, 4, 5, 3, 5, 1, 4, -9, 8, 2, 1, 1...
$ BYS36A <dbl+lbl> 0, 1, 1, 2, 1, 0, 1, 1, -9, 3, 3, 1, 2...
$ BYS36B <dbl+lbl> 4, 2, 5, 3, 3, 0, 1, 2, -9, 8, 2, 1, 1...
$ BYS37 <dbl+lbl> 3, 4, 4, 4, 4, 3, 2, 4, 4, 4, 3, 4, 3, 4, 4, 3, 3,...
$ BYS38A <dbl+lbl> 2, 2, 1, 1, 4, 2, 3, 2, 4, 1, 2, 1, 1...
$ BYS38B <dbl+lbl> 3, 1, 1, -9, 4, 1, 4, 2, 4, 1, 2, 1, 1...
$ BYS38C <dbl+lbl> 2, 1, 2, 2, 4, 2, 3, 2, 4, 1, 2, 1, 2...
$ BYS39A <dbl+lbl> -9, 2, 2, 2, 3, 2, 2, 1, 2, 1, 2, 2, 2...
$ BYS39B <dbl+lbl> 3, 2, 2, 2, 3, 2, 2, 1, 2, 1, 2, 3, 2...
$ BYS39C <dbl+lbl> 3, 2, 2, 3, 3, 2, 2, 1, 2, 1, 2, 2, 2...
$ BYS39D <dbl+lbl> 3, 2, 2, 2, 3, 2, 2, 1, 2, 1, 2, 2, 2...
$ BYS39E <dbl+lbl> -9, 2, 2, 2, 3, 2, 2, 1, 2, 1, 2, 2, 2...
$ BYS39F <dbl+lbl> 3, 2, 3, 3, 3, 3, 3, 3, 2, 1, 2, 2, 3...
$ BYS39G <dbl+lbl> 3, 2, 2, -9, 3, 2, 2, 1, 2, 2, 2, 2, 2...
$ BYS39H <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 3, 3...
$ BYS40AA <dbl+lbl> 0, 0, 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, 1, 1...
$ BYS40AC <dbl+lbl> 0, 0, 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, 0, 0...
$ BYS40AE <dbl+lbl> 0, 0, 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, 0, 0...
$ BYS40BB <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1...
$ BYS40BC <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0...
$ BYS40BD <dbl+lbl> 0, 0, 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, 0, 0...
$ BYS40CA <dbl+lbl> 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...
$ BYS40CC <dbl+lbl> 0, 0, 0, 1, 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...
$ BYS40CE <dbl+lbl> 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, 0, 0...
$ BYS40DB <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1...
$ BYS40DC <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0...
$ BYS40DD <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0...
$ BYS40DE <dbl+lbl> 0, 0, 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, 0, 0...
$ BYS40EB <dbl+lbl> 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYS40EC <dbl+lbl> 0, 0, 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, 0, 0...
$ BYS40EE <dbl+lbl> 0, 0, 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, 0, 1...
$ BYS40FB <dbl+lbl> 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0...
$ BYS40FC <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0...
$ BYS40FD <dbl+lbl> 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0...
$ BYS40FE <dbl+lbl> 0, 0, 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, 0, 0...
$ BYS40GB <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1...
$ BYS40GC <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0...
$ BYS40GD <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0...
$ BYS40GE <dbl+lbl> 0, 0, 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, 0, 1...
$ BYS40HB <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0...
$ BYS40HC <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0...
$ BYS40HD <dbl+lbl> 0, 0, 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, 0, 0...
$ BYS41A <dbl+lbl> 0, 0, 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, 0, 0,...
$ BYS41C <dbl+lbl> 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,...
$ BYS41D <dbl+lbl> 0, 0, 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, 0, 0,...
$ BYS41F <dbl+lbl> 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,...
$ BYS41G <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,...
$ BYS41H <dbl+lbl> 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, -9, 1...
$ BYS41I <dbl+lbl> 0, 0, 0, -9, 0, 0, 0, 0, 0, 0, 0, 0, 1...
$ BYS42 <dbl+lbl> 0, 2, 2, 14, 0, 8, 5, 18, -9, 2, 18, 15, 2...
$ BYS43 <dbl+lbl> 2, 2, 3, 5, 2, 12, -9, 0, -9, 2, 0, 2, 0...
$ BYS44A <dbl+lbl> 4, 2, 3, 3, 2, 4, 4, 2, 2, 2, 2, 3, 3...
$ BYS44B <dbl+lbl> 4, 2, 2, 1, 2, 1, 3, 3, 3, 3, 4, 1, 1...
$ BYS44C <dbl+lbl> 1, 3, 1, 1, 1, 1, 4, 1, 1, 1, 1, 3, 1...
$ BYS44D <dbl+lbl> 4, 3, 3, 3, 4, 1, 2, 1, 2, 4, 1, 3, 3...
$ BYS44E <dbl+lbl> 4, 4, 1, 4, 1, 4, 3, -9, 3, 3, 1, 4, 3...
$ BYS44F <dbl+lbl> 3, 1, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYS44G <dbl+lbl> 1, 1, 1, 1, 1, 4, 2, -9, 1, 2, 1, 4, 1...
$ BYS44H <dbl+lbl> 1, 1, 3, 1, -9, 1, 3, -9, 3, 2, 1, 3, 1...
$ BYS45A <dbl+lbl> 4, 5, 5, 5, 5, 3, 5, 5, 5, 3, 5, 5, 2...
$ BYS45B <dbl+lbl> 2, 4, 4, 4, 4, 2, 4, 4, 5, 4, 4, 4, 3...
$ BYS45C <dbl+lbl> 4, 4, 4, 5, 5, 1, 4, 4, 5, 5, 5, 5, 2...
$ BYS46A <dbl+lbl> 1, 1, 1, 2, 3, 0, 1, 2, -9, 2, 3, 1, 2...
$ BYS46B <dbl+lbl> 3, 5, 1, 4, 3, 0, 3, 6, -9, 3, 6, 3, 1...
$ BYS47A <dbl+lbl> 4, 5, 5, 5, 5, 3, 5, 5, 5, 4, 5, 5, 3...
$ BYS47B <dbl+lbl> 3, 2, 3, 3, 3, 2, 3, 5, 5, 3, 3, 4, 3...
$ BYS47C <dbl+lbl> 2, 2, 2, 2, 2, 2, 3, 2, 5, 2, 2, 2, 2...
$ BYS47D <dbl+lbl> 2, 3, 2, 2, 2, 2, 3, 3, 2, 2, 2, 3, 3...
$ BYS47E <dbl+lbl> 3, 2, 2, -9, 2, 2, 3, 2, 2, 2, 2, 2, 2...
$ BYS48A <dbl+lbl> 6, 2, 1, 6, 4, 3, 1, 2, -9, 3, 6, 1, 4...
$ BYS48B <dbl+lbl> 6, 4, 6, 6, 4, 6, 1, 6, -9, 2, 6, 5, 3...
$ BYS49A <dbl+lbl> 0, 2, 0, 1, 0, 0, 1, 1, -9, 0, 6, 0, 0...
$ BYS49B <dbl+lbl> 1, 3, 2, 3, 0, 2, 1, 5, -9, 2, 6, 0, 0...
$ BYS50 <dbl+lbl> 0, 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, 3, 2...
$ BYS51B <dbl+lbl> -3, 2, 2, 3, 2, 2, 3, 2, 4, 3, 2, 3, 2...
$ BYS51C <dbl+lbl> -3, 1, 1, 3, 2, 1, 3, 2, 2, 3, 1, 3, 2...
$ BYS51D <dbl+lbl> -3, 4, 4, 4, 2, 1, 4, 3, 3, 4, 3, 3, 2...
$ BYS51E <dbl+lbl> -3, 1, 1, 4, 1, 1, 2, 1, 2, 2, 1, 2, 1...
$ BYS51F <dbl+lbl> -3, 1, 1, 4, 1, 1, 1, 1, 1, 2, 1, 2, 1...
$ BYS51G <dbl+lbl> -3, 2, 1, -9, 1, 1, 1, 1, 3, 1, 1, 2, 1...
$ BYS51H <dbl+lbl> -3, 1, 1, -9, 1, 1, 1, 1, 3, 1, 1, 2, 1...
$ BYS51I <dbl+lbl> -3, 1, 1, -9, 2, 1, 2, 2, 3, 2, 2, 3, 2...
$ BYS52 <dbl+lbl> -3, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 3...
$ BYS53A <dbl+lbl> -3, 3, 2, 1, 2, 2, 1, 2, 3, 3, 2, 1, 3...
$ BYS53B <dbl+lbl> -3, 4, 2, 2, 2, 2, 2, 4, 3, 3, 2, 1, 3...
$ BYS53C <dbl+lbl> -3, 4, 4, 1, 2, 2, 2, 4, 3, 2, 2, 1, 3...
$ BYS54A <dbl+lbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3,...
$ BYS54B <dbl+lbl> 1, 3, 3, 3, 3, 3, 2, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3,...
$ BYS54C <dbl+lbl> 1, 2, 2, 3, 2, 3, 2, 3, 3, 2, 3, 2, 3, 3, 2, 3, 1,...
$ BYS54D <dbl+lbl> 3, 3, 3, 3, 3, 2, 3, 3, 2, 1, 3, 3, 3, 3, 3, 2, 2,...
$ BYS54E <dbl+lbl> 3, 3, 3, 3, 3, 3, 2, -9, 3, 3, 3, 3, 3...
$ BYS54F <dbl+lbl> 3, 2, 2, 3, 3, 2, 2, 2, 1, 3, 2, 2, 3, 3, 2, 1, 2,...
$ BYS54G <dbl+lbl> 2, 3, 3, 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, 3, 2,...
$ BYS54I <dbl+lbl> 3, 2, 2, 1, 3, 1, 1, 3, 2, 3, 2, 3, 3, 2, 1, 2, 1,...
$ BYS54J <dbl+lbl> 2, 2, 2, 3, 3, 2, 2, 2, 1, 2, 2, 2, 3, 3, 1, 1, 3,...
$ BYS54K <dbl+lbl> 1, 2, 2, 3, 3, 1, 1, 3, 2, 1, 2, 3, 1, 3, 2, 1, 2,...
$ BYS54L <dbl+lbl> 3, 3, 3, 3, 3, 3, 2, 3, 2, 3, 2, 3, 3, 3, 3, 2, 3,...
$ BYS54N <dbl+lbl> 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 1, 3,...
$ BYS54O <dbl+lbl> 3, 3, 3, 3, 3, 3, 2, -9, 3, 3, 3, 3, 3...
$ BYS55A <dbl+lbl> 1, 4, 4, 4, 4, 4, 3, 3, 2, 4, 4, 4, 4...
$ BYS55B <dbl+lbl> 5, 4, 5, 1, 4, 1, 5, 4, 5, 4, 5, 4, 2...
$ BYS55C <dbl+lbl> 1, 4, 1, 1, 4, 2, 2, 4, 2, 4, 4, 1, 2...
$ BYS55D <dbl+lbl> 4, 1, 1, 1, 3, 2, 2, 1, 2, 4, 5, 1, 3...
$ BYS56 <dbl+lbl> 3, 7, -1, 5, 5, 4, -1, 6, 7, 6, -1, 6, 6...
$ BYS57 <dbl+lbl> 3, 1, 1, 1, 4, 1, 2, 1, 1, 1, 1, 1, 1...
$ BYS58 <dbl+lbl> 3, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYS59A <dbl+lbl> 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0...
$ BYS59B <dbl+lbl> 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0...
$ BYS59C <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0...
$ BYS59D <dbl+lbl> 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0...
$ BYS59E <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1...
$ BYS59F <dbl+lbl> 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1...
$ BYS59G <dbl+lbl> 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0...
$ BYS59H <dbl+lbl> 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0...
$ BYS59I <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0...
$ BYS59J <dbl+lbl> 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0...
$ BYS59K <dbl+lbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0...
$ BYS60 <dbl+lbl> 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1...
$ BYS61 <dbl+lbl> -3, -3, -3, 1, 0, 1, 1, 1, -3, 1, 1, 1, 1...
$ BYS62A <dbl+lbl> -3, -3, -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, -3, -3...
$ BYS62C <dbl+lbl> -3, -3, -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, -3, -3...
$ BYS62E <dbl+lbl> -3, -3, -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, -3, -3...
$ BYS62G <dbl+lbl> -3, -3, -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, -3, -3...
$ BYS65A <dbl+lbl> -9, 7, 5, 3, 1, -9, -1, 5, 7, 7, -9, 6, 6...
$ BYS65B <dbl+lbl> 3, 7, 5, 4, 1, -9, -1, -9, 7, -9, 5, 5, 6...
$ BYS66A <dbl+lbl> 4, 1, 1, 1, 1, 6, 1, 1, 1, 4, -9, 1, 1...
$ BYS66B <dbl+lbl> 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYS66C <dbl+lbl> 3, -1, -1, 6, 1, -3, -1, 6, 1, 6, 1, 1, 1...
$ BYS66D <dbl+lbl> 3, 1, -1, 1, 1, -9, 1, 1, 1, -1, 1, 1, 1...
$ BYS66E <dbl+lbl> 1, -1, -1, 1, -9, -3, 1, 1, 1, 1, 1, 1, 1...
$ BYS66F <dbl+lbl> 1, 1, -1, 1, 1, -3, 1, 1, 1, 1, 1, 1, -3...
$ BYS66G <dbl+lbl> -3, -3, -1, 1, -3, -9, 1, 1, -3, -1, 1, 1, -3...
$ BYS67 <dbl+lbl> 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,...
$ BYS69A <dbl+lbl> -3, 4, -3, -3, 4, 2, -3, -3, -3, -3, -3, 3, -3...
$ BYS69B <dbl+lbl> -3, 2, -3, -3, 4, 2, -3, -3, -3, -3, -3, 2, -3...
$ BYS69C <dbl+lbl> -3, 2, -3, -3, 4, 2, -3, -3, -3, -3, -3, 2, -3...
$ BYS69D <dbl+lbl> -3, 1, -3, -3, 2, 2, -3, -3, -3, -3, -3, 4, -3...
$ BYS70A <dbl+lbl> -3, 1, -3, -3, 1, 1, -3, -3, -3, -3, -3, 1, -3...
$ BYS70B <dbl+lbl> -3, 1, -3, -3, 1, 1, -3, -3, -3, -3, -3, 1, -3...
$ BYS70C <dbl+lbl> -3, 1, -3, -3, 1, 1, -3, -3, -3, -3, -3, 1, -3...
$ BYS70D <dbl+lbl> -3, 1, -3, -3, 1, 1, -3, -3, -3, -3, -3, 1, -3...
$ BYS71A <dbl+lbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1...
$ BYS71B <dbl+lbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0...
$ BYS71C <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0...
$ BYS71D <dbl+lbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0...
$ BYS71E <dbl+lbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0...
$ BYS71F <dbl+lbl> 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0...
$ BYS71G <dbl+lbl> 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0...
$ BYS72 <dbl+lbl> 2, 1, 1, 1, 2, 1, 2, 1, 3, 3, 1, 1, 2...
$ BYS73 <dbl+lbl> -3, -3, -3, -3, -3, -3, ...
$ BYS74 <dbl+lbl> 200203, -3, -3, -3, 200105, -3, 20...
$ BYS75 <dbl+lbl> 27, -3, -3, -3, 14, -3, 5, -3, -9, -9, -3, -3, 29...
$ BYS76 <dbl+lbl> 17, -3, -3, -3, 15, -3, 0, -3, -9, -9, -3, -3, 14...
$ BYS77 <dbl+lbl> 1, -3, -3, -3, 4, -3, -9, -3, -6, 9, -3, -3, 3...
$ BYS79 <dbl+lbl> 3, -3, -3, -3, 3, -3, 1, -3, -6, 1, -3, -3, 7...
$ BYS80 <dbl+lbl> 3, -3, -3, -3, 2, -3, 3, -3, 3, 3, -3, -3, 3...
$ BYS83A <dbl+lbl> 2, 5, 2, 2, 1, -9, -1, -1, -1, 2, -1, 6, 2...
$ BYS83B <dbl+lbl> 4, 7, 4, 2, 1, -9, -1, -1, -1, 2, -1, 2, 5...
$ BYS84A <dbl+lbl> 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1,...
$ BYS84B <dbl+lbl> 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0,...
$ BYS84C <dbl+lbl> 1, 1, 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, 0, 1,...
$ BYS84E <dbl+lbl> 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1...
$ BYS84F <dbl+lbl> 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0,...
$ BYS84G <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,...
$ BYS84H <dbl+lbl> 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0,...
$ BYS84I <dbl+lbl> 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 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, 0, 0,...
$ BYS85A <dbl+lbl> 4, 4, 4, 4, 4, 4, 2, 4, 4, 4, 2, 4, 2...
$ BYS85B <dbl+lbl> 3, 4, 2, 4, 4, 4, 3, 2, 4, 3, 2, 4, 2...
$ BYS85C <dbl+lbl> 3, 4, 3, 4, 4, 4, 4, 2, 4, 4, 4, 4, 3...
$ BYS85D <dbl+lbl> 3, 4, -9, -9, 2, 4, 4, 2, 4, 2, 3, 4, 2...
$ BYS85E <dbl+lbl> 3, 2, 3, -9, 4, 4, 4, 3, 4, 3, 4, 4, 3...
$ BYS85F <dbl+lbl> 2, 2, 3, 2, 2, 1, 3, 2, 3, 1, 3, 1, 1...
$ BYS85G <dbl+lbl> 3, 3, 4, 2, 3, 3, 4, 2, 4, 4, 3, 1, 3...
$ BYS86A <dbl+lbl> 2, 3, 1, 3, 2, 1, 3, 2, 3, 3, 2, 2, 2...
$ BYS86B <dbl+lbl> 2, 2, 2, 3, 2, 1, 3, 3, 3, 2, 2, 2, 2...
$ BYS86C <dbl+lbl> 2, 3, 2, 3, 2, -9, 3, 3, 3, 2, 2, 2, 1...
$ BYS86D <dbl+lbl> 3, 2, 2, 3, 3, -9, 3, 3, 3, 3, 2, 2, 2...
$ BYS86E <dbl+lbl> 1, 1, 1, 1, 2, -9, 1, 1, 2, 2, 1, 1, 1...
$ BYS86F <dbl+lbl> 1, 3, 1, 1, 2, -9, 2, 3, 3, 2, 2, 2, 1...
$ BYS86G <dbl+lbl> 2, 3, 2, 3, 2, 3, 3, 3, 3, 3, 2, 3, 3...
$ BYS86H <dbl+lbl> 2, 2, 2, 2, 2, 1, 1, 3, 1, 1, 1, 2, 1...
$ BYS86I <dbl+lbl> 2, 1, 2, 3, 2, 1, 2, 2, 3, 3, -9, 3, 2...
$ BYS87A <dbl+lbl> 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2...
$ BYS87B <dbl+lbl> 3, 2, 1, 2, 2, 2, 3, 3, 2, 3, 3, 2, 3...
$ BYS87C <dbl+lbl> 3, 2, 3, 2, 2, 2, 3, 3, 3, 3, 3, 2, 3...
$ BYS87D <dbl+lbl> 3, 2, 1, 2, 2, 3, 3, 3, 2, 2, -9, 2, 3...
$ BYS87E <dbl+lbl> 2, 2, 1, 2, 2, -9, 2, 2, 2, 2, 1, 2, 2...
$ BYS87F <dbl+lbl> 3, 2, 3, 1, 2, 1, 3, 1, 2, 1, 2, 2, 2...
$ BYS88A <dbl+lbl> 3, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 2, 2...
$ BYS88B <dbl+lbl> 2, 3, 3, 3, 2, 4, 4, 3, 3, 4, 3, 3, 4...
$ BYS89A <dbl+lbl> 2, 4, 3, 4, 2, 2, 2, 4, 3, 4, 1, 2, 2...
$ BYS89B <dbl+lbl> 1, 3, 2, 3, 2, 2, 1, 3, 3, 3, 2, 2, 2...
$ BYS89C <dbl+lbl> 2, 3, 2, 3, 2, 4, 1, 3, 3, 3, 2, 2, 2...
$ BYS89D <dbl+lbl> 1, 3, 2, 4, 3, -9, 2, 2, 3, 4, 2, 1, 3...
$ BYS89E <dbl+lbl> 1, 3, 3, 4, 3, 3, 3, 3, 3, 4, 2, 1, 4...
$ BYS89F <dbl+lbl> 1, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 2, 3...
$ BYS89G <dbl+lbl> 2, 3, 3, 4, 3, 3, 4, 2, 3, 3, 3, 1, 4...
$ BYS89H <dbl+lbl> 1, 3, 2, 4, 3, 3, 3, 2, 3, 4, 2, 1, 4...
$ BYS89I <dbl+lbl> 3, 3, 2, 4, 3, 3, 3, 3, 3, 4, 3, 1, 4...
$ BYS89J <dbl+lbl> 1, 4, 3, 4, 3, 3, 3, 2, 3, 4, 3, 1, 4...
$ BYS89K <dbl+lbl> 3, 3, 2, 4, 3, 3, 2, 3, 3, 4, 3, 1, 4...
$ BYS89L <dbl+lbl> 2, 4, 2, 3, 3, 3, 2, 3, 3, 3, 2, 1, 2...
$ BYS89M <dbl+lbl> 2, 3, 2, 4, 3, 3, 2, 3, 3, 3, 3, 1, 4...
$ BYS89N <dbl+lbl> 3, 3, 3, 4, 3, 3, 3, 4, 3, 3, 2, 1, 4...
$ BYS89O <dbl+lbl> 2, 4, 3, 3, 3, 3, 3, 4, 3, 3, 2, 1, 3...
$ BYS89P <dbl+lbl> 2, 4, 3, 4, 3, 3, 4, 2, 3, 4, 3, 1, 4...
$ BYS89Q <dbl+lbl> 1, 3, 2, 1, 3, 3, 3, 4, 3, 3, 2, 1, 2...
$ BYS89R <dbl+lbl> 2, 4, 3, 3, 3, 3, 3, 3, 3, 3, 2, 1, 3...
$ BYS89S <dbl+lbl> 2, 3, 3, 4, 3, 3, 4, 3, 3, 3, 2, 1, 3...
$ BYS89T <dbl+lbl> 2, 3, 3, -9, 3, -9, 3, 4, 3, 3, 2, 1, 3...
$ BYS89U <dbl+lbl> 1, 4, 2, 4, 3, -9, 3, 3, 3, 3, 1, 1, 3...
$ BYS89V <dbl+lbl> 2, 3, 3, 4, 3, -9, 4, 2, 3, 4, 3, 1, 4...
$ BYS90A <dbl+lbl> 2, 3, 2, 3, 3, 3, 3, 2, 3, 2, 2, 3, 3...
$ BYS90B <dbl+lbl> 2, 3, 2, 3, 3, 3, 2, 2, 3, 2, 2, 2, 3...
$ BYS90C <dbl+lbl> 3, 1, 1, 2, 3, 3, 2, 3, 1, 2, 2, 3, 2...
$ BYS90D <dbl+lbl> 2, 2, 2, 3, 3, 3, 3, 2, 3, 3, 2, 3, 2...
$ BYS90E <dbl+lbl> 2, 3, 1, 3, 3, 3, 2, 3, 1, 1, 2, 2, 1...
$ BYS90F <dbl+lbl> 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 2, 3, 3...
$ BYS90G <dbl+lbl> 2, 3, 1, 3, 1, 1, 2, 2, 1, 1, 2, 3, 2...
$ BYS90H <dbl+lbl> 2, 3, 2, 2, 3, 3, 3, 2, 3, 3, 2, 3, 3...
$ BYS90J <dbl+lbl> 1, 2, 2, 2, 3, 3, 2, 2, 1, 1, 2, 2, 2...
$ BYS90K <dbl+lbl> 3, 2, 2, 3, 3, 3, 2, 2, 1, 2, 2, 3, 3...
$ BYS90L <dbl+lbl> 3, 3, 2, 3, 2, 3, 3, 3, 2, 2, 2, 3, 3...
$ BYS90M <dbl+lbl> 3, 3, 2, 3, 1, -9, 3, 3, 1, 1, 2, 3, 3...
$ BYS90Q <dbl+lbl> 3, 3, 2, 3, 2, 3, 2, 3, 1, 2, 2, 3, 3...
$ BYS91 <dbl+lbl> 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1...
$ BYS92A <dbl+lbl> 1, 2, 1, 1, 3, 1, 2, 1, 2, 2, 2, 1, 1...
$ BYS92B <dbl+lbl> 4, 3, 2, 2, 3, 1, 2, 3, 2, 3, 2, 3, 4...
$ BYS92C <dbl+lbl> 1, 2, 2, 1, 3, 1, 1, 1, 2, 2, 2, 2, 3...
$ BYS92D <dbl+lbl> 1, 2, -9, 3, 3, 3, 3, 4, 2, 2, 1, 1, 1...
$ BYS94 <dbl+lbl> 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYS96 <dbl+lbl> 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0...
$ BYS97A <dbl+lbl> -3, 1, -3, 1, 0, 1, 1, 1, -3, -3, 1, 1, -3...
$ BYS97B <dbl+lbl> -3, 0, -3, 0, 1, 0, 0, 0, -3, -3, 0, 0, -3...
$ BYS97C <dbl+lbl> -3, 0, -3, 0, 0, 0, 0, 0, -3, -3, 0, 0, -3...
$ BYS97D <dbl+lbl> -3, 0, -3, 0, 0, 0, 0, 0, -3, -3, 0, 0, -3...
$ BYS97E <dbl+lbl> -3, 0, -3, 0, 0, 0, 0, 0, -3, -3, 0, 0, -3...
$ BYP01 <dbl+lbl> 2, 2, 1, -4, 1, 1, 1, 1, 2, 1, 2, 1, 1...
$ BYP02 <dbl+lbl> -3, -3, -3, -4, -3, -3, -3, -3, -3, -3, -3, -3, -3...
$ BYP03 <dbl+lbl> 1, -9, 1, -4, 1, -9, 1, 1, 1, 1, 1, 0, 1...
$ BYP04 <dbl+lbl> 5, 1, 2, -4, 2, -9, 2, 2, 1, 2, 5, -3, 2...
$ BYP05 <dbl+lbl> 1, 1, 1, -4, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYP06 <dbl+lbl> 1, 8, 2, -4, 3, 3, -7, 4, 2, -7, 3, 2, 5...
$ BYP07A <dbl+lbl> 0, -9, 1, -4, 1, 1, -7, 2, 0, -7, 1, 0, -9...
$ BYP07B <dbl+lbl> 0, -9, 0, -4, 0, 0, -7, 0, 0, -7, 0, 0, -9...
$ BYP07C <dbl+lbl> 0, -9, 0, -4, 0, 0, -7, 0, 0, -7, 0, 0, -9...
$ BYP07D <dbl+lbl> 0, -9, 0, -4, 2, 1, -7, 1, 1, -7, 1, 1, -9...
$ BYP07E <dbl+lbl> 0, -9, 0, -4, 0, 0, -7, 0, 0, -7, 0, 0, -9...
$ BYP07F <dbl+lbl> 0, -9, 0, -4, 0, 0, -7, 0, 0, -7, 0, 0, -9...
$ BYP07G <dbl+lbl> 0, -9, 0, -4, 0, 0, -7, 0, 2, -7, 0, 0, -9...
$ BYP07H <dbl+lbl> 0, -9, 0, -4, 0, 0, -7, 0, 0, -7, 0, 0, -9...
$ BYP07I <dbl+lbl> 0, -9, 0, -4, 0, 0, -7, 0, 0, -7, 0, 0, -9...
$ BYP07J <dbl+lbl> 0, -9, 0, -4, 0, 0, -7, 0, 0, -7, 1, 0, -9...
$ BYP07K <dbl+lbl> 0, -9, 0, -4, 0, 0, -7, 0, 0, -7, 0, 0, -9...
$ BYP07L <dbl+lbl> 0, -9, 0, -4, 0, 0, -7, 0, 0, -7, 0, 0, -9...
$ BYP08 <dbl+lbl> 6, 5, 1, -4, 5, 2, -7, 3, 2, -7, 2, 5, 4...
$ BYP09 <dbl+lbl> 3, 0, -1, -4, 0, 0, -7, 0, 0, -7, 0, 0, 0...
$ BYP10 <dbl+lbl> 1, 1, 1, -4, 1, 2, 1, 1, 1, 1, 1, 5, 1...
$ BYP11 <dbl+lbl> 1959, 1953, 1965, -4, 1964, 1970, 1962, 1955, 19...
$ BYP12 <dbl+lbl> 1945, 1964, 1963, -4, 1955, 1966, 1960, 1955, 19...
$ BYP13 <dbl+lbl> 0, 0, 0, -4, 1, 1, 1, 0, 1, 0, 0, 1, 0...
$ BYP17 <dbl+lbl> 2, 3, 1, -4, 2, 1, 2, 1, 2, 1, -1, 1, 1...
$ BYP18 <dbl+lbl> 25, 13, -3, -4, 30, -3, -7, -3, 21, -3, -3, -3, -3...
$ BYP20 <dbl+lbl> 1, 3, 1, -4, 2, 1, 1, 1, 2, 1, -1, 2, 1...
$ BYP21 <dbl+lbl> -3, 13, -3, -4, 40, -3, -3, -3, 43, -3, -3, 36, -3...
$ BYP23 <dbl+lbl> 1, 3, 1, -4, 1, 1, 1, 1, 1, 1, 3, 1, 1...
$ BYP24 <dbl+lbl> -3, 2, -3, -4, -3, -3, -3, -3, -3, -3, 11, -3, -3...
$ BYP25 <dbl+lbl> 0, 0, 0, -4, 0, 0, -7, 0, 0, -7, 0, 0, 0...
$ BYP26A <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP26B <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP26C <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP26D <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP26E <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP26F <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP26G <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP26H <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP26I <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP26J <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP26K <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP26L <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP27 <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP28 <dbl+lbl> 1, 0, 1, -4, 0, 1, 0, 1, 0, 1, 1, 1, 1...
$ BYP30A <dbl+lbl> -3, 3, -3, -4, 4, -3, -7, -3, 4, -3, -3, -3, -3...
$ BYP30B <dbl+lbl> -3, 1, -3, -4, 4, -3, -7, -3, 2, -3, -3, -3, -3...
$ BYP30C <dbl+lbl> -3, 2, -3, -4, 4, -3, -7, -3, 4, -3, -3, -3, -3...
$ BYP30D <dbl+lbl> -3, 2, -3, -4, 4, -3, -7, -3, 4, -3, -3, -3, -3...
$ BYP31A <dbl+lbl> -3, 1, -3, -4, 2, -3, -7, -3, 1, -3, -3, -3, -3...
$ BYP31B <dbl+lbl> -3, 1, -3, -4, 2, -3, -7, -3, 1, -3, -3, -3, -3...
$ BYP31C <dbl+lbl> -3, 1, -3, -4, 2, -3, -7, -3, 3, -3, -3, -3, -3...
$ BYP31D <dbl+lbl> -3, 1, -3, -4, 2, -3, -7, -3, 4, -3, -3, -3, -3...
$ BYP32A <dbl+lbl> -3, 0, -3, -4, 0, -3, -7, -3, 1, -3, -3, -3, -3...
$ BYP32B <dbl+lbl> -3, 0, -3, -4, 0, -3, -7, -3, 1, -3, -3, -3, -3...
$ BYP32C <dbl+lbl> -3, 0, -3, -4, 0, -3, -7, -3, 0, -3, -3, -3, -3...
$ BYP32D <dbl+lbl> -3, 0, -3, -4, 0, -3, -7, -3, 0, -3, -3, -3, -3...
$ BYP32E <dbl+lbl> -3, 0, -3, -4, 0, -3, -7, -3, 0, -3, -3, -3, -3...
$ BYP33 <dbl+lbl> 6, 8, -1, -4, 1, 2, -7, 1, 1, -7, 2, 1, 11...
$ BYP34A <dbl+lbl> 5, 5, 2, -4, 1, 2, 6, 2, 1, 1, 4, 4, 3...
$ BYP34B <dbl+lbl> 1, -9, 2, -4, 1, 1, 3, 2, 2, 1, 6, -3, 4...
$ BYP35A <dbl+lbl> 1, 1, 1, -4, -1, -2, -7, 2, 1, -7, -1, 2, 2...
$ BYP35B <dbl+lbl> 2, 7, 1, -4, 2, -2, -7, 2, 1, -7, -1, 1, 4...
$ BYP35C <dbl+lbl> -1, 1, 2, -4, -1, -2, -7, 2, -1, -7, -1, -3, -9...
$ BYP35D <dbl+lbl> -1, 8, 2, -4, -1, -2, -7, 2, -1, -7, -1, -3, -9...
$ BYP36 <dbl+lbl> 1, 1, 1, -4, 3, 3, 1, 3, 1, 1, 1, 1, 1...
$ BYP37 <dbl+lbl> -3, -3, -3, -4, 2, 2, -3, 1, -3, -3, -3, -3, -3...
$ BYP38 <dbl+lbl> 1, 1, 1, -4, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYP39C <dbl+lbl> 6, 9, 5, -4, 8, 5, 14, 1, -9, 9, 7, 14, 15...
$ BYP40 <dbl+lbl> 1, 3, 1, -4, 1, 3, 1, 1, 1, 3, -3, -3, 3...
$ BYP41 <dbl+lbl> -3, -9, -3, -4, -3, 1, -3, -3, -3, 4, -3, -3, 3...
$ BYP42 <dbl+lbl> 1, 0, 1, -4, 1, 1, 1, 1, 1, 1, -3, -3, 1...
$ BYP43C <dbl+lbl> 8, -3, 5, -4, 5, 8, 15, 12, 5, 5, -3, -3, 15...
$ BYP44A <dbl+lbl> 0, 1, 0, -4, 0, 0, -7, 1, 0, -7, 0, 1, -9...
$ BYP44B <dbl+lbl> 1, 1, 1, -4, 0, 0, -7, 1, 1, -7, 1, 1, -9...
$ BYP44C <dbl+lbl> 0, 0, 0, -4, 1, 0, -7, 0, 0, -7, 0, 0, -9...
$ BYP44D <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 1, 1, -7, 1, 1, -9...
$ BYP45 <dbl+lbl> 4, 0, 2, -4, 0, 0, -7, 2, 0, -7, 4, 0, 3...
$ BYP46 <dbl+lbl> 0, 0, 0, -4, 0, 0, -7, 0, 0, -7, 0, 0, 1...
$ BYP47A <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, 0...
$ BYP47B <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, 1...
$ BYP47C <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, 0...
$ BYP48A <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, 0...
$ BYP48B <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, 0...
$ BYP48C <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, 0...
$ BYP48D <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, 1...
$ BYP48E <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, 0...
$ BYP48F <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, 0...
$ BYP48G <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, 0...
$ BYP48H <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, 0...
$ BYP48I <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, 0...
$ BYP48J <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, 0...
$ BYP48K <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, 0...
$ BYP49 <dbl+lbl> 0, 0, 0, -4, 0, 0, -7, 0, 0, -7, 0, 0, 0...
$ BYP50A <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP50B <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP50C <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP50D <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP50E <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP50F <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP50G <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP50H <dbl+lbl> -3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, -3, -3...
$ BYP51 <dbl+lbl> 0, 0, 0, -4, 0, 0, -7, 0, 0, -7, 0, 0, 0...
$ BYP52A <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 2, 2, -7, 1, 2, 1...
$ BYP52B <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 3, 1, -7, 3, 1, 1...
$ BYP52C <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 1, 1, -7, 2, 1, 1...
$ BYP52D <dbl+lbl> 2, 1, 1, -4, 1, 1, -7, 1, 1, -7, 2, 1, 1...
$ BYP52E <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 1, 1, -7, 1, 1, 2...
$ BYP52F <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 1, 1, -7, 1, 1, 1...
$ BYP52G <dbl+lbl> 1, 1, -1, -4, 1, 1, -7, 1, 4, -7, 1, 2, 2...
$ BYP52H <dbl+lbl> 1, 2, 1, -4, 1, 1, -7, 1, 1, -7, 1, 1, 2...
$ BYP52I <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 1, 1, -7, 1, 1, 2...
$ BYP52J <dbl+lbl> 1, 1, 2, -4, 2, 1, -7, 1, 1, -7, 2, 2, 2...
$ BYP53A <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 2, 1, -7, 1, 3, 1...
$ BYP53B <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 2, 1, -7, 3, 2, -9...
$ BYP53C <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 2, 1, -7, 2, 2, -9...
$ BYP53D <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 2, 1, -7, 2, 2, -9...
$ BYP53E <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 2, 1, -7, 1, 1, -9...
$ BYP53F <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 2, 2, -7, 1, 1, -9...
$ BYP53G <dbl+lbl> 1, 2, 1, -4, 1, 1, -7, 2, 1, -7, 1, 2, -9...
$ BYP53H <dbl+lbl> 1, 2, 1, -4, 1, 1, -7, 2, 1, -7, 1, 2, -9...
$ BYP53I <dbl+lbl> 1, 2, 1, -4, 1, 1, -7, 2, 1, -7, 1, 3, -9...
$ BYP53J <dbl+lbl> 1, 1, 2, -4, 1, 1, -7, 2, 1, -7, 2, 2, -9...
$ BYP54A <dbl+lbl> 0, 0, 0, -4, 0, 0, -7, 0, 0, -7, 0, 1, 0...
$ BYP54B <dbl+lbl> 0, 1, 0, -4, 1, 0, -7, 0, 0, -7, 0, 1, -9...
$ BYP54C <dbl+lbl> 0, 1, 0, -4, 0, 0, -7, 0, 0, -7, 0, 1, -9...
$ BYP54D <dbl+lbl> 0, 1, 0, -4, 0, 0, -7, 0, 0, -7, 0, 1, -9...
$ BYP54E <dbl+lbl> 1, 0, 0, -4, 0, 0, -7, 0, 0, -7, 0, 1, 1...
$ BYP55A <dbl+lbl> 3, 2, 4, -4, 4, 2, -7, 4, 4, -7, 2, 3, 3...
$ BYP55B <dbl+lbl> 4, 2, 4, -4, 4, 4, -7, 4, 4, -7, 4, 4, 4...
$ BYP55C <dbl+lbl> 4, 3, 4, -4, 4, 4, -7, 4, 4, -7, 3, 4, -9...
$ BYP55D <dbl+lbl> 4, 1, 4, -4, 4, 4, -7, 4, 4, -7, 4, 4, 4...
$ BYP56A <dbl+lbl> 2, 3, -1, -4, 3, 3, -7, 3, 3, -7, 1, 3, 2...
$ BYP56B <dbl+lbl> 2, 3, 2, -4, 3, 3, -7, 3, 3, -7, 1, 2, 2...
$ BYP56C <dbl+lbl> 2, 2, -1, -4, 3, 3, -7, 2, 3, -7, 2, 1, 2...
$ BYP56D <dbl+lbl> 3, 3, 1, -4, 3, 3, -7, 2, 1, -7, 2, 2, 2...
$ BYP56E <dbl+lbl> 2, 3, 1, -4, 1, 3, -7, 2, 2, -7, 1, 2, 2...
$ BYP56F <dbl+lbl> 2, 2, 3, -4, 1, 3, -7, 2, 1, -7, 1, 3, 2...
$ BYP57A <dbl+lbl> 2, 4, 1, -4, 4, 4, -7, 3, 3, -7, 3, 3, 3...
$ BYP57B <dbl+lbl> 2, 4, 1, -4, 4, 4, -7, 3, 4, -7, 3, 3, 3...
$ BYP57C <dbl+lbl> 1, 4, 3, -4, 4, 4, -7, 1, 3, -7, 3, 3, 3...
$ BYP57D <dbl+lbl> 4, 3, 1, -4, 2, 4, -7, 3, 2, -7, 1, 2, 3...
$ BYP57E <dbl+lbl> 4, 3, 1, -4, 4, 4, -7, 3, 4, -7, 1, 4, 3...
$ BYP57F <dbl+lbl> 4, 4, 4, -4, 4, 4, -7, 3, 2, -7, 1, 3, 3...
$ BYP57G <dbl+lbl> 3, 2, 4, -4, 4, 4, -7, 2, 2, -7, 3, 2, 3...
$ BYP57H <dbl+lbl> 3, 2, 1, -4, 2, 4, -7, 1, 4, -7, 1, 3, 3...
$ BYP57I <dbl+lbl> 4, 4, 4, -4, 4, 4, -7, 3, 3, -7, 1, 3, 3...
$ BYP57J <dbl+lbl> 4, 3, 3, -4, 4, 4, -7, 3, 3, -7, 1, 3, 3...
$ BYP57K <dbl+lbl> 4, 3, 4, -4, 4, 4, -7, 4, 4, -7, 1, 3, 3...
$ BYP57L <dbl+lbl> 3, 3, 3, -4, 4, 4, -7, 4, 3, -7, 1, 3, 3...
$ BYP58A <dbl+lbl> 1, 2, 2, -4, 2, 2, -7, 2, 1, -7, 2, 2, 2...
$ BYP58B <dbl+lbl> 4, 3, 2, -4, 2, 3, -7, 3, 2, -7, 3, 3, 3...
$ BYP59BA <dbl+lbl> 0, 1, 1, -4, 0, 1, -7, 1, 1, -7, 1, 1, 1...
$ BYP59CA <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 1, 1, -7, 1, 1, 1...
$ BYP59DA <dbl+lbl> 1, 1, 1, -4, 1, 0, -7, 1, 1, -7, 0, 1, 1...
$ BYP59EA <dbl+lbl> 1, 1, 1, -4, 1, 0, -7, 1, 1, -7, 0, 1, 0...
$ BYP59BB <dbl+lbl> 1, 1, 1, -4, -3, 1, -7, 1, -3, -7, 1, 1, 1...
$ BYP59CB <dbl+lbl> 1, 1, 1, -4, -3, 1, -7, 1, -3, -7, 1, 1, 1...
$ BYP59DB <dbl+lbl> 1, 1, 1, -4, -3, 1, -7, 1, -3, -7, 0, 1, 1...
$ BYP59EB <dbl+lbl> 0, 1, 1, -4, -3, 1, -7, 1, -3, -7, 0, 0, 0...
$ BYP59BC <dbl+lbl> 0, -3, 1, -4, -3, 1, -7, 1, -3, -7, 1, 1, 1...
$ BYP59CC <dbl+lbl> 1, -3, 1, -4, -3, 1, -7, 1, -3, -7, 0, 1, 1...
$ BYP59DC <dbl+lbl> 1, -3, 1, -4, -3, 0, -7, 0, -3, -7, 0, 0, 1...
$ BYP59EC <dbl+lbl> 1, -3, 1, -4, -3, 1, -7, 0, -3, -7, 0, 0, 1...
$ BYP60A <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 1, 1, -7, 1, 2, 2...
$ BYP60B <dbl+lbl> 4, 1, 1, -4, 1, 1, -7, 2, 1, -7, 1, 4, 2...
$ BYP60C <dbl+lbl> 4, 2, 1, -4, 3, 1, -7, 2, 1, -7, 1, 4, 2...
$ BYP60D <dbl+lbl> 1, 1, 2, -4, 3, 2, -7, 1, 1, -7, 2, 1, 2...
$ BYP61 <dbl+lbl> 1, 0, 0, -4, 0, 0, -7, 0, 0, -7, 0, 1, 0...
$ BYP62 <dbl+lbl> 1, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, 1, -3...
$ BYP63 <dbl+lbl> 3, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, 2, -3...
$ BYP64A <dbl+lbl> 0, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, 0, -3...
$ BYP64B <dbl+lbl> 0, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, 0, -3...
$ BYP64C <dbl+lbl> 1, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, 0, -3...
$ BYP64D <dbl+lbl> 0, -3, -3, -4, -3, -3, -7, -3, -3, -7, -3, 1, -3...
$ BYP65 <dbl+lbl> 13, 6, 17, -4, 23, 0, -7, 14, 19, -7, 6, 1, 7...
$ BYP66 <dbl+lbl> 1, 1, -1, -4, 1, 1, -7, 1, 2, -7, 2, 1, 2...
$ BYP67 <dbl+lbl> 2, 3, 3, -4, 3, 3, -7, 3, 3, -7, 3, 3, 3...
$ BYP68 <dbl+lbl> 2, 1, 1, -4, 3, 1, -7, 1, 1, -7, 2, 1, 2...
$ BYP69A <dbl+lbl> 1, 1, 1, -4, 0, 1, -7, 1, 0, -7, 1, 1, 1...
$ BYP69B <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 1, 1, -7, 1, 1, 1...
$ BYP69C <dbl+lbl> 1, 0, -1, -4, 1, 1, -7, 1, 1, -7, 1, 1, 1...
$ BYP69D <dbl+lbl> 1, 1, -1, -4, 0, 1, -7, 0, 1, -7, 1, 1, 0...
$ BYP70 <dbl+lbl> 5, 2, 6, -4, 7, 5, 5, 4, 2, 7, 4, 5, 3...
$ BYP71 <dbl+lbl> 1, 1, 1, -4, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYP72 <dbl+lbl> 1, 1, 1, -4, 1, 1, -7, 1, 1, -7, 1, 1, 1...
$ BYP73 <dbl+lbl> 3, -6, 3, -4, 1, 3, -7, 3, 3, -7, 3, 1, 1...
$ BYP74A <dbl+lbl> -3, 1, -3, -4, 1, -3, -7, -3, -3, -7, -3, 4, 2...
$ BYP74B <dbl+lbl> -3, 1, -3, -4, 3, -3, -7, -3, -3, -7, -3, 5, 2...
$ BYP74C <dbl+lbl> -3, 1, -3, -4, 5, -3, -7, -3, -3, -7, -3, 2, 2...
$ BYP74D <dbl+lbl> -3, 2, -3, -4, 3, -3, -7, -3, -3, -7, -3, 2, 4...
$ BYP74E <dbl+lbl> -3, 2, -3, -4, 1, -3, -7, -3, -3, -7, -3, 3, 2...
$ BYP75 <dbl+lbl> -1, 1, 1, -4, 0, 1, -7, 1, 1, -7, 0, 1, 1...
$ BYP76 <dbl+lbl> -3, 2, 1, -4, -3, 1, -7, 1, 3, -7, -3, 3, 1...
$ BYP77A <dbl+lbl> 2, 3, 4, -4, 2, 1, -7, 3, 2, -7, 3, 3, 3...
$ BYP77B <dbl+lbl> 4, 2, 1, -4, 2, 1, -7, 2, 1, -7, 2, 2, 2...
$ BYP77C <dbl+lbl> 3, 3, 1, -4, 2, 1, -7, 2, 1, -7, 2, 2, 1...
$ BYP77D <dbl+lbl> 2, 2, 1, -4, 2, 3, -7, 2, 1, -7, 2, 3, 2...
$ BYP77E <dbl+lbl> 2, 2, 1, -4, 3, 1, -7, 2, 1, -7, 2, 2, 1...
$ BYP77F <dbl+lbl> 2, 2, 2, -4, 2, 2, -7, 2, 1, -7, 2, 4, 3...
$ BYP77G <dbl+lbl> 2, 4, 2, -4, 2, 1, -7, 2, 1, -7, 2, 4, 2...
$ BYP77H <dbl+lbl> 2, 4, 2, -4, 2, 1, -7, 2, 4, -7, 2, -1, 1...
$ BYP77I <dbl+lbl> 2, 4, 2, -4, 2, 1, -7, 2, 1, -7, 2, 2, 3...
$ BYP77J <dbl+lbl> 3, -1, -1, -4, 3, 3, -7, 3, 3, -7, 4, -1, 4...
$ BYP77K <dbl+lbl> 3, -1, -1, -4, 2, 3, -7, -1, 3, -7, 4, -1, -1...
$ BYP77L <dbl+lbl> 2, -1, 2, -4, 2, 3, -7, -1, 3, -7, 4, -1, -1...
$ BYP77M <dbl+lbl> 2, -1, 2, -4, 2, 3, -7, 2, 3, -7, 2, 1, 3...
$ BYP77N <dbl+lbl> 3, -1, 2, -4, 2, 3, -7, 3, 3, -7, 3, 2, 3...
$ BYP77O <dbl+lbl> 3, 4, 3, -4, 2, 1, -7, 3, 3, -7, 2, 2, 3...
$ BYP78 <dbl+lbl> 2, 2, 1, -4, 2, 2, 1, 1, 1, 2, 2, 2, 1...
$ BYP79 <dbl+lbl> 5, 7, 7, -4, 2, 3, 5, 5, 5, 2, 5, 3, 6...
$ BYP80A <dbl+lbl> 3, 2, 2, -4, -3, 1, -7, 2, 1, -3, 1, 1, 1...
$ BYP80B <dbl+lbl> 2, 1, 2, -4, -3, 1, -7, 2, 1, -3, 1, 1, 1...
$ BYP80C <dbl+lbl> 2, 1, 2, -4, -3, 1, -7, 2, 1, -3, 1, 1, 1...
$ BYP80D <dbl+lbl> 3, 2, 3, -4, -3, 1, -7, 2, 1, -3, 2, 1, 2...
$ BYP80E <dbl+lbl> 3, 3, 3, -4, -3, 2, -7, 2, 1, -3, 2, 1, 3...
$ BYP80F <dbl+lbl> 2, 2, 2, -4, -3, 1, -7, 2, 1, -3, 2, 1, 1...
$ BYP80G <dbl+lbl> 2, 2, 3, -4, -3, 2, -7, 2, 3, -3, 2, 3, 2...
$ BYP80H <dbl+lbl> 2, 3, 3, -4, -3, 3, -7, 2, 1, -3, 3, 1, 1...
$ BYP80I <dbl+lbl> 1, 2, 1, -4, -3, 1, -7, 2, 1, -3, 1, 1, 1...
$ BYP80J <dbl+lbl> 1, 2, 2, -4, -3, 1, -7, 2, 1, -3, 2, 1, 1...
$ BYP80K <dbl+lbl> 1, 2, 2, -4, -3, 1, -7, 2, 1, -3, 2, 1, 1...
$ BYP80L <dbl+lbl> 1, 2, 2, -4, -3, 1, -7, 2, 1, -3, 2, 1, 1...
$ BYP80M <dbl+lbl> 3, 2, 2, -4, -3, 1, -7, 2, 1, -3, 2, 1, 1...
$ BYP80N <dbl+lbl> 3, 2, 3, -4, -3, 2, -7, 2, 1, -3, 3, 1, 3...
$ BYP80O <dbl+lbl> 3, 2, 3, -4, -3, 2, -7, 2, 1, -3, 2, 1, 3...
$ BYP81 <dbl+lbl> 2, 7, 6, -4, 5, 3, -7, 5, 5, -7, 5, 3, 6...
$ BYP82 <dbl+lbl> -3, 1, 0, -4, 0, 0, -7, 0, 1, -7, 0, 1, -9...
$ BYP83A <dbl+lbl> -3, 0, -3, -4, -3, -3, -7, -3, 1, -7, -3, 1, 1...
$ BYP83B <dbl+lbl> -3, 1, -3, -4, -3, -3, -7, -3, 1, -7, -3, 1, 1...
$ BYP83C <dbl+lbl> -3, 0, -3, -4, -3, -3, -7, -3, 0, -7, -3, 0, 0...
$ BYP83D <dbl+lbl> -3, 1, -3, -4, -3, -3, -7, -3, 0, -7, -3, 1, 1...
$ BYP83E <dbl+lbl> -3, 0, -3, -4, -3, -3, -7, -3, 0, -7, -3, 0, 0...
$ BYP83F <dbl+lbl> -3, -9, -3, -4, -3, -3, -7, -3, 0, -7, -3, 0, 1...
$ BYP83G <dbl+lbl> -3, 1, -3, -4, -3, -3, -7, -3, 0, -7, -3, 1, 1...
$ BYP83H <dbl+lbl> -3, 0, -3, -4, -3, -3, -7, -3, 0, -7, -3, 0, 1...
$ BYP83I <dbl+lbl> -3, 0, -3, -4, -3, -3, -7, -3, 0, -7, -3, 1, 1...
$ BYP83J <dbl+lbl> -3, 0, -3, -4, -3, -3, -7, -3, 0, -7, -3, 0, 0...
$ BYP83K <dbl+lbl> -3, 1, -3, -4, -3, -3, -7, -3, 0, -7, -3, 0, 0...
$ BYP83L <dbl+lbl> -3, 0, -3, -4, -3, -3, -7, -3, 0, -7, -3, 0, 1...
$ BYP83M <dbl+lbl> -3, 0, -3, -4, -3, -3, -7, -3, 0, -7, -3, 0, 0...
$ BYP84 <dbl+lbl> -3, 4, -3, -4, -3, -3, -7, -3, 4, -7, -3, 5, 1...
$ BYP85 <dbl+lbl> 10, 11, 10, -4, 6, -9, 10, 10, 8, -9, 8, 8, 5...
$ BYP86 <dbl+lbl> 2, 1, 2, -4, 2, -2, -7, 1, 2, -7, 1, 1, 2...
$ BYP97 <dbl+lbl> 0, 0, 0, -4, 0, 0, -9, 0, 0, -9, 1, 0, 1...
$ BYP98A <dbl+lbl> -3, -3, -3, -4, -3, -3, -9, -3, -3, -9, 1, -3, 1...
$ BYP98B <dbl+lbl> -3, -3, -3, -4, -3, -3, -9, -3, -3, -9, 0, -3, 0...
$ BYP98C <dbl+lbl> -3, -3, -3, -4, -3, -3, -9, -3, -3, -9, 0, -3, 0...
$ BYP98D <dbl+lbl> -3, -3, -3, -4, -3, -3, -9, -3, -3, -9, 0, -3, 0...
$ BYP98E <dbl+lbl> -3, -3, -3, -4, -3, -3, -9, -3, -3, -9, 0, -3, 0...
$ BYP99 <dbl+lbl> 200207, 200206, 200207, -4, 200207, 200208, 20...
$ BYTE01 <dbl+lbl> 1, 1, 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, 1, 2...
$ BYTE03 <dbl+lbl> 1, 1, 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, 0, 0...
$ BYTE05 <dbl+lbl> 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1...
$ BYTE06 <dbl+lbl> 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0...
$ BYTE07 <dbl+lbl> 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1...
$ BYTE08A <dbl+lbl> 1, -3, 0, 0, 0, 0, 1, -3, 0, -3, 0, 1, 1...
$ BYTE08B <dbl+lbl> -3, -3, 0, 0, 0, 0, 0, -3, -3, -3, 1, 0, -3...
$ BYTE08C <dbl+lbl> 1, -3, 0, 0, 0, 0, 1, -3, 0, -3, 0, 1, 1...
$ BYTE08D <dbl+lbl> 1, -3, 0, 0, 0, 0, 0, -3, -3, -3, 0, 0, -3...
$ BYTE08E <dbl+lbl> 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1...
$ BYTE09 <dbl+lbl> 2, 2, -1, 3, 1, 2, 2, 2, 2, 1, 1, 2, 2...
$ BYTE10 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 2, 1, 3...
$ BYTE11 <dbl+lbl> 1, 0, 0, 0, 0, 0, 0, 0, -1, 0, 1, 0, 0...
$ BYTE12 <dbl+lbl> 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1...
$ BYTE12A <dbl+lbl> 0, -3, 0, -3, -3, -3, -3, -3, 0, -3, -3, 0, 0...
$ BYTE12B <dbl+lbl> 0, -3, 0, -3, -3, -3, -3, -3, 0, -3, -3, 0, 0...
$ BYTE12C <dbl+lbl> 0, -3, 0, -3, -3, -3, -3, -3, 0, -3, -3, 0, 0...
$ BYTE12D <dbl+lbl> 1, -3, 1, -3, -3, -3, -3, -3, 1, -3, -3, 1, 1...
$ BYTE12E <dbl+lbl> 1, -3, 0, -3, -3, -3, -3, -3, 1, -3, -3, 1, 1...
$ BYTE13 <dbl+lbl> 3, 5, 3, 4, 4, -9, 4, 4, 3, 4, 4, 4, 3...
$ BYTE14 <dbl+lbl> 2, 1, 2, 2, 2, 2, 2, 2, 3, 3, 2, 2, 2...
$ BYTE15 <dbl+lbl> 1, 1, 1, 1, 1, 3, 1, 1, 3, 1, 2, 1, 1...
$ BYTE16 <dbl+lbl> 4, 5, 3, 4, 4, 5, 4, 4, 2, 4, 3, 4, 4...
$ BYTE17 <dbl+lbl> 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2...
$ BYTE18A <dbl+lbl> 1, -3, 0, -3, -3, -3, 1, -3, 1, -3, 0, 0, 1...
$ BYTE18B <dbl+lbl> -3, -3, 0, -3, -9, -3, 0, -3, -3, -3, 1, 0, -3...
$ BYTE19 <dbl+lbl> -3, -3, 1, -3, 0, 0, 0, 1, 0, 0, 0, 0, -3...
$ BYTE20 <dbl+lbl> 6, 6, 5, 3, 3, 3, 3, 5, 3, 5, 6, 3, 5...
$ BYTE21A <dbl+lbl> 2, 2, 2, 3, 3, 3, 3, 2, 3, 3, 1, 3, 3...
$ BYTE21B <dbl+lbl> 2, 2, 3, 3, 3, 4, 3, 2, 4, 3, 2, 3, 2...
$ BYTE21C <dbl+lbl> 2, 2, 2, 3, 4, 4, 4, 3, 5, 4, 1, 3, 3...
$ BYTE21D <dbl+lbl> 1, 1, 1, 4, 4, 4, 3, 2, 5, 5, 2, 2, 2...
$ BYTE22 <dbl+lbl> 2, 2, 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, 0, 0...
$ BYTE25 <dbl+lbl> 1942, 1942, 1950, 1976, 1959, 1965, 1953, 1947, 19...
$ BYTE26A <dbl+lbl> 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 1, -9, 0...
$ BYTE26B <dbl+lbl> 25, 25, 25, 3, 3, 4, 26, 5, 1, 1, 4, -9, 25...
$ BYTE26C <dbl+lbl> 25, 25, 25, 3, 3, 5, 26, 7, 1, 1, 5, 21, 25...
$ BYTE27 <dbl+lbl> 25, 25, 27, 2, 3, 3, 5, 5, 1, 1, 3, 17, 25...
$ BYTE28 <dbl+lbl> 1, 1, 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, 1, 1...
$ BYTE30A <dbl+lbl> 0, 0, 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, 0, 0...
$ BYTE30C <dbl+lbl> 1, 1, 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, 0, 0...
$ BYTE30E <dbl+lbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0...
$ BYTE30F <dbl+lbl> 0, 0, 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, 0, 0...
$ BYTE31A <dbl+lbl> 1, 1, 1, 2, 1, 2, -9, 2, 2, 2, 2, 2, 1...
$ BYTE31B <dbl+lbl> 2, 2, 2, 1, -3, 1, -9, 1, 1, 1, 1, 1, 2...
$ BYTE32A <dbl+lbl> -3, -3, -3, -3, -3, -3, 10, -3, -3, -3, -3, -3, -3...
$ BYTE32B <dbl+lbl> -3, -3, -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, 5, 5...
$ BYTE33B <dbl+lbl> 5, 5, 5, -3, -3, 2, 2, 2, -3, -3, 2, 2, 5...
$ BYTE34 <dbl+lbl> 1, 1, 3, 1, 1, 1, 4, 1, 1, 1, 1, 4, 1...
$ BYTE35A <dbl+lbl> 5, 5, 5, 4, 4, 5, 5, 4, 6, 6, 5, 4, 5...
$ BYTE35B <dbl+lbl> 4, 4, 5, 4, 2, 4, 3, 3, 4, 4, 4, 2, 4...
$ BYTE35C <dbl+lbl> 3, 3, 3, 3, 2, 4, 3, 2, 3, 3, 4, 1, 3...
$ BYTE35D <dbl+lbl> 3, 3, 3, 3, 2, 3, 1, 2, 2, 2, 3, 1, 3...
$ BYTE35E <dbl+lbl> 1, 1, 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, 1, 2...
$ BYTE35G <dbl+lbl> 2, 2, 1, 2, 1, 3, 1, 2, 2, 2, 3, 1, 2...
$ BYTE35H <dbl+lbl> 2, 2, 3, 2, 1, 2, 1, 2, 3, 3, 2, 1, 2...
$ BYTE35I <dbl+lbl> 3, 3, 6, 6, 1, 1, 1, 3, 6, 6, 1, 1, 3...
$ BYTE35J <dbl+lbl> 2, 2, 3, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2...
$ BYTE35K <dbl+lbl> 6, 6, 6, 1, 3, 6, 4, 1, 6, 6, 6, 4, 6...
$ BYTE35L <dbl+lbl> 4, 4, 3, 3, 3, 6, 2, 3, 4, 4, 6, 3, 4...
$ BYTE35M <dbl+lbl> 3, 3, 3, 5, 1, 3, 2, 2, 3, 3, 3, 2, 3...
$ BYTE35N <dbl+lbl> 1, 1, 1, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYTE36 <dbl+lbl> 24, 24, 6, 15, 40, 0, 0, 4, 0, 0, 0, 0, 24...
$ BYTE37 <dbl+lbl> 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...
$ BYTE38A <dbl+lbl> 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1...
$ BYTE38B <dbl+lbl> 1, 1, 1, 1, 1, 1, 0, -9, 1, 1, 1, 0, 1...
$ BYTE38C <dbl+lbl> 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1...
$ BYTE38D <dbl+lbl> 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1...
$ BYTE38E <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1...
$ BYTE38F <dbl+lbl> 1, 1, 1, 1, 0, 0, -9, 0, 1, 1, 0, 0, 1...
$ BYTE39 <dbl+lbl> 10, 10, 5, 6, 2, 1, 3, 2, -9, -9, 1, 2, 10...
$ BYTE40 <dbl+lbl> 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYTE41 <dbl+lbl> -3, -3, -3, 1, -3, -3, -3, -3, -3, -3, -3, -3, -3...
$ BYTE42 <dbl+lbl> 3, 3, 0, 3, 1, 0, 0, 1, 1, 1, 0, 0, 3...
$ BYTE43 <dbl+lbl> 1, 1, -3, 0, 1, -3, -3, 1, 0, 0, -3, -3, 1...
$ BYTE44A <dbl+lbl> 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2...
$ BYTE44B <dbl+lbl> 2, 2, 2, 2, 3, 2, 2, 2, 3, 3, 2, 1, 2...
$ BYTE44C <dbl+lbl> 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1...
$ BYTE44D <dbl+lbl> 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 2, 1...
$ BYTE44E <dbl+lbl> 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1...
$ BYTE44F <dbl+lbl> 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYTE47 <dbl+lbl> 200204, 200204, 200204, 200204, 200204, 200207, 20...
$ BYTM01 <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1...
$ BYTM02 <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, -3, -3, 1, -3, 1...
$ BYTM03 <dbl+lbl> 1, 1, 1, -9, 1, -9, 1, 1, 1, 1, 0, 1, 1...
$ BYTM04 <dbl+lbl> 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1...
$ BYTM05 <dbl+lbl> 1, 1, 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, 1, 0...
$ BYTM07 <dbl+lbl> 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0...
$ BYTM08A <dbl+lbl> 0, 0, 0, 0, -3, 0, 1, 0, 0, 0, 0, 0, -3...
$ BYTM08B <dbl+lbl> 0, 0, 0, -3, -3, -3, 1, 0, -3, -3, 0, -3, 0...
$ BYTM08C <dbl+lbl> 0, 0, 0, -3, -3, 0, 1, 0, 1, 0, 0, 1, 0...
$ BYTM08D <dbl+lbl> 0, 0, 0, -3, -3, -3, -3, 0, 0, -3, 0, -3, 0...
$ BYTM08E <dbl+lbl> 1, 1, 0, 0, 0, 0, 1, 0, 0, -3, 0, 0, 0...
$ BYTM09 <dbl+lbl> 2, 1, 2, -1, 2, -1, 1, 2, -1, -1, 2, -1, -1...
$ BYTM10 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3...
$ BYTM11 <dbl+lbl> 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, -1, 0...
$ BYTM12 <dbl+lbl> 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1...
$ BYTM12A <dbl+lbl> 1, -3, -3, -3, -3, 0, 1, -3, -9, -3, -3, 0, 0...
$ BYTM12B <dbl+lbl> 0, -3, -3, -3, -3, 0, 0, -3, -9, -3, -3, 0, 0...
$ BYTM12C <dbl+lbl> 0, -3, -3, -3, -3, 0, 0, -3, -9, -3, -3, 0, 1...
$ BYTM12D <dbl+lbl> 0, -3, -3, -3, -3, 1, 1, -3, -9, -3, -3, 1, 1...
$ BYTM12E <dbl+lbl> 0, -3, -3, -3, -3, 0, 0, -3, -9, -3, -3, 0, 0...
$ BYTM13 <dbl+lbl> 5, 5, 5, 4, 5, 3, 4, 5, 3, 3, 5, 3, 4...
$ BYTM14 <dbl+lbl> 2, 2, 2, 2, 2, 3, 2, 2, 3, 3, 2, 2, 3...
$ BYTM15 <dbl+lbl> 1, 1, 2, 1, 1, 2, 1, 2, 3, 2, 2, 1, 3...
$ BYTM16 <dbl+lbl> 5, 5, 5, 5, 5, 4, 4, 5, 3, 3, 4, 3, 3...
$ BYTM17 <dbl+lbl> 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2...
$ BYTM18A <dbl+lbl> 1, -3, 0, -3, -3, -3, 1, 0, -3, -3, 0, -3, 1...
$ BYTM18B <dbl+lbl> -3, -3, 0, -3, -3, -3, 1, 0, -3, -3, -9, -3, 1...
$ BYTM19 <dbl+lbl> 0, 1, 1, -3, -3, 0, 1, 1, 0, 0, 1, 0, 1...
$ BYTM20 <dbl+lbl> 3, 7, 5, 3, 2, 3, 5, 5, 2, 3, 5, 4, 3...
$ BYTM22 <dbl+lbl> 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2...
$ BYTM23 <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYTM25 <dbl+lbl> 1964, 1964, 1964, 1967, 1968, 1967, 1958, 1964, 19...
$ BYTM26A <dbl+lbl> 0, 0, 0, 0, 1, 0, 0, 0, -9, 0, 0, -9, 0...
$ BYTM26B <dbl+lbl> 10, 10, 14, 13, 4, 13, 6, 14, -9, 7, 28, -9, 6...
$ BYTM26C <dbl+lbl> -9, -9, 14, 13, 5, 13, 6, 14, 19, 7, 28, 19, 6...
$ BYTM27 <dbl+lbl> 10, 10, 13, 13, 4, 13, 3, 13, 19, 4, 25, 19, 3...
$ BYTM28 <dbl+lbl> 1, 1, 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, 1, 1...
$ BYTM30A <dbl+lbl> 0, 0, 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, 1, 0...
$ BYTM30C <dbl+lbl> 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYTM30D <dbl+lbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1...
$ BYTM30E <dbl+lbl> 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0...
$ BYTM30F <dbl+lbl> 0, 0, 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, 0, 0...
$ BYTM31A <dbl+lbl> -9, -9, 3, 1, 10, 1, 3, 3, -9, -9, 3, -9, 3...
$ BYTM31B <dbl+lbl> -9, -9, 1, 3, -3, 3, 1, 1, -9, 10, 10, -9, 1...
$ BYTM32A <dbl+lbl> -3, -3, 3, -3, 1, -3, -9, 3, -3, -3, -3, -3, -9...
$ BYTM32B <dbl+lbl> -3, -3, -3, -3, -3, -3, -9, -3, -3, -3, -9, -3, -9...
$ BYTM33C <dbl+lbl> -9, -9, 5, 5, -3, 5, 5, 5, 5, 5, 5, 5, 5...
$ BYTM33D <dbl+lbl> -9, -9, 5, -3, -3, -3, 2, 5, 1, -3, 5, 1, 2...
$ BYTM34 <dbl+lbl> 1, 1, 4, 1, 2, 1, 4, 4, 1, 1, 2, 1, 4...
$ BYTM35A <dbl+lbl> 4, 4, 5, 2, 5, 2, 4, 5, 4, 3, 4, 4, 4...
$ BYTM35B <dbl+lbl> 1, 1, 2, 3, 2, 3, 3, 2, -9, 2, 4, -9, 3...
$ BYTM35C <dbl+lbl> 1, 1, 2, 1, 2, 1, 2, 2, 3, 1, 4, 3, 2...
$ BYTM35D <dbl+lbl> 1, 1, 2, 1, 2, 1, 3, 2, 3, 1, 4, 3, 3...
$ BYTM35E <dbl+lbl> 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 4, 1, 1...
$ BYTM35F <dbl+lbl> 1, 1, 2, 1, 1, 1, 1, 2, 1, 3, 4, 1, 1...
$ BYTM35G <dbl+lbl> 3, 3, 2, 1, 1, 1, 2, 2, 2, 2, 4, 2, 2...
$ BYTM35H <dbl+lbl> 1, 1, 2, 1, 2, 1, 2, 2, 1, 2, 4, 1, 2...
$ BYTM35I <dbl+lbl> 1, 1, 1, 1, 6, 1, 3, 1, 6, 1, 4, 6, 3...
$ BYTM35J <dbl+lbl> 1, 1, 2, 1, 1, 1, 2, 2, 2, 2, 4, 2, 2...
$ BYTM35K <dbl+lbl> 3, 3, 3, 4, 4, 4, 3, 3, 3, 2, 4, 3, 3...
$ BYTM35L <dbl+lbl> 2, 2, 3, 3, 3, 3, 1, 3, 3, 3, 4, 3, 1...
$ BYTM35M <dbl+lbl> 1, 1, 3, 1, 3, 1, 1, 3, 5, 4, 4, 5, 1...
$ BYTM35N <dbl+lbl> 1, 1, 1, 1, 6, 1, 1, 1, 6, 5, 4, 6, 1...
$ BYTM36 <dbl+lbl> 0, 0, 3, 30, 50, 30, 3, 3, 0, 22, 10, 0, 3...
$ BYTM37 <dbl+lbl> 0, 0, 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, 1, 0...
$ BYTM38B <dbl+lbl> 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1...
$ BYTM38C <dbl+lbl> 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0...
$ BYTM38D <dbl+lbl> 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1...
$ BYTM38E <dbl+lbl> 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1...
$ BYTM38F <dbl+lbl> 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0...
$ BYTM39 <dbl+lbl> 10, 10, 2, 1, 1, 1, 2, 2, 40, 2, 0, 40, 2...
$ BYTM40 <dbl+lbl> 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0...
$ BYTM41 <dbl+lbl> 0, 0, -3, -3, 0, -3, -3, -3, -3, -3, -3, -3, -3...
$ BYTM42 <dbl+lbl> 1, 1, 0, 3, 0, 3, 3, 0, 0, 0, 0, 0, 3...
$ BYTM43 <dbl+lbl> 0, 0, -3, 0, -3, 0, 1, -3, -3, -3, -3, -3, 1...
$ BYTM44A <dbl+lbl> 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 2, 2...
$ BYTM44B <dbl+lbl> 2, 2, 2, 2, 2, 2, 3, 2, 2, -9, 2, 2, 3...
$ BYTM44C <dbl+lbl> 1, 1, 1, 1, 2, 1, 2, 1, 2, -9, 2, 2, 2...
$ BYTM44D <dbl+lbl> 2, 2, 1, 2, 3, 2, 2, 1, 2, -9, 2, 2, 2...
$ BYTM44E <dbl+lbl> 1, 1, 2, 2, 3, 2, 2, 2, 2, -9, 2, 2, 2...
$ BYTM44F <dbl+lbl> 1, 1, 2, 1, 3, 1, 1, 2, 1, -9, 2, 1, 1...
$ BYTM45A <dbl+lbl> 2, 2, 2, 2, 3, 2, 1, 2, 1, 2, 2, 1, 1...
$ BYTM45B <dbl+lbl> 3, 3, 3, 3, 2, 3, 4, 3, 4, 2, 3, 4, 4...
$ BYTM47 <dbl+lbl> 200205, 200205, 200208, 200204, 200204, 200204, 20...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, 3, 4, ...
$ BYS21B <dbl+lbl> 3, 3, 3, -9, 4, 2, 3, 3, 3, 2, 3, 2, 4, ...
$ BYS20G <dbl+lbl> 3, 2, 3, 2, 3, 2, 2, 2, 3, 1, 2, 2, 3, ...
$ BYS20F <dbl+lbl> 2, 2, 2, 2, 2, 1, 2, 2, 3, 2, 1, 2, 3, ...
$ BYS20H <dbl+lbl> 1, 4, 3, -9, 3, 3, 4, 4, 3, 3, 3, 3, 3, ...
$ BYS20A <dbl+lbl> 2, 2, 3, -9, 2, 2, 3, 1, 3, 2, 2, 2, 3, ...
$ BYS20I <dbl+lbl> 3, 4, 3, -9, 3, 3, 4, 3, 3, 4, 3, 3, 4, ...
$ BYS20C <dbl+lbl> 1, 2, 3, -9, 2, 2, 1, 2, 3, 2, 1, 3, 2, ...
$ BYS88B <dbl+lbl> 2, 3, 3, 3, 2, 4, 4, 3, 3, 4, 3, 3, 4, ...
$ BYS88A <dbl+lbl> 3, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 2, 2, ...
$ BYS89R <dbl+lbl> 2, 4, 3, 3, 3, 3, 3, 3, 3, 3, 2, 1, 3, ...
$ BYS89A <dbl+lbl> 2, 4, 3, 4, 2, 2, 2, 4, 3, 4, 1, 2, 2, ...
$ BYS89B <dbl+lbl> 1, 3, 2, 3, 2, 2, 1, 3, 3, 3, 2, 2, 2, ...
$ BYS89U <dbl+lbl> 1, 4, 2, 4, 3, -9, 3, 3, 3, 3, 1, 1, 3, ...
$ BYS89L <dbl+lbl> 2, 4, 2, 3, 3, 3, 2, 3, 3, 3, 2, 1, 2, ...
$ BYS29B <dbl+lbl> 2, 5, 4, 2, 5, 2, 2, 5, 4, 5, 5, 5, 2, ...
$ BYS29E <dbl+lbl> 2, 3, 5, 3, 4, 4, 4, 4, 5, 4, 5, 5, 5, ...
$ BYS29C <dbl+lbl> 2, 5, 5, -9, 5, -9, 5, 5, 5, 4, 4, 5, 5, ...
$ BYS29J <dbl+lbl> 5, 2, 1, -9, 5, 2, 1, 1, 2, 5, 5, 4, 3, ...
$ SCH_ID <dbl> 1011, 1011, 1011, 1011, 1011, 1011, 1011, 1011, 1011, 10...
$ STU_ID <dbl> 101101, 101102, 101104, 101105, 101106, 101107, 101108, ...
$ SEX <dbl+lbl> 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2...
$ RACE <dbl+lbl> 5, 2, 7, 3, 4, 4, 4, 7, 4, 3, 3, 4, 3, 2, 2, 3, 3, 4...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))
filter(! (-9))`filter_()` is deprecated as of dplyr 0.7.0.
Please use `filter()` instead.
See vignette('programming') for more help
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.Error in UseMethod("filter_") :
no applicable method for 'filter_' applied to an object of class "logical"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 growth.mindset
alpha 0.33 0.66 0.93 0.49
Standard errors of unstandardized Alpha:
school.climate.school School.climate.teacher math.se growth.mindset
ASE 0.019 0.0094 0.0044 0.018
Standardized Alpha of observed scales:
school.climate.school School.climate.teacher math.se growth.mindset
[1,] 0.33 0.66 0.93 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 math.se
sch.clim.sch1 0.41 0.45 0.12
sch.clim.sch2.recode 0.45 0.52 0.15
sch.clim.tea1.recode 0.55 0.57 0.18
sch.clim.tea2.recode 0.70 0.68 0.18
sch.clim.tea3 0.65 0.48 0.16
sch.clim.tea4.recode 0.64 0.50 0.16
math.se1 0.30 0.28 0.87
math.se2 0.26 0.27 0.83
math.se3 0.22 0.22 0.84
math.se4 0.31 0.28 0.86
math.se5 0.23 0.25 0.86
mindset.growth.recode 0.23 0.20 0.29
mindset.entity 0.08 0.07 0.10
growth.mindset
sch.clim.sch1 0.10
sch.clim.sch2.recode 0.16
sch.clim.tea1.recode 0.18
sch.clim.tea2.recode 0.17
sch.clim.tea3 0.20
sch.clim.tea4.recode 0.06
math.se1 0.34
math.se2 0.34
math.se3 0.28
math.se4 0.34
math.se5 0.33
mindset.growth.recode 0.54
mindset.entity 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)Error in as_tibble(my.scales$scores) :
could not find function "as_tibble"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 + race.fac:sch_level_school.climate.racial + (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 + race.fac:sch_level_school.climate.racial +
(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 -0.015library(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 |
NALS0tDQp0aXRsZTogIlIgTm90ZWJvb2siDQpvdXRwdXQ6IGh0bWxfbm90ZWJvb2sNCi0tLQ0KDQpUaGlzIGlzIGFuIFtSIE1hcmtkb3duXShodHRwOi8vcm1hcmtkb3duLnJzdHVkaW8uY29tKSBOb3RlYm9vay4gV2hlbiB5b3UgZXhlY3V0ZSBjb2RlIHdpdGhpbiB0aGUgbm90ZWJvb2ssIHRoZSByZXN1bHRzIGFwcGVhciBiZW5lYXRoIHRoZSBjb2RlLiANCg0KVHJ5IGV4ZWN1dGluZyB0aGlzIGNodW5rIGJ5IGNsaWNraW5nIHRoZSAqUnVuKiBidXR0b24gd2l0aGluIHRoZSBjaHVuayBvciBieSBwbGFjaW5nIHlvdXIgY3Vyc29yIGluc2lkZSBpdCBhbmQgcHJlc3NpbmcgKkN0cmwrU2hpZnQrRW50ZXIqLiANCg0KYGBge3J9DQpwbG90KGNhcnMpDQpgYGANCg0KYGBge3J9DQpsaWJyYXJ5KHRpZHl2ZXJzZSkNCmxpYnJhcnkocHN5Y2gpDQpsaWJyYXJ5KGxtZTQpDQpgYGANCg0KDQoNCmBgYHtyfQ0KWDA0Mjc1XzAwMDFfRGF0YSA8LSByZWFkX2R0YSgiRURVUyA2NTEvSUNQU1JfMDQyNzUtVjEvSUNQU1JfMDQyNzUvRFMwMDAxLzA0Mjc1LTAwMDEtRGF0YS5kdGEiKQ0KZ2xpbXBzZShYMDQyNzVfMDAwMV9EYXRhKQ0KYGBgDQpgYGB7cn0NCkVMUy5jbGVhbiA8LSBYMDQyNzVfMDAwMV9EYXRhDQpgYGANCg0KYGBge3J9DQpFTFMuY2xlYW4gPC0gRUxTLmNsZWFuICU+JQ0KICBzZWxlY3QoLiwgQllTMjBKLA0KICAgICAgICAgQllTMjFCLA0KICAgICAgICAgQllTMjBHLA0KICAgICAgICAgQllTMjBGLA0KICAgICAgICAgQllTMjBILA0KICAgICAgICAgQllTMjBBLA0KICAgICAgICAgQllTMjBJLA0KICAgICAgICAgQllTMjBDLA0KICAgICAgICAgQllTODhCLA0KICAgICAgICAgQllTODhBLA0KICAgICAgICAgQllTODlSLA0KICAgICAgICAgQllTODlBLA0KICAgICAgICAgQllTODlCLA0KICAgICAgICAgQllTODlVLA0KICAgICAgICAgQllTODlMLA0KICAgICAgICAgQllTMjlCLA0KICAgICAgICAgQllTMjlFLA0KICAgICAgICAgQllTMjlDLA0KICAgICAgICAgQllTMjlKLA0KICAgICAgICAgU0NIX0lELA0KICAgICAgICAgU1RVX0lELA0KICAgICAgICAgU0VYLA0KICAgICAgICAgUkFDRSkNCmdsaW1wc2UoRUxTLmNsZWFuKQ0KYGBgDQpgYGB7cn0NCkVMUy5jbGVhbi5tdSA8LSBFTFMuY2xlYW4gJT4lDQptdXRhdGUoLiwNCiAgICAgICBzY2guY2xpbS5zY2gxID0gQllTMjBKLA0KICAgICAgIHNjaC5jbGltLnNjaDIgPSBCWVMyMUIsDQogICAgICAgc2NoLmNsaW0udGVhMSA9IEJZUzIwRywNCiAgICAgICBzY2guY2xpbS50ZWEyID0gQllTMjBGLA0KICAgICAgIHNjaC5jbGltLnRlYTMgPSBCWVMyMEgsDQogICAgICAgc2NoLmNsaW0udGVhNCA9IEJZUzIwQSwNCiAgICAgICBzY2guY2xpbS5zdDEgPSBCWVMyMEksDQogICAgICAgc2NoLmNsaW0ucmFjaWFsMSA9IEJZUzIwQywNCiAgICAgICBtaW5kc2V0LmVudGl0eSA9IEJZUzg4QiwNCiAgICAgICBtaW5kc2V0Lmdyb3d0aCA9IEJZUzg4QSwNCiAgICAgICBtYXRoLnNlMSA9IEJZUzg5UiwNCiAgICAgICBtYXRoLnNlMiA9IEJZUzg5QSwNCiAgICAgICBtYXRoLnNlMyA9IEJZUzg5QiwNCiAgICAgICBtYXRoLnNlNCA9IEJZUzg5VSwNCiAgICAgICBtYXRoLnNlNSA9IEJZUzg5TCwNCiAgICAgICBtYXRoLmVuZ2FnZTEgPSBCWVMyOUIsDQogICAgICAgbWF0aC5lbmdhZ2UyID0gQllTMjlFLA0KICAgICAgIG1hdGguZW5nYWdlMyA9IEJZUzI5QywNCiAgICAgICBtYXRoLmVuZ2FnZTQgPSBCWVMyOUosDQogICAgICAgU0NIX0lELmZhYyA9IGFzX2ZhY3RvcihTQ0hfSUQpLA0KICAgICAgIFNUVV9JRC5mYWMgPSBhc19mYWN0b3IoU1RVX0lEKSwNCiAgICAgICBzZXguZmFjID0gYXNfZmFjdG9yKFNFWCksDQogICAgICAgcmFjZS5mYWMgPSBhc19mYWN0b3IoUkFDRSkpDQoNCmBgYA0KDQpgYGB7cn0NCnRhYmxlKEVMUy5jbGVhbi5tdSRzY2guY2xpbS5zY2gxKQ0KYGBgDQoNCg0KYGBge3J9DQpFTFMuY2xlYW4uZmlsdGVyIDwtIEVMUy5jbGVhbi5tdSAlPiUNCiAgZmlsdGVyKC4sDQogICAgICAgICAhc2NoLmNsaW0uc2NoMSAlaW4lIA0KICAgICAgICAgICAgICgtOSksIA0KICAgICAgICAgIXNjaC5jbGltLnNjaDEgJWluJSANCiAgICAgICAgICAgICAoLTcpLA0KICAgICAgICAgIXNjaC5jbGltLnNjaDEgJWluJSANCiAgICAgICAgICAgICAoLTYpLA0KICAgICAgICAgIXNjaC5jbGltLnNjaDIgJWluJSANCiAgICAgICAgICAgICAoLTkpLCANCiAgICAgICAgICFzY2guY2xpbS5zY2gyICVpbiUgDQogICAgICAgICAgICAgKC03KSwNCiAgICAgICAgICFzY2guY2xpbS5zY2gyICVpbiUgDQogICAgICAgICAgICAgKC02KSwNCiAgICAgICAgICFzY2guY2xpbS50ZWExICVpbiUgDQogICAgICAgICAgICAgKC05KSwgDQogICAgICAgICAhc2NoLmNsaW0udGVhMSAlaW4lIA0KICAgICAgICAgICAgICgtNyksDQogICAgICAgICAhc2NoLmNsaW0udGVhMSAlaW4lIA0KICAgICAgICAgICAgICgtNiksDQogICAgICAgICAhc2NoLmNsaW0udGVhMiAlaW4lIA0KICAgICAgICAgICAgICgtOSksIA0KICAgICAgICAgIXNjaC5jbGltLnRlYTIgJWluJSANCiAgICAgICAgICAgICAoLTcpLA0KICAgICAgICAgIXNjaC5jbGltLnRlYTIgJWluJSANCiAgICAgICAgICAgICAoLTYpLA0KICAgICAgICAgIXNjaC5jbGltLnRlYTMgJWluJSANCiAgICAgICAgICAgICAoLTkpLCANCiAgICAgICAgICFzY2guY2xpbS50ZWEzICVpbiUgDQogICAgICAgICAgICAgKC03KSwNCiAgICAgICAgICFzY2guY2xpbS50ZWEzICVpbiUgDQogICAgICAgICAgICAgKC02KSwNCiAgICAgICAgICFzY2guY2xpbS50ZWE0ICVpbiUgDQogICAgICAgICAgICAgKC05KSwgDQogICAgICAgICAhc2NoLmNsaW0udGVhNCAlaW4lIA0KICAgICAgICAgICAgICgtNyksDQogICAgICAgICAhc2NoLmNsaW0udGVhNCAlaW4lIA0KICAgICAgICAgICAgICgtNiksDQogICAgICAgICAhc2NoLmNsaW0uc3QxICVpbiUgDQogICAgICAgICAgICAgKC05KSwgDQogICAgICAgICAhc2NoLmNsaW0uc3QxICVpbiUgDQogICAgICAgICAgICAgKC03KSwNCiAgICAgICAgICFzY2guY2xpbS5zdDEgJWluJSANCiAgICAgICAgICAgICAoLTYpLA0KICAgICAgICAgIXNjaC5jbGltLnJhY2lhbDEgJWluJSANCiAgICAgICAgICAgICAoLTkpLCANCiAgICAgICAgICFzY2guY2xpbS5yYWNpYWwxICVpbiUgDQogICAgICAgICAgICAgKC03KSwNCiAgICAgICAgICFzY2guY2xpbS5yYWNpYWwxICVpbiUgDQogICAgICAgICAgICAgKC02KSwNCiAgICAgICAgICFtaW5kc2V0LmVudGl0eSAlaW4lIA0KICAgICAgICAgICAgICgtOSksIA0KICAgICAgICAgIW1pbmRzZXQuZW50aXR5ICVpbiUgDQogICAgICAgICAgICAgKC03KSwNCiAgICAgICAgICFtaW5kc2V0LmVudGl0eSAlaW4lIA0KICAgICAgICAgICAgICgtNiksDQogICAgICAgICAhbWluZHNldC5ncm93dGggJWluJSANCiAgICAgICAgICAgICAoLTkpLCANCiAgICAgICAgICFtaW5kc2V0Lmdyb3d0aCAlaW4lIA0KICAgICAgICAgICAgICgtNyksDQogICAgICAgICAhbWluZHNldC5ncm93dGggJWluJSANCiAgICAgICAgICAgICAoLTYpLA0KICAgICAgICAgIW1hdGguc2UxICVpbiUgDQogICAgICAgICAgICAgKC05KSwgDQogICAgICAgICAhbWF0aC5zZTEgJWluJSANCiAgICAgICAgICAgICAoLTcpLA0KICAgICAgICAgIW1hdGguc2UxICVpbiUgDQogICAgICAgICAgICAgKC02KSwNCiAgICAgICAgIW1hdGguc2UyICVpbiUgDQogICAgICAgICAgICAgKC05KSwgDQogICAgICAgICAhbWF0aC5zZTIgJWluJSANCiAgICAgICAgICAgICAoLTcpLA0KICAgICAgICAgIW1hdGguc2UyICVpbiUgDQogICAgICAgICAgICAgKC02KSwNCiAgICAgICAgICFtYXRoLnNlMyAlaW4lIA0KICAgICAgICAgICAgICgtOSksIA0KICAgICAgICAgIW1hdGguc2UzICVpbiUgDQogICAgICAgICAgICAgKC03KSwNCiAgICAgICAgICFtYXRoLnNlMyAlaW4lIA0KICAgICAgICAgICAgICgtNiksDQogICAgICAgICAhbWF0aC5zZTQgJWluJSANCiAgICAgICAgICAgICAoLTkpLCANCiAgICAgICAgICFtYXRoLnNlNCAlaW4lIA0KICAgICAgICAgICAgICgtNyksDQogICAgICAgICAhbWF0aC5zZTQgJWluJSANCiAgICAgICAgICAgICAoLTYpLA0KICAgICAgICAgIW1hdGguc2U1ICVpbiUgDQogICAgICAgICAgICAgKC05KSwgDQogICAgICAgICAhbWF0aC5zZTUgJWluJSANCiAgICAgICAgICAgICAoLTcpLA0KICAgICAgICAgIW1hdGguc2U1ICVpbiUgDQogICAgICAgICAgICAgKC02KSwNCiAgICAgICAgICFtYXRoLmVuZ2FnZTEgJWluJSANCiAgICAgICAgICAgICAoLTkpLCANCiAgICAgICAgICFtYXRoLmVuZ2FnZTEgJWluJSANCiAgICAgICAgICAgICAoLTcpLA0KICAgICAgICAgIW1hdGguZW5nYWdlMSAlaW4lIA0KICAgICAgICAgICAgICgtNiksDQogICAgICAgICFtYXRoLmVuZ2FnZTIgJWluJSANCiAgICAgICAgICAgICAoLTkpLCANCiAgICAgICAgICFtYXRoLmVuZ2FnZTIgJWluJSANCiAgICAgICAgICAgICAoLTcpLA0KICAgICAgICAgIW1hdGguZW5nYWdlMiAlaW4lIA0KICAgICAgICAgICAgICgtNiksDQogICAgICAgICFtYXRoLmVuZ2FnZTMgJWluJSANCiAgICAgICAgICAgICAoLTkpLCANCiAgICAgICAgICFtYXRoLmVuZ2FnZTMgJWluJSANCiAgICAgICAgICAgICAoLTcpLA0KICAgICAgICAgIW1hdGguZW5nYWdlMyAlaW4lIA0KICAgICAgICAgICAgICgtNiksDQogICAgICAgICFtYXRoLmVuZ2FnZTQgJWluJSANCiAgICAgICAgICAgICAoLTkpLCANCiAgICAgICAgICFtYXRoLmVuZ2FnZTQgJWluJSANCiAgICAgICAgICAgICAoLTcpLA0KICAgICAgICAgIW1hdGguZW5nYWdlNCAlaW4lIA0KICAgICAgICAgICAgICgtNikpDQogICAgICAgIA0KYGBgDQoNCg0KYGBge3J9DQpFTFMuY2xlYW4uZmlsdGVyIDwtIEVMUy5jbGVhbi5maWx0ZXIgJT4lDQogIG11dGF0ZSguLA0KICAgICAgICAgc2NoLmNsaW0uc2NoMi5yZWNvZGUgPSBjYXNlX3doZW4oDQogICAgICAgICAgIHNjaC5jbGltLnNjaDIgPT0gMSB+IDQsDQogICAgICAgICAgIHNjaC5jbGltLnNjaDIgPT0gMiB+IDMsDQogICAgICAgICAgIHNjaC5jbGltLnNjaDIgPT0gMyB+IDIsDQogICAgICAgICAgIHNjaC5jbGltLnNjaDIgPT0gNCB+IDEpLA0KICAgICAgICAgc2NoLmNsaW0udGVhMS5yZWNvZGUgPSBjYXNlX3doZW4oDQogICAgICAgICAgIHNjaC5jbGltLnRlYTEgPT0gMSB+IDQsDQogICAgICAgICAgIHNjaC5jbGltLnRlYTEgPT0gMiB+IDMsDQogICAgICAgICAgIHNjaC5jbGltLnRlYTEgPT0gMyB+IDIsDQogICAgICAgICAgIHNjaC5jbGltLnRlYTEgPT0gNCB+IDEpLA0KICAgICAgICAgc2NoLmNsaW0udGVhMi5yZWNvZGUgPSBjYXNlX3doZW4oDQogICAgICAgICAgIHNjaC5jbGltLnRlYTIgPT0gMSB+IDQsDQogICAgICAgICAgIHNjaC5jbGltLnRlYTIgPT0gMiB+IDMsDQogICAgICAgICAgIHNjaC5jbGltLnRlYTIgPT0gMyB+IDIsDQogICAgICAgICAgIHNjaC5jbGltLnRlYTIgPT0gNCB+IDEpLA0KICAgICAgICBzY2guY2xpbS50ZWE0LnJlY29kZSA9IGNhc2Vfd2hlbigNCiAgICAgICAgICAgc2NoLmNsaW0udGVhNCA9PSAxIH4gNCwNCiAgICAgICAgICAgc2NoLmNsaW0udGVhNCA9PSAyIH4gMywNCiAgICAgICAgICAgc2NoLmNsaW0udGVhNCA9PSAzIH4gMiwNCiAgICAgICAgICAgc2NoLmNsaW0udGVhNCA9PSA0IH4gMSksDQogICAgICAgIHNjaC5jbGltLnJhY2lhbDEucmVjb2RlID0gY2FzZV93aGVuKA0KICAgICAgICAgICBzY2guY2xpbS5yYWNpYWwxID09IDEgfiA0LA0KICAgICAgICAgICBzY2guY2xpbS5yYWNpYWwxID09IDIgfiAzLA0KICAgICAgICAgICBzY2guY2xpbS5yYWNpYWwxID09IDMgfiAyLA0KICAgICAgICAgICBzY2guY2xpbS5yYWNpYWwxID09IDQgfiAxKSwNCiAgICAgICAgbWluZHNldC5ncm93dGgucmVjb2RlID0gY2FzZV93aGVuKA0KICAgICAgICAgICBtaW5kc2V0Lmdyb3d0aCA9PSAxIH4gNCwNCiAgICAgICAgICAgbWluZHNldC5ncm93dGggPT0gMiB+IDMsDQogICAgICAgICAgIG1pbmRzZXQuZ3Jvd3RoID09IDMgfiAyLA0KICAgICAgICAgICBtaW5kc2V0Lmdyb3d0aCA9PSA0IH4gMSkpDQpgYGANCg0KYGBge3J9DQpzZV9pdGVtcyA8LSBFTFMuY2xlYW4uZmlsdGVyICU+JQ0KICBzZWxlY3QoLiwNCiAgICAgICAgIG1hdGguc2UxLA0KICAgICAgIG1hdGguc2UyLA0KICAgICAgIG1hdGguc2UzLA0KICAgICAgIG1hdGguc2U0LA0KICAgICAgIG1hdGguc2U1KQ0KYWxwaGEoc2VfaXRlbXMpDQpgYGANCmBgYHtyfQ0KZW5nYWdlX2l0ZW1zIDwtIEVMUy5jbGVhbi5maWx0ZXIgJT4lDQogIHNlbGVjdCguLA0KICAgICAgIG1hdGguZW5nYWdlMSwNCiAgICAgICBtYXRoLmVuZ2FnZTMsDQogICAgICAgbWF0aC5lbmdhZ2U0KQ0KYWxwaGEoZW5nYWdlX2l0ZW1zKQ0KYGBgDQoNCg0KDQoNCg0KYGBge3J9DQpteS5rZXlzLmxpc3QgPC0gbGlzdChzY2hvb2wuY2xpbWF0ZS5zY2hvb2wgPSBjKCJzY2guY2xpbS5zY2gxIiwgInNjaC5jbGltLnNjaDIucmVjb2RlIiksDQogICAgICAgICAgICAgICAgICAgICBTY2hvb2wuY2xpbWF0ZS50ZWFjaGVyID0gYygic2NoLmNsaW0udGVhMS5yZWNvZGUiLCAic2NoLmNsaW0udGVhMi5yZWNvZGUiLCAic2NoLmNsaW0udGVhMyIsICJzY2guY2xpbS50ZWE0LnJlY29kZSIpLA0KICAgICAgICAgICAgICAgICAgICAgbWF0aC5zZSA9IGMoIm1hdGguc2UxIiwgIm1hdGguc2UyIiwgIm1hdGguc2UzIiwgIm1hdGguc2U0IiwgIm1hdGguc2U1IiksDQogICAgICAgICAgICAgICAgICAgICBncm93dGgubWluZHNldCA9IGMoIm1pbmRzZXQuZ3Jvd3RoLnJlY29kZSIsICJtaW5kc2V0LmVudGl0eSIpKQ0KICAgICAgICAgICAgICAgICAgICAgDQpteS5zY2FsZXMgPC0gc2NvcmVJdGVtcyhteS5rZXlzLmxpc3QsIEVMUy5jbGVhbi5maWx0ZXIsIGltcHV0ZSA9ICJub25lIikNCmBgYA0KDQpgYGB7cn0NCnByaW50KG15LnNjYWxlcywgc2hvcnQgPSBGQUxTRSkNCmBgYA0KDQpgYGB7cn0NCnNjaG9vbF9pdGVtcyA8LSBFTFMuY2xlYW4uZmlsdGVyICU+JQ0KICBzZWxlY3QoLiwNCiAgICAgICAgIHNjaC5jbGltLnNjaDEsDQogICAgICAgICBzY2guY2xpbS5zY2gyLnJlY29kZSkNCmFscGhhKHNjaG9vbF9pdGVtcykNCmBgYA0KDQpgYGB7cn0NCiBFTFMuY2xlYW4uZmlsdGVyIDwtIEVMUy5jbGVhbi5maWx0ZXIgJT4lDQogIGdyb3VwX2J5KFNDSF9JRC5mYWMpICU+JSANCiAgbXV0YXRlKC4sDQogICAgICAgICAgICBzY2hfc2NoLmNsaW0uc2NoMSA9IG1lYW4oc2NoLmNsaW0uc2NoMSwgbmEucm0gPSBUUlVFKSwNCiAgICAgICAgIHNjaF9zY2guY2xpbS5zY2gyLnJlY29kZSA9IG1lYW4oc2NoLmNsaW0uc2NoMi5yZWNvZGUsIG5hLnJtID0gVFJVRSkpICU+JQ0KICB1bmdyb3VwKCkNCmBgYA0KDQpgYGB7cn0NCnNjaF9zY2hvb2xfaXRlbXMgPC0gRUxTLmNsZWFuLmZpbHRlciAlPiUNCiAgc2VsZWN0KC4sDQogICAgICAgICBzY2hfc2NoLmNsaW0uc2NoMSwNCiAgICAgICAgIHNjaF9zY2guY2xpbS5zY2gyLnJlY29kZSkNCg0KYGBgDQoNCg0KYGBge3J9DQphbHBoYShzY2hfc2Nob29sX2l0ZW1zKQ0KYGBgDQpgYGB7cn0NCm15LnNjb3JlcyA8LSBhc190aWJibGUobXkuc2NhbGVzJHNjb3JlcykNCmBgYA0KDQpgYGB7cn0NCkVMUy5jbGVhbi5maWx0ZXIuMSA8LWJpbmRfY29scyhFTFMuY2xlYW4uZmlsdGVyLCBteS5zY29yZXMpDQpgYGANCg0KDQoNCg0KYGBge3J9DQogRUxTLmNsZWFuLmZpbHRlci4xIDwtIEVMUy5jbGVhbi5maWx0ZXIuMSAlPiUNCiAgZ3JvdXBfYnkoU0NIX0lELmZhYykgJT4lIA0KICBtdXRhdGUoLiwNCiAgICAgICAgICAgIHNjaF9sZXZlbF9zY2hvb2wuY2xpbWF0ZSA9IG1lYW4oc2NoLmNsaW0uc2NoMSwgbmEucm0gPSBUUlVFKSwNCiAgICAgICAgIHNjaF9sZXZlbF9zY2hvb2wuY2xpbWF0ZS50ZWFjaGVyID0gbWVhbihTY2hvb2wuY2xpbWF0ZS50ZWFjaGVyLCBuYS5ybSA9IFRSVUUpLA0KICAgICAgICAgc2NoX2xldmVsX3NjaG9vbC5jbGltYXRlLnN0dWRlbnQgPSBtZWFuKHNjaC5jbGltLnN0MSwgbmEucm0gPSBUUlVFKSwNCiAgICAgICAgIHNjaF9sZXZlbF9zY2hvb2wuY2xpbWF0ZS5yYWNpYWwgPSBtZWFuKHNjaC5jbGltLnJhY2lhbDEsIG5hLnJtID0gVFJVRSkpICU+JQ0KICB1bmdyb3VwKCkNCmBgYA0KDQpgYGB7cn0NCkVMUy5jbGVhbi5maWx0ZXIuMSA8LSBFTFMuY2xlYW4uZmlsdGVyLjEgJT4lICAgICAgIA0KICBtdXRhdGUoLiwNCiAgICAgICAgIG1pbmRzZXQuZW50aXR5LnJlY29kZSA9IGNhc2Vfd2hlbigNCiAgICAgICAgICAgbWluZHNldC5lbnRpdHkgPT0gMSB+IDQsDQogICAgICAgICAgIG1pbmRzZXQuZW50aXR5ID09IDIgfiAzLA0KICAgICAgICAgICBtaW5kc2V0LmVudGl0eSA9PSAzIH4gMiwNCiAgICAgICAgICAgbWluZHNldC5lbnRpdHkgPT0gNCB+IDEpKQ0KYGBgDQoNCg0KYGBge3J9DQpFTFMuZmluYWwgPC0gRUxTLmNsZWFuLmZpbHRlci4xICU+JQ0KICBzZWxlY3QoLiwNCiAgICAgICAgIHNjaF9sZXZlbF9zY2hvb2wuY2xpbWF0ZSwNCiAgICAgICAgIHNjaF9sZXZlbF9zY2hvb2wuY2xpbWF0ZS50ZWFjaGVyLA0KICAgICAgICAgc2NoX2xldmVsX3NjaG9vbC5jbGltYXRlLnN0dWRlbnQsDQogICAgICAgICBzY2hfbGV2ZWxfc2Nob29sLmNsaW1hdGUucmFjaWFsLA0KICAgICAgICAgbWluZHNldC5lbnRpdHkucmVjb2RlLA0KICAgICAgICAgbWluZHNldC5ncm93dGgucmVjb2RlLA0KICAgICAgICAgbWF0aC5zZSwNCiAgICAgICAgIHNleC5mYWMsDQogICAgICAgICByYWNlLmZhYywNCiAgICAgICAgIFNDSF9JRC5mYWMsDQogICAgICAgICBTVFVfSUQuZmFjLA0KICAgICAgICAgU0NIX0lEKQ0KDQpgYGANCg0KYGBge3J9DQptb2RlbC5udWxsIDwtIGxtZXIobWF0aC5zZSB+ICgxfFNDSF9JRC5mYWMpLCBSRU1MID0gRkFMU0UsIGRhdGEgPSBFTFMuZmluYWwpDQpzdW1tYXJ5KG1vZGVsLm51bGwpDQpgYGANCmBgYHtyfQ0KSUNDIDwtIDAuMDIvKDAuMDIgKyAwLjY3KQ0KSUNDDQpgYGANCg0KYGBge3J9DQptb2RlbC4xIDwtIGxtZXIobWF0aC5zZSB+IHJhY2UuZmFjICsgc2V4LmZhYyArIG1pbmRzZXQuZW50aXR5LnJlY29kZSArIG1pbmRzZXQuZ3Jvd3RoLnJlY29kZSArICgxfFNDSF9JRC5mYWMpLCBSRU1MID0gRkFMU0UsIGRhdGEgPSBFTFMuZmluYWwpDQpzdW1tYXJ5KG1vZGVsLjEpDQpgYGANCm1pbmRzZXQuZW50aXR5IGlzbid0IHNpZ25pZmljYW50LCB0YWtpbmcgb3V0IG9mIG1vZGVsDQoNCmBgYHtyfQ0KbW9kZWwuMiA8LSBsbWVyKG1hdGguc2UgfiBzZXguZmFjICsgbWluZHNldC5ncm93dGgucmVjb2RlICsgc2NoX2xldmVsX3NjaG9vbC5jbGltYXRlICsNCiAgICAgICAgIHNjaF9sZXZlbF9zY2hvb2wuY2xpbWF0ZS50ZWFjaGVyICsgc2NoX2xldmVsX3NjaG9vbC5jbGltYXRlLnN0dWRlbnQgKw0KICAgICAgICAgc2NoX2xldmVsX3NjaG9vbC5jbGltYXRlLnJhY2lhbCArICgxfFNDSF9JRC5mYWMpLCBSRU1MID0gRkFMU0UsIGRhdGEgPSBFTFMuZmluYWwpDQpzdW1tYXJ5KG1vZGVsLjIpDQpgYGANCnNjaF9sZXZlbF9zY2hvb2wuY2xpbWF0ZS5yYWNpYWwgbm90IHNpZ25pZmljYW50LW1pZ2h0IHRyeSB3aXRoIGFuIGludGVyYWN0aW9uIHdpdGggcmFjZQ0KDQpzY2hfbGV2ZWxfc2Nob29sLmNsaW1hdGUuc3R1ZGVudCBub3Qgc2lnbmlmaWNhbnQNCnNjaF9sZXZlbF9zY2hvb2wuY2xpbWF0ZSBub3Qgc2lnbmlmaWNhbnQNCmBgYHtyfQ0KbW9kZWwuMyA8LSBsbWVyKG1hdGguc2UgfiBzZXguZmFjICsgbWluZHNldC5ncm93dGgucmVjb2RlICsgc2NoX2xldmVsX3NjaG9vbC5jbGltYXRlLnJhY2lhbCArIHNjaF9sZXZlbF9zY2hvb2wuY2xpbWF0ZS50ZWFjaGVyICsgcmFjZS5mYWMgKyByYWNlLmZhYzpzY2hfbGV2ZWxfc2Nob29sLmNsaW1hdGUucmFjaWFsICsgKDF8U0NIX0lELmZhYyksIFJFTUwgPSBGQUxTRSwgZGF0YSA9IEVMUy5maW5hbCkNCnN1bW1hcnkobW9kZWwuMykNCmBgYA0KYGBge3J9DQppbnRlcnBsb3Q6OmludGVycGxvdChtb2RlbC5pbnRlcmFjdGlvbiwgdmFyMSA9ICJyYWNlLmZhYyIsIHZhcjIgPSAic2NoX2xldmVsX3NjaG9vbC5jbGltYXRlLnJhY2lhbCIpDQpgYGANCg0KDQoNCg0KDQpgYGB7cn0NCm1vZGVsLjQgPC0gbG1lcihtYXRoLnNlIH4gc2V4LmZhYyArIG1pbmRzZXQuZ3Jvd3RoLnJlY29kZSArIA0KICAgICAgICAgc2NoX2xldmVsX3NjaG9vbC5jbGltYXRlLnRlYWNoZXIgKyAocmFjZS5mYWN8U0NIX0lELmZhYyksIFJFTUwgPSBGQUxTRSwgZGF0YSA9IEVMUy5maW5hbCkNCnN1bW1hcnkobW9kZWwuNCkNCmBgYA0KYGBge3J9DQptb2RlbC41IDwtIGxtZXIobWF0aC5zZSB+IHNleC5mYWMgKyBtaW5kc2V0Lmdyb3d0aC5yZWNvZGUgKyANCiAgICAgICAgIHNjaF9sZXZlbF9zY2hvb2wuY2xpbWF0ZS50ZWFjaGVyICsgKDF8U0NIX0lELmZhYyksIFJFTUwgPSBGQUxTRSwgZGF0YSA9IEVMUy5maW5hbCkNCnN1bW1hcnkobW9kZWwuNSkNCmBgYA0KDQoNCg0KDQpgYGB7cn0NCmxpYnJhcnkobW9kZWxzdW1tYXJ5KQ0KbGlicmFyeShicm9vbS5taXhlZCkNCmxpYnJhcnkodGFibGVzKQ0KYGBgDQpgYGB7cn0NCm1vZGVscyA8LSBsaXN0KG1vZGVsLjEsIG1vZGVsLjIsIG1vZGVsLjMsIG1vZGVsLjQsIG1vZGVsLjUpDQptb2RlbHN1bW1hcnkobW9kZWxzLCBvdXRwdXQgPSAibWFya2Rvd24iKQ0KYGBgDQoNCmBgYHtyfQ0KZGVzY3JpYmUoRUxTLmZpbmFsLCBmYXN0ID0gVFJVRSkNCmBgYA0KDQpgYGB7cn0NCmRpYWdub3N0aWNzIDwtIGF1Z21lbnQobW9kZWwuNSkNCmBgYA0KDQoNCg0KYGBge3J9DQpnZ3Bsb3QoZGF0YSA9IGRpYWdub3N0aWNzLCBtYXBwaW5nID0gYWVzKHggPSAucmVzaWQpKSArDQogIGdlb21faGlzdG9ncmFtKGJpbndpZHRoID0gLjI1KSArIHRoZW1lX2NsYXNzaWMoKSArIA0KICBsYWJzKHRpdGxlID0gIkhpc3RvZ3JhbSBvZiBSZXNpZHVhbHMgZm9yIEVkdWNhdGlvbiBMb25naXR1ZGluYWwgU3R1ZHkgTW9kZWwiLA0KICAgICAgICAgICAgICAgICAgICAgIHggPSAiUmVzaWR1YWwgVmFsdWUiKSArDQogIGdlb21fdmxpbmUoeGludGVyY2VwdCA9IGMoLTIuNSwgMi41KSwgbGluZXR5cGUgPSAiZG90dGVkIikNCmBgYA0KDQoNCkFzc2VzcyBOb3JtYWxpdHkgb2YgUmVzaWR1YWxzDQpWaXN1YWxseSwgd2l0aCBhIEhpc3RvZ3JhbTogQSBsaXR0bGUgc2tld2VkDQpgYGB7cn0NCnNoYXBpcm8udGVzdChkaWFnbm9zdGljcyQucmVzaWQpDQpgYGANCnRvbyBtYW55IG9ic2VydmFpdG9ucyB0byBjaGVjaw0KYGBge3J9DQpnZ3Bsb3QoZGF0YSA9IGRpYWdub3N0aWNzLCBtYXBwaW5nID0gYWVzKHggPSAuZml0dGVkLCB5ID0gLnJlc2lkKSkgKw0KICBnZW9tX3BvaW50KCkgKyBsYWJzKHRpdGxlID0gIlJWRiBQbG90IGZvciBFZHVjYXRpb24gTG9uZ2l0dWRpbmFsIFN0dWR5IE1vZGVsIiwNCiAgICAgICAgICAgICAgICAgICAgICB4ID0gIlByZWRpY3RlZCBWYWx1ZSwgbWF0aCBzZWxmLWVmZmljYWN5IiwNCiAgICAgICAgICAgICAgICAgICAgICB5ID0gIlJlc2lkdWFsIFZhbHVlIikgKyB0aGVtZV9jbGFzc2ljKCkNCmBgYA0KVXNlIFJlc2lkdWFscyB2cy4gRml0dGVkIChSVkYpIFBsb3QgdG8gQXNzZXNzIEhvbW9za2VkYXN0aWNpdHkgb2YgRXJyb3JzOiBMb29rcyBsaWtlIHRoZXJlIGlzIGEgcGF0dGVybg0KYGBge3J9DQpnZ3Bsb3QoZGF0YSA9IGRpYWdub3N0aWNzLCBtYXBwaW5nID0gYWVzKHggPSAuZml0dGVkLCB5ID0gLmNvb2tzZCwgbGFiZWwgPSBTQ0hfSUQuZmFjKSkgKw0KICBnZW9tX3BvaW50KCkgKyBnZW9tX3RleHQobnVkZ2VfeCA9IC4yNSkgKyB0aGVtZV9jbGFzc2ljKCkgKyANCiAgbGFicyh0aXRsZSA9ICJDb29rJ3MgRGlzdGFuY2UgUGxvdCBmb3IgU2Nob29sIEVkdWNhdGlvbiBMb25naXR1ZGluYWwgU3R1ZHkgTW9kZWwiLA0KICAgICAgICAgICAgICAgICAgICAgIHggPSAiUHJlZGljdGVkIFZhbHVlLCBtYXRoIHNlbGYtZWZmaWNhY3kiLA0KICAgICAgICAgICAgICAgICAgICAgIHkgPSAiQ29vaydzIERpc3RhbmNlIikgKyANCiAgZ2VvbV9obGluZSh5aW50ZXJjZXB0ID0gNC84MTYsIGxpbmV0eXBlID0gImRvdHRlZCIpDQpgYGANCmBgYHtyfQ0KcHJvZC50cmltbWVkIDwtIGRpYWdub3N0aWNzICU+JQ0KICBmaWx0ZXIoLiwgLmNvb2tzZCA8IC4zMCkNCm1vZGVsLnRyaW1tZWQgPC0gbG1lcihtYXRoLnNlIH4gc2V4LmZhYyArIG1pbmRzZXQuZ3Jvd3RoLnJlY29kZSArIA0KICAgICAgICAgc2NoX2xldmVsX3NjaG9vbC5jbGltYXRlLnRlYWNoZXIgKyAoMXxTQ0hfSUQuZmFjKSwgUkVNTCA9IEZBTFNFLCBkYXRhID0gcHJvZC50cmltbWVkKQ0Kc3VtbWFyeShtb2RlbC50cmltbWVkKQ0KYGBgDQoNCmBgYHtyfQ0KbW9kZWxzIDwtIGxpc3QobW9kZWwuMSwgbW9kZWwuMiwgbW9kZWwuMywgbW9kZWwuNCwgbW9kZWwuNSkNCm1vZGVsc3VtbWFyeShtb2RlbHMsIG91dHB1dCA9ICJodG1sIikNCmBgYA0KDQpgDQpgYGB7cn0NCm1vZGVscy4xIDwtIGxpc3QobW9kZWwuMSwgbW9kZWwuMiwgbW9kZWwuMywgbW9kZWwuNSkNCm1vZGVsc3VtbWFyeShtb2RlbHMuMSwgb3V0cHV0ID0gImh0bWwiKQ0KYGBgDQoNCg0KYGBge3J9DQptb2RlbHMuMiA8LSBsaXN0KG1vZGVsLjUsIG1vZGVsLnRyaW1tZWQpDQptb2RlbHN1bW1hcnkobW9kZWxzLjIsIG91dHB1dCA9ICJodG1sIikNCg0KYGBgDQoNCg0KDQo=