24/09/2020
Taller I. miercoles 30 sept 16-18:30
Taller II. viernes 23 de octubre 16-18:30
Taller III. sabado 7 de noviembre 10-12
Alvaro Passi-Solar linkedin twitter scholar arpassi@uc.cl
Thanks to Dr Shaun Scholes. I used some slides from the class “Basic Statistics for Medical Sciences” (Stata), November 29th, 2019
• Identify complex survey designs features
• Specify survey design
• Perform survey analysis: mean, cross-tabulations, and proportions
• Perform survey analysis: linear and logistic regressions
• Perform sub-population analysis (correctly)
• Recognise the importance of the design effect and estimate the design effect (DEFF) or design factor
Objetivo de la tarea:
Adquirir habilidades para realizar análisis de variables incluidas en las bases de datos de la Encuesta Nacional de Salud chilena, utilizando un paquete estadístico para “Muestras Complejas”.
Instrucciones:Archivo 1. Un archivo con 3 tablas (ejemplo):
Archivo 2. Un archivo con la sintaxis utilizada para realizar estos análisis.
Para realizar este trabajo contará con el siguiente apoyo:
Fecha de entrega: miércoles 28 de octubre (hasta las 23.59), enviar archivos rotulados como 1_su nombre.xls y 2_su nombre.doc a Francisco Valenzuela: fjvalen2@uc.cl
Dr Shaun Scholes, 2019
Stratify (partition the population into groups called strata), sampling independently from each and every stratum. Examples? Region; Faculty.
Sample from the set of clusters in each stratum (these are known as the Primary Sampling Units: PSUs)
Why? For reasons of cost and convenience!
But….there is a statistical price to pay. In this session, we treat the clustering of data as a nuisance (not as something of primary interest)
Example: Precision for a proportion
\[SE=1.96~x~\sqrt\frac{P*(1-P)}{n}\]
Both stratification and clustering can influence the magnitude of the standard errors (SEs): and hence the value of test statistics and width of confidence intervals (CIs).
Because SEs affect significance levels (P-values), the conclusions drawn from an analysis that does not take these complex survey design features into account may be misleading.
Usually misleading in one direction: under-estimation of amount of uncertainty in the survey data (p-values too small; CIs too narrow)
Correct inference requires taking the features of the complex survey design into account.
When using complex survey designs, the study might have any or all of the three following features:
• Stratification
• Clustering (selection of PSUs within strata)
• Weighting
In practice, these are variables in a complex survey dataset that can be used to enable you to use packages such as Stata and R to perform appropriate statistical inference (generalise from your sample to the underlying population)
http://epi.minsal.cl/condiciones-de-uso/ http://epi.minsal.cl/bases-de-datos/
– ENS 2003 (SPSS)
– Base de datos ENS 2003 – Región y comuna (SPSS)
– ENS 2009-2010
(a.1) ENS 2009-2010 Comuna (SPSS)
– ENS 2016-17
(a.1) Base Formulario 1-Formulario 2 y exámenes – comuna y variables complejas (SPSS)
(a.2) Base Formulario1-Formulario 2 y exámenes – Metales Pesados (SPSS)
Base Formulario 4
Manual de uso (Actualización 14/02/2018)
Libro de códigos F1, F2 , F3 y F4
Base de Medicamentos
Base formuarios y libro de codigos F3
Base Minsal ENS2017 en formato .sav solo se puede abrir en SPSS
Por alguna razón library(rio) no logra importar este archivo.sav
Descarga ENS2017 en formato .dta (Stata) en la web del curso https://cursos.canvas.uc.cl/courses/22528
Esta base importaremos a R
https://rstudio.cloud/project/1676149
No requiere instalar R ni RStudio
Puedes abrirlo en tu celular
Trae la base ENS2017 y el taller de hoy
Memoria limitada a 1GB
Subscribe https://rpubs.com/
RStudio te ofrece installar los paquetes por ti. Guarda el archivo. No demora en aparecer un mensaje en amarillo ofreciendo instalar 
# install.packages("dplyr")
Puedes activar el código borrando el # antes del texto
Puedes cambiar el “nombre del paquete” a instalar
Comillas en install.packages() son importantes
library(rio) #To import and export data# library(reshape2) #Modifiy data set shape design with grammar library(dplyr) #dplyr: a grammar of data manipulation# library(ggplot2) #plot your results library(survey) #Analise using survey design library(srvyr) #Analise using survey design with grammar
Knit: Do you see that blue button with knitting sticks? Press it 
# Current wd getwd()
## [1] "/cloud/project"
# Setting wd
# setwd('~/Dropbox/PhD_web/ENS/static/Referencias ENS')
# df0 <- import('F1_F2_EX_V9_20AUG18AP5.dta')
df0 <- rio::import('F1_F2_EX_V9_20AUG18AP5.dta')
rio:: lo utilizas cuando no has cargado la librería “rio” o para asegurar que usas la función “import” del paquete “rio” y no de “otro paquete”.
# View(df0) # WARNING View() de bases gigante # ENS no es gigante # nombres de variables / columnas # names(df0) # total columnas length(names(df0))
## [1] 1160
# total filas length(df0$IdEncuesta)
## [1] 6233
# options: # nrow(df0) # ncol(df0) # dim(df0)
# A veces es mejor generar un elemento ("namesdf0")
namesdf0 <- names(df0)
# y abrirlo en otra ventana
# View(namesdf0)
namesdf0[1:40]
## [1] "IdEncuesta" "FechaInicioF1" "Region" "Zona" ## [5] "IdSegmento" "IdPersona_1" "Ident7" "Edad" ## [9] "Edad_Codificada" "Sexo" "c1" "c1_esp" ## [13] "c2" "c2a" "c2b" "c3" ## [17] "c3a" "c3b" "c3c" "c5" ## [21] "c5_otro" "c5b" "c6" "c7_0_nino" ## [25] "c7_1_nino" "c7_1_cuidador" "c7_2_nino" "c7_2_cuidador" ## [29] "c7_3_nino" "c7_3_cuidador" "c7_4_nino" "c7_4_cuidador" ## [33] "c7_5_nino" "c7_5_cuidador" "e1" "e2a" ## [37] "e2b" "e2c" "e2d" "e3_1"
summary(df0[1:10])
## IdEncuesta FechaInicioF1 Region Zona ## Min. :20006 Min. :2016-08-04 Min. : 1.000 Min. :1.000 ## 1st Qu.:22684 1st Qu.:2016-10-14 1st Qu.: 5.000 1st Qu.:1.000 ## Median :25698 Median :2016-11-06 Median : 7.000 Median :1.000 ## Mean :25764 Mean :2016-11-07 Mean : 7.851 Mean :1.159 ## 3rd Qu.:28501 3rd Qu.:2016-11-28 3rd Qu.:11.000 3rd Qu.:1.000 ## Max. :70000 Max. :2017-02-23 Max. :15.000 Max. :2.000 ## IdSegmento IdPersona_1 Ident7 Edad ## Min. : 1101101 Min. : 1.0 Min. :1918-11-26 Min. :15.00 ## 1st Qu.: 5109123 1st Qu.: 40.0 1st Qu.:1952-04-23 1st Qu.:33.00 ## Median : 8301107 Median : 82.0 Median :1966-10-30 Median :50.00 ## Mean : 8503764 Mean :106.6 Mean :1967-06-20 Mean :48.91 ## 3rd Qu.:13101108 3rd Qu.:146.0 3rd Qu.:1983-07-25 3rd Qu.:64.00 ## Max. :15101204 Max. :590.0 Max. :2002-01-19 Max. :98.00 ## Edad_Codificada Sexo ## Min. :1.000 Min. :1.000 ## 1st Qu.:2.000 1st Qu.:1.000 ## Median :3.000 Median :2.000 ## Mean :2.684 Mean :1.629 ## 3rd Qu.:3.000 3rd Qu.:2.000 ## Max. :4.000 Max. :2.000
names(df0)[grepl("Vitamina",names(df0))]
## [1] "v_25_OH_Vitamina_D2" "aux_25_OH_Vitamina_D2" ## [3] "v_25_OH_Vitamina_D2_D3" "v_25_OH_Vitamina_D3" ## [5] "v_25_OH_Vitamina_D2_corr" "v_25_OH_Vitamina_D3_corr" ## [7] "v_25_OH_Vitamina_D2_D3_corr"
names(df0)[grepl("Fexp",names(df0))]
## [1] "Fexp_F1p_Corr" "Fexp_F2p_Corr" "Fexp_F1F2p_Corr" ## [4] "Fexp_EX1p_Corr" "Fexp_F1F2EX1p_Corr" "Fexp_EX2p_Corr" ## [7] "Fexp_F1F2EX2p_Corr" "Fexp_EX3p_Corr" "Fexp_F1F2EX3p_Corr"
names(df0)[grepl("Congl",names(df0))]
## [1] "Conglomerado"
names(df0)[grepl("Estr",names(df0))]
## [1] "Estrato"
df0_ext <- df0[c( "IdEncuesta","FechaInicioF1","Region","Zona", "Edad", "Edad_Codificada","Sexo","NEDU1", "Fexp_F1p_Corr","Fexp_F2p_Corr","Fexp_F1F2p_Corr", "Fexp_EX1p_Corr","Fexp_F1F2EX1p_Corr", "Fexp_EX2p_Corr", "Fexp_F1F2EX2p_Corr" ,"Fexp_EX3p_Corr","Fexp_F1F2EX3p_Corr", "Conglomerado","Estrato","v_25_OH_Vitamina_D2_D3_corr")] summary(df0_ext)
## IdEncuesta FechaInicioF1 Region Zona ## Min. :20006 Min. :2016-08-04 Min. : 1.000 Min. :1.000 ## 1st Qu.:22684 1st Qu.:2016-10-14 1st Qu.: 5.000 1st Qu.:1.000 ## Median :25698 Median :2016-11-06 Median : 7.000 Median :1.000 ## Mean :25764 Mean :2016-11-07 Mean : 7.851 Mean :1.159 ## 3rd Qu.:28501 3rd Qu.:2016-11-28 3rd Qu.:11.000 3rd Qu.:1.000 ## Max. :70000 Max. :2017-02-23 Max. :15.000 Max. :2.000 ## ## Edad Edad_Codificada Sexo NEDU1 ## Min. :15.00 Min. :1.000 Min. :1.000 Min. :1.000 ## 1st Qu.:33.00 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000 ## Median :50.00 Median :3.000 Median :2.000 Median :2.000 ## Mean :48.91 Mean :2.684 Mean :1.629 Mean :1.983 ## 3rd Qu.:64.00 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:2.000 ## Max. :98.00 Max. :4.000 Max. :2.000 Max. :3.000 ## NA's :59 ## Fexp_F1p_Corr Fexp_F2p_Corr Fexp_F1F2p_Corr ## Min. : 2.594 Min. : 3.131 Min. : 3.131 ## 1st Qu.: 414.818 1st Qu.: 454.585 1st Qu.: 454.585 ## Median : 1084.403 Median : 1209.348 Median : 1209.348 ## Mean : 2329.371 Mean : 2630.248 Mean : 2630.248 ## 3rd Qu.: 2633.405 3rd Qu.: 3017.059 3rd Qu.: 3017.059 ## Max. :24000.000 Max. :24000.000 Max. :24000.000 ## NA's :713 NA's :713 ## Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## Min. : 3.163 Min. : 3.163 Min. : 4.32 Min. : 4.34 ## 1st Qu.: 464.486 1st Qu.: 465.550 1st Qu.: 726.64 1st Qu.: 728.37 ## Median : 1232.532 Median : 1241.019 Median : 1781.00 Median : 1791.02 ## Mean : 2666.477 Mean : 2674.828 Mean : 3765.63 Mean : 3774.10 ## 3rd Qu.: 3071.311 3rd Qu.: 3071.912 3rd Qu.: 4341.99 3rd Qu.: 4349.47 ## Max. :24000.000 Max. :24000.000 Max. :34000.00 Max. :34000.00 ## NA's :788 NA's :805 NA's :2386 NA's :2386 ## Fexp_EX3p_Corr Fexp_F1F2EX3p_Corr Conglomerado Estrato ## Min. : 10.27 Min. : 10.41 Min. : 1101101 Min. : 11.00 ## 1st Qu.: 1808.85 1st Qu.: 1821.60 1st Qu.: 5109123 1st Qu.: 51.00 ## Median : 4823.59 Median : 4832.53 Median : 8301107 Median : 81.00 ## Mean :10532.52 Mean :10551.58 Mean : 8503691 Mean : 84.27 ## 3rd Qu.:12958.39 3rd Qu.:12962.88 3rd Qu.:13101108 3rd Qu.:131.00 ## Max. :98000.00 Max. :98000.00 Max. :15101204 Max. :152.00 ## NA's :4857 NA's :4857 ## v_25_OH_Vitamina_D2_D3_corr ## Min. : 1.30 ## 1st Qu.:13.50 ## Median :18.90 ## Mean :19.74 ## 3rd Qu.:24.90 ## Max. :59.70 ## NA's :3347
by(df0_ext, df0_ext$Sexo, summary)
## df0_ext$Sexo: 1 ## IdEncuesta FechaInicioF1 Region Zona ## Min. :20013 Min. :2016-09-03 Min. : 1.000 Min. :1.000 ## 1st Qu.:22616 1st Qu.:2016-10-14 1st Qu.: 5.000 1st Qu.:1.000 ## Median :25585 Median :2016-11-04 Median : 7.000 Median :1.000 ## Mean :25608 Mean :2016-11-06 Mean : 7.803 Mean :1.143 ## 3rd Qu.:28369 3rd Qu.:2016-11-27 3rd Qu.:11.000 3rd Qu.:1.000 ## Max. :31839 Max. :2017-02-23 Max. :15.000 Max. :2.000 ## ## Edad Edad_Codificada Sexo NEDU1 ## Min. :15.00 Min. :1.000 Min. :1 Min. :1.000 ## 1st Qu.:31.00 1st Qu.:2.000 1st Qu.:1 1st Qu.:2.000 ## Median :49.00 Median :3.000 Median :1 Median :2.000 ## Mean :47.93 Mean :2.634 Mean :1 Mean :2.043 ## 3rd Qu.:63.00 3rd Qu.:3.000 3rd Qu.:1 3rd Qu.:2.000 ## Max. :95.00 Max. :4.000 Max. :1 Max. :3.000 ## NA's :18 ## Fexp_F1p_Corr Fexp_F2p_Corr Fexp_F1F2p_Corr ## Min. : 6.424 Min. : 8.888 Min. : 8.888 ## 1st Qu.: 568.766 1st Qu.: 639.303 1st Qu.: 639.303 ## Median : 1507.263 Median : 1786.075 Median : 1786.075 ## Mean : 3080.486 Mean : 3532.108 Mean : 3532.108 ## 3rd Qu.: 3768.953 3rd Qu.: 4395.049 3rd Qu.: 4395.049 ## Max. :24000.000 Max. :24000.000 Max. :24000.000 ## NA's :296 NA's :296 ## Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## Min. : 8.888 Min. : 8.888 Min. : 9.11 Min. : 9.11 ## 1st Qu.: 662.982 1st Qu.: 665.465 1st Qu.: 1038.81 1st Qu.: 1048.51 ## Median : 1827.887 Median : 1837.309 Median : 2561.75 Median : 2579.03 ## Mean : 3567.447 Mean : 3583.581 Mean : 5053.01 Mean : 5068.46 ## 3rd Qu.: 4470.406 3rd Qu.: 4514.441 3rd Qu.: 6300.13 3rd Qu.: 6323.65 ## Max. :24000.000 Max. :24000.000 Max. :34000.00 Max. :34000.00 ## NA's :316 NA's :325 NA's :908 NA's :908 ## Fexp_EX3p_Corr Fexp_F1F2EX3p_Corr Conglomerado Estrato ## Min. : 64.65 Min. : 64.65 Min. : 1101101 Min. : 11.00 ## 1st Qu.: 2810.20 1st Qu.: 2813.99 1st Qu.: 5301101 1st Qu.: 51.00 ## Median : 6550.06 Median : 6554.51 Median : 8301106 Median : 81.00 ## Mean :14537.63 Mean :14553.73 Mean : 8501529 Mean : 84.21 ## 3rd Qu.:20210.62 3rd Qu.:20237.63 3rd Qu.:13101108 3rd Qu.:131.00 ## Max. :98000.00 Max. :98000.00 Max. :15101204 Max. :152.00 ## NA's :1825 NA's :1825 ## v_25_OH_Vitamina_D2_D3_corr ## Min. : 1.30 ## 1st Qu.:14.60 ## Median :20.45 ## Mean :21.14 ## 3rd Qu.:26.75 ## Max. :50.10 ## NA's :1851 ## ------------------------------------------------------------ ## df0_ext$Sexo: 2 ## IdEncuesta FechaInicioF1 Region Zona ## Min. :20006 Min. :2016-08-04 Min. : 1.00 Min. :1.000 ## 1st Qu.:22707 1st Qu.:2016-10-14 1st Qu.: 5.00 1st Qu.:1.000 ## Median :25778 Median :2016-11-07 Median : 7.00 Median :1.000 ## Mean :25856 Mean :2016-11-08 Mean : 7.88 Mean :1.168 ## 3rd Qu.:28584 3rd Qu.:2016-11-28 3rd Qu.:11.00 3rd Qu.:1.000 ## Max. :70000 Max. :2017-02-22 Max. :15.00 Max. :2.000 ## ## Edad Edad_Codificada Sexo NEDU1 ## Min. :15.00 Min. :1.000 Min. :2 Min. :1.000 ## 1st Qu.:34.00 1st Qu.:2.000 1st Qu.:2 1st Qu.:1.000 ## Median :50.00 Median :3.000 Median :2 Median :2.000 ## Mean :49.49 Mean :2.713 Mean :2 Mean :1.948 ## 3rd Qu.:64.00 3rd Qu.:3.000 3rd Qu.:2 3rd Qu.:2.000 ## Max. :98.00 Max. :4.000 Max. :2 Max. :3.000 ## NA's :41 ## Fexp_F1p_Corr Fexp_F2p_Corr Fexp_F1F2p_Corr ## Min. : 2.594 Min. : 3.131 Min. : 3.131 ## 1st Qu.: 346.014 1st Qu.: 387.065 1st Qu.: 387.065 ## Median : 876.152 Median : 965.907 Median : 965.907 ## Mean : 1885.565 Mean : 2110.152 Mean : 2110.152 ## 3rd Qu.: 2138.313 3rd Qu.: 2402.234 3rd Qu.: 2402.234 ## Max. :24000.000 Max. :24000.000 Max. :24000.000 ## NA's :417 NA's :417 ## Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## Min. : 3.163 Min. : 3.163 Min. : 4.32 Min. : 4.34 ## 1st Qu.: 393.154 1st Qu.: 393.644 1st Qu.: 590.95 1st Qu.: 591.24 ## Median : 983.557 Median : 984.762 Median : 1446.51 Median : 1448.54 ## Mean : 2143.831 Mean : 2148.820 Mean : 3023.28 Mean : 3027.72 ## 3rd Qu.: 2458.421 3rd Qu.: 2461.515 3rd Qu.: 3504.26 3rd Qu.: 3511.23 ## Max. :24000.000 Max. :24000.000 Max. :34000.00 Max. :34000.00 ## NA's :472 NA's :480 NA's :1478 NA's :1478 ## Fexp_EX3p_Corr Fexp_F1F2EX3p_Corr Conglomerado Estrato ## Min. : 10.27 Min. : 10.41 Min. : 1101101 Min. : 11.00 ## 1st Qu.: 1473.66 1st Qu.: 1482.27 1st Qu.: 5109122 1st Qu.: 51.00 ## Median : 3715.37 Median : 3722.69 Median : 8301108 Median : 81.00 ## Mean : 8317.50 Mean : 8338.20 Mean : 8504969 Mean : 84.32 ## 3rd Qu.: 9521.39 3rd Qu.: 9521.39 3rd Qu.:13101109 3rd Qu.:131.00 ## Max. :98000.00 Max. :98000.00 Max. :15101204 Max. :152.00 ## NA's :3032 NA's :3032 ## v_25_OH_Vitamina_D2_D3_corr ## Min. : 2.00 ## 1st Qu.:13.20 ## Median :18.60 ## Mean :19.47 ## 3rd Qu.:24.60 ## Max. :59.70 ## NA's :1496
df0_ext <- df0[c( "IdEncuesta","Fexp_F1p_Corr","Fexp_F1F2EX1p_Corr",
"Conglomerado","Estrato","v_25_OH_Vitamina_D2_D3_corr")]
df0_ext[1:10,]
## IdEncuesta Fexp_F1p_Corr Fexp_F1F2EX1p_Corr Conglomerado Estrato ## 1 20006 98.31527 NA 13101101 131 ## 2 20008 49.15763 52.35046 13101101 131 ## 3 20011 222.45004 275.63080 13101102 131 ## 4 20012 124.96051 133.12834 13101102 131 ## 5 20013 162.73767 NA 13101102 131 ## 6 20017 132.35255 140.94896 13101102 131 ## 7 20018 86.41464 97.63442 13101102 131 ## 8 20025 60.19316 64.12758 13101104 131 ## 9 20026 156.78064 180.03816 13101104 131 ## 10 20031 165.95195 190.56998 13101105 131 ## v_25_OH_Vitamina_D2_D3_corr ## 1 NA ## 2 13.4 ## 3 12.6 ## 4 4.3 ## 5 NA ## 6 20.0 ## 7 NA ## 8 23.5 ## 9 NA ## 10 NA
• Weights provided with complex datasets are positive values associated with each (response) unit in the sample. There may be different weights for different stages of a survey (e.g. interview; nurse examination; collection of blood samples)
• Sampling weights: “inverse probability weights” are the inverse / reciprocal of the probability of selection
• The main purpose is to reduce bias (systematic error) in population estimates by up-weighting population subgroups that are under-represented in the sample and down-weighting subgroups that are over-represented in the sample
• Unequal probability of selection
• Differences in response rates
• Post-stratification: adjust the sample distribution for key variables of interest (e.g. age, sex, region) so that the weighted sample conforms to a known population distribution in analysis (i.e. an adjustment after data has been collected)
• Does this sound familiar to you?
• Weights for analysis can include all 3 components: selection × (non-response × poststratification)
We won’t worry today about how weights are calculated, but note that subjects aged 65 y or more were deliberately oversampled to ensure adequate numbers for subgroup analysis (and bearing in mind their response rates); and men (especially young men) are less likely to respond to surveys than women.
To achieve nationally-representative estimates (avoid bias) you would expect the weighting scheme to show: (1) lower weights for people aged 65 or more (compensate for over-sampling); and (2) higher weights for young men (adjust for lower response)
SEs (estimate of variability) attached to estimates from complex surveys tend to be larger than those from a Simple Random Sample (of the same size)…this is the statistical price to be paid for the cost and convenience of complex designs.
Typically Stratification reduces SEs; clustering increases SEs
The difference in precision of estimates produced by a complex design relative to a SRS is estimated by the Design Effect (DEFF)
\[DEFF=\frac{variance~estimate~(complex~design)}{variance~estimate~(SRS)}\] * Is the ratio of the actual variance of an estimateunder the sampling method used (e.g. stratification & clustering) to the variance calculated under the assumption of SRS.
The deff increases both as cluster size increases and as rho increases (fewer clusters and large homogeneity within them: intraclass correlation)
Deff = 1 implies similar precision for the complex design and SRS
Magnitude of the design effect is estimate-specific
For a simple one-stage cluster sample: deff = 1 + rho (n-1) deff = 1 + (ρ × (n-1))
deff1 <- 1 + 0.05*(10-1) n1<-c(5,10,15,100) p1<-c(0.05,0.10,0.15,0.1) df1<-cbind(n1,p1) df1<-as.data.frame(df1) df1$deff<-1+df1$p1*(df1$n1-1) df1
## n1 p1 deff ## 1 5 0.05 1.2 ## 2 10 0.10 1.9 ## 3 15 0.15 3.1 ## 4 100 0.10 10.9
Where:
• n is the size of each cluster
• rho (ρ) is the intraclass correlation: which reflects the homogeneity of the sample:
ρ = 0 means elements of clusters are completely heterogeneous (members of the same cluster are diverse)
ρ = 1 means elements of clusters are completely homogeneous (members are completely alike)
deff increases both as cluster size increases and as ρ increases (sample more people who are alike)
Interpretation? A deff = 2.0 is that the sampling variance for that estimate is twice as large than it would be if the survey was based on the same size sample but was SRS (hypothetical). A guide to the larger imprecision in the estimates.
Alternative interpretation: only a half as many sample cases would be needed to infer estimates with the same precision if you had used a SRS (effective sample size = ncomplex / deff)
But remember the costs and convenience!
The SE is the √ variance.
Similarly, the design factor (deft) is the √ deff
A deft of 2.0 indicates that the SEs are twice as large as they would have been had the design been SRS.
deft is typically greater than 1: so SEs for complex surveys are larger than those assuming SRS.
Specify survey design features
Compute estimates of interest: means; crosstabulations; proportions; regression coefficients taking into account those design features
Possible post-estimation commands giving more information: e.g. design effects; design factors; appropriate statistical testing for complex survey data
# # Hmisc Trae conflictos con paquetes library(dplyr) y library(srvyr) library(summarytools)
## Warning in fun(libname, pkgname): couldn't connect to display ":0"
https://dabblingwithdata.wordpress.com/2018/01/02/my-favourite-r-package-for-summarising-data/
mydata <- descr(df0_ext) mydata
## Descriptive Statistics ## df0_ext$IdEncuesta ## Label: IdEncuesta ## N: 6233 ## ## Conglomerado Estrato Fexp_F1F2EX1p_Corr Fexp_F1p_Corr IdEncuesta ## ----------------- -------------- --------- -------------------- --------------- ------------ ## Mean 8503691.47 84.27 2674.83 2329.37 25763.61 ## Std.Dev 4187166.26 42.10 3933.49 3526.21 3651.47 ## Min 1101101.00 11.00 3.16 2.59 20006.00 ## Q1 5109123.00 51.00 465.53 414.82 22684.00 ## Median 8301107.00 81.00 1241.02 1084.40 25698.00 ## Q3 13101108.00 131.00 3072.37 2633.41 28501.00 ## Max 15101204.00 152.00 24000.00 24000.00 70000.00 ## MAD 5633900.76 59.30 1424.99 1223.40 4308.44 ## IQR 7991985.00 80.00 2606.36 2218.59 5817.00 ## CV 0.49 0.50 1.47 1.51 0.14 ## Skewness -0.10 -0.08 3.06 3.34 1.36 ## SE.Skewness 0.03 0.03 0.03 0.03 0.03 ## Kurtosis -1.16 -1.19 10.99 13.68 13.25 ## N.Valid 6233.00 6233.00 5428.00 6233.00 6233.00 ## Pct.Valid 100.00 100.00 87.08 100.00 100.00 ## ## Table: Table continues below ## ## ## ## v_25_OH_Vitamina_D2_D3_corr ## ----------------- ----------------------------- ## Mean 19.74 ## Std.Dev 8.37 ## Min 1.30 ## Q1 13.50 ## Median 18.90 ## Q3 24.90 ## Max 59.70 ## MAD 8.45 ## IQR 11.40 ## CV 0.42 ## Skewness 0.59 ## SE.Skewness 0.05 ## Kurtosis 0.38 ## N.Valid 2886.00 ## Pct.Valid 46.30
# View(mydata)
FactoresExp1 <- rio::import('ENS2017_FactoresExp.xlsx', sheet=1)
FactoresExp3 <- rio::import('ENS2017_FactoresExp.xlsx', sheet=3)
FactoresExp2 <- rio::import('ENS2017_FactoresExp.xlsx', sheet=2)
FactoresExp2
## Examen FactorExp F1-Examen FactorExp, F2-Examen ## 1 v_25_OH_Vitamina_D2 Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 2 aux_25_OH_Vitamina_D2 Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 3 v_25_OH_Vitamina_D2_D3 Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 4 v_25_OH_Vitamina_D3 Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 5 Anticuerpos_Anti_Peptido_C Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 6 aux_Anticuerpos_Anti_Peptido_C Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 7 Anticuerpos_Anti_Peroxidasa_T Fexp_EX3p_Corr Fexp_F1F2EX3p_Corr ## 8 aux_Anticuerpos_Anti_Peroxidasa Fexp_EX3p_Corr Fexp_F1F2EX3p_Corr ## 9 Anticuerpos_anti_Trypanosoma_c Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 10 aux_A_Anticuerpos_anti_Tryp_c_ Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 11 aux_B_Anticuerpos_anti_Tryp_c_ Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 12 Arsenico_ENS Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 13 Cadmio_ENS Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 14 Colesterol_HDL Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 15 Colesterol_LDL_Calculado Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 16 Colesterol_No_HDL_Calculado Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 17 Colesterol_Total Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 18 Colesterol_VLDL_Calculado Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 19 Creatinina_en_Orina Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 20 Creatinina_en_Sangre Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 21 Factor_Reumatoideo Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 22 aux_Factor_Reumatoideo Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 23 Filtrado_Glomerular_CKD_EPI Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 24 Folato_Serico Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 25 GGT_Gamma_Glutamil_Transferasa Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 26 Glucosa Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 27 Hemoglobina Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 28 Hemoglobina_A1C Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 29 aux_Hemoglobina_A1C Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 30 Hormona_Estimulante_Tiroides_TSH Fexp_EX3p_Corr Fexp_F1F2EX3p_Corr ## 31 aux_Hormona_Estimulante_TSH Fexp_EX3p_Corr Fexp_F1F2EX3p_Corr ## 32 Mercurio_ENS Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 33 Microalbuminuria Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 34 aux_Microalbuminuria Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 35 Microalbuminuria_Creatinina Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 36 Plomo_ENS Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 37 Potasio_K_en_Orina Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 38 Potasio_Creatinina Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 39 Proteina_C_Reactiva_cuantitativa Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 40 aux_Proteina_C_Reactiva_cuant Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 41 Sangre_deposiciones_1_muestra Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 42 Sarampion_IgG Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 43 Sodio_Na_en_Orina Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 44 Sodio_Creatinina Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 45 Tiroxina_Libre_FT4 Fexp_EX3p_Corr Fexp_F1F2EX3p_Corr ## 46 Transaminasa_Glutamico_Piruvica Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 47 Trigliceridos Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr ## 48 VPH_alto_riesgo Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 49 VPH_genotipo_16 Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 50 VPH_genotipo_18 Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 51 VPH_resultado Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr ## 52 Yoduria Fexp_EX3p_Corr Fexp_F1F2EX3p_Corr ## 53 aux_Yoduria Fexp_EX3p_Corr Fexp_F1F2EX3p_Corr ## 54 Yoduria_mas_Creatinina Fexp_EX3p_Corr Fexp_F1F2EX3p_Corr ## 55 Conf_ISP_Trypanosoma_cruzi Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr
table(df0$NEDU1)
## ## 1 2 3 ## 1477 3323 1374
table(df0$NEDU1, useNA = "ifany")
## ## 1 2 3 <NA> ## 1477 3323 1374 59
prop.table(table(df0$NEDU1))
## ## 1 2 3 ## 0.2392290 0.5382248 0.2225462
# Explorar paquetes # library(epiDisplay) # tab1(df0$NEDU1, sort.group = "decreasing", cum.percent = TRUE) # library(janitor) # tabyl(df0$NEDU1, sort = TRUE) # library(summarytools) # summarytools::freq(df0$NEDU1, order = "freq") # library(questionr) # questionr::freq(df0$NEDU1, cum = TRUE, sort = "dec", total = TRUE)
hist(df0$v_25_OH_Vitamina_D2_D3_corr)
df0$Gender <- factor(df0$Sexo,
levels=c("1","2"),
labels=c("Male",
"Female"))
df0$Educational_level <- factor(df0$NEDU1,
levels=c("1","2","3"),
labels=c("Low",
"Mid",
"High"))
df0$Area <- factor(df0$Zona,
levels=c("1","2"),
labels=c("Urban",
"Rural"))
df0$Age <- factor(df0$Edad_Codificada,
levels=c("1","2","3","4"),
labels=c("17-24",
"25-44",
"45-64",
"65+"))
df0$Region_n <- as.numeric(df0$Region)
df0$RM <- NA
df0$RM[(df0$Region_n==7)] <- 1
df0$RM[(df0$Region_n!=7)] <- 2
df0$ENS <- 2017
df0$person <- 1
df0$Conglomerado_ <- NA df0$Conglomerado_ <- df0$Conglomerado df0$strata_ <- NA df0$strata_ <- df0$Estrato df0$fexp <- df0$Fexp_F1F2EX1p_Corr
Explore
res_0b <- df0 %>% group_by(Region,Area,strata_) %>% summarize(Conglomerado_l = length(unique(Conglomerado_))) res_0b
## # A tibble: 30 x 4 ## # Groups: Region, Area [30] ## Region Area strata_ Conglomerado_l ## <dbl> <fct> <dbl> <int> ## 1 1 Urban 151 55 ## 2 1 Rural 152 3 ## 3 2 Urban 11 52 ## 4 2 Rural 12 3 ## 5 3 Urban 21 58 ## 6 3 Rural 22 1 ## 7 4 Urban 31 58 ## 8 4 Rural 32 3 ## 9 5 Urban 41 49 ## 10 5 Rural 42 6 ## # … with 20 more rows
res_0b <- df0 %>% group_by(Region,Area,strata_) %>% summarize(Conglomerado_l = length(unique(Conglomerado_))) res_0b
## # A tibble: 30 x 4 ## # Groups: Region, Area [30] ## Region Area strata_ Conglomerado_l ## <dbl> <fct> <dbl> <int> ## 1 1 Urban 151 55 ## 2 1 Rural 152 3 ## 3 2 Urban 11 52 ## 4 2 Rural 12 3 ## 5 3 Urban 21 58 ## 6 3 Rural 22 1 ## 7 4 Urban 31 58 ## 8 4 Rural 32 3 ## 9 5 Urban 41 49 ## 10 5 Rural 42 6 ## # … with 20 more rows
res_0b <- df0 %>%
group_by(Region,Area,strata_) %>%
summarize(
is.naFexp_F1F2EX1p_Corr = sum(is.na(Fexp_F1F2EX1p_Corr)),
Fexp_F1F2EX1p_Corr_valid = sum(!is.na(Fexp_F1F2EX1p_Corr)),
mean = mean(Fexp_F1F2EX1p_Corr, na.rm=TRUE)
)
res_0b
## # A tibble: 30 x 6 ## # Groups: Region, Area [30] ## Region Area strata_ is.naFexp_F1F2EX1p_Corr Fexp_F1F2EX1p_Corr_valid mean ## <dbl> <fct> <dbl> <int> <int> <dbl> ## 1 1 Urban 151 58 261 699. ## 2 1 Rural 152 1 39 185. ## 3 2 Urban 11 68 241 1061. ## 4 2 Rural 12 8 24 245. ## 5 3 Urban 21 74 254 1920. ## 6 3 Rural 22 0 10 280. ## 7 4 Urban 31 26 250 938. ## 8 4 Rural 32 4 24 433. ## 9 5 Urban 41 34 236 2062. ## 10 5 Rural 42 9 51 2604. ## # … with 20 more rows
res_0b <- df0 %>%
group_by(Age) %>%
summarize(
is.naFexp_F1F2EX1p_Corr = sum(is.na(Fexp_F1F2EX1p_Corr)),
Fexp_F1F2EX1p_Corr_valid = sum(!is.na(Fexp_F1F2EX1p_Corr)),
mean_ = mean(Fexp_F1F2EX1p_Corr, na.rm=TRUE),
sum_ = sum(Fexp_F1F2EX1p_Corr, na.rm=TRUE),
n_ = n()
)
res_0b
## # A tibble: 4 x 6 ## Age is.naFexp_F1F2EX1p_Corr Fexp_F1F2EX1p_Corr_valid mean_ sum_ n_ ## <fct> <int> <int> <dbl> <dbl> <int> ## 1 17-24 129 708 3867. 2737931. 837 ## 2 25-44 267 1548 3498. 5414690. 1815 ## 3 45-64 233 1831 2424. 4437480. 2064 ## 4 65+ 176 1341 1438. 1928868. 1517
Explore
length(unique(df0$Conglomerado_))
## [1] 1077
table(df0$Conglomerado_,df0$strata_)
## ## 11 12 21 22 31 32 41 42 51 52 61 62 71 72 81 82 91 92 101 102 111 ## 1101101 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101102 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101103 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101104 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101105 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101106 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101107 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101108 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101109 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101110 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101111 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101112 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101114 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101115 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101119 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101120 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101121 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101122 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101123 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101124 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101125 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101126 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101127 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101130 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101131 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101132 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101133 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101136 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1101137 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1105101 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1105102 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1105103 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1105201 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1107101 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1107102 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1107103 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1107104 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1107105 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1107106 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1107107 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1107108 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1107109 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1107111 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1107112 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1107113 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1107114 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1107115 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1107116 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1107117 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1107118 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1107119 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1107120 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1107121 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1401201 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1401202 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101101 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101102 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101103 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101104 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101105 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101106 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101107 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101108 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101109 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101110 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101111 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101112 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101113 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101114 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101115 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101116 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101118 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101119 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101120 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101121 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101123 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101125 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101126 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101128 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101129 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101130 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101131 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101132 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101133 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101134 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101135 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101136 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101137 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101138 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101139 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2101140 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2201101 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2201102 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2201103 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2201104 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2201105 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2201106 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2201107 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2201108 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2201109 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2201110 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2201111 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2201113 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2201114 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2201115 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2201116 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2201117 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2201118 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2203101 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2203102 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2203104 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2203105 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2203106 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 2203201 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101101 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101102 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101103 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101104 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101105 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101106 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101107 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101108 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101109 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101110 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101111 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101112 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101113 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101114 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101115 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101116 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101117 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101118 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101119 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101120 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101121 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101122 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101123 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101124 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101125 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101126 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101127 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101128 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101129 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101130 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101131 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101132 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101133 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101134 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3101135 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3103101 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3103102 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3103103 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3103104 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3103105 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3103106 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3103107 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3103201 0 0 0 0 0 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3301101 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3301102 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3301103 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3301104 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3301105 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3301106 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3301107 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3301108 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3301109 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3301110 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3302201 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3302202 0 0 0 0 0 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3304101 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3304102 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3304103 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3304104 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3304105 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 3304106 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4101101 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4101102 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4101105 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4101106 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4101108 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4101109 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4101110 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4101111 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4101112 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4101113 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4101114 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4101115 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4101116 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4101117 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4102101 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4102102 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4102104 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4102105 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4102106 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4102107 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4102108 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4102109 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4102110 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4102111 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4102112 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4102113 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4102114 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4102115 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4102117 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4102118 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4102119 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4106201 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4106202 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4201101 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4201102 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4201103 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4201104 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4201105 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4301101 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4301102 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4301103 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4301104 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4301105 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4301106 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4301107 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4301108 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4301201 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4301202 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4303101 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4303102 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4303103 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4303104 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4303105 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4304201 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 4304202 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 5101102 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 ## 5101103 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 ## 5101104 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5101105 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 ## 5101106 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 ## 5101107 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 ## 5101108 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 ## 5101109 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5101111 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 ## 5101112 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 ## 5101113 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 ## 5101115 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5101116 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5101117 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 ## 5101118 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 ## 5101119 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 ## 5101120 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 ## 5101121 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5101122 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 ## 5102101 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 ## 5102102 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 ## 5102103 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5102104 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5102105 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 ## 5103103 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5103104 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109101 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109102 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109105 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109106 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109107 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109108 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109109 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109111 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109112 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109113 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109114 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109115 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109116 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109117 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109118 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109119 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109120 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109121 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109122 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109123 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 ## 5109124 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 ## 5301101 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5301102 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 ## 5301103 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 ## 5301104 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 ## 5304201 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 ## 5304202 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 ## 5402101 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 ## 5402102 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 ## 5402103 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 ## 5402104 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 ## 5402105 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 ## 5501101 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 ## 5501102 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 ## 5501103 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 ## 5501104 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 ## 5501105 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 ## 5502101 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 ## 5502102 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 ## 5502103 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 ## 5502104 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 ## 5503201 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 ## 5503202 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 ## 5601101 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 ## 5601102 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 ## 5601103 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 ## 5601104 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 ## 5601105 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 ## 5601106 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 ## 5602101 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 ## 5602102 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5602103 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 ## 5602104 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 ## 5602105 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 ## 5701101 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5701102 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 ## 5701103 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5701104 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5703101 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 ## 5703102 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5703103 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 ## 5703104 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 ## 5703105 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 ## 5705201 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 ## 5705202 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 ## 5801101 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 ## 5801102 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 ## 5801103 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 ## 5801104 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 ## 5801105 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5801106 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 ## 5801107 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5801108 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 ## 5801109 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5801110 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 ## 5801111 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5802101 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 ## 5802102 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5802103 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 ## 5804101 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5804102 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 ## 5804103 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5804104 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 ## 5804105 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5804107 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 ## 5804108 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 ## 5804109 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 ## 5804110 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 ## 6101102 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 ## 6101103 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 ## 6101104 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 ## 6101105 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 ## 6101106 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 ## 6101107 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 ## 6101108 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 ## 6101109 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 ## 6101111 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 ## 6101112 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 ## 6101113 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 ## 6101114 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 ## 6101115 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 ## 6101116 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 ## 6101117 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 ## 6101118 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 ## 6105101 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 ## 6105102 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 ## 6105103 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 ## 6105104 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 ## 6105105 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 ## 6107201 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 ## 6107202 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 ## 6108101 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 ## 6108102 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 ## 6108103 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 ## 6108104 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 ## 6110101 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 ## 6110102 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 ## 6110103 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 ## 6110104 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 ## 6110105 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 ## 6114201 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 ## 6114202 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 ## 6115101 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 ## 6115102 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 ## 6115103 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 ## 6117101 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 ## 6117102 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 ## 6117103 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 ## 6117104 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 ## 6117105 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 ## 6117201 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 ## 6117202 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 ## 6301101 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 ## 6301102 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 ## 6301103 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 ## 6301104 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 ## 6303201 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 ## 6303202 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 ## 6305101 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 ## 6305102 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 ## 6305103 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 ## 6305104 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 ## 6305105 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 ## 6310201 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 ## 6310202 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 ## 7101101 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 ## 7101102 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 ## 7101103 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 ## 7101104 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 ## 7101105 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 ## 7101106 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 ## 7101107 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 ## 7101108 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 ## 7101109 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 ## 7101110 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 ## 7101111 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 ## 7101112 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 ## 7101113 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 ## 7101114 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 ## 7102101 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 ## 7102102 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 ## 7102103 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 ## 7104201 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 ## 7104202 0 0 0 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 0 ## 7105101 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 ## 7105102 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 ## 7105103 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 ## 7105104 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 ## 7105105 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 ## 7109201 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 ## 7109202 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 ## 7201101 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 ## 7201102 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 ## 7301101 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 ## 7301102 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 ## 7301103 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 ## 7301104 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 ## 7301105 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 ## 7301106 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 ## 7301107 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 ## 7301108 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 ## 7301201 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 ## 7301202 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 ## 7304101 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 ## 7304102 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 ## 7307201 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 ## 7307202 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 ## 7401101 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 ## 7401102 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 ## 7401103 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 ## 7401104 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 ## 7401105 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 ## 7402201 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 ## 7402202 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 ## 7404101 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 ## 7404102 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 ## 7404103 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 ## 7404104 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 ## 7404105 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 ## 7404106 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 ## 7405201 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 ## 7405202 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 ## 8101101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8101102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 ## 8101103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8101104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 ## 8101105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 ## 8101106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 ## 8101107 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 ## 8101108 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 ## 8101110 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 ## 8101112 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 ## 8101113 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 ## 8101114 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 ## 8102101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8102102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 ## 8102103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8102104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 ## 8102105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 ## 8102106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 ## 8103101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 ## 8103102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8103103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8103104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8103105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 ## 8103106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8106101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 ## 8106102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 ## 8106103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8107101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 ## 8107102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8107103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 ## 8108101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 ## 8108102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 ## 8108103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 ## 8108104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 ## 8108105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 ## 8109101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8109102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 ## 8109103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 ## 8109104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8109105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8109106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8110101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8110102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8110103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 ## 8110104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 ## 8110105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 ## 8110106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 ## 8110107 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8110108 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 ## 8110109 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 ## 8110110 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8110201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 ## 8110202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 ## 8111101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 ## 8111102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8111103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8112101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 ## 8112102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 ## 8112103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8112104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 ## 8112105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 ## 8112106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 ## 8205101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8205102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 ## 8206101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8206102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 ## 8206103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 ## 8206104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8206105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 ## 8206106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8301101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 ## 8301102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8301103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8301104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8301105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 ## 8301106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 ## 8301107 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8301108 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8301201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 ## 8301202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 ## 8307101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8307102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 ## 8307103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8307104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8307105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 ## 8307106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8309201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 ## 8309202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 ## 8401101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 ## 8401102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 ## 8401103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8401104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8401105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 ## 8401106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8401107 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 ## 8401108 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8401109 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8405201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 0 0 0 0 0 ## 8405202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15 0 0 0 0 0 ## 8406101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8406102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8406103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8406104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 ## 8406105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8406106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8416101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 ## 8416102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 ## 8416201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 0 0 0 0 0 ## 8416202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15 0 0 0 0 0 ## 9101102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 ## 9101103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 ## 9101104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 ## 9101105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 ## 9101106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 ## 9101107 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 ## 9101108 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 ## 9101109 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 ## 9101110 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 ## 9101111 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 ## 9101112 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 ## 9101113 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 ## 9101114 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 ## 9101115 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 ## 9101116 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 ## 9101117 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 ## 9101118 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 ## 9101119 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 ## 9101201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 ## 9101202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 ## 9105101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 ## 9105102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 ## 9105103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 ## 9105104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 ## 9105105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 ## 9106201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 ## 9106202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 0 0 0 ## 9111101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 ## 9111102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 ## 9111103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 ## 9111104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 ## 9111105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 ## 9112101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 ## 9112102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 ## 9112103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 ## 9112201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 ## 9112202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 ## 9116201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 ## 9116202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 ## 9118101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 ## 9118102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 ## 9118103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 ## 9118104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 ## 9118105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 ## 9120101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 ## 9120102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 ## 9201101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 ## 9201102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 ## 9201103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 ## 9202201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 ## 9202202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 ## 9209101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 ## 9209102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 ## 9209103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 ## 9209104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 ## 9209105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 ## 10101101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 ## 10101102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 ## 10101103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 ## 10101104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 ## 10101105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 ## 10101106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 ## 10101107 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 ## 10101108 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 ## 10101109 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 ## 10101112 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 ## 10101113 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 ## 10101114 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 ## 10101115 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 ## 10101116 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 ## 10101117 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 ## 10101118 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 ## 10101201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15 0 ## 10101202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 18 0 ## 10105101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 ## 10105102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 ## 10105103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 ## 10105104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 ## 10105201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 ## 10105202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13 0 ## 10109101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 ## 10109102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 ## 10201101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 ## 10201102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 ## 10201103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 ## 10201201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 ## 10201202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 ## 10202101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 ## 10202102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 ## 10203101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 ## 10203102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 ## 10203103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 ## 10203104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 ## 10207201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 ## 10207202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 ## 10301101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 ## 10301102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 ## 10301103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 ## 10301104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 ## 10301105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 ## 10301106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 ## 10301107 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 ## 10301108 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 ## 10301109 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 ## 10301110 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 ## 10301111 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 ## 10303101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 ## 10303102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 ## 10303103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 ## 10303104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 ## 10303201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 ## 10303202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 ## 11101101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 ## 11101102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 ## 11101103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 ## 11101105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 ## 11101106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 ## 11101107 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 ## 11101108 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 ## 11101109 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 ## 11101110 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 ## 11101111 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 ## 11101112 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ## 11101113 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 ## 11101114 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 ## 11101115 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 ## 11101116 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 ## 11101117 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 ## 11101118 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 ## 11101120 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 ## 11101121 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 ## 11101122 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 ## 11101123 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 ## 11101124 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 ## 11101125 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 ## 11101127 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 ## 11101128 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 ## 11101129 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 ## 11101130 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 ## 11101131 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 ## 11101132 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 ## 11101133 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 ## 11101134 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ## 11101135 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 ## 11101136 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 ## 11101137 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 ## 11101201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 11101202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 11101203 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 11201101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 ## 11201102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 ## 11201103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 ## 11201104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 ## 11201105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 ## 11201106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 ## 11201107 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 ## 11201108 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 ## 11201109 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 ## 11201110 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 ## 11201111 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 ## 11201112 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 ## 11201113 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 ## 11201114 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 ## 11201115 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 ## 11201116 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 ## 11201117 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 ## 11201118 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 ## 11201119 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 ## 11201201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 11201202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101107 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101108 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101109 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101110 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101111 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101112 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101113 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101114 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101115 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101116 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101117 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101118 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101119 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101120 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101121 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101122 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101123 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101124 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101125 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101126 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101127 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101128 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101129 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101131 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101132 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101133 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101134 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101135 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101136 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101137 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101138 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101139 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101140 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101141 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101142 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101143 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101144 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101145 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101146 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101147 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101149 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101150 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12101201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12301101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12301102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12301103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12301104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12301105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12301106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12301107 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12301108 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12301109 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12301110 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 12401201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13101101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13101102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13101104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13101105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13101106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13101107 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13101108 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13101109 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13101110 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13102101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13102102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13103101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13103102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13103103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13103104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13104101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13104102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13104103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13105101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13105102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13105103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13105104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13105105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13106101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13106102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13106103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13106104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13107101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13107102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13108101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13108102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13109101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13109102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13110101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13110102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13110103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13110104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13110105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13110106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13110108 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13110109 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13110110 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13110111 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13111101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13111102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13111103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13111104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13112101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13112102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13112103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13112104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13112105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13113102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13113103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13114101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13114103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13114105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13114107 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13115101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13115102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13116101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13116102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13116103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13117101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13117102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13117103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13118101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13118102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13118103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13119101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13119102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13119103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13119104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13119105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13119106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13119107 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13119108 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13119109 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13119110 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13119111 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13119112 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13119113 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13120102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13120104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13120105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13120106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13121101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13121102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13121103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13122101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13122102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13122104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13122105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13122106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13122107 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13123101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13123102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13123105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13124101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13124102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13124103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13124104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13124105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13124106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13125101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13125102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13125103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13125104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13125105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13126101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13126102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13126103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13127101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13127102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13127103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13127104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13128101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13128102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13128103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13128104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13129101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13129102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13129104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13130101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13130103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13131101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13131102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13132101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13132102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13201102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13201103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13201106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13201107 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13201109 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13201110 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13201111 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13201112 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13201113 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13201114 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13201115 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13201116 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13201117 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13301101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13301102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13301201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13301202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13302101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13302102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13401101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13401102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13401103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13401104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13401105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13401106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13401107 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13402102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13403101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13404101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13501101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13501102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13501201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13501202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13601101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13602101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13603101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13604101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13605101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 13605102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101107 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101108 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101109 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101110 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101111 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101112 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101113 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101114 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101115 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101117 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101118 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101119 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101120 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101121 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101122 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101123 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101124 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101125 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14101202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14104101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14104102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14104103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14104104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14104105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14104201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14104202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14107101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14107102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14107103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14107104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14107105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14107201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14107202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14201101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14201102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14201103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14201104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14201105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14201201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14201202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14203201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14203202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14204101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14204102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14204103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14204104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 14204105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101107 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101108 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101109 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101110 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101111 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101112 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101113 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101114 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101115 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101116 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101118 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101119 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101120 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101121 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101122 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101123 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101124 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101125 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101126 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101127 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101128 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101129 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101130 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101131 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101132 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101133 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101134 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101135 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101136 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101137 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101138 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101139 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101140 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101141 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101142 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101143 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101144 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101146 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101149 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101150 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101151 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101152 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101153 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101154 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101155 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101156 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101157 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101158 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101203 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 15101204 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## ## 112 121 122 131 132 141 142 151 152 ## 1101101 0 0 0 0 0 0 0 0 0 ## 1101102 0 0 0 0 0 0 0 0 0 ## 1101103 0 0 0 0 0 0 0 0 0 ## 1101104 0 0 0 0 0 0 0 0 0 ## 1101105 0 0 0 0 0 0 0 0 0 ## 1101106 0 0 0 0 0 0 0 0 0 ## 1101107 0 0 0 0 0 0 0 0 0 ## 1101108 0 0 0 0 0 0 0 0 0 ## 1101109 0 0 0 0 0 0 0 0 0 ## 1101110 0 0 0 0 0 0 0 0 0 ## 1101111 0 0 0 0 0 0 0 0 0 ## 1101112 0 0 0 0 0 0 0 0 0 ## 1101114 0 0 0 0 0 0 0 0 0 ## 1101115 0 0 0 0 0 0 0 0 0 ## 1101119 0 0 0 0 0 0 0 0 0 ## 1101120 0 0 0 0 0 0 0 0 0 ## 1101121 0 0 0 0 0 0 0 0 0 ## 1101122 0 0 0 0 0 0 0 0 0 ## 1101123 0 0 0 0 0 0 0 0 0 ## 1101124 0 0 0 0 0 0 0 0 0 ## 1101125 0 0 0 0 0 0 0 0 0 ## 1101126 0 0 0 0 0 0 0 0 0 ## 1101127 0 0 0 0 0 0 0 0 0 ## 1101130 0 0 0 0 0 0 0 0 0 ## 1101131 0 0 0 0 0 0 0 0 0 ## 1101132 0 0 0 0 0 0 0 0 0 ## 1101133 0 0 0 0 0 0 0 0 0 ## 1101136 0 0 0 0 0 0 0 0 0 ## 1101137 0 0 0 0 0 0 0 0 0 ## 1105101 0 0 0 0 0 0 0 0 0 ## 1105102 0 0 0 0 0 0 0 0 0 ## 1105103 0 0 0 0 0 0 0 0 0 ## 1105201 0 0 0 0 0 0 0 0 0 ## 1107101 0 0 0 0 0 0 0 0 0 ## 1107102 0 0 0 0 0 0 0 0 0 ## 1107103 0 0 0 0 0 0 0 0 0 ## 1107104 0 0 0 0 0 0 0 0 0 ## 1107105 0 0 0 0 0 0 0 0 0 ## 1107106 0 0 0 0 0 0 0 0 0 ## 1107107 0 0 0 0 0 0 0 0 0 ## 1107108 0 0 0 0 0 0 0 0 0 ## 1107109 0 0 0 0 0 0 0 0 0 ## 1107111 0 0 0 0 0 0 0 0 0 ## 1107112 0 0 0 0 0 0 0 0 0 ## 1107113 0 0 0 0 0 0 0 0 0 ## 1107114 0 0 0 0 0 0 0 0 0 ## 1107115 0 0 0 0 0 0 0 0 0 ## 1107116 0 0 0 0 0 0 0 0 0 ## 1107117 0 0 0 0 0 0 0 0 0 ## 1107118 0 0 0 0 0 0 0 0 0 ## 1107119 0 0 0 0 0 0 0 0 0 ## 1107120 0 0 0 0 0 0 0 0 0 ## 1107121 0 0 0 0 0 0 0 0 0 ## 1401201 0 0 0 0 0 0 0 0 0 ## 1401202 0 0 0 0 0 0 0 0 0 ## 2101101 0 0 0 0 0 0 0 0 0 ## 2101102 0 0 0 0 0 0 0 0 0 ## 2101103 0 0 0 0 0 0 0 0 0 ## 2101104 0 0 0 0 0 0 0 0 0 ## 2101105 0 0 0 0 0 0 0 0 0 ## 2101106 0 0 0 0 0 0 0 0 0 ## 2101107 0 0 0 0 0 0 0 0 0 ## 2101108 0 0 0 0 0 0 0 0 0 ## 2101109 0 0 0 0 0 0 0 0 0 ## 2101110 0 0 0 0 0 0 0 0 0 ## 2101111 0 0 0 0 0 0 0 0 0 ## 2101112 0 0 0 0 0 0 0 0 0 ## 2101113 0 0 0 0 0 0 0 0 0 ## 2101114 0 0 0 0 0 0 0 0 0 ## 2101115 0 0 0 0 0 0 0 0 0 ## 2101116 0 0 0 0 0 0 0 0 0 ## 2101118 0 0 0 0 0 0 0 0 0 ## 2101119 0 0 0 0 0 0 0 0 0 ## 2101120 0 0 0 0 0 0 0 0 0 ## 2101121 0 0 0 0 0 0 0 0 0 ## 2101123 0 0 0 0 0 0 0 0 0 ## 2101125 0 0 0 0 0 0 0 0 0 ## 2101126 0 0 0 0 0 0 0 0 0 ## 2101128 0 0 0 0 0 0 0 0 0 ## 2101129 0 0 0 0 0 0 0 0 0 ## 2101130 0 0 0 0 0 0 0 0 0 ## 2101131 0 0 0 0 0 0 0 0 0 ## 2101132 0 0 0 0 0 0 0 0 0 ## 2101133 0 0 0 0 0 0 0 0 0 ## 2101134 0 0 0 0 0 0 0 0 0 ## 2101135 0 0 0 0 0 0 0 0 0 ## 2101136 0 0 0 0 0 0 0 0 0 ## 2101137 0 0 0 0 0 0 0 0 0 ## 2101138 0 0 0 0 0 0 0 0 0 ## 2101139 0 0 0 0 0 0 0 0 0 ## 2101140 0 0 0 0 0 0 0 0 0 ## 2201101 0 0 0 0 0 0 0 0 0 ## 2201102 0 0 0 0 0 0 0 0 0 ## 2201103 0 0 0 0 0 0 0 0 0 ## 2201104 0 0 0 0 0 0 0 0 0 ## 2201105 0 0 0 0 0 0 0 0 0 ## 2201106 0 0 0 0 0 0 0 0 0 ## 2201107 0 0 0 0 0 0 0 0 0 ## 2201108 0 0 0 0 0 0 0 0 0 ## 2201109 0 0 0 0 0 0 0 0 0 ## 2201110 0 0 0 0 0 0 0 0 0 ## 2201111 0 0 0 0 0 0 0 0 0 ## 2201113 0 0 0 0 0 0 0 0 0 ## 2201114 0 0 0 0 0 0 0 0 0 ## 2201115 0 0 0 0 0 0 0 0 0 ## 2201116 0 0 0 0 0 0 0 0 0 ## 2201117 0 0 0 0 0 0 0 0 0 ## 2201118 0 0 0 0 0 0 0 0 0 ## 2203101 0 0 0 0 0 0 0 0 0 ## 2203102 0 0 0 0 0 0 0 0 0 ## 2203104 0 0 0 0 0 0 0 0 0 ## 2203105 0 0 0 0 0 0 0 0 0 ## 2203106 0 0 0 0 0 0 0 0 0 ## 2203201 0 0 0 0 0 0 0 0 0 ## 3101101 0 0 0 0 0 0 0 0 0 ## 3101102 0 0 0 0 0 0 0 0 0 ## 3101103 0 0 0 0 0 0 0 0 0 ## 3101104 0 0 0 0 0 0 0 0 0 ## 3101105 0 0 0 0 0 0 0 0 0 ## 3101106 0 0 0 0 0 0 0 0 0 ## 3101107 0 0 0 0 0 0 0 0 0 ## 3101108 0 0 0 0 0 0 0 0 0 ## 3101109 0 0 0 0 0 0 0 0 0 ## 3101110 0 0 0 0 0 0 0 0 0 ## 3101111 0 0 0 0 0 0 0 0 0 ## 3101112 0 0 0 0 0 0 0 0 0 ## 3101113 0 0 0 0 0 0 0 0 0 ## 3101114 0 0 0 0 0 0 0 0 0 ## 3101115 0 0 0 0 0 0 0 0 0 ## 3101116 0 0 0 0 0 0 0 0 0 ## 3101117 0 0 0 0 0 0 0 0 0 ## 3101118 0 0 0 0 0 0 0 0 0 ## 3101119 0 0 0 0 0 0 0 0 0 ## 3101120 0 0 0 0 0 0 0 0 0 ## 3101121 0 0 0 0 0 0 0 0 0 ## 3101122 0 0 0 0 0 0 0 0 0 ## 3101123 0 0 0 0 0 0 0 0 0 ## 3101124 0 0 0 0 0 0 0 0 0 ## 3101125 0 0 0 0 0 0 0 0 0 ## 3101126 0 0 0 0 0 0 0 0 0 ## 3101127 0 0 0 0 0 0 0 0 0 ## 3101128 0 0 0 0 0 0 0 0 0 ## 3101129 0 0 0 0 0 0 0 0 0 ## 3101130 0 0 0 0 0 0 0 0 0 ## 3101131 0 0 0 0 0 0 0 0 0 ## 3101132 0 0 0 0 0 0 0 0 0 ## 3101133 0 0 0 0 0 0 0 0 0 ## 3101134 0 0 0 0 0 0 0 0 0 ## 3101135 0 0 0 0 0 0 0 0 0 ## 3103101 0 0 0 0 0 0 0 0 0 ## 3103102 0 0 0 0 0 0 0 0 0 ## 3103103 0 0 0 0 0 0 0 0 0 ## 3103104 0 0 0 0 0 0 0 0 0 ## 3103105 0 0 0 0 0 0 0 0 0 ## 3103106 0 0 0 0 0 0 0 0 0 ## 3103107 0 0 0 0 0 0 0 0 0 ## 3103201 0 0 0 0 0 0 0 0 0 ## 3301101 0 0 0 0 0 0 0 0 0 ## 3301102 0 0 0 0 0 0 0 0 0 ## 3301103 0 0 0 0 0 0 0 0 0 ## 3301104 0 0 0 0 0 0 0 0 0 ## 3301105 0 0 0 0 0 0 0 0 0 ## 3301106 0 0 0 0 0 0 0 0 0 ## 3301107 0 0 0 0 0 0 0 0 0 ## 3301108 0 0 0 0 0 0 0 0 0 ## 3301109 0 0 0 0 0 0 0 0 0 ## 3301110 0 0 0 0 0 0 0 0 0 ## 3302201 0 0 0 0 0 0 0 0 0 ## 3302202 0 0 0 0 0 0 0 0 0 ## 3304101 0 0 0 0 0 0 0 0 0 ## 3304102 0 0 0 0 0 0 0 0 0 ## 3304103 0 0 0 0 0 0 0 0 0 ## 3304104 0 0 0 0 0 0 0 0 0 ## 3304105 0 0 0 0 0 0 0 0 0 ## 3304106 0 0 0 0 0 0 0 0 0 ## 4101101 0 0 0 0 0 0 0 0 0 ## 4101102 0 0 0 0 0 0 0 0 0 ## 4101105 0 0 0 0 0 0 0 0 0 ## 4101106 0 0 0 0 0 0 0 0 0 ## 4101108 0 0 0 0 0 0 0 0 0 ## 4101109 0 0 0 0 0 0 0 0 0 ## 4101110 0 0 0 0 0 0 0 0 0 ## 4101111 0 0 0 0 0 0 0 0 0 ## 4101112 0 0 0 0 0 0 0 0 0 ## 4101113 0 0 0 0 0 0 0 0 0 ## 4101114 0 0 0 0 0 0 0 0 0 ## 4101115 0 0 0 0 0 0 0 0 0 ## 4101116 0 0 0 0 0 0 0 0 0 ## 4101117 0 0 0 0 0 0 0 0 0 ## 4102101 0 0 0 0 0 0 0 0 0 ## 4102102 0 0 0 0 0 0 0 0 0 ## 4102104 0 0 0 0 0 0 0 0 0 ## 4102105 0 0 0 0 0 0 0 0 0 ## 4102106 0 0 0 0 0 0 0 0 0 ## 4102107 0 0 0 0 0 0 0 0 0 ## 4102108 0 0 0 0 0 0 0 0 0 ## 4102109 0 0 0 0 0 0 0 0 0 ## 4102110 0 0 0 0 0 0 0 0 0 ## 4102111 0 0 0 0 0 0 0 0 0 ## 4102112 0 0 0 0 0 0 0 0 0 ## 4102113 0 0 0 0 0 0 0 0 0 ## 4102114 0 0 0 0 0 0 0 0 0 ## 4102115 0 0 0 0 0 0 0 0 0 ## 4102117 0 0 0 0 0 0 0 0 0 ## 4102118 0 0 0 0 0 0 0 0 0 ## 4102119 0 0 0 0 0 0 0 0 0 ## 4106201 0 0 0 0 0 0 0 0 0 ## 4106202 0 0 0 0 0 0 0 0 0 ## 4201101 0 0 0 0 0 0 0 0 0 ## 4201102 0 0 0 0 0 0 0 0 0 ## 4201103 0 0 0 0 0 0 0 0 0 ## 4201104 0 0 0 0 0 0 0 0 0 ## 4201105 0 0 0 0 0 0 0 0 0 ## 4301101 0 0 0 0 0 0 0 0 0 ## 4301102 0 0 0 0 0 0 0 0 0 ## 4301103 0 0 0 0 0 0 0 0 0 ## 4301104 0 0 0 0 0 0 0 0 0 ## 4301105 0 0 0 0 0 0 0 0 0 ## 4301106 0 0 0 0 0 0 0 0 0 ## 4301107 0 0 0 0 0 0 0 0 0 ## 4301108 0 0 0 0 0 0 0 0 0 ## 4301201 0 0 0 0 0 0 0 0 0 ## 4301202 0 0 0 0 0 0 0 0 0 ## 4303101 0 0 0 0 0 0 0 0 0 ## 4303102 0 0 0 0 0 0 0 0 0 ## 4303103 0 0 0 0 0 0 0 0 0 ## 4303104 0 0 0 0 0 0 0 0 0 ## 4303105 0 0 0 0 0 0 0 0 0 ## 4304201 0 0 0 0 0 0 0 0 0 ## 4304202 0 0 0 0 0 0 0 0 0 ## 5101102 0 0 0 0 0 0 0 0 0 ## 5101103 0 0 0 0 0 0 0 0 0 ## 5101104 0 0 0 0 0 0 0 0 0 ## 5101105 0 0 0 0 0 0 0 0 0 ## 5101106 0 0 0 0 0 0 0 0 0 ## 5101107 0 0 0 0 0 0 0 0 0 ## 5101108 0 0 0 0 0 0 0 0 0 ## 5101109 0 0 0 0 0 0 0 0 0 ## 5101111 0 0 0 0 0 0 0 0 0 ## 5101112 0 0 0 0 0 0 0 0 0 ## 5101113 0 0 0 0 0 0 0 0 0 ## 5101115 0 0 0 0 0 0 0 0 0 ## 5101116 0 0 0 0 0 0 0 0 0 ## 5101117 0 0 0 0 0 0 0 0 0 ## 5101118 0 0 0 0 0 0 0 0 0 ## 5101119 0 0 0 0 0 0 0 0 0 ## 5101120 0 0 0 0 0 0 0 0 0 ## 5101121 0 0 0 0 0 0 0 0 0 ## 5101122 0 0 0 0 0 0 0 0 0 ## 5102101 0 0 0 0 0 0 0 0 0 ## 5102102 0 0 0 0 0 0 0 0 0 ## 5102103 0 0 0 0 0 0 0 0 0 ## 5102104 0 0 0 0 0 0 0 0 0 ## 5102105 0 0 0 0 0 0 0 0 0 ## 5103103 0 0 0 0 0 0 0 0 0 ## 5103104 0 0 0 0 0 0 0 0 0 ## 5109101 0 0 0 0 0 0 0 0 0 ## 5109102 0 0 0 0 0 0 0 0 0 ## 5109105 0 0 0 0 0 0 0 0 0 ## 5109106 0 0 0 0 0 0 0 0 0 ## 5109107 0 0 0 0 0 0 0 0 0 ## 5109108 0 0 0 0 0 0 0 0 0 ## 5109109 0 0 0 0 0 0 0 0 0 ## 5109111 0 0 0 0 0 0 0 0 0 ## 5109112 0 0 0 0 0 0 0 0 0 ## 5109113 0 0 0 0 0 0 0 0 0 ## 5109114 0 0 0 0 0 0 0 0 0 ## 5109115 0 0 0 0 0 0 0 0 0 ## 5109116 0 0 0 0 0 0 0 0 0 ## 5109117 0 0 0 0 0 0 0 0 0 ## 5109118 0 0 0 0 0 0 0 0 0 ## 5109119 0 0 0 0 0 0 0 0 0 ## 5109120 0 0 0 0 0 0 0 0 0 ## 5109121 0 0 0 0 0 0 0 0 0 ## 5109122 0 0 0 0 0 0 0 0 0 ## 5109123 0 0 0 0 0 0 0 0 0 ## 5109124 0 0 0 0 0 0 0 0 0 ## 5301101 0 0 0 0 0 0 0 0 0 ## 5301102 0 0 0 0 0 0 0 0 0 ## 5301103 0 0 0 0 0 0 0 0 0 ## 5301104 0 0 0 0 0 0 0 0 0 ## 5304201 0 0 0 0 0 0 0 0 0 ## 5304202 0 0 0 0 0 0 0 0 0 ## 5402101 0 0 0 0 0 0 0 0 0 ## 5402102 0 0 0 0 0 0 0 0 0 ## 5402103 0 0 0 0 0 0 0 0 0 ## 5402104 0 0 0 0 0 0 0 0 0 ## 5402105 0 0 0 0 0 0 0 0 0 ## 5501101 0 0 0 0 0 0 0 0 0 ## 5501102 0 0 0 0 0 0 0 0 0 ## 5501103 0 0 0 0 0 0 0 0 0 ## 5501104 0 0 0 0 0 0 0 0 0 ## 5501105 0 0 0 0 0 0 0 0 0 ## 5502101 0 0 0 0 0 0 0 0 0 ## 5502102 0 0 0 0 0 0 0 0 0 ## 5502103 0 0 0 0 0 0 0 0 0 ## 5502104 0 0 0 0 0 0 0 0 0 ## 5503201 0 0 0 0 0 0 0 0 0 ## 5503202 0 0 0 0 0 0 0 0 0 ## 5601101 0 0 0 0 0 0 0 0 0 ## 5601102 0 0 0 0 0 0 0 0 0 ## 5601103 0 0 0 0 0 0 0 0 0 ## 5601104 0 0 0 0 0 0 0 0 0 ## 5601105 0 0 0 0 0 0 0 0 0 ## 5601106 0 0 0 0 0 0 0 0 0 ## 5602101 0 0 0 0 0 0 0 0 0 ## 5602102 0 0 0 0 0 0 0 0 0 ## 5602103 0 0 0 0 0 0 0 0 0 ## 5602104 0 0 0 0 0 0 0 0 0 ## 5602105 0 0 0 0 0 0 0 0 0 ## 5701101 0 0 0 0 0 0 0 0 0 ## 5701102 0 0 0 0 0 0 0 0 0 ## 5701103 0 0 0 0 0 0 0 0 0 ## 5701104 0 0 0 0 0 0 0 0 0 ## 5703101 0 0 0 0 0 0 0 0 0 ## 5703102 0 0 0 0 0 0 0 0 0 ## 5703103 0 0 0 0 0 0 0 0 0 ## 5703104 0 0 0 0 0 0 0 0 0 ## 5703105 0 0 0 0 0 0 0 0 0 ## 5705201 0 0 0 0 0 0 0 0 0 ## 5705202 0 0 0 0 0 0 0 0 0 ## 5801101 0 0 0 0 0 0 0 0 0 ## 5801102 0 0 0 0 0 0 0 0 0 ## 5801103 0 0 0 0 0 0 0 0 0 ## 5801104 0 0 0 0 0 0 0 0 0 ## 5801105 0 0 0 0 0 0 0 0 0 ## 5801106 0 0 0 0 0 0 0 0 0 ## 5801107 0 0 0 0 0 0 0 0 0 ## 5801108 0 0 0 0 0 0 0 0 0 ## 5801109 0 0 0 0 0 0 0 0 0 ## 5801110 0 0 0 0 0 0 0 0 0 ## 5801111 0 0 0 0 0 0 0 0 0 ## 5802101 0 0 0 0 0 0 0 0 0 ## 5802102 0 0 0 0 0 0 0 0 0 ## 5802103 0 0 0 0 0 0 0 0 0 ## 5804101 0 0 0 0 0 0 0 0 0 ## 5804102 0 0 0 0 0 0 0 0 0 ## 5804103 0 0 0 0 0 0 0 0 0 ## 5804104 0 0 0 0 0 0 0 0 0 ## 5804105 0 0 0 0 0 0 0 0 0 ## 5804107 0 0 0 0 0 0 0 0 0 ## 5804108 0 0 0 0 0 0 0 0 0 ## 5804109 0 0 0 0 0 0 0 0 0 ## 5804110 0 0 0 0 0 0 0 0 0 ## 6101102 0 0 0 0 0 0 0 0 0 ## 6101103 0 0 0 0 0 0 0 0 0 ## 6101104 0 0 0 0 0 0 0 0 0 ## 6101105 0 0 0 0 0 0 0 0 0 ## 6101106 0 0 0 0 0 0 0 0 0 ## 6101107 0 0 0 0 0 0 0 0 0 ## 6101108 0 0 0 0 0 0 0 0 0 ## 6101109 0 0 0 0 0 0 0 0 0 ## 6101111 0 0 0 0 0 0 0 0 0 ## 6101112 0 0 0 0 0 0 0 0 0 ## 6101113 0 0 0 0 0 0 0 0 0 ## 6101114 0 0 0 0 0 0 0 0 0 ## 6101115 0 0 0 0 0 0 0 0 0 ## 6101116 0 0 0 0 0 0 0 0 0 ## 6101117 0 0 0 0 0 0 0 0 0 ## 6101118 0 0 0 0 0 0 0 0 0 ## 6105101 0 0 0 0 0 0 0 0 0 ## 6105102 0 0 0 0 0 0 0 0 0 ## 6105103 0 0 0 0 0 0 0 0 0 ## 6105104 0 0 0 0 0 0 0 0 0 ## 6105105 0 0 0 0 0 0 0 0 0 ## 6107201 0 0 0 0 0 0 0 0 0 ## 6107202 0 0 0 0 0 0 0 0 0 ## 6108101 0 0 0 0 0 0 0 0 0 ## 6108102 0 0 0 0 0 0 0 0 0 ## 6108103 0 0 0 0 0 0 0 0 0 ## 6108104 0 0 0 0 0 0 0 0 0 ## 6110101 0 0 0 0 0 0 0 0 0 ## 6110102 0 0 0 0 0 0 0 0 0 ## 6110103 0 0 0 0 0 0 0 0 0 ## 6110104 0 0 0 0 0 0 0 0 0 ## 6110105 0 0 0 0 0 0 0 0 0 ## 6114201 0 0 0 0 0 0 0 0 0 ## 6114202 0 0 0 0 0 0 0 0 0 ## 6115101 0 0 0 0 0 0 0 0 0 ## 6115102 0 0 0 0 0 0 0 0 0 ## 6115103 0 0 0 0 0 0 0 0 0 ## 6117101 0 0 0 0 0 0 0 0 0 ## 6117102 0 0 0 0 0 0 0 0 0 ## 6117103 0 0 0 0 0 0 0 0 0 ## 6117104 0 0 0 0 0 0 0 0 0 ## 6117105 0 0 0 0 0 0 0 0 0 ## 6117201 0 0 0 0 0 0 0 0 0 ## 6117202 0 0 0 0 0 0 0 0 0 ## 6301101 0 0 0 0 0 0 0 0 0 ## 6301102 0 0 0 0 0 0 0 0 0 ## 6301103 0 0 0 0 0 0 0 0 0 ## 6301104 0 0 0 0 0 0 0 0 0 ## 6303201 0 0 0 0 0 0 0 0 0 ## 6303202 0 0 0 0 0 0 0 0 0 ## 6305101 0 0 0 0 0 0 0 0 0 ## 6305102 0 0 0 0 0 0 0 0 0 ## 6305103 0 0 0 0 0 0 0 0 0 ## 6305104 0 0 0 0 0 0 0 0 0 ## 6305105 0 0 0 0 0 0 0 0 0 ## 6310201 0 0 0 0 0 0 0 0 0 ## 6310202 0 0 0 0 0 0 0 0 0 ## 7101101 0 0 0 0 0 0 0 0 0 ## 7101102 0 0 0 0 0 0 0 0 0 ## 7101103 0 0 0 0 0 0 0 0 0 ## 7101104 0 0 0 0 0 0 0 0 0 ## 7101105 0 0 0 0 0 0 0 0 0 ## 7101106 0 0 0 0 0 0 0 0 0 ## 7101107 0 0 0 0 0 0 0 0 0 ## 7101108 0 0 0 0 0 0 0 0 0 ## 7101109 0 0 0 0 0 0 0 0 0 ## 7101110 0 0 0 0 0 0 0 0 0 ## 7101111 0 0 0 0 0 0 0 0 0 ## 7101112 0 0 0 0 0 0 0 0 0 ## 7101113 0 0 0 0 0 0 0 0 0 ## 7101114 0 0 0 0 0 0 0 0 0 ## 7102101 0 0 0 0 0 0 0 0 0 ## 7102102 0 0 0 0 0 0 0 0 0 ## 7102103 0 0 0 0 0 0 0 0 0 ## 7104201 0 0 0 0 0 0 0 0 0 ## 7104202 0 0 0 0 0 0 0 0 0 ## 7105101 0 0 0 0 0 0 0 0 0 ## 7105102 0 0 0 0 0 0 0 0 0 ## 7105103 0 0 0 0 0 0 0 0 0 ## 7105104 0 0 0 0 0 0 0 0 0 ## 7105105 0 0 0 0 0 0 0 0 0 ## 7109201 0 0 0 0 0 0 0 0 0 ## 7109202 0 0 0 0 0 0 0 0 0 ## 7201101 0 0 0 0 0 0 0 0 0 ## 7201102 0 0 0 0 0 0 0 0 0 ## 7301101 0 0 0 0 0 0 0 0 0 ## 7301102 0 0 0 0 0 0 0 0 0 ## 7301103 0 0 0 0 0 0 0 0 0 ## 7301104 0 0 0 0 0 0 0 0 0 ## 7301105 0 0 0 0 0 0 0 0 0 ## 7301106 0 0 0 0 0 0 0 0 0 ## 7301107 0 0 0 0 0 0 0 0 0 ## 7301108 0 0 0 0 0 0 0 0 0 ## 7301201 0 0 0 0 0 0 0 0 0 ## 7301202 0 0 0 0 0 0 0 0 0 ## 7304101 0 0 0 0 0 0 0 0 0 ## 7304102 0 0 0 0 0 0 0 0 0 ## 7307201 0 0 0 0 0 0 0 0 0 ## 7307202 0 0 0 0 0 0 0 0 0 ## 7401101 0 0 0 0 0 0 0 0 0 ## 7401102 0 0 0 0 0 0 0 0 0 ## 7401103 0 0 0 0 0 0 0 0 0 ## 7401104 0 0 0 0 0 0 0 0 0 ## 7401105 0 0 0 0 0 0 0 0 0 ## 7402201 0 0 0 0 0 0 0 0 0 ## 7402202 0 0 0 0 0 0 0 0 0 ## 7404101 0 0 0 0 0 0 0 0 0 ## 7404102 0 0 0 0 0 0 0 0 0 ## 7404103 0 0 0 0 0 0 0 0 0 ## 7404104 0 0 0 0 0 0 0 0 0 ## 7404105 0 0 0 0 0 0 0 0 0 ## 7404106 0 0 0 0 0 0 0 0 0 ## 7405201 0 0 0 0 0 0 0 0 0 ## 7405202 0 0 0 0 0 0 0 0 0 ## 8101101 0 0 0 0 0 0 0 0 0 ## 8101102 0 0 0 0 0 0 0 0 0 ## 8101103 0 0 0 0 0 0 0 0 0 ## 8101104 0 0 0 0 0 0 0 0 0 ## 8101105 0 0 0 0 0 0 0 0 0 ## 8101106 0 0 0 0 0 0 0 0 0 ## 8101107 0 0 0 0 0 0 0 0 0 ## 8101108 0 0 0 0 0 0 0 0 0 ## 8101110 0 0 0 0 0 0 0 0 0 ## 8101112 0 0 0 0 0 0 0 0 0 ## 8101113 0 0 0 0 0 0 0 0 0 ## 8101114 0 0 0 0 0 0 0 0 0 ## 8102101 0 0 0 0 0 0 0 0 0 ## 8102102 0 0 0 0 0 0 0 0 0 ## 8102103 0 0 0 0 0 0 0 0 0 ## 8102104 0 0 0 0 0 0 0 0 0 ## 8102105 0 0 0 0 0 0 0 0 0 ## 8102106 0 0 0 0 0 0 0 0 0 ## 8103101 0 0 0 0 0 0 0 0 0 ## 8103102 0 0 0 0 0 0 0 0 0 ## 8103103 0 0 0 0 0 0 0 0 0 ## 8103104 0 0 0 0 0 0 0 0 0 ## 8103105 0 0 0 0 0 0 0 0 0 ## 8103106 0 0 0 0 0 0 0 0 0 ## 8106101 0 0 0 0 0 0 0 0 0 ## 8106102 0 0 0 0 0 0 0 0 0 ## 8106103 0 0 0 0 0 0 0 0 0 ## 8107101 0 0 0 0 0 0 0 0 0 ## 8107102 0 0 0 0 0 0 0 0 0 ## 8107103 0 0 0 0 0 0 0 0 0 ## 8108101 0 0 0 0 0 0 0 0 0 ## 8108102 0 0 0 0 0 0 0 0 0 ## 8108103 0 0 0 0 0 0 0 0 0 ## 8108104 0 0 0 0 0 0 0 0 0 ## 8108105 0 0 0 0 0 0 0 0 0 ## 8109101 0 0 0 0 0 0 0 0 0 ## 8109102 0 0 0 0 0 0 0 0 0 ## 8109103 0 0 0 0 0 0 0 0 0 ## 8109104 0 0 0 0 0 0 0 0 0 ## 8109105 0 0 0 0 0 0 0 0 0 ## 8109106 0 0 0 0 0 0 0 0 0 ## 8110101 0 0 0 0 0 0 0 0 0 ## 8110102 0 0 0 0 0 0 0 0 0 ## 8110103 0 0 0 0 0 0 0 0 0 ## 8110104 0 0 0 0 0 0 0 0 0 ## 8110105 0 0 0 0 0 0 0 0 0 ## 8110106 0 0 0 0 0 0 0 0 0 ## 8110107 0 0 0 0 0 0 0 0 0 ## 8110108 0 0 0 0 0 0 0 0 0 ## 8110109 0 0 0 0 0 0 0 0 0 ## 8110110 0 0 0 0 0 0 0 0 0 ## 8110201 0 0 0 0 0 0 0 0 0 ## 8110202 0 0 0 0 0 0 0 0 0 ## 8111101 0 0 0 0 0 0 0 0 0 ## 8111102 0 0 0 0 0 0 0 0 0 ## 8111103 0 0 0 0 0 0 0 0 0 ## 8112101 0 0 0 0 0 0 0 0 0 ## 8112102 0 0 0 0 0 0 0 0 0 ## 8112103 0 0 0 0 0 0 0 0 0 ## 8112104 0 0 0 0 0 0 0 0 0 ## 8112105 0 0 0 0 0 0 0 0 0 ## 8112106 0 0 0 0 0 0 0 0 0 ## 8205101 0 0 0 0 0 0 0 0 0 ## 8205102 0 0 0 0 0 0 0 0 0 ## 8206101 0 0 0 0 0 0 0 0 0 ## 8206102 0 0 0 0 0 0 0 0 0 ## 8206103 0 0 0 0 0 0 0 0 0 ## 8206104 0 0 0 0 0 0 0 0 0 ## 8206105 0 0 0 0 0 0 0 0 0 ## 8206106 0 0 0 0 0 0 0 0 0 ## 8301101 0 0 0 0 0 0 0 0 0 ## 8301102 0 0 0 0 0 0 0 0 0 ## 8301103 0 0 0 0 0 0 0 0 0 ## 8301104 0 0 0 0 0 0 0 0 0 ## 8301105 0 0 0 0 0 0 0 0 0 ## 8301106 0 0 0 0 0 0 0 0 0 ## 8301107 0 0 0 0 0 0 0 0 0 ## 8301108 0 0 0 0 0 0 0 0 0 ## 8301201 0 0 0 0 0 0 0 0 0 ## 8301202 0 0 0 0 0 0 0 0 0 ## 8307101 0 0 0 0 0 0 0 0 0 ## 8307102 0 0 0 0 0 0 0 0 0 ## 8307103 0 0 0 0 0 0 0 0 0 ## 8307104 0 0 0 0 0 0 0 0 0 ## 8307105 0 0 0 0 0 0 0 0 0 ## 8307106 0 0 0 0 0 0 0 0 0 ## 8309201 0 0 0 0 0 0 0 0 0 ## 8309202 0 0 0 0 0 0 0 0 0 ## 8401101 0 0 0 0 0 0 0 0 0 ## 8401102 0 0 0 0 0 0 0 0 0 ## 8401103 0 0 0 0 0 0 0 0 0 ## 8401104 0 0 0 0 0 0 0 0 0 ## 8401105 0 0 0 0 0 0 0 0 0 ## 8401106 0 0 0 0 0 0 0 0 0 ## 8401107 0 0 0 0 0 0 0 0 0 ## 8401108 0 0 0 0 0 0 0 0 0 ## 8401109 0 0 0 0 0 0 0 0 0 ## 8405201 0 0 0 0 0 0 0 0 0 ## 8405202 0 0 0 0 0 0 0 0 0 ## 8406101 0 0 0 0 0 0 0 0 0 ## 8406102 0 0 0 0 0 0 0 0 0 ## 8406103 0 0 0 0 0 0 0 0 0 ## 8406104 0 0 0 0 0 0 0 0 0 ## 8406105 0 0 0 0 0 0 0 0 0 ## 8406106 0 0 0 0 0 0 0 0 0 ## 8416101 0 0 0 0 0 0 0 0 0 ## 8416102 0 0 0 0 0 0 0 0 0 ## 8416201 0 0 0 0 0 0 0 0 0 ## 8416202 0 0 0 0 0 0 0 0 0 ## 9101102 0 0 0 0 0 0 0 0 0 ## 9101103 0 0 0 0 0 0 0 0 0 ## 9101104 0 0 0 0 0 0 0 0 0 ## 9101105 0 0 0 0 0 0 0 0 0 ## 9101106 0 0 0 0 0 0 0 0 0 ## 9101107 0 0 0 0 0 0 0 0 0 ## 9101108 0 0 0 0 0 0 0 0 0 ## 9101109 0 0 0 0 0 0 0 0 0 ## 9101110 0 0 0 0 0 0 0 0 0 ## 9101111 0 0 0 0 0 0 0 0 0 ## 9101112 0 0 0 0 0 0 0 0 0 ## 9101113 0 0 0 0 0 0 0 0 0 ## 9101114 0 0 0 0 0 0 0 0 0 ## 9101115 0 0 0 0 0 0 0 0 0 ## 9101116 0 0 0 0 0 0 0 0 0 ## 9101117 0 0 0 0 0 0 0 0 0 ## 9101118 0 0 0 0 0 0 0 0 0 ## 9101119 0 0 0 0 0 0 0 0 0 ## 9101201 0 0 0 0 0 0 0 0 0 ## 9101202 0 0 0 0 0 0 0 0 0 ## 9105101 0 0 0 0 0 0 0 0 0 ## 9105102 0 0 0 0 0 0 0 0 0 ## 9105103 0 0 0 0 0 0 0 0 0 ## 9105104 0 0 0 0 0 0 0 0 0 ## 9105105 0 0 0 0 0 0 0 0 0 ## 9106201 0 0 0 0 0 0 0 0 0 ## 9106202 0 0 0 0 0 0 0 0 0 ## 9111101 0 0 0 0 0 0 0 0 0 ## 9111102 0 0 0 0 0 0 0 0 0 ## 9111103 0 0 0 0 0 0 0 0 0 ## 9111104 0 0 0 0 0 0 0 0 0 ## 9111105 0 0 0 0 0 0 0 0 0 ## 9112101 0 0 0 0 0 0 0 0 0 ## 9112102 0 0 0 0 0 0 0 0 0 ## 9112103 0 0 0 0 0 0 0 0 0 ## 9112201 0 0 0 0 0 0 0 0 0 ## 9112202 0 0 0 0 0 0 0 0 0 ## 9116201 0 0 0 0 0 0 0 0 0 ## 9116202 0 0 0 0 0 0 0 0 0 ## 9118101 0 0 0 0 0 0 0 0 0 ## 9118102 0 0 0 0 0 0 0 0 0 ## 9118103 0 0 0 0 0 0 0 0 0 ## 9118104 0 0 0 0 0 0 0 0 0 ## 9118105 0 0 0 0 0 0 0 0 0 ## 9120101 0 0 0 0 0 0 0 0 0 ## 9120102 0 0 0 0 0 0 0 0 0 ## 9201101 0 0 0 0 0 0 0 0 0 ## 9201102 0 0 0 0 0 0 0 0 0 ## 9201103 0 0 0 0 0 0 0 0 0 ## 9202201 0 0 0 0 0 0 0 0 0 ## 9202202 0 0 0 0 0 0 0 0 0 ## 9209101 0 0 0 0 0 0 0 0 0 ## 9209102 0 0 0 0 0 0 0 0 0 ## 9209103 0 0 0 0 0 0 0 0 0 ## 9209104 0 0 0 0 0 0 0 0 0 ## 9209105 0 0 0 0 0 0 0 0 0 ## 10101101 0 0 0 0 0 0 0 0 0 ## 10101102 0 0 0 0 0 0 0 0 0 ## 10101103 0 0 0 0 0 0 0 0 0 ## 10101104 0 0 0 0 0 0 0 0 0 ## 10101105 0 0 0 0 0 0 0 0 0 ## 10101106 0 0 0 0 0 0 0 0 0 ## 10101107 0 0 0 0 0 0 0 0 0 ## 10101108 0 0 0 0 0 0 0 0 0 ## 10101109 0 0 0 0 0 0 0 0 0 ## 10101112 0 0 0 0 0 0 0 0 0 ## 10101113 0 0 0 0 0 0 0 0 0 ## 10101114 0 0 0 0 0 0 0 0 0 ## 10101115 0 0 0 0 0 0 0 0 0 ## 10101116 0 0 0 0 0 0 0 0 0 ## 10101117 0 0 0 0 0 0 0 0 0 ## 10101118 0 0 0 0 0 0 0 0 0 ## 10101201 0 0 0 0 0 0 0 0 0 ## 10101202 0 0 0 0 0 0 0 0 0 ## 10105101 0 0 0 0 0 0 0 0 0 ## 10105102 0 0 0 0 0 0 0 0 0 ## 10105103 0 0 0 0 0 0 0 0 0 ## 10105104 0 0 0 0 0 0 0 0 0 ## 10105201 0 0 0 0 0 0 0 0 0 ## 10105202 0 0 0 0 0 0 0 0 0 ## 10109101 0 0 0 0 0 0 0 0 0 ## 10109102 0 0 0 0 0 0 0 0 0 ## 10201101 0 0 0 0 0 0 0 0 0 ## 10201102 0 0 0 0 0 0 0 0 0 ## 10201103 0 0 0 0 0 0 0 0 0 ## 10201201 0 0 0 0 0 0 0 0 0 ## 10201202 0 0 0 0 0 0 0 0 0 ## 10202101 0 0 0 0 0 0 0 0 0 ## 10202102 0 0 0 0 0 0 0 0 0 ## 10203101 0 0 0 0 0 0 0 0 0 ## 10203102 0 0 0 0 0 0 0 0 0 ## 10203103 0 0 0 0 0 0 0 0 0 ## 10203104 0 0 0 0 0 0 0 0 0 ## 10207201 0 0 0 0 0 0 0 0 0 ## 10207202 0 0 0 0 0 0 0 0 0 ## 10301101 0 0 0 0 0 0 0 0 0 ## 10301102 0 0 0 0 0 0 0 0 0 ## 10301103 0 0 0 0 0 0 0 0 0 ## 10301104 0 0 0 0 0 0 0 0 0 ## 10301105 0 0 0 0 0 0 0 0 0 ## 10301106 0 0 0 0 0 0 0 0 0 ## 10301107 0 0 0 0 0 0 0 0 0 ## 10301108 0 0 0 0 0 0 0 0 0 ## 10301109 0 0 0 0 0 0 0 0 0 ## 10301110 0 0 0 0 0 0 0 0 0 ## 10301111 0 0 0 0 0 0 0 0 0 ## 10303101 0 0 0 0 0 0 0 0 0 ## 10303102 0 0 0 0 0 0 0 0 0 ## 10303103 0 0 0 0 0 0 0 0 0 ## 10303104 0 0 0 0 0 0 0 0 0 ## 10303201 0 0 0 0 0 0 0 0 0 ## 10303202 0 0 0 0 0 0 0 0 0 ## 11101101 0 0 0 0 0 0 0 0 0 ## 11101102 0 0 0 0 0 0 0 0 0 ## 11101103 0 0 0 0 0 0 0 0 0 ## 11101105 0 0 0 0 0 0 0 0 0 ## 11101106 0 0 0 0 0 0 0 0 0 ## 11101107 0 0 0 0 0 0 0 0 0 ## 11101108 0 0 0 0 0 0 0 0 0 ## 11101109 0 0 0 0 0 0 0 0 0 ## 11101110 0 0 0 0 0 0 0 0 0 ## 11101111 0 0 0 0 0 0 0 0 0 ## 11101112 0 0 0 0 0 0 0 0 0 ## 11101113 0 0 0 0 0 0 0 0 0 ## 11101114 0 0 0 0 0 0 0 0 0 ## 11101115 0 0 0 0 0 0 0 0 0 ## 11101116 0 0 0 0 0 0 0 0 0 ## 11101117 0 0 0 0 0 0 0 0 0 ## 11101118 0 0 0 0 0 0 0 0 0 ## 11101120 0 0 0 0 0 0 0 0 0 ## 11101121 0 0 0 0 0 0 0 0 0 ## 11101122 0 0 0 0 0 0 0 0 0 ## 11101123 0 0 0 0 0 0 0 0 0 ## 11101124 0 0 0 0 0 0 0 0 0 ## 11101125 0 0 0 0 0 0 0 0 0 ## 11101127 0 0 0 0 0 0 0 0 0 ## 11101128 0 0 0 0 0 0 0 0 0 ## 11101129 0 0 0 0 0 0 0 0 0 ## 11101130 0 0 0 0 0 0 0 0 0 ## 11101131 0 0 0 0 0 0 0 0 0 ## 11101132 0 0 0 0 0 0 0 0 0 ## 11101133 0 0 0 0 0 0 0 0 0 ## 11101134 0 0 0 0 0 0 0 0 0 ## 11101135 0 0 0 0 0 0 0 0 0 ## 11101136 0 0 0 0 0 0 0 0 0 ## 11101137 0 0 0 0 0 0 0 0 0 ## 11101201 9 0 0 0 0 0 0 0 0 ## 11101202 8 0 0 0 0 0 0 0 0 ## 11101203 8 0 0 0 0 0 0 0 0 ## 11201101 0 0 0 0 0 0 0 0 0 ## 11201102 0 0 0 0 0 0 0 0 0 ## 11201103 0 0 0 0 0 0 0 0 0 ## 11201104 0 0 0 0 0 0 0 0 0 ## 11201105 0 0 0 0 0 0 0 0 0 ## 11201106 0 0 0 0 0 0 0 0 0 ## 11201107 0 0 0 0 0 0 0 0 0 ## 11201108 0 0 0 0 0 0 0 0 0 ## 11201109 0 0 0 0 0 0 0 0 0 ## 11201110 0 0 0 0 0 0 0 0 0 ## 11201111 0 0 0 0 0 0 0 0 0 ## 11201112 0 0 0 0 0 0 0 0 0 ## 11201113 0 0 0 0 0 0 0 0 0 ## 11201114 0 0 0 0 0 0 0 0 0 ## 11201115 0 0 0 0 0 0 0 0 0 ## 11201116 0 0 0 0 0 0 0 0 0 ## 11201117 0 0 0 0 0 0 0 0 0 ## 11201118 0 0 0 0 0 0 0 0 0 ## 11201119 0 0 0 0 0 0 0 0 0 ## 11201201 9 0 0 0 0 0 0 0 0 ## 11201202 16 0 0 0 0 0 0 0 0 ## 12101101 0 3 0 0 0 0 0 0 0 ## 12101102 0 6 0 0 0 0 0 0 0 ## 12101103 0 6 0 0 0 0 0 0 0 ## 12101104 0 4 0 0 0 0 0 0 0 ## 12101105 0 4 0 0 0 0 0 0 0 ## 12101106 0 6 0 0 0 0 0 0 0 ## 12101107 0 5 0 0 0 0 0 0 0 ## 12101108 0 2 0 0 0 0 0 0 0 ## 12101109 0 7 0 0 0 0 0 0 0 ## 12101110 0 6 0 0 0 0 0 0 0 ## 12101111 0 5 0 0 0 0 0 0 0 ## 12101112 0 2 0 0 0 0 0 0 0 ## 12101113 0 5 0 0 0 0 0 0 0 ## 12101114 0 5 0 0 0 0 0 0 0 ## 12101115 0 4 0 0 0 0 0 0 0 ## 12101116 0 5 0 0 0 0 0 0 0 ## 12101117 0 4 0 0 0 0 0 0 0 ## 12101118 0 4 0 0 0 0 0 0 0 ## 12101119 0 4 0 0 0 0 0 0 0 ## 12101120 0 3 0 0 0 0 0 0 0 ## 12101121 0 9 0 0 0 0 0 0 0 ## 12101122 0 2 0 0 0 0 0 0 0 ## 12101123 0 9 0 0 0 0 0 0 0 ## 12101124 0 6 0 0 0 0 0 0 0 ## 12101125 0 4 0 0 0 0 0 0 0 ## 12101126 0 2 0 0 0 0 0 0 0 ## 12101127 0 4 0 0 0 0 0 0 0 ## 12101128 0 6 0 0 0 0 0 0 0 ## 12101129 0 6 0 0 0 0 0 0 0 ## 12101131 0 5 0 0 0 0 0 0 0 ## 12101132 0 5 0 0 0 0 0 0 0 ## 12101133 0 4 0 0 0 0 0 0 0 ## 12101134 0 3 0 0 0 0 0 0 0 ## 12101135 0 3 0 0 0 0 0 0 0 ## 12101136 0 6 0 0 0 0 0 0 0 ## 12101137 0 5 0 0 0 0 0 0 0 ## 12101138 0 6 0 0 0 0 0 0 0 ## 12101139 0 5 0 0 0 0 0 0 0 ## 12101140 0 5 0 0 0 0 0 0 0 ## 12101141 0 5 0 0 0 0 0 0 0 ## 12101142 0 6 0 0 0 0 0 0 0 ## 12101143 0 4 0 0 0 0 0 0 0 ## 12101144 0 4 0 0 0 0 0 0 0 ## 12101145 0 6 0 0 0 0 0 0 0 ## 12101146 0 7 0 0 0 0 0 0 0 ## 12101147 0 8 0 0 0 0 0 0 0 ## 12101148 0 4 0 0 0 0 0 0 0 ## 12101149 0 1 0 0 0 0 0 0 0 ## 12101150 0 3 0 0 0 0 0 0 0 ## 12101201 0 0 15 0 0 0 0 0 0 ## 12301101 0 4 0 0 0 0 0 0 0 ## 12301102 0 5 0 0 0 0 0 0 0 ## 12301103 0 2 0 0 0 0 0 0 0 ## 12301104 0 3 0 0 0 0 0 0 0 ## 12301105 0 3 0 0 0 0 0 0 0 ## 12301106 0 5 0 0 0 0 0 0 0 ## 12301107 0 6 0 0 0 0 0 0 0 ## 12301108 0 3 0 0 0 0 0 0 0 ## 12301109 0 4 0 0 0 0 0 0 0 ## 12301110 0 4 0 0 0 0 0 0 0 ## 12401201 0 0 18 0 0 0 0 0 0 ## 13101101 0 0 0 2 0 0 0 0 0 ## 13101102 0 0 0 5 0 0 0 0 0 ## 13101104 0 0 0 2 0 0 0 0 0 ## 13101105 0 0 0 6 0 0 0 0 0 ## 13101106 0 0 0 4 0 0 0 0 0 ## 13101107 0 0 0 9 0 0 0 0 0 ## 13101108 0 0 0 8 0 0 0 0 0 ## 13101109 0 0 0 5 0 0 0 0 0 ## 13101110 0 0 0 4 0 0 0 0 0 ## 13102101 0 0 0 2 0 0 0 0 0 ## 13102102 0 0 0 5 0 0 0 0 0 ## 13103101 0 0 0 5 0 0 0 0 0 ## 13103102 0 0 0 3 0 0 0 0 0 ## 13103103 0 0 0 5 0 0 0 0 0 ## 13103104 0 0 0 6 0 0 0 0 0 ## 13104101 0 0 0 5 0 0 0 0 0 ## 13104102 0 0 0 6 0 0 0 0 0 ## 13104103 0 0 0 4 0 0 0 0 0 ## 13105101 0 0 0 5 0 0 0 0 0 ## 13105102 0 0 0 3 0 0 0 0 0 ## 13105103 0 0 0 3 0 0 0 0 0 ## 13105104 0 0 0 8 0 0 0 0 0 ## 13105105 0 0 0 7 0 0 0 0 0 ## 13106101 0 0 0 7 0 0 0 0 0 ## 13106102 0 0 0 5 0 0 0 0 0 ## 13106103 0 0 0 7 0 0 0 0 0 ## 13106104 0 0 0 5 0 0 0 0 0 ## 13107101 0 0 0 5 0 0 0 0 0 ## 13107102 0 0 0 6 0 0 0 0 0 ## 13108101 0 0 0 5 0 0 0 0 0 ## 13108102 0 0 0 5 0 0 0 0 0 ## 13109101 0 0 0 5 0 0 0 0 0 ## 13109102 0 0 0 5 0 0 0 0 0 ## 13110101 0 0 0 6 0 0 0 0 0 ## 13110102 0 0 0 5 0 0 0 0 0 ## 13110103 0 0 0 2 0 0 0 0 0 ## 13110104 0 0 0 4 0 0 0 0 0 ## 13110105 0 0 0 7 0 0 0 0 0 ## 13110106 0 0 0 8 0 0 0 0 0 ## 13110108 0 0 0 7 0 0 0 0 0 ## 13110109 0 0 0 6 0 0 0 0 0 ## 13110110 0 0 0 5 0 0 0 0 0 ## 13110111 0 0 0 6 0 0 0 0 0 ## 13111101 0 0 0 4 0 0 0 0 0 ## 13111102 0 0 0 5 0 0 0 0 0 ## 13111103 0 0 0 5 0 0 0 0 0 ## 13111104 0 0 0 4 0 0 0 0 0 ## 13112101 0 0 0 4 0 0 0 0 0 ## 13112102 0 0 0 6 0 0 0 0 0 ## 13112103 0 0 0 5 0 0 0 0 0 ## 13112104 0 0 0 7 0 0 0 0 0 ## 13112105 0 0 0 6 0 0 0 0 0 ## 13113102 0 0 0 6 0 0 0 0 0 ## 13113103 0 0 0 3 0 0 0 0 0 ## 13114101 0 0 0 1 0 0 0 0 0 ## 13114103 0 0 0 3 0 0 0 0 0 ## 13114105 0 0 0 2 0 0 0 0 0 ## 13114107 0 0 0 6 0 0 0 0 0 ## 13115101 0 0 0 2 0 0 0 0 0 ## 13115102 0 0 0 7 0 0 0 0 0 ## 13116101 0 0 0 5 0 0 0 0 0 ## 13116102 0 0 0 7 0 0 0 0 0 ## 13116103 0 0 0 6 0 0 0 0 0 ## 13117101 0 0 0 2 0 0 0 0 0 ## 13117102 0 0 0 3 0 0 0 0 0 ## 13117103 0 0 0 6 0 0 0 0 0 ## 13118101 0 0 0 8 0 0 0 0 0 ## 13118102 0 0 0 9 0 0 0 0 0 ## 13118103 0 0 0 4 0 0 0 0 0 ## 13119101 0 0 0 4 0 0 0 0 0 ## 13119102 0 0 0 7 0 0 0 0 0 ## 13119103 0 0 0 7 0 0 0 0 0 ## 13119104 0 0 0 8 0 0 0 0 0 ## 13119105 0 0 0 9 0 0 0 0 0 ## 13119106 0 0 0 2 0 0 0 0 0 ## 13119107 0 0 0 8 0 0 0 0 0 ## 13119108 0 0 0 4 0 0 0 0 0 ## 13119109 0 0 0 8 0 0 0 0 0 ## 13119110 0 0 0 6 0 0 0 0 0 ## 13119111 0 0 0 7 0 0 0 0 0 ## 13119112 0 0 0 4 0 0 0 0 0 ## 13119113 0 0 0 2 0 0 0 0 0 ## 13120102 0 0 0 6 0 0 0 0 0 ## 13120104 0 0 0 6 0 0 0 0 0 ## 13120105 0 0 0 6 0 0 0 0 0 ## 13120106 0 0 0 5 0 0 0 0 0 ## 13121101 0 0 0 4 0 0 0 0 0 ## 13121102 0 0 0 3 0 0 0 0 0 ## 13121103 0 0 0 5 0 0 0 0 0 ## 13122101 0 0 0 6 0 0 0 0 0 ## 13122102 0 0 0 3 0 0 0 0 0 ## 13122104 0 0 0 4 0 0 0 0 0 ## 13122105 0 0 0 8 0 0 0 0 0 ## 13122106 0 0 0 6 0 0 0 0 0 ## 13122107 0 0 0 6 0 0 0 0 0 ## 13123101 0 0 0 2 0 0 0 0 0 ## 13123102 0 0 0 6 0 0 0 0 0 ## 13123105 0 0 0 6 0 0 0 0 0 ## 13124101 0 0 0 7 0 0 0 0 0 ## 13124102 0 0 0 6 0 0 0 0 0 ## 13124103 0 0 0 3 0 0 0 0 0 ## 13124104 0 0 0 6 0 0 0 0 0 ## 13124105 0 0 0 7 0 0 0 0 0 ## 13124106 0 0 0 4 0 0 0 0 0 ## 13125101 0 0 0 6 0 0 0 0 0 ## 13125102 0 0 0 4 0 0 0 0 0 ## 13125103 0 0 0 5 0 0 0 0 0 ## 13125104 0 0 0 5 0 0 0 0 0 ## 13125105 0 0 0 7 0 0 0 0 0 ## 13126101 0 0 0 5 0 0 0 0 0 ## 13126102 0 0 0 3 0 0 0 0 0 ## 13126103 0 0 0 5 0 0 0 0 0 ## 13127101 0 0 0 4 0 0 0 0 0 ## 13127102 0 0 0 6 0 0 0 0 0 ## 13127103 0 0 0 5 0 0 0 0 0 ## 13127104 0 0 0 4 0 0 0 0 0 ## 13128101 0 0 0 5 0 0 0 0 0 ## 13128102 0 0 0 4 0 0 0 0 0 ## 13128103 0 0 0 5 0 0 0 0 0 ## 13128104 0 0 0 6 0 0 0 0 0 ## 13129101 0 0 0 4 0 0 0 0 0 ## 13129102 0 0 0 3 0 0 0 0 0 ## 13129104 0 0 0 6 0 0 0 0 0 ## 13130101 0 0 0 7 0 0 0 0 0 ## 13130103 0 0 0 6 0 0 0 0 0 ## 13131101 0 0 0 6 0 0 0 0 0 ## 13131102 0 0 0 7 0 0 0 0 0 ## 13132101 0 0 0 4 0 0 0 0 0 ## 13132102 0 0 0 3 0 0 0 0 0 ## 13201102 0 0 0 7 0 0 0 0 0 ## 13201103 0 0 0 6 0 0 0 0 0 ## 13201106 0 0 0 8 0 0 0 0 0 ## 13201107 0 0 0 6 0 0 0 0 0 ## 13201109 0 0 0 4 0 0 0 0 0 ## 13201110 0 0 0 6 0 0 0 0 0 ## 13201111 0 0 0 3 0 0 0 0 0 ## 13201112 0 0 0 4 0 0 0 0 0 ## 13201113 0 0 0 5 0 0 0 0 0 ## 13201114 0 0 0 6 0 0 0 0 0 ## 13201115 0 0 0 7 0 0 0 0 0 ## 13201116 0 0 0 6 0 0 0 0 0 ## 13201117 0 0 0 8 0 0 0 0 0 ## 13301101 0 0 0 9 0 0 0 0 0 ## 13301102 0 0 0 8 0 0 0 0 0 ## 13301201 0 0 0 0 7 0 0 0 0 ## 13301202 0 0 0 0 8 0 0 0 0 ## 13302101 0 0 0 7 0 0 0 0 0 ## 13302102 0 0 0 4 0 0 0 0 0 ## 13401101 0 0 0 6 0 0 0 0 0 ## 13401102 0 0 0 8 0 0 0 0 0 ## 13401103 0 0 0 9 0 0 0 0 0 ## 13401104 0 0 0 6 0 0 0 0 0 ## 13401105 0 0 0 2 0 0 0 0 0 ## 13401106 0 0 0 6 0 0 0 0 0 ## 13401107 0 0 0 3 0 0 0 0 0 ## 13402102 0 0 0 7 0 0 0 0 0 ## 13403101 0 0 0 6 0 0 0 0 0 ## 13404101 0 0 0 7 0 0 0 0 0 ## 13501101 0 0 0 9 0 0 0 0 0 ## 13501102 0 0 0 4 0 0 0 0 0 ## 13501201 0 0 0 0 15 0 0 0 0 ## 13501202 0 0 0 0 12 0 0 0 0 ## 13601101 0 0 0 5 0 0 0 0 0 ## 13602101 0 0 0 5 0 0 0 0 0 ## 13603101 0 0 0 3 0 0 0 0 0 ## 13604101 0 0 0 5 0 0 0 0 0 ## 13605101 0 0 0 4 0 0 0 0 0 ## 13605102 0 0 0 6 0 0 0 0 0 ## 14101101 0 0 0 0 0 7 0 0 0 ## 14101103 0 0 0 0 0 5 0 0 0 ## 14101104 0 0 0 0 0 10 0 0 0 ## 14101105 0 0 0 0 0 1 0 0 0 ## 14101106 0 0 0 0 0 7 0 0 0 ## 14101107 0 0 0 0 0 1 0 0 0 ## 14101108 0 0 0 0 0 7 0 0 0 ## 14101109 0 0 0 0 0 6 0 0 0 ## 14101110 0 0 0 0 0 7 0 0 0 ## 14101111 0 0 0 0 0 7 0 0 0 ## 14101112 0 0 0 0 0 2 0 0 0 ## 14101113 0 0 0 0 0 7 0 0 0 ## 14101114 0 0 0 0 0 6 0 0 0 ## 14101115 0 0 0 0 0 6 0 0 0 ## 14101117 0 0 0 0 0 5 0 0 0 ## 14101118 0 0 0 0 0 1 0 0 0 ## 14101119 0 0 0 0 0 7 0 0 0 ## 14101120 0 0 0 0 0 6 0 0 0 ## 14101121 0 0 0 0 0 8 0 0 0 ## 14101122 0 0 0 0 0 8 0 0 0 ## 14101123 0 0 0 0 0 7 0 0 0 ## 14101124 0 0 0 0 0 1 0 0 0 ## 14101125 0 0 0 0 0 3 0 0 0 ## 14101201 0 0 0 0 0 0 10 0 0 ## 14101202 0 0 0 0 0 0 10 0 0 ## 14104101 0 0 0 0 0 4 0 0 0 ## 14104102 0 0 0 0 0 5 0 0 0 ## 14104103 0 0 0 0 0 8 0 0 0 ## 14104104 0 0 0 0 0 5 0 0 0 ## 14104105 0 0 0 0 0 5 0 0 0 ## 14104201 0 0 0 0 0 0 10 0 0 ## 14104202 0 0 0 0 0 0 10 0 0 ## 14107101 0 0 0 0 0 5 0 0 0 ## 14107102 0 0 0 0 0 5 0 0 0 ## 14107103 0 0 0 0 0 6 0 0 0 ## 14107104 0 0 0 0 0 3 0 0 0 ## 14107105 0 0 0 0 0 6 0 0 0 ## 14107201 0 0 0 0 0 0 10 0 0 ## 14107202 0 0 0 0 0 0 10 0 0 ## 14201101 0 0 0 0 0 5 0 0 0 ## 14201102 0 0 0 0 0 5 0 0 0 ## 14201103 0 0 0 0 0 5 0 0 0 ## 14201104 0 0 0 0 0 5 0 0 0 ## 14201105 0 0 0 0 0 6 0 0 0 ## 14201201 0 0 0 0 0 0 10 0 0 ## 14201202 0 0 0 0 0 0 10 0 0 ## 14203201 0 0 0 0 0 0 10 0 0 ## 14203202 0 0 0 0 0 0 10 0 0 ## 14204101 0 0 0 0 0 3 0 0 0 ## 14204102 0 0 0 0 0 5 0 0 0 ## 14204103 0 0 0 0 0 4 0 0 0 ## 14204104 0 0 0 0 0 4 0 0 0 ## 14204105 0 0 0 0 0 4 0 0 0 ## 15101101 0 0 0 0 0 0 0 1 0 ## 15101102 0 0 0 0 0 0 0 5 0 ## 15101103 0 0 0 0 0 0 0 5 0 ## 15101104 0 0 0 0 0 0 0 7 0 ## 15101105 0 0 0 0 0 0 0 4 0 ## 15101106 0 0 0 0 0 0 0 5 0 ## 15101107 0 0 0 0 0 0 0 7 0 ## 15101108 0 0 0 0 0 0 0 7 0 ## 15101109 0 0 0 0 0 0 0 5 0 ## 15101110 0 0 0 0 0 0 0 6 0 ## 15101111 0 0 0 0 0 0 0 7 0 ## 15101112 0 0 0 0 0 0 0 7 0 ## 15101113 0 0 0 0 0 0 0 4 0 ## 15101114 0 0 0 0 0 0 0 4 0 ## 15101115 0 0 0 0 0 0 0 6 0 ## 15101116 0 0 0 0 0 0 0 7 0 ## 15101118 0 0 0 0 0 0 0 6 0 ## 15101119 0 0 0 0 0 0 0 4 0 ## 15101120 0 0 0 0 0 0 0 6 0 ## 15101121 0 0 0 0 0 0 0 6 0 ## 15101122 0 0 0 0 0 0 0 5 0 ## 15101123 0 0 0 0 0 0 0 6 0 ## 15101124 0 0 0 0 0 0 0 7 0 ## 15101125 0 0 0 0 0 0 0 5 0 ## 15101126 0 0 0 0 0 0 0 4 0 ## 15101127 0 0 0 0 0 0 0 6 0 ## 15101128 0 0 0 0 0 0 0 5 0 ## 15101129 0 0 0 0 0 0 0 6 0 ## 15101130 0 0 0 0 0 0 0 5 0 ## 15101131 0 0 0 0 0 0 0 7 0 ## 15101132 0 0 0 0 0 0 0 4 0 ## 15101133 0 0 0 0 0 0 0 5 0 ## 15101134 0 0 0 0 0 0 0 6 0 ## 15101135 0 0 0 0 0 0 0 7 0 ## 15101136 0 0 0 0 0 0 0 5 0 ## 15101137 0 0 0 0 0 0 0 6 0 ## 15101138 0 0 0 0 0 0 0 7 0 ## 15101139 0 0 0 0 0 0 0 6 0 ## 15101140 0 0 0 0 0 0 0 4 0 ## 15101141 0 0 0 0 0 0 0 7 0 ## 15101142 0 0 0 0 0 0 0 6 0 ## 15101143 0 0 0 0 0 0 0 7 0 ## 15101144 0 0 0 0 0 0 0 7 0 ## 15101146 0 0 0 0 0 0 0 7 0 ## 15101148 0 0 0 0 0 0 0 5 0 ## 15101149 0 0 0 0 0 0 0 7 0 ## 15101150 0 0 0 0 0 0 0 6 0 ## 15101151 0 0 0 0 0 0 0 7 0 ## 15101152 0 0 0 0 0 0 0 7 0 ## 15101153 0 0 0 0 0 0 0 7 0 ## 15101154 0 0 0 0 0 0 0 7 0 ## 15101155 0 0 0 0 0 0 0 6 0 ## 15101156 0 0 0 0 0 0 0 6 0 ## 15101157 0 0 0 0 0 0 0 6 0 ## 15101158 0 0 0 0 0 0 0 6 0 ## 15101201 0 0 0 0 0 0 0 0 20 ## 15101203 0 0 0 0 0 0 0 0 10 ## 15101204 0 0 0 0 0 0 0 0 10
Explore using ggplot
ggplot(df0)+ geom_point(aes(x=Edad, y=Fexp_F1F2EX1p_Corr))
ggplot(df0)+
geom_point(aes(x=Edad, y=Fexp_F1F2EX1p_Corr,
col=Educational_level, shape=Gender))
create a subset with valid values: fexp, strata_ Conglomerado_
# drop cases df0 <- subset(df0,!is.na(fexp)& !is.na(strata_)& !is.na(Conglomerado_)) # I do not step over df0 in case I want to use another fexp
df0$exposure <- df0$Educational_level df0$outcome <- df0$v_25_OH_Vitamina_D2_D3_corr df0$outcome1 <- as.numeric(df0$v_25_OH_Vitamina_D2_D3_corr<30) df0$outcome2 <- as.numeric(df0$v_25_OH_Vitamina_D2_D3_corr<20) df0$outcome3 <- as.numeric(df0$v_25_OH_Vitamina_D2_D3_corr<12) df0$outcome5_ <- NA df0$outcome5_[df0$v_25_OH_Vitamina_D2_D3_corr>=30] <- 0 df0$outcome5_[df0$v_25_OH_Vitamina_D2_D3_corr<30] <- 1 df0$outcome5_[df0$v_25_OH_Vitamina_D2_D3_corr<20] <- 2 df0$outcome5_[df0$v_25_OH_Vitamina_D2_D3_corr<12] <- 3 df0$outcome5_ <- as.factor(df0$outcome5_)
ALL VARIABLES NEEDS TO BE CREATED BEFORE USING THE “as_survey_design”
survey_design <- df0 %>%
as_survey_design(id=Conglomerado_,
# nest=TRUE,
weight = fexp,
strata=strata_
)
options(survey.lonely.psu="certainty")
# nest=TRUE IS REQUIERED TO ANALISE TRENDS USING ENS.
# PSU LABELS ARE SHARED
res_0b <- df0 %>% group_by(Region,Area,strata_) %>% summarize(Conglomerado_l = length(unique(Conglomerado_))) Single_PSU <- subset(res_0b,Conglomerado_l==1) Single_PSU
## # A tibble: 1 x 4 ## # Groups: Region, Area [1] ## Region Area strata_ Conglomerado_l ## <dbl> <fct> <dbl> <int> ## 1 3 Rural 22 1
http://r-survey.r-forge.r-project.org/survey/exmample-lonely.html
If only one PSU is sampled from a particular stratum the variance can’t be computed (there is no unbiased estimator and the standard estimator gives 0/0). Variance estimation in sample surveys involves variances computed within primary sampling units.
One exception to this is “certainty” PSUs in sampling without replacement, where the population has only one PSU in the stratum. With 100% sampling, there is no contribution to the variance from the first stage of sampling in this stratum. A single-PSU stratum makes no contribution to the variance (for multistage sampling it makes no contribution at that level of sampling).
“As a general rule when working with complex survey data such as NHANES, you should never drop records from your analysis dataset before executing your analysis procedures. Instead, use the special statements provided in your software’s analysis procedure to perform subgroup analyses.” https://wwwn.cdc.gov/nchs/nhanes/tutorials/module4.aspx
survey_design <- subset(survey_design,
!is.na(outcome) &!is.na(exposure) & Edad>=15)
df0 <- rio::import('F1_F2_EX_V9_20AUG18AP5.dta')
df0$Gender <- factor(df0$Sexo,
levels=c("1","2"),
labels=c("Male","Female"))
df0$Educational_level <- factor(df0$NEDU1,
levels=c("1","2","3"),
labels=c("Low","Mid","High"))
df0$Area <- factor(df0$Zona,
levels=c("1","2"),
labels=c("Urban","Rural"))
df0$Age <- factor(df0$Edad_Codificada,
levels=c("1","2","3","4"),
labels=c("17-24","25-44","45-64","65+"))
df0$exposure <- df0$Educational_level
df0$outcome <- df0$v_25_OH_Vitamina_D2_D3_corr
df0$outcome1 <- as.numeric(df0$v_25_OH_Vitamina_D2_D3_corr<30)
df0$outcome2 <- as.numeric(df0$v_25_OH_Vitamina_D2_D3_corr<20)
df0$outcome3 <- as.numeric(df0$v_25_OH_Vitamina_D2_D3_corr<12)
df0$outcome5_ <- NA df0$outcome5_[df0$v_25_OH_Vitamina_D2_D3_corr>=30] <- 0 df0$outcome5_[df0$v_25_OH_Vitamina_D2_D3_corr<30] <- 1 df0$outcome5_[df0$v_25_OH_Vitamina_D2_D3_corr<20] <- 2 df0$outcome5_[df0$v_25_OH_Vitamina_D2_D3_corr<12] <- 3 df0$outcome5_ <- as.factor(df0$outcome5_) df0$ENS <- 2017 df0$person <- 1 df0$Conglomerado_ <- NA df0$Conglomerado_ <- df0$Conglomerado df0$strata_ <- NA df0$strata_ <- df0$Estrato df0$fexp <- df0$Fexp_F1F2EX1p_Corr df0 <- subset(df0,!is.na(fexp)& !is.na(strata_)& !is.na(Conglomerado_))
# https://www.rdocumentation.org/packages/srvyr/versions/0.1.1/topics/as_survey_design
survey_design <- df0 %>%
as_survey_design(id=Conglomerado_,
weight = fexp,
strata=strata_)
options(survey.lonely.psu="certainty")
## Survey design: SUBSET
survey_design <- subset(survey_design,
!is.na(outcome) &!is.na(exposure) & Edad>=15)
# MEAN
# svyby(~outcome, ~person, survey_design, svymean)
# svyby(~outcome, ~person, survey_design, svymean, deff=TRUE)
# svyby(~outcome, ~exposure, survey_design, svymean, vartype=c("se","ci"), deff=TRUE)
# svyby(~outcome, ~exposure+Gender, survey_design, svymean, vartype=c("se","ci"), deff=TRUE)
# svyby(~outcome+Edad, ~exposure+Gender, survey_design, svymean, vartype=c("se","ci"), deff=TRUE)
# PROPORTION
# svyby(~outcome1, ~person, survey_design, svymean)
# svyby(~outcome1+outcome2, ~exposure+Gender, survey_design, svymean,
# vartype=c("se","ci"), deff=TRUE, proportion = TRUE, prop_method = c("logit"))
# svyby(~outcome1, ~exposure+Gender, survey_design, svytotal,
# vartype=c("se","ci"), deff=TRUE, proportion = TRUE, prop_method = c("logit"))
# CROSSTAB
# tbl <- svytable(~exposure+Area, survey_design)
# summary(tbl, statistic="Chisq")
# LINEAR REGRESSION
# svyglm(outcome~ Area+Edad, survey_design)
# svyglm(outcome~ Area+Edad, survey_design, deff=TRUE)
# LOGISTIC REGRESSION
# svyglm(outcome1 ~Area+Edad, survey_design,
# family = quasibinomial(link = "logit"))
# svyglm(outcome1 ~Area+Edad, survey_design,
# family = quasibinomial(link = "logit"), deff=TRUE)
tables
csv files to export data
plots with ggplot2
svyby(~outcome, ~person, survey_design, svymean, vartype=c(“se”,“ci”), deff=TRUE)
svyby(~outcome, ~exposure, survey_design, svymean, vartype=c(“se”,“ci”), deff=TRUE)
svyby(~outcome, ~exposure+Gender, survey_design, svymean, vartype=c(“se”,“ci”), deff=TRUE)
svyby(~outcome+Edad, ~exposure+Gender, survey_design, svymean, vartype=c(“se”,“ci”), deff=TRUE)
We created this variable before df0$person <- 1 “person” is a “variable” with constant value=1 I use “person” to calculate mean in total sample using svyby instead of svymean()
svyby(~outcome, ~person, survey_design, svymean, vartype=c("se","ci"),
deff=TRUE)
## person outcome se ci_l ci_u DEff.outcome ## 1 1 19.8146 0.2964862 19.2335 20.3957 3.751351
svyby(~outcome, ~exposure, survey_design, svymean, vartype=c("se","ci"),
deff=TRUE)
## exposure outcome se ci_l ci_u DEff.outcome ## Low Low 19.78772 0.6054778 18.60101 20.97444 4.084561 ## Mid Mid 19.78517 0.3667705 19.06632 20.50403 3.238398 ## High High 19.89291 0.6933440 18.53398 21.25184 3.811599
# svymean, vartype=c("se","ci"), method="beta"
Generar tabla de medias, se, n e IC95%
res0 <- svyby(~outcome, ~exposure, survey_design, svymean,
vartype=c("se","ci"), deff=TRUE)
res0$exposure_ <- "Age"
res0b <- survey_design %>%
group_by(person,exposure) %>%
summarize(n = unweighted(n()))
res0 <- full_join(res0,res0b)
res0$outcome_ <- "Mean"
res_0 <- svyby(~outcome, ~person, survey_design, svymean,
vartype=c("se","ci"), deff=TRUE)
res_0$exposure <- "All"
res_0b <- survey_design %>%
group_by(person) %>%
summarize(n = unweighted(n()))
res_0 <- full_join(res_0,res_0b)
res_0$outcome_ <- "Mean"
res_0 <- full_join(res_0,res_0b)
res0 <- full_join(res_0,res0)
res0
## person outcome se ci_l ci_u DEff.outcome exposure n ## 1 1 19.81460 0.2964862 19.23350 20.39570 3.751351 All 2859 ## 2 1 19.78772 0.6054778 18.60101 20.97444 4.084561 Low 857 ## 3 1 19.78517 0.3667705 19.06632 20.50403 3.238398 Mid 1391 ## 4 1 19.89291 0.6933440 18.53398 21.25184 3.811599 High 611 ## outcome_ exposure_ ## 1 Mean <NA> ## 2 Mean Age ## 3 Mean Age ## 4 Mean Age
survey_design <- subset(survey_design,Edad<50 & Sexo==2)
# Déficit Vit D (M1 y ADM) 2499
# Criterio de impresión de etiqueta: ( (ser mujer) & (edad>=15 & edad<=49) ) Ó (edad>=65)
# Criterio de aplicación de Examen: ( (ser mujer) & (edad>=15 & edad<=49) ) Ó (edad>=65)
res0 <- svyby(~outcome, ~person+exposure, survey_design, svymean,
vartype=c("se","ci"), deff=TRUE)
res0$exposure_ <- "Age"
res0b <- survey_design %>%
group_by(person,exposure) %>%
summarize(n = unweighted(n()))
res0 <- full_join(res0,res0b)
res0$outcome_ <- "Mean"
res_0 <- svyby(~outcome, ~person, survey_design, svymean,
vartype=c("se","ci"), deff=TRUE)
res_0$exposure <- "All"
res_0b <- survey_design %>%
group_by(person) %>%
summarize(n = unweighted(n()))
res_0 <- full_join(res_0,res_0b)
res_0$outcome_ <- "Mean"
res_0 <- full_join(res_0,res_0b)
res0 <- full_join(res_0,res0)
# res0$value <- res0$outcome
# res0$value <- res0$outcome*100
res0$CI_i <- (res0$outcome-1.96*res0$se)
res0$CI_s <- (res0$outcome+1.96*res0$se)
# svyciprop(formula, design, method = c("logit", "likelihood", "asin", "beta","mean"), level = 0.95, ...)
# idem que cofint---
confint(svyby(~outcome, ~person+exposure, survey_design, svymean,
vartype=c("se","ci"), deff=TRUE))
## 2.5 % 97.5 % ## 1.Low 19.88154 25.47994 ## 1.Mid 19.24538 20.87599 ## 1.High 18.03851 21.11203
# confint(svyby(~outcome, ~person+exposure+Area, survey_design, svymean, # method="likelihood")) # confint(svyby(~outcome, ~person+exposure+Area, survey_design, svymean, # method="logit")) # confint(svyby(~outcome, ~person+exposure+Area, survey_design, svymean, # method="asin")) # confint(svyby(~outcome, ~person+exposure+Area, survey_design, svymean, # method="beta")) # confint(svyby(~outcome, ~person+exposure+Area, survey_design, svymean, # method="mean"))
res0
## person outcome se ci_l ci_u DEff.outcome exposure n ## 1 1 20.06962 0.3680455 19.34827 20.79098 3.392954 All 1584 ## 2 1 22.68074 1.4281898 19.88154 25.47994 3.958762 Low 113 ## 3 1 20.06069 0.4159796 19.24538 20.87599 3.032236 Mid 976 ## 4 1 19.57527 0.7840747 18.03851 21.11203 3.935770 High 495 ## outcome_ exposure_ CI_i CI_s ## 1 Mean <NA> 19.34825 20.79099 ## 2 Mean Age 19.88149 25.47999 ## 3 Mean Age 19.24537 20.87601 ## 4 Mean Age 18.03849 21.11206
# names(res0) ggplot(res0)+ geom_point(aes(x=exposure, y=outcome))
ggplot(res0)+
geom_point(aes(x=exposure, y=outcome, col=exposure))+
geom_errorbar(aes(x=exposure, y=outcome,
ymin = CI_i, ymax = CI_s),
width = 0.1,
size = 0.1,
position = position_dodge(0.9)
)
plot_1<-ggplot(res0)+
geom_point(aes(x=exposure, y=outcome, col=exposure))+
geom_text(aes(x=exposure, y=outcome, label=round(outcome,1)))+
geom_errorbar(aes(x=exposure, y=outcome,
ymin = CI_i, ymax = CI_s),
width = 0.1,
size = 0.1,
position = position_dodge(0.9)
)+
ylab("Media VitD ng/dL")+
xlab(paste("\n ","\n ","Educational level",sep=""))+
labs(col = " ")+
labs(shape = " ")+
theme(plot.title = element_text(size=22),
plot.caption = element_text(size=22),
legend.position = "none",
legend.text = element_text(size = 12,
colour = "black",
angle = 0),
strip.text.x = element_text(size = 12,
colour = "black",
angle = 0),
axis.ticks = element_blank(),
axis.text.x = element_text(size = 12,
colour = "black",
angle = 0,
hjust = 1),
axis.text.y = element_text(size = 12,
colour = "black",
angle = 0,
hjust = 1),
axis.title = element_text(size = 12,
colour = "black",
angle = 0),
axis.line = element_line(colour = "grey40",
size = 1,
linetype = "solid"),
panel.grid.minor.y = element_line(colour="grey90", size=0.1),
panel.background = element_rect(fill="white"))
plot_1
svyby(~outcome1, ~person, survey_design, svymean, vartype=c(“se”,“ci”), deff=TRUE, proportion = TRUE, prop_method = c(“logit”))
svyby(~outcome1, ~exposure+Gender, survey_design, svymean, vartype=c(“se”,“ci”), deff=TRUE, proportion = TRUE, prop_method = c(“logit”))
svyby(~outcome1+outcome2, ~exposure+Gender, survey_design, svymean, vartype=c(“se”,“ci”), deff=TRUE, proportion = TRUE, prop_method = c(“logit”))
svyby(~outcome5_, ~exposure+Gender, survey_design, svymean, vartype=c(“se”,“ci”), deff=TRUE, proportion = TRUE, prop_method = c(“logit”))
svyby(~outcome1, ~exposure, survey_design, svytotal, vartype=c(“se”,“ci”), deff=TRUE, proportion = TRUE, prop_method = c(“logit”))
svyby(~outcome1, ~exposure+Gender, survey_design, svytotal, vartype=c(“se”,“ci”), deff=TRUE, proportion = TRUE, prop_method = c(“logit”))
res0a <- svyby(~outcome1, ~person+exposure, survey_design, svytotal,
vartype=c("se","ci"), deff=TRUE, proportion = TRUE,
prop_method = c("logit"))
res0b <- svyby(~outcome1, ~person+exposure, survey_design, svymean,
vartype=c("se","ci"), deff=TRUE, proportion = TRUE,
prop_method = c("logit"))
res1 <- svyby(~outcome1, ~person+exposure, survey_design, svymean,
vartype=c("se","ci"), deff=TRUE, proportion = TRUE,
prop_method = c("logit"))
res1$exposure_ <- "Educational levels"
res1b <- survey_design %>%
group_by(person,exposure) %>%
summarize(n = unweighted(n()))
res1 <- full_join(res1,res1b)
res1$outcome1_ <- "Mean"
res_1 <- svyby(~outcome1, ~person, survey_design, svymean,
vartype=c("se","ci"), deff=TRUE, proportion = TRUE,
prop_method = c("logit"))
res_1$exposure <- "All"
res_1b <- survey_design %>%
group_by(person) %>%
summarize(n = unweighted(n()))
res_1 <- full_join(res_1,res_1b)
res_1$outcome1_ <- "Mean"
res_1 <- full_join(res_1,res_1b)
res1 <- full_join(res_1,res1)
# res1$value <- res1$outcome1
# res1$value <- res1$outcome1*111
res1$CI_i <- (res1$outcome1-1.96*res1$se)
res1$CI_s <- (res1$outcome1+1.96*res1$se)
res2 <- svyby(~outcome2, ~person+exposure, survey_design, svymean,
vartype=c("se","ci"), deff=TRUE, proportion = TRUE,
prop_method = c("logit"))
res2$exposure_ <- "Educational levels"
res2b <- survey_design %>%
group_by(person,exposure) %>%
summarize(n = unweighted(n()))
res2 <- full_join(res2,res2b)
res2$outcome_ <- "Mean"
res_2 <- svyby(~outcome2, ~person, survey_design, svymean,
vartype=c("se","ci"), deff=TRUE, proportion = TRUE,
prop_method = c("logit"))
res_2$exposure <- "All"
res_2b <- survey_design %>%
group_by(person) %>%
summarize(n = unweighted(n()))
res_2 <- full_join(res_2,res_2b)
res_2$outcome_ <- "Mean"
res_2 <- full_join(res_2,res_2b)
res2 <- full_join(res_2,res2)
# res2$value <- res2$outcome
# res2$value <- res2$outcome*100
res2$CI_i <- (res2$outcome2-1.96*res2$se)
res2$CI_s <- (res2$outcome2+1.96*res2$se)
res3 <- svyby(~outcome3, ~person+exposure, survey_design, svymean,
vartype=c("se","ci"), deff=TRUE, proportion = TRUE,
prop_method = c("logit"))
res3$exposure_ <- "Educational level"
res3b <- survey_design %>%
group_by(person,exposure) %>%
summarize(n = unweighted(n()))
res3 <- full_join(res3,res3b)
res3$outcome_ <- "Mean"
res_3 <- svyby(~outcome3, ~person, survey_design, svymean,
vartype=c("se","ci"), deff=TRUE, proportion = TRUE,
prop_method = c("logit"))
res_3$exposure <- "All"
res_3b <- survey_design %>%
group_by(person) %>%
summarize(n = unweighted(n()))
res_3 <- full_join(res_3,res_3b)
res_3$outcome_ <- "Mean"
res_3 <- full_join(res_3,res_3b)
res3 <- full_join(res_3,res3)
# res3$value <- res3$outcome
# res3$value <- res3$outcome*100
res3$CI_i <- (res3$outcome3-1.96*res3$se)
res3$CI_s <- (res3$outcome3+1.96*res3$se)
res1
## person outcome1 se ci_l ci_u DEff.outcome1 exposure n ## 1 1 0.8900505 0.01325489 0.8640713 0.9160296 2.843039 All 1584 ## 2 1 0.8512115 0.07232704 0.7094531 0.9929699 4.627977 Low 113 ## 3 1 0.8890693 0.01539621 0.8588932 0.9192453 2.344242 Mid 976 ## 4 1 0.8995363 0.02479161 0.8509456 0.9481270 3.360949 High 495 ## outcome1_ exposure_ CI_i CI_s ## 1 Mean <NA> 0.8640709 0.9160300 ## 2 Mean Educational levels 0.7094505 0.9929725 ## 3 Mean Educational levels 0.8588927 0.9192458 ## 4 Mean Educational levels 0.8509448 0.9481279
res2
## person outcome2 se ci_l ci_u DEff.outcome2 exposure n ## 1 1 0.5224473 0.02393065 0.4755441 0.5693505 3.634828 All 1584 ## 2 1 0.3645948 0.07862445 0.2104938 0.5186959 2.989858 Low 113 ## 3 1 0.5030312 0.03049085 0.4432702 0.5627922 3.627249 Mid 976 ## 4 1 0.5905336 0.03962239 0.5128751 0.6681920 3.208475 High 495 ## outcome_ exposure_ CI_i CI_s ## 1 Mean <NA> 0.4755432 0.5693514 ## 2 Mean Educational levels 0.2104909 0.5186988 ## 3 Mean Educational levels 0.4432691 0.5627933 ## 4 Mean Educational levels 0.5128737 0.6681935
res3
## person outcome3 se ci_l ci_u DEff.outcome3 exposure n ## 1 1 0.16048978 0.01612776 0.12887994 0.1920996 3.057127 All 1584 ## 2 1 0.06202279 0.02318446 0.01658208 0.1074635 1.035254 Low 113 ## 3 1 0.15350749 0.02081670 0.11270751 0.1943075 3.252621 Mid 976 ## 4 1 0.19314277 0.03000457 0.13433489 0.2519506 2.854827 High 495 ## outcome_ exposure_ CI_i CI_s ## 1 Mean <NA> 0.12887936 0.1921002 ## 2 Mean Educational level 0.01658125 0.1074643 ## 3 Mean Educational level 0.11270676 0.1943082 ## 4 Mean Educational level 0.13433381 0.2519517
names(res1)<-gsub("come1","come",names(res1))
names(res2)<-gsub("come2","come",names(res2))
names(res3)<-gsub("come3","come",names(res2))
res1$type<-"oucome1"
res2$type<-"oucome2"
res3$type<-"oucome3"
res0 <- rbind(res1,res2,res3)
res0
## person outcome se ci_l ci_u DEff.outcome exposure n ## 1 1 0.89005046 0.01325489 0.86407135 0.9160296 2.843039 All 1584 ## 2 1 0.85121154 0.07232704 0.70945314 0.9929699 4.627977 Low 113 ## 3 1 0.88906925 0.01539621 0.85889324 0.9192453 2.344242 Mid 976 ## 4 1 0.89953632 0.02479161 0.85094565 0.9481270 3.360949 High 495 ## 5 1 0.52244729 0.02393065 0.47554407 0.5693505 3.634828 All 1584 ## 6 1 0.36459484 0.07862445 0.21049376 0.5186959 2.989858 Low 113 ## 7 1 0.50303119 0.03049085 0.44327022 0.5627922 3.627249 Mid 976 ## 8 1 0.59053356 0.03962239 0.51287510 0.6681920 3.208475 High 495 ## 9 1 0.16048978 0.01612776 0.12887994 0.1920996 3.057127 All 1584 ## 10 1 0.06202279 0.02318446 0.01658208 0.1074635 1.035254 Low 113 ## 11 1 0.15350749 0.02081670 0.11270751 0.1943075 3.252621 Mid 976 ## 12 1 0.19314277 0.03000457 0.13433489 0.2519506 2.854827 High 495 ## outcome_ exposure_ CI_i CI_s type ## 1 Mean <NA> 0.86407087 0.9160300 oucome1 ## 2 Mean Educational levels 0.70945054 0.9929725 oucome1 ## 3 Mean Educational levels 0.85889269 0.9192458 oucome1 ## 4 Mean Educational levels 0.85094475 0.9481279 oucome1 ## 5 Mean <NA> 0.47554321 0.5693514 oucome2 ## 6 Mean Educational levels 0.21049092 0.5186988 oucome2 ## 7 Mean Educational levels 0.44326913 0.5627933 oucome2 ## 8 Mean Educational levels 0.51287367 0.6681935 oucome2 ## 9 Mean <NA> 0.12887936 0.1921002 oucome3 ## 10 Mean Educational level 0.01658125 0.1074643 oucome3 ## 11 Mean Educational level 0.11270676 0.1943082 oucome3 ## 12 Mean Educational level 0.13433381 0.2519517 oucome3
plot2<-ggplot(res0)+
geom_point(aes(x=exposure, y=outcome, col=type))+
geom_errorbar(aes(x=exposure, y=outcome,
ymin = CI_i, ymax = CI_s),
width = 0.1,size = 0.1, position = position_dodge(0.9))
plot2
plot2<-plot2 + facet_wrap(type~ . , scales = "free_y", nrow = 3) plot2
# https://ggplot2.tidyverse.org/reference/labs.html # https://www.r-graph-gallery.com/275-add-text-labels-with-ggplot2.html
res0a <- svyby(~outcome5_, ~person+exposure, survey_design, svytotal,
vartype=c("se","ci"), deff=TRUE, proportion = TRUE,
prop_method = c("logit"))
res0a
## person exposure outcome5_0 outcome5_1 outcome5_2 outcome5_3 ## 1.Low 1 Low 40896.22 133752.2 83165.41 17047.68 ## 1.Mid 1 Mid 297973.16 1036944.1 938862.16 412339.35 ## 1.High 1 High 140974.84 433605.6 557635.39 271026.02 ## se.outcome5_0 se.outcome5_1 se.outcome5_2 se.outcome5_3 ci_l.outcome5_0 ## 1.Low 22076.61 30697.49 28029.96 5876.561 -2373.128 ## 1.Mid 45819.89 107061.77 92261.07 59179.669 208167.819 ## 1.High 37482.16 65943.28 65035.51 48892.137 67511.154 ## ci_l.outcome5_1 ci_l.outcome5_2 ci_l.outcome5_3 ci_u.outcome5_0 ## 1.Low 73586.23 28227.71 5529.828 84165.57 ## 1.Mid 827106.88 758033.79 296349.332 387778.49 ## 1.High 304359.13 430168.13 175199.189 214438.54 ## ci_u.outcome5_1 ci_u.outcome5_2 ci_u.outcome5_3 DEff.outcome5_0 ## 1.Low 193918.2 138103.1 28565.53 5.707246 ## 1.Mid 1246781.3 1119690.5 528329.37 2.877615 ## 1.High 562852.1 685102.7 366852.84 3.901545 ## DEff.outcome5_1 DEff.outcome5_2 DEff.outcome5_3 ## 1.Low 5.594298 5.521860 0.8803831 ## 1.Mid 6.537463 5.061053 3.6433794 ## 1.High 5.111138 4.432643 3.8496213
write.csv(res0a,file="check.csv")
res0 <- svyby(~outcome5_, ~person+exposure, survey_design, svymean,
vartype=c("se","ci"), deff=TRUE, proportion = TRUE,
prop_method = c("logit"))
res0
## person exposure outcome5_0 outcome5_1 outcome5_2 outcome5_3 ## 1.Low 1 Low 0.1487885 0.4866167 0.3025721 0.06202279 ## 1.Mid 1 Mid 0.1109307 0.3860381 0.3495237 0.15350749 ## 1.High 1 High 0.1004637 0.3090028 0.3973908 0.19314277 ## se.outcome5_0 se.outcome5_1 se.outcome5_2 se.outcome5_3 ci_l.outcome5_0 ## 1.Low 0.07232704 0.08151790 0.07835190 0.02318446 0.007030071 ## 1.Mid 0.01539621 0.02790336 0.02756623 0.02081670 0.080754731 ## 1.High 0.02479161 0.03449155 0.03588078 0.03000457 0.051873013 ## ci_l.outcome5_1 ci_l.outcome5_2 ci_l.outcome5_3 ci_u.outcome5_0 ## 1.Low 0.3268446 0.1490052 0.01658208 0.2905469 ## 1.Mid 0.3313485 0.2954949 0.11270751 0.1411068 ## 1.High 0.2414005 0.3270658 0.13433489 0.1490544 ## ci_u.outcome5_1 ci_u.outcome5_2 ci_u.outcome5_3 DEff.outcome5_0 ## 1.Low 0.6463888 0.4561390 0.1074635 4.627977 ## 1.Mid 0.4407276 0.4035525 0.1943075 2.344242 ## 1.High 0.3766050 0.4677158 0.2519506 3.360949 ## DEff.outcome5_1 DEff.outcome5_2 DEff.outcome5_3 ## 1.Low 2.980395 3.259624 1.035254 ## 1.Mid 3.204083 3.259937 3.252621 ## 1.High 2.753386 2.656748 2.854827
res0 <- svyby(~outcome5_, ~person+exposure, survey_design, svymean,
vartype=c("se","ci"), deff=TRUE, proportion = TRUE,
prop_method = c("logit"))
res0$exposure_ <- "Educational level"
res0b <- survey_design %>%
group_by(person,exposure) %>%
summarize(n = unweighted(n()))
res0 <- full_join(res0,res0b)
res0$outcome5__ <- "Mean"
res_0 <- svyby(~outcome5_, ~person, survey_design, svymean,
vartype=c("se","ci"), deff=TRUE, proportion = TRUE,
prop_method = c("logit"))
res_0$exposure <- "All"
res_0b <- survey_design %>%
group_by(person) %>%
summarize(n = unweighted(n()))
res_0 <- full_join(res_0,res_0b)
res_0$outcome5__ <- "Mean"
res_0 <- full_join(res_0,res_0b)
res0 <- full_join(res_0,res0)
res0$person <- NULL
res0$outcome5__ <- NULL
#Guardo data con unweighted n
res0n <- res0
# names(res0)
res0<-reshape(res0, idvar = c("exposure","n","exposure_"),
varying = list(c(1:4),c(5:8)),
v.names = c("outcome5_", "se"),
direction = "long")
res0$se <- res0$se*100
res0$outcome5_ <- res0$outcome5_*100
#calculo intervalo de confianza 95%
res0$CI_i <- (res0$outcome5_-1.96*res0$se)
res0$CI_s <- (res0$outcome5_+1.96*res0$se)
names(res0) <- make.unique(names(res0))
res0n <- res0n[c("exposure","n")]
res0 <- left_join(res0,res0n)
res0 <- res0[order(res0$exposure_),]
# res0 <- res0[c("exposure","variable_","outcome5_",
# "se","CI_i","CI_s")]
write.csv(res0,file="res0.csv")
res0
## ci_l.outcome5_0 ci_l.outcome5_1 ci_l.outcome5_2 ci_l.outcome5_3 ## 2 0.007030071 0.3268446 0.1490052 0.01658208 ## 3 0.080754731 0.3313485 0.2954949 0.11270751 ## 4 0.051873013 0.2414005 0.3270658 0.13433489 ## 6 0.007030071 0.3268446 0.1490052 0.01658208 ## 7 0.080754731 0.3313485 0.2954949 0.11270751 ## 8 0.051873013 0.2414005 0.3270658 0.13433489 ## 10 0.007030071 0.3268446 0.1490052 0.01658208 ## 11 0.080754731 0.3313485 0.2954949 0.11270751 ## 12 0.051873013 0.2414005 0.3270658 0.13433489 ## 14 0.007030071 0.3268446 0.1490052 0.01658208 ## 15 0.080754731 0.3313485 0.2954949 0.11270751 ## 16 0.051873013 0.2414005 0.3270658 0.13433489 ## 1 0.083970433 0.3243274 0.3189673 0.12887994 ## 5 0.083970433 0.3243274 0.3189673 0.12887994 ## 9 0.083970433 0.3243274 0.3189673 0.12887994 ## 13 0.083970433 0.3243274 0.3189673 0.12887994 ## ci_u.outcome5_0 ci_u.outcome5_1 ci_u.outcome5_2 ci_u.outcome5_3 ## 2 0.2905469 0.6463888 0.4561390 0.1074635 ## 3 0.1411068 0.4407276 0.4035525 0.1943075 ## 4 0.1490544 0.3766050 0.4677158 0.2519506 ## 6 0.2905469 0.6463888 0.4561390 0.1074635 ## 7 0.1411068 0.4407276 0.4035525 0.1943075 ## 8 0.1490544 0.3766050 0.4677158 0.2519506 ## 10 0.2905469 0.6463888 0.4561390 0.1074635 ## 11 0.1411068 0.4407276 0.4035525 0.1943075 ## 12 0.1490544 0.3766050 0.4677158 0.2519506 ## 14 0.2905469 0.6463888 0.4561390 0.1074635 ## 15 0.1411068 0.4407276 0.4035525 0.1943075 ## 16 0.1490544 0.3766050 0.4677158 0.2519506 ## 1 0.1359287 0.4108790 0.4049478 0.1920996 ## 5 0.1359287 0.4108790 0.4049478 0.1920996 ## 9 0.1359287 0.4108790 0.4049478 0.1920996 ## 13 0.1359287 0.4108790 0.4049478 0.1920996 ## DEff.outcome5_0 DEff.outcome5_1 DEff.outcome5_2 DEff.outcome5_3 exposure ## 2 4.627977 2.980395 3.259624 1.035254 Low ## 3 2.344242 3.204083 3.259937 3.252621 Mid ## 4 3.360949 2.753386 2.656748 2.854827 High ## 6 4.627977 2.980395 3.259624 1.035254 Low ## 7 2.344242 3.204083 3.259937 3.252621 Mid ## 8 3.360949 2.753386 2.656748 2.854827 High ## 10 4.627977 2.980395 3.259624 1.035254 Low ## 11 2.344242 3.204083 3.259937 3.252621 Mid ## 12 3.360949 2.753386 2.656748 2.854827 High ## 14 4.627977 2.980395 3.259624 1.035254 Low ## 15 2.344242 3.204083 3.259937 3.252621 Mid ## 16 3.360949 2.753386 2.656748 2.854827 High ## 1 2.843039 3.320961 3.298945 3.057127 All ## 5 2.843039 3.320961 3.298945 3.057127 All ## 9 2.843039 3.320961 3.298945 3.057127 All ## 13 2.843039 3.320961 3.298945 3.057127 All ## n exposure_ time outcome5_ se CI_i CI_s ## 2 113 Educational level 1 14.878846 7.232704 0.7027466 29.05495 ## 3 976 Educational level 1 11.093075 1.539621 8.0754177 14.11073 ## 4 495 Educational level 1 10.046368 2.479161 5.1872121 14.90552 ## 6 113 Educational level 2 48.661669 8.151790 32.6841614 64.63918 ## 7 976 Educational level 2 38.603806 2.790336 33.1347487 44.07286 ## 8 495 Educational level 2 30.900275 3.449155 24.1399307 37.66062 ## 10 113 Educational level 3 30.257206 7.835190 14.9002342 45.61418 ## 11 976 Educational level 3 34.952370 2.756623 29.5493881 40.35535 ## 12 495 Educational level 3 39.739079 3.588078 32.7064468 46.77171 ## 14 113 Educational level 4 6.202279 2.318446 1.6581250 10.74643 ## 15 976 Educational level 4 15.350749 2.081670 11.2706763 19.43082 ## 16 495 Educational level 4 19.314277 3.000457 13.4333810 25.19517 ## 1 1584 <NA> 1 10.994954 1.325489 8.3969956 13.59291 ## 5 1584 <NA> 2 36.760317 2.207990 32.4326570 41.08798 ## 9 1584 <NA> 3 36.195751 2.193420 31.8966473 40.49485 ## 13 1584 <NA> 4 16.048978 1.612776 12.8879361 19.21002
tbl <- svytable(~exposure+Area, survey_design) summary(tbl, statistic="Chisq")
## Area ## exposure Urban Rural ## Low 215501 59360 ## Mid 2283632 402487 ## High 1365540 37702 ## ## Pearson's X^2: Rao & Scott adjustment ## ## data: NextMethod() ## X-squared = 60.731, df = 2, p-value = 1.061e-10
svyglm(outcome~ Area+Edad, survey_design, deff=TRUE)
Primero describo y luego modelo
#Describo
svyby(~outcome, ~Area, survey_design, svymean, vartype=c("se","ci"), deff=TRUE)
## Area outcome se ci_l ci_u DEff.outcome ## Urban Urban 19.53875 0.4051727 18.74462 20.33287 3.545769 ## Rural Rural 24.17664 0.6420721 22.91820 25.43508 1.849969
#Modelo fit2 <- svyglm(outcome~ Area+Edad, survey_design, deff=TRUE) summary(fit2)
## ## Call: ## svyglm(formula = outcome ~ Area + Edad, design = survey_design, ## deff = TRUE) ## ## Survey design: ## subset(survey_design, Edad < 50 & Sexo == 2) ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 19.729818 1.176567 16.769 < 2e-16 *** ## AreaRural 4.649011 0.763475 6.089 1.8e-09 *** ## Edad -0.006081 0.034642 -0.176 0.861 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## (Dispersion parameter for gaussian family taken to be 61.07582) ## ## Number of Fisher Scoring iterations: 2
fit2 <- svyglm(outcome~ Area+Edad, survey_design, deff=TRUE) fit2 <- summary(fit2) fit2 <- as.data.frame(coef(fit2)) fit2$name <- rownames(fit2) fit2$outcome <- "Vit D numeric"
fit2
## Estimate Std. Error t value Pr(>|t|) name ## (Intercept) 19.729818178 1.1765675 16.7689647 6.174439e-54 (Intercept) ## AreaRural 4.649010986 0.7634752 6.0892754 1.799608e-09 AreaRural ## Edad -0.006080526 0.0346421 -0.1755242 8.607145e-01 Edad ## outcome ## (Intercept) Vit D numeric ## AreaRural Vit D numeric ## Edad Vit D numeric
write.csv(fit2,file="fit2.csv")
svyglm(outcome1 ~Area+Edad, survey_design,
family = quasibinomial(link = "logit"), deff=TRUE)
## Stratified 1 - level Cluster Sampling design (with replacement) ## With (792) clusters. ## subset(survey_design, Edad < 50 & Sexo == 2) ## Sampling variables: ## - ids: Conglomerado_ ## - strata: strata_ ## - weights: fexp ## ## Call: svyglm(formula = outcome1 ~ Area + Edad, design = survey_design, ## family = quasibinomial(link = "logit"), deff = TRUE) ## ## Coefficients: ## (Intercept) AreaRural Edad ## 2.29419 -0.99606 -0.00142 ## ## Degrees of Freedom: 1583 Total (i.e. Null); 760 Residual ## Null Deviance: 1097 ## Residual Deviance: 1075 AIC: NA
fit2<-svyglm(outcome1 ~Area+Edad, survey_design,
family = quasibinomial(link = "logit"), deff=TRUE)
summary(fit2)
## ## Call: ## svyglm(formula = outcome1 ~ Area + Edad, design = survey_design, ## family = quasibinomial(link = "logit"), deff = TRUE) ## ## Survey design: ## subset(survey_design, Edad < 50 & Sexo == 2) ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 2.29419 0.43864 5.230 2.19e-07 *** ## AreaRural -0.99606 0.25706 -3.875 0.000116 *** ## Edad -0.00142 0.01309 -0.108 0.913645 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## (Dispersion parameter for quasibinomial family taken to be 1.000614) ## ## Number of Fisher Scoring iterations: 4
res1a <- svyby(~outcome1, ~Area, survey_design, svymean, vartype=c("se","ci"),
deff=TRUE)
fit_t <- svyglm(outcome1 ~Area+Edad, survey_design,
family = quasibinomial(link = "logit"), deff=TRUE)
fit_t <- summary(fit_t)
fit_t <- as.data.frame(coef(fit_t))
fit_t$name <- rownames(fit_t)
fit_t$outcome <- "VitD<30"
fit_t$OR <- round(exp(fit_t$Estimate),digits=2)
fit_t$OR_i <- round(exp((fit_t$Estimate)-(1.96*fit_t$`Std. Error`)),2)
fit_t$OR_s <- round(exp((fit_t$Estimate)+(1.96*fit_t$`Std. Error`)),2)
fit_t$Estimate <- NULL
fit_t$`Std. Error` <- NULL
fit_t$`t value` <- NULL
fit_t$pvalue <- round(fit_t$`Pr(>|t|)`,digits=4)
fit_t$`Pr(>|t|)` <- NULL
fit_t <- subset(fit_t,name!="(Intercept)")
fit_t$name <- NULL
res1a
## Area outcome1 se ci_l ci_u DEff.outcome1 ## Urban Urban 0.9046057 0.01422092 0.8767333 0.9324782 3.127374 ## Rural Rural 0.7774459 0.03355157 0.7116861 0.8432058 1.614315
fit_t
## outcome OR OR_i OR_s pvalue ## AreaRural VitD<30 0.37 0.22 0.61 0.0001 ## Edad VitD<30 1.00 0.97 1.02 0.9136
fit_t <- svyglm(outcome1 ~Area+Edad, survey_design,
family = quasibinomial(link = "logit"), deff=TRUE)
summary(fit_t)
## ## Call: ## svyglm(formula = outcome1 ~ Area + Edad, design = survey_design, ## family = quasibinomial(link = "logit"), deff = TRUE) ## ## Survey design: ## subset(survey_design, Edad < 50 & Sexo == 2) ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 2.29419 0.43864 5.230 2.19e-07 *** ## AreaRural -0.99606 0.25706 -3.875 0.000116 *** ## Edad -0.00142 0.01309 -0.108 0.913645 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## (Dispersion parameter for quasibinomial family taken to be 1.000614) ## ## Number of Fisher Scoring iterations: 4
fit_t <- svyglm(outcome1 ~Area+Edad, survey_design,
family = quasibinomial(link = "logit"), deff=TRUE)
fit_t <- summary(fit_t)
fit_t <- as.data.frame(coef(fit_t))
fit_t$name <- rownames(fit_t)
fit_t$outcome <- "VitD<30"
fit_t$OR <- round(exp(fit_t$Estimate),digits=2)
fit_t$OR_i <- round(exp((fit_t$Estimate)-(1.96*fit_t$`Std. Error`)),2)
fit_t$OR_s <- round(exp((fit_t$Estimate)+(1.96*fit_t$`Std. Error`)),2)
fit_t$Estimate <- NULL
fit_t$`Std. Error` <- NULL
fit_t$`t value` <- NULL
fit_t$pvalue <- round(fit_t$`Pr(>|t|)`,digits=4)
fit_t$`Pr(>|t|)` <- NULL
fit_t <- subset(fit_t,name!="(Intercept)")
fit_t$name <- NULL
fit_t1<-fit_t
fit_t <- svyglm(outcome2 ~Area+Edad, survey_design,
family = quasibinomial(link = "logit"), deff=TRUE)
fit_t <- summary(fit_t)
fit_t <- as.data.frame(coef(fit_t))
fit_t$name <- rownames(fit_t)
fit_t$outcome <- "VitD<20"
fit_t$OR <- round(exp(fit_t$Estimate),digits=2)
fit_t$OR_i <- round(exp((fit_t$Estimate)-(1.96*fit_t$`Std. Error`)),2)
fit_t$OR_s <- round(exp((fit_t$Estimate)+(1.96*fit_t$`Std. Error`)),2)
fit_t$Estimate <- NULL
fit_t$`Std. Error` <- NULL
fit_t$`t value` <- NULL
fit_t$pvalue <- round(fit_t$`Pr(>|t|)`,digits=4)
fit_t$`Pr(>|t|)` <- NULL
fit_t <- subset(fit_t,name!="(Intercept)")
fit_t$name <- NULL
fit_t2<-fit_t
fit_t <- svyglm(outcome3 ~Area+Edad, survey_design,
family = quasibinomial(link = "logit"), deff=TRUE)
fit_t <- summary(fit_t)
fit_t <- as.data.frame(coef(fit_t))
fit_t$name <- rownames(fit_t)
fit_t$outcome <- "VitD<12"
fit_t$OR <- round(exp(fit_t$Estimate),digits=2)
fit_t$OR_i <- round(exp((fit_t$Estimate)-(1.96*fit_t$`Std. Error`)),2)
fit_t$OR_s <- round(exp((fit_t$Estimate)+(1.96*fit_t$`Std. Error`)),2)
fit_t$Estimate <- NULL
fit_t$`Std. Error` <- NULL
fit_t$`t value` <- NULL
fit_t$pvalue <- round(fit_t$`Pr(>|t|)`,digits=4)
fit_t$`Pr(>|t|)` <- NULL
fit_t <- subset(fit_t,name!="(Intercept)")
fit_t$name <- NULL
fit_t3<-fit_t
fit_t <- svyglm(outcome1 ~Area+Educational_level, survey_design,
family = quasibinomial(link = "logit"), deff=TRUE)
fit_t <- summary(fit_t)
fit_t <- as.data.frame(coef(fit_t))
fit_t$name <- rownames(fit_t)
fit_t$outcome <- "VitD<30"
fit_t$OR <- round(exp(fit_t$Estimate),digits=2)
fit_t$OR_i <- round(exp((fit_t$Estimate)-(1.96*fit_t$`Std. Error`)),2)
fit_t$OR_s <- round(exp((fit_t$Estimate)+(1.96*fit_t$`Std. Error`)),2)
fit_t$Estimate <- NULL
fit_t$`Std. Error` <- NULL
fit_t$`t value` <- NULL
fit_t$pvalue <- round(fit_t$`Pr(>|t|)`,digits=4)
fit_t$`Pr(>|t|)` <- NULL
fit_t <- subset(fit_t,name!="(Intercept)")
fit_t$name <- NULL
fit_t1b<-fit_t
fit_t <- svyglm(outcome2 ~Area+Educational_level, survey_design,
family = quasibinomial(link = "logit"), deff=TRUE)
fit_t <- summary(fit_t)
fit_t <- as.data.frame(coef(fit_t))
fit_t$name <- rownames(fit_t)
fit_t$outcome <- "VitD<20"
fit_t$OR <- round(exp(fit_t$Estimate),digits=2)
fit_t$OR_i <- round(exp((fit_t$Estimate)-(1.96*fit_t$`Std. Error`)),2)
fit_t$OR_s <- round(exp((fit_t$Estimate)+(1.96*fit_t$`Std. Error`)),2)
fit_t$Estimate <- NULL
fit_t$`Std. Error` <- NULL
fit_t$`t value` <- NULL
fit_t$pvalue <- round(fit_t$`Pr(>|t|)`,digits=4)
fit_t$`Pr(>|t|)` <- NULL
fit_t <- subset(fit_t,name!="(Intercept)")
fit_t$name <- NULL
fit_t2b<-fit_t
fit_t <- svyglm(outcome3 ~Area+Educational_level, survey_design,
family = quasibinomial(link = "logit"), deff=TRUE)
fit_t <- summary(fit_t)
fit_t <- as.data.frame(coef(fit_t))
fit_t$name <- rownames(fit_t)
fit_t$outcome <- "VitD<12"
fit_t$OR <- round(exp(fit_t$Estimate),digits=2)
fit_t$OR_i <- round(exp((fit_t$Estimate)-(1.96*fit_t$`Std. Error`)),2)
fit_t$OR_s <- round(exp((fit_t$Estimate)+(1.96*fit_t$`Std. Error`)),2)
fit_t$Estimate <- NULL
fit_t$`Std. Error` <- NULL
fit_t$`t value` <- NULL
fit_t$pvalue <- round(fit_t$`Pr(>|t|)`,digits=4)
fit_t$`Pr(>|t|)` <- NULL
fit_t <- subset(fit_t,name!="(Intercept)")
fit_t$name <- NULL
fit_t3b<-fit_t
fit_123a<-rbind(fit_t1,fit_t2,fit_t3,fit_t1b,fit_t2b,fit_t3b)
fit_123a$type <- fit_123a$outcome
fit_123a$outcome <- fit_123a$OR
fit_123a$CI_i <- fit_123a$OR_i
fit_123a$CI_s <- fit_123a$OR_s
fit_123a$exposure <- fit_123a$name
res0 <- fit_123a
res0$exposure<-rownames(res0)
res0$exposure<-gsub("\\d+","",res0$exposure)
res0$exposure_lbl<-res0$exposure
res0$exposure<-as.factor(res0$exposure)
# res0$exposure<-as.numeric(res0$exposure)
rownames(res0)<-NULL
res0
## outcome OR OR_i OR_s pvalue type CI_i CI_s exposure ## 1 0.37 0.37 0.22 0.61 0.0001 VitD<30 0.22 0.61 AreaRural ## 2 1.00 1.00 0.97 1.02 0.9136 VitD<30 0.97 1.02 Edad ## 3 0.28 0.28 0.17 0.46 0.0000 VitD<20 0.17 0.46 AreaRural ## 4 1.00 1.00 0.98 1.02 0.8521 VitD<20 0.98 1.02 Edad ## 5 0.28 0.28 0.11 0.73 0.0090 VitD<12 0.11 0.73 AreaRural ## 6 0.99 0.99 0.96 1.01 0.3617 VitD<12 0.96 1.01 Edad ## 7 0.37 0.37 0.22 0.63 0.0003 VitD<30 0.22 0.63 AreaRural ## 8 1.30 1.30 0.38 4.42 0.6727 VitD<30 0.38 4.42 Educational_levelMid ## 9 1.23 1.23 0.32 4.76 0.7610 VitD<30 0.32 4.76 Educational_levelHigh ## 10 0.30 0.30 0.18 0.51 0.0000 VitD<20 0.18 0.51 AreaRural ## 11 1.68 1.68 0.81 3.48 0.1631 VitD<20 0.81 3.48 Educational_levelMid ## 12 2.09 2.09 0.97 4.49 0.0595 VitD<20 0.97 4.49 Educational_levelHigh ## 13 0.31 0.31 0.12 0.79 0.0148 VitD<12 0.12 0.79 AreaRural ## 14 2.62 2.62 1.10 6.22 0.0297 VitD<12 1.10 6.22 Educational_levelMid ## 15 3.12 3.12 1.29 7.58 0.0121 VitD<12 1.29 7.58 Educational_levelHigh ## exposure_lbl ## 1 AreaRural ## 2 Edad ## 3 AreaRural ## 4 Edad ## 5 AreaRural ## 6 Edad ## 7 AreaRural ## 8 Educational_levelMid ## 9 Educational_levelHigh ## 10 AreaRural ## 11 Educational_levelMid ## 12 Educational_levelHigh ## 13 AreaRural ## 14 Educational_levelMid ## 15 Educational_levelHigh
res0
## outcome OR OR_i OR_s pvalue type CI_i CI_s exposure ## 1 0.37 0.37 0.22 0.61 0.0001 VitD<30 0.22 0.61 AreaRural ## 2 1.00 1.00 0.97 1.02 0.9136 VitD<30 0.97 1.02 Edad ## 3 0.28 0.28 0.17 0.46 0.0000 VitD<20 0.17 0.46 AreaRural ## 4 1.00 1.00 0.98 1.02 0.8521 VitD<20 0.98 1.02 Edad ## 5 0.28 0.28 0.11 0.73 0.0090 VitD<12 0.11 0.73 AreaRural ## 6 0.99 0.99 0.96 1.01 0.3617 VitD<12 0.96 1.01 Edad ## 7 0.37 0.37 0.22 0.63 0.0003 VitD<30 0.22 0.63 AreaRural ## 8 1.30 1.30 0.38 4.42 0.6727 VitD<30 0.38 4.42 Educational_levelMid ## 9 1.23 1.23 0.32 4.76 0.7610 VitD<30 0.32 4.76 Educational_levelHigh ## 10 0.30 0.30 0.18 0.51 0.0000 VitD<20 0.18 0.51 AreaRural ## 11 1.68 1.68 0.81 3.48 0.1631 VitD<20 0.81 3.48 Educational_levelMid ## 12 2.09 2.09 0.97 4.49 0.0595 VitD<20 0.97 4.49 Educational_levelHigh ## 13 0.31 0.31 0.12 0.79 0.0148 VitD<12 0.12 0.79 AreaRural ## 14 2.62 2.62 1.10 6.22 0.0297 VitD<12 1.10 6.22 Educational_levelMid ## 15 3.12 3.12 1.29 7.58 0.0121 VitD<12 1.29 7.58 Educational_levelHigh ## exposure_lbl ## 1 AreaRural ## 2 Edad ## 3 AreaRural ## 4 Edad ## 5 AreaRural ## 6 Edad ## 7 AreaRural ## 8 Educational_levelMid ## 9 Educational_levelHigh ## 10 AreaRural ## 11 Educational_levelMid ## 12 Educational_levelHigh ## 13 AreaRural ## 14 Educational_levelMid ## 15 Educational_levelHigh
# res0$exposure<-as.factor(res0$exposure)
res0<-subset(res0,exposure!="AreaRural")
plot_1<-
ggplot(res0)+
geom_point(aes(x=exposure, y=outcome, col=exposure))+
# geom_text(aes(x=exposure, y=outcome, label=round(outcome,1)))+
geom_errorbar(aes(x=exposure, y=outcome,
ymin = CI_i, ymax = CI_s),
width = 0.1,
size = 0.1,
position = position_dodge(0.9)
)+
geom_hline(yintercept =c(1),col=c("red"),size=0.1)+
labs(title = "",
subtitle = "",
caption = "")+
ylab("OR")+
xlab("")+
labs(col = " ")+
labs(shape = " ")+
theme(plot.title = element_text(size=22),
plot.caption = element_text(size=22),
legend.position = "right",
legend.text = element_text(size = 12,
colour = "black",
angle = 0),
strip.text.x = element_text(size = 12,
colour = "black",
angle = 0),
axis.ticks = element_blank(),
axis.text.x = element_text(size = 6,
colour = "black",
angle = 40,
hjust = 1),
axis.text.y = element_text(size = 12,
colour = "black",
angle = 0,
hjust = 1),
axis.title = element_text(size = 12,
colour = "black",
angle = 0),
axis.line = element_line(colour = "grey40",
size = 1,
linetype = "solid"),
panel.grid.minor.y = element_line(colour="grey90", size=0.1),
panel.background = element_rect(fill="white"))+
# coord_flip()+
# facet_wrap(type~ exposure, ncol=4, scales = "free")
facet_wrap(.~ type, scales = "fixed")
plot_1
plot_1
unique(cbind(res0$exposure,res0$exposure_lbl))
## [,1] [,2] ## [1,] "2" "Edad" ## [2,] "4" "Educational_levelMid" ## [3,] "3" "Educational_levelHigh"
Do you see a the blue “Publish” button? Press it, select RPubs and follow instructions 
https://faculty.washington.edu/tlumley/old-survey/survey-wss.pdf
https://wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx
https://github.com/pachamaltese/casen/blob/master/R/descriptive_statistics.R
https://www.tidyverse.org/packages/
https://www.datanovia.com/en/blog/the-a-z-of-rcolorbrewer-palette/
https://jef.works/art-with-code/
https://medium.com/@alinastepanova/tiny-art-with-r-d6d93d110619