Resultados
Laura Lucía Domínguez
2022-07-13
Todas las variables CALI Y PALMIRA
#factominer
library(bootstrap)
library(factoextra)## Loading required package: ggplot2## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(FactoMineR)
library(haven)
library(ade4)##
## Attaching package: 'ade4'## The following object is masked from 'package:FactoMineR':
##
## reconstlibrary(xtable)
library(readr)
library(data.table)
#library(ff)
#library(ffbase)
library(bigmemory)
library(foreach)
library(doParallel)## Loading required package: iterators## Loading required package: parallellibrary(biglm)## Loading required package: DBIlibrary(Factoshiny)## Loading required package: shiny## Loading required package: FactoInvestigatelibrary(readxl)
CaliyPalmira<-read_excel("C:/LAURA LUCIA/U/9/Tesis/MARZO/CaliyPalmira-TAINA.xlsx")
names(CaliyPalmira)## [1] "Total_act_sociales" "Total_lug_act_sociales" "conoce_enf"
## [4] "p26" "S_sintomas" "conoce_preven"
## [7] "S_prevención" "creenvirus" "contac_covid"
## [10] "dx_covid" "conf_presi" "conf_alcaldia"
## [13] "conf_gobern" "conf_mensgobierno" "p40"
## [16] "p42" "medios" "conf_mediocomu"
## [19] "p46" "p47" "p48"
## [22] "p49" "p50_1" "p50_2"
## [25] "p50_3" "p51" "p52"
## [28] "p53" "p54" "p55"
## [31] "p56" "p57" "p58"
## [34] "p60" "cumple_lavamanos" "cumple_tapaboca"
## [37] "cumple_distancia" "cumple_desinfecmano" "Total_tapaboca"
## [40] "Total_distancia" "ID" "Municipio"CaliyPalmira$creenvirus<-as.factor(CaliyPalmira$creenvirus)
CaliyPalmira$contac_covid<-as.factor(CaliyPalmira$contac_covid)
CaliyPalmira$dx_covid<-as.factor(CaliyPalmira$dx_covid)names(CaliyPalmira)<-c(
#1.voluntariedad
"x11",
"x12",
#2.conocimiento
"x21",
"x22", #p26
"x23",
"x24",
"x25", #p30
#3.incertidumbre
"x31", #p33
"x32", #p35
"x33", #p36
#4.gubernamental
"x41",
"x42",
"x43",
"x44",#"recomen_efectiva",
#5.salud
"x51",
#"p41", #factor
"x52",
#6.medios de comunicación
"x61", #total_medios_comu
"x62", #p43
#"mensaje", #categórica p72
#7.probabilidad de contagio
"x71",
"x72",
"x73",
"x74",
"x75",
"x76",
"x77",
#8.severidad
"x81",
"x82",
"x83",
"x84",
#9.susceptibilidad
"x91",
"x92",
"x93",
"x94",
#"p59_1", #factor
#"p59_2", #factor
#"p59_3", #factor
#"p59_4", #factor
"x95",
#10.cumplimiento
"x101", #p61
"x102",
"x103",
"x104",
"x105", #p76
"x106",
#otras
"id",
"Municipio"
)#recodificar la voluntariedad
library(car)## Loading required package: carDatasummary(CaliyPalmira$x11) #de 0 a 5## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 1.000 1.265 2.000 5.000CaliyPalmira$x11 <- recode(CaliyPalmira$x11,"5=0; 4=1; 3=2; 2=3; 1=4; 0=5")
summary(CaliyPalmira$x12) #de 0 a 8## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 1.000 1.633 2.000 8.000CaliyPalmira$x12 <- recode(CaliyPalmira$x12,"8=0; 7=1; 6=2; 5=3; 4=4; 3=5; 2=6; 1=7; 0=8")#x75 [23]
x75N<-as.factor(CaliyPalmira$x75)
summary(x75N)## 1 2 3 4 5
## 288 82 104 245 724#x77 [25]
x77N<-as.factor(CaliyPalmira$x77)
summary(x77N)## 1 2 3 4 5
## 43 42 151 269 938AFM CON TODAS LAS VARIABLES
##Análisis factorial múltiple
CaliyPalmira.FMA<-MFA(CaliyPalmira[,c(19:34)],
group=c(#2,
#5,
#3,
#4,
#2,
#2, #3
7,
4,
5
#6
),
type=c(#'s',
#'s',
#'n',
#'s', #n
#'s',
#'s', #n
's',
's',
's'#,
#'s'
),
name.group=c(#"Voluntariedad",
#"Conocimiento",
#"Incertidumbre",
#"Confianza gubernamental",
#"Confianza sector salud",
#"Confianza medios",
"Probabilidad de contagio",
"Severidad",
"Susceptibilidad"), #,
#"Cumplimiento"),
#num.group.sup=c(3),
graph=FALSE)
CaliyPalmira.FMA$eig## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 1.95371969 37.354849 37.35485
## comp 2 0.76278418 14.584328 51.93918
## comp 3 0.46061251 8.806847 60.74602
## comp 4 0.31307706 5.985990 66.73201
## comp 5 0.27980231 5.349781 72.08179
## comp 6 0.24472973 4.679198 76.76099
## comp 7 0.21919631 4.191003 80.95200
## comp 8 0.19355882 3.700818 84.65281
## comp 9 0.16004627 3.060062 87.71288
## comp 10 0.14217163 2.718302 90.43118
## comp 11 0.11679648 2.233132 92.66431
## comp 12 0.10867422 2.077836 94.74215
## comp 13 0.08312730 1.589382 96.33153
## comp 14 0.07144414 1.366002 97.69753
## comp 15 0.06704119 1.281818 98.97935
## comp 16 0.05338169 1.020651 100.00000CaliyPalmira.FMA$group$contrib## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## Probabilidad de contagio 22.07821 77.726865 15.65889 91.047532 84.042533
## Severidad 38.70329 13.287416 45.28145 6.939909 9.329046
## Susceptibilidad 39.21850 8.985719 39.05966 2.012559 6.628421CaliyPalmira.FMA$group$correlation[,1:3]## Dim.1 Dim.2 Dim.3
## Probabilidad de contagio 0.6636843 0.7801080 0.4438594
## Severidad 0.8746823 0.3278311 0.5848514
## Susceptibilidad 0.8775728 0.2757276 0.4690437Coordenadas<-round(CaliyPalmira.FMA$quanti.var$coord[,c(1,2,3)],3);Coordenadas## Dim.1 Dim.2 Dim.3
## x71 0.534 0.705 -0.037
## x72 0.529 0.644 -0.040
## x73 0.531 0.659 -0.043
## x74 0.451 0.444 0.230
## x75 0.178 0.067 0.180
## x76 0.396 0.570 0.063
## x77 0.354 -0.037 0.365
## x81 0.608 -0.274 0.570
## x82 0.792 -0.295 -0.026
## x83 0.641 -0.310 0.494
## x84 0.810 -0.135 0.003
## x91 0.342 -0.197 -0.528
## x92 0.795 -0.165 -0.369
## x93 0.793 -0.182 -0.252
## x94 0.818 -0.177 -0.297
## x95 0.652 -0.300 -0.117Contribu<-round(CaliyPalmira.FMA$quanti.var$contrib[,c(1,2,3)],3);Contribu## Dim.1 Dim.2 Dim.3
## x71 4.620 20.610 0.094
## x72 4.527 17.215 0.109
## x73 4.562 18.004 0.128
## x74 3.287 8.178 3.647
## x75 0.516 0.187 2.237
## x76 2.533 13.476 0.271
## x77 2.034 0.057 9.172
## x81 6.924 3.594 25.829
## x82 11.764 4.187 0.055
## x83 7.713 4.627 19.396
## x84 12.301 0.880 0.001
## x91 1.851 1.583 18.745
## x92 10.029 1.113 9.175
## x93 9.969 1.351 4.290
## x94 10.615 1.276 5.929
## x95 6.755 3.664 0.920Tabla<-cbind(Coordenadas,Contribu);Tabla## Dim.1 Dim.2 Dim.3 Dim.1 Dim.2 Dim.3
## x71 0.534 0.705 -0.037 4.620 20.610 0.094
## x72 0.529 0.644 -0.040 4.527 17.215 0.109
## x73 0.531 0.659 -0.043 4.562 18.004 0.128
## x74 0.451 0.444 0.230 3.287 8.178 3.647
## x75 0.178 0.067 0.180 0.516 0.187 2.237
## x76 0.396 0.570 0.063 2.533 13.476 0.271
## x77 0.354 -0.037 0.365 2.034 0.057 9.172
## x81 0.608 -0.274 0.570 6.924 3.594 25.829
## x82 0.792 -0.295 -0.026 11.764 4.187 0.055
## x83 0.641 -0.310 0.494 7.713 4.627 19.396
## x84 0.810 -0.135 0.003 12.301 0.880 0.001
## x91 0.342 -0.197 -0.528 1.851 1.583 18.745
## x92 0.795 -0.165 -0.369 10.029 1.113 9.175
## x93 0.793 -0.182 -0.252 9.969 1.351 4.290
## x94 0.818 -0.177 -0.297 10.615 1.276 5.929
## x95 0.652 -0.300 -0.117 6.755 3.664 0.920plot.MFA(CaliyPalmira.FMA, choix="group",title="Representación de grupos")
#plot.MFA(CaliyPalmira.FMA, choix="ind",lab.par=FALSE)
library(ggrepel)
options(ggrepel.max.overlaps = Inf)
#dim 1-2
plot.MFA(CaliyPalmira.FMA, choix="var",habillage='group',title="Círculo de correlación", repel = TRUE)
IPRG
#--------------------------ÍNDICE DE PERCEPCIÓN GLOBAL-----------------------------------------------#####
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Severidad=CaliyPalmira[,c(19:34)]
Coord1_severidad <-res.mfa_severidad$global.pca$var$coord[,1];Coord1_severidad## x71 x72 x73 x74 x75 x76 x77 x81
## 0.5341653 0.5287287 0.5308074 0.4505285 0.1784305 0.3955389 0.3544335 0.6076142
## x82 x83 x84 x91 x92 x93 x94 x95
## 0.7919927 0.6412808 0.8098680 0.3415608 0.7949994 0.7926213 0.8178959 0.6524851lp_severidad<-res.mfa_severidad$eig[1];lp_severidad #VALOR PROPIO## [1] 1.95372Vp_severidad<-Coord1_severidad/sqrt(lp_severidad);Vp_severidad #VECTOR PROPIO## x71 x72 x73 x74 x75 x76 x77 x81
## 0.3821594 0.3782699 0.3797571 0.3223229 0.1276550 0.2829815 0.2535734 0.4347071
## x82 x83 x84 x91 x92 x93 x94 x95
## 0.5666176 0.4587934 0.5794061 0.2443638 0.5687687 0.5670673 0.5851496 0.4668093Pesos_severidad<-(Vp_severidad/sum(Vp_severidad));Pesos_severidad # PESOS RELATIVOS DE LAS VARIABLES## x71 x72 x73 x74 x75 x76 x77
## 0.05791696 0.05732750 0.05755288 0.04884863 0.01934636 0.04288637 0.03842951
## x81 x82 x83 x84 x91 x92 x93
## 0.06588067 0.08587194 0.06953098 0.08781007 0.03703379 0.08619794 0.08594010
## x94 x95
## 0.08868050 0.07074581sum(Pesos_severidad)## [1] 1data.frame(round(Pesos_severidad,3))## round.Pesos_severidad..3.
## x71 0.058
## x72 0.057
## x73 0.058
## x74 0.049
## x75 0.019
## x76 0.043
## x77 0.038
## x81 0.066
## x82 0.086
## x83 0.070
## x84 0.088
## x91 0.037
## x92 0.086
## x93 0.086
## x94 0.089
## x95 0.071res.mfa_severidad$eig## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 1.95371969 37.354849 37.35485
## comp 2 0.76278418 14.584328 51.93918
## comp 3 0.46061251 8.806847 60.74602
## comp 4 0.31307706 5.985990 66.73201
## comp 5 0.27980231 5.349781 72.08179
## comp 6 0.24472973 4.679198 76.76099
## comp 7 0.21919631 4.191003 80.95200
## comp 8 0.19355882 3.700818 84.65281
## comp 9 0.16004627 3.060062 87.71288
## comp 10 0.14217163 2.718302 90.43118
## comp 11 0.11679648 2.233132 92.66431
## comp 12 0.10867422 2.077836 94.74215
## comp 13 0.08312730 1.589382 96.33153
## comp 14 0.07144414 1.366002 97.69753
## comp 15 0.06704119 1.281818 98.97935
## comp 16 0.05338169 1.020651 100.00000Ind_severidad<-as.matrix(Severidad)%*%Pesos_severidad
Imin_severidad<-min(Ind_severidad);Imin_severidad## [1] 0.9629662Imax_severidad<-max(Ind_severidad);Imax_severidad## [1] 6.613507Ind_2_severidad3<-round(((Ind_severidad-Imin_severidad)/(Imax_severidad-Imin_severidad))*100,2) #con este índice se hace el cluster
min(Ind_2_severidad3)## [1] 0max(Ind_2_severidad3)## [1] 100C8<-cbind(Ind_2_severidad3,CaliyPalmira)
summary(C8$Ind_2_severidad3);sd(C8$Ind_2_severidad3)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 41.24 53.47 54.10 66.20 100.00## [1] 17.83391K-means
set.seed(1234)
kmeans <- kmeans(C8$Ind_2_severidad3, 3, iter.max = 1000, nstart = 10);kmeans## K-means clustering with 3 clusters of sizes 646, 433, 364
##
## Cluster means:
## [,1]
## 1 54.69625
## 2 33.72363
## 3 77.28025
##
## Clustering vector:
## [1] 2 2 1 1 1 3 2 1 1 2 1 2 1 1 1 1 2 1 3 1 3 2 2 2 1 2 2 1 2 1 1 1 2 3 1 2 1
## [38] 3 3 1 3 2 1 1 1 2 2 2 2 1 2 2 2 3 1 1 2 1 2 2 3 2 2 2 1 1 1 1 3 3 1 2 1 2
## [75] 2 1 2 1 3 1 3 1 1 1 2 3 2 2 3 1 2 1 1 1 2 3 1 2 2 1 3 2 2 1 3 3 3 3 1 1 3
## [112] 3 1 3 2 2 2 1 2 1 3 3 1 1 2 1 3 1 3 2 3 1 3 2 1 2 2 3 1 2 3 1 1 1 2 1 1 2
## [149] 1 1 2 2 3 1 3 2 1 1 1 3 1 3 1 1 1 3 1 2 3 2 3 3 1 2 1 1 2 1 1 1 3 2 3 2 3
## [186] 1 2 1 1 1 1 1 2 2 3 2 1 1 2 1 2 1 1 3 1 3 1 1 2 3 3 2 1 1 1 1 1 3 2 3 1 1
## [223] 1 1 1 2 1 2 2 1 3 1 1 1 1 2 2 1 2 2 1 1 1 2 2 2 1 3 2 3 2 2 1 1 1 2 1 2 3
## [260] 1 3 1 1 2 2 1 2 1 1 2 3 2 2 2 2 1 1 3 3 1 1 1 3 1 2 1 3 2 1 1 1 1 2 1 2 2
## [297] 2 1 1 3 2 1 2 2 3 1 2 1 1 3 1 3 2 2 1 1 1 2 3 2 1 1 2 2 1 3 3 2 1 2 1 1 3
## [334] 3 1 2 1 3 2 2 2 1 1 2 1 2 2 1 1 3 3 1 2 1 1 2 3 2 2 3 1 3 2 2 1 2 3 2 3 2
## [371] 1 1 3 1 3 1 3 2 2 1 1 3 2 3 3 1 3 2 2 1 2 2 1 2 1 1 2 1 2 2 1 2 2 2 1 2 2
## [408] 2 1 1 3 1 2 1 1 3 1 1 2 1 1 2 3 3 3 1 3 1 1 1 1 3 2 2 3 3 1 2 1 2 2 2 1 3
## [445] 2 2 3 1 3 3 1 3 3 1 1 1 1 3 1 3 2 1 1 2 1 1 1 1 3 1 3 1 2 3 1 1 3 1 1 1 2
## [482] 3 1 1 1 2 3 3 3 2 1 1 2 2 3 1 1 1 1 3 2 3 1 2 1 1 1 2 2 2 2 1 1 1 2 2 3 1
## [519] 2 2 2 3 2 2 1 1 3 2 2 1 1 2 1 1 1 2 2 2 1 2 2 1 3 1 3 1 1 1 1 2 1 3 1 2 2
## [556] 3 2 2 1 2 2 2 1 2 1 2 1 2 1 2 1 3 1 1 2 1 2 1 1 1 1 1 1 2 2 2 2 1 2 1 1 2
## [593] 1 1 1 1 1 2 1 1 1 1 1 2 2 2 1 2 1 3 2 1 1 3 1 3 2 3 3 2 3 3 2 1 1 1 1 3 2
## [630] 2 3 1 2 3 1 1 2 3 2 2 2 2 1 1 2 1 1 1 2 3 3 3 1 1 1 3 1 3 1 1 3 2 3 1 3 1
## [667] 2 1 2 1 3 1 3 1 3 1 1 2 3 1 1 1 1 1 1 2 2 1 2 1 2 1 1 1 3 1 3 1 3 2 2 1 1
## [704] 1 3 3 1 1 2 1 1 3 2 2 1 1 2 1 1 1 1 2 1 1 1 3 1 2 1 2 2 1 2 1 1 2 2 1 1 1
## [741] 1 3 3 3 1 3 1 3 1 1 3 1 3 3 1 1 3 1 1 1 1 1 2 2 1 2 1 2 1 2 1 3 3 3 2 1 2
## [778] 1 1 3 3 1 1 1 3 2 1 1 2 3 1 1 1 3 2 3 2 1 2 2 1 3 2 2 1 1 3 3 2 2 2 1 3 3
## [815] 1 1 3 1 1 1 2 1 2 2 1 1 1 1 3 1 2 1 3 1 3 3 2 3 1 1 1 2 3 1 1 1 1 1 1 3 1
## [852] 1 2 3 3 1 2 1 1 2 3 2 1 3 3 2 1 3 1 2 1 1 1 2 3 3 2 3 2 1 1 3 1 2 1 1 1 3
## [889] 1 2 2 2 1 3 1 2 1 2 2 1 1 2 2 2 3 3 2 3 2 1 1 1 2 1 1 3 1 3 1 1 1 1 2 1 1
## [926] 3 1 1 2 3 1 2 1 3 1 2 3 1 2 3 2 2 3 3 3 1 1 2 2 3 3 3 3 1 1 3 2 3 2 1 3 1
## [963] 2 2 2 2 1 1 1 3 3 1 1 3 3 2 3 1 3 1 1 2 2 2 2 2 1 1 3 3 3 3 2 2 1 2 1 3 1
## [1000] 1 2 2 3 3 3 1 3 1 1 1 2 1 2 3 3 1 2 2 3 3 2 3 1 3 1 1 2 3 3 2 1 3 3 2 3 1
## [1037] 2 2 1 2 2 3 1 3 3 3 3 1 2 3 1 2 1 1 3 1 3 1 1 1 1 2 1 2 3 2 3 3 1 1 3 1 2
## [1074] 2 1 2 1 3 1 2 3 1 1 1 3 3 1 1 2 2 1 1 3 2 3 2 2 2 2 3 2 1 3 2 1 2 1 3 1 3
## [1111] 1 1 1 1 3 1 3 2 2 1 1 1 2 3 1 3 1 1 1 1 2 1 1 1 3 1 3 3 1 1 2 2 2 1 1 3 2
## [1148] 3 1 3 2 1 1 3 1 2 2 1 1 2 1 3 1 1 2 2 1 3 3 2 2 2 1 2 1 2 1 3 1 1 2 1 2 2
## [1185] 2 2 3 2 1 1 3 1 2 2 1 1 1 1 1 1 3 1 2 3 3 2 2 3 3 2 3 2 1 1 3 2 3 3 2 3 2
## [1222] 2 2 3 3 3 3 1 1 1 1 3 1 3 1 1 1 1 2 1 1 3 3 3 2 3 3 3 1 1 3 1 3 1 3 1 2 1
## [1259] 1 1 2 3 3 3 3 3 2 1 1 1 2 1 2 1 2 1 3 2 3 2 2 2 3 3 2 3 1 2 3 1 3 3 1 3 2
## [1296] 1 3 1 2 3 1 2 2 1 1 3 3 1 3 1 3 1 1 1 1 3 1 3 2 2 3 2 3 1 3 1 1 3 3 3 3 2
## [1333] 3 3 3 3 1 3 2 3 1 1 1 3 1 2 3 1 3 2 3 1 2 1 2 2 3 3 2 1 3 2 3 1 1 2 2 3 1
## [1370] 1 2 2 1 1 2 1 1 1 1 2 3 3 3 3 2 1 3 2 1 2 1 1 1 1 1 2 1 1 3 2 3 2 2 1 2 3
## [1407] 1 1 2 1 1 1 1 1 2 3 3 1 1 2 3 1 1 1 1 1 3 3 1 1 3 1 3 1 1 3 1 2 1 1 1 3 1
##
## Within cluster sum of squares by cluster:
## [1] 23747.37 32212.71 27070.04
## (between_SS / total_SS = 81.9 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"#windows();fviz_cluster(kmeans, data = C8)
C8$cluster <- kmeans$cluster
summary(C8)## Ind_2_severidad3 x11 x12 x21
## Min. : 0.00 Min. :0.000 Min. :0.000 Min. :1.000
## 1st Qu.: 41.24 1st Qu.:3.000 1st Qu.:6.000 1st Qu.:5.000
## Median : 53.47 Median :4.000 Median :7.000 Median :5.000
## Mean : 54.10 Mean :3.735 Mean :6.367 Mean :5.375
## 3rd Qu.: 66.20 3rd Qu.:5.000 3rd Qu.:7.000 3rd Qu.:6.000
## Max. :100.00 Max. :5.000 Max. :8.000 Max. :7.000
## x22 x23 x24 x25 x31
## Min. :1.000 Min. :0.000 Min. :1.000 Min. :0.000 No : 21
## 1st Qu.:3.000 1st Qu.:7.000 1st Qu.:5.000 1st Qu.:6.000 No sabe: 20
## Median :4.000 Median :8.000 Median :6.000 Median :7.000 Si :1402
## Mean :3.633 Mean :7.319 Mean :5.796 Mean :6.517
## 3rd Qu.:4.000 3rd Qu.:8.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :4.000 Max. :8.000 Max. :7.000 Max. :7.000
## x32 x33 x41 x42 x43
## No :702 0: 7 Min. :1.000 Min. :1.000 Min. :1.00
## No sabe:124 1:1303 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.00
## Si :617 2: 133 Median :4.000 Median :4.000 Median :4.00
## Mean :3.671 Mean :3.773 Mean :3.96
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00
## Max. :7.000 Max. :7.000 Max. :7.00
## x44 x51 x52 x61 x62
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :0.000 Min. :1.00
## 1st Qu.:2.00 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:2.00
## Median :3.00 Median :5.000 Median :5.000 Median :2.000 Median :4.00
## Mean :3.45 Mean :4.459 Mean :5.252 Mean :2.256 Mean :3.43
## 3rd Qu.:5.00 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:3.000 3rd Qu.:5.00
## Max. :7.00 Max. :7.000 Max. :7.000 Max. :6.000 Max. :7.00
## x71 x72 x73 x74
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:5.000
## Median :4.000 Median :5.000 Median :4.000 Median :6.000
## Mean :4.283 Mean :4.578 Mean :4.112 Mean :5.415
## 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x75 x76 x77 x81
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.500 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :3.000 Median :5.000 Median :5.000
## Mean :3.717 Mean :3.286 Mean :4.398 Mean :4.995
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :7.000
## x82 x83 x84 x91
## Min. :1.000 Min. :1.000 Min. :1.0 Min. : 0.0000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.0 1st Qu.: 0.0000
## Median :4.000 Median :4.000 Median :4.0 Median : 0.0000
## Mean :4.084 Mean :4.236 Mean :3.9 Mean : 0.6189
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.0 3rd Qu.: 1.0000
## Max. :7.000 Max. :7.000 Max. :7.0 Max. :13.0000
## x92 x93 x94 x95
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.500 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :5.000
## Mean :3.625 Mean :3.685 Mean :3.743 Mean :4.604
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x101 x102 x103 x104
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :4.000 Median :5.000
## Mean :4.426 Mean :4.639 Mean :4.131 Mean :4.516
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## x105 x106 id Municipio
## Min. : 0.000 Min. : 0.000 Min. : 1.0 Length:1443
## 1st Qu.: 4.000 1st Qu.: 2.000 1st Qu.: 368.5 Class :character
## Median : 9.000 Median : 6.000 Median : 736.0 Mode :character
## Mean : 8.069 Mean : 5.881 Mean : 734.0
## 3rd Qu.:12.000 3rd Qu.:10.000 3rd Qu.:1099.5
## Max. :14.000 Max. :12.000 Max. :1460.0
## cluster
## Min. :1.000
## 1st Qu.:1.000
## Median :2.000
## Mean :1.805
## 3rd Qu.:3.000
## Max. :3.000kmeans$centers## [,1]
## 1 54.69625
## 2 33.72363
## 3 77.28025tapply(Ind_2_severidad3,kmeans$cluster,summary)## $`1`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 44.26 49.44 54.52 54.70 59.80 65.94
##
## $`2`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 29.76 35.91 33.72 40.02 44.17
##
## $`3`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 66.05 70.30 75.33 77.28 82.27 100.00BASES CON IPRG K-MEANS
#descriptivas final ####
#recodificar la voluntariedad
library(car)
#summary(C8$cluster) #de 1 a 3
#C8$cluster <- recode(C8$cluster, "1=Bajo; 2=Alto; 3=Medio")
C8$cluster<-factor(C8$cluster,levels=c("2","1","3"),labels = c("1.Bajo","2.Medio","3.Alto"))
summary(C8)## Ind_2_severidad3 x11 x12 x21
## Min. : 0.00 Min. :0.000 Min. :0.000 Min. :1.000
## 1st Qu.: 41.24 1st Qu.:3.000 1st Qu.:6.000 1st Qu.:5.000
## Median : 53.47 Median :4.000 Median :7.000 Median :5.000
## Mean : 54.10 Mean :3.735 Mean :6.367 Mean :5.375
## 3rd Qu.: 66.20 3rd Qu.:5.000 3rd Qu.:7.000 3rd Qu.:6.000
## Max. :100.00 Max. :5.000 Max. :8.000 Max. :7.000
## x22 x23 x24 x25 x31
## Min. :1.000 Min. :0.000 Min. :1.000 Min. :0.000 No : 21
## 1st Qu.:3.000 1st Qu.:7.000 1st Qu.:5.000 1st Qu.:6.000 No sabe: 20
## Median :4.000 Median :8.000 Median :6.000 Median :7.000 Si :1402
## Mean :3.633 Mean :7.319 Mean :5.796 Mean :6.517
## 3rd Qu.:4.000 3rd Qu.:8.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :4.000 Max. :8.000 Max. :7.000 Max. :7.000
## x32 x33 x41 x42 x43
## No :702 0: 7 Min. :1.000 Min. :1.000 Min. :1.00
## No sabe:124 1:1303 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.00
## Si :617 2: 133 Median :4.000 Median :4.000 Median :4.00
## Mean :3.671 Mean :3.773 Mean :3.96
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00
## Max. :7.000 Max. :7.000 Max. :7.00
## x44 x51 x52 x61 x62
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :0.000 Min. :1.00
## 1st Qu.:2.00 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:2.00
## Median :3.00 Median :5.000 Median :5.000 Median :2.000 Median :4.00
## Mean :3.45 Mean :4.459 Mean :5.252 Mean :2.256 Mean :3.43
## 3rd Qu.:5.00 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:3.000 3rd Qu.:5.00
## Max. :7.00 Max. :7.000 Max. :7.000 Max. :6.000 Max. :7.00
## x71 x72 x73 x74
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:5.000
## Median :4.000 Median :5.000 Median :4.000 Median :6.000
## Mean :4.283 Mean :4.578 Mean :4.112 Mean :5.415
## 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x75 x76 x77 x81
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.500 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :3.000 Median :5.000 Median :5.000
## Mean :3.717 Mean :3.286 Mean :4.398 Mean :4.995
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :7.000
## x82 x83 x84 x91
## Min. :1.000 Min. :1.000 Min. :1.0 Min. : 0.0000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.0 1st Qu.: 0.0000
## Median :4.000 Median :4.000 Median :4.0 Median : 0.0000
## Mean :4.084 Mean :4.236 Mean :3.9 Mean : 0.6189
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.0 3rd Qu.: 1.0000
## Max. :7.000 Max. :7.000 Max. :7.0 Max. :13.0000
## x92 x93 x94 x95
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.500 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :5.000
## Mean :3.625 Mean :3.685 Mean :3.743 Mean :4.604
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x101 x102 x103 x104
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :4.000 Median :5.000
## Mean :4.426 Mean :4.639 Mean :4.131 Mean :4.516
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## x105 x106 id Municipio
## Min. : 0.000 Min. : 0.000 Min. : 1.0 Length:1443
## 1st Qu.: 4.000 1st Qu.: 2.000 1st Qu.: 368.5 Class :character
## Median : 9.000 Median : 6.000 Median : 736.0 Mode :character
## Mean : 8.069 Mean : 5.881 Mean : 734.0
## 3rd Qu.:12.000 3rd Qu.:10.000 3rd Qu.:1099.5
## Max. :14.000 Max. :12.000 Max. :1460.0
## cluster
## 1.Bajo :433
## 2.Medio:646
## 3.Alto :364
##
##
## table(C8$cluster)##
## 1.Bajo 2.Medio 3.Alto
## 433 646 364DESCRIPTIVAS
###------------------------------descriptivas-------------------------------####
library(readxl)
exploratorio <- read_excel("C:/LAURA LUCIA/U/9/Tesis/MARZO/exploratorio/exploratorio.xlsx")
C9<-cbind(C8$Ind_2_severidad,C8$cluster,exploratorio)
summary(C9)## C8$Ind_2_severidad C8$cluster ID acep_participa
## Min. : 0.00 1.Bajo :433 Min. : 1.0 Length:1443
## 1st Qu.: 41.24 2.Medio:646 1st Qu.: 368.5 Class :character
## Median : 53.47 3.Alto :364 Median : 736.0 Mode :character
## Mean : 54.10 Mean : 734.0
## 3rd Qu.: 66.20 3rd Qu.:1099.5
## Max. :100.00 Max. :1460.0
## acep_nuevainv mayoredad vivtrab Edad
## Length:1443 Length:1443 Min. :0.000 Min. :18.00
## Class :character Class :character 1st Qu.:1.000 1st Qu.:28.00
## Mode :character Mode :character Median :1.000 Median :36.00
## Mean :1.431 Mean :37.74
## 3rd Qu.:2.000 3rd Qu.:46.00
## Max. :2.000 Max. :75.00
## Edad_g Sexo Sexo_Cat Raza
## Length:1443 Length:1443 Min. :0.0000 Length:1443
## Class :character Class :character 1st Qu.:0.0000 Class :character
## Mode :character Mode :character Median :1.0000 Mode :character
## Mean :0.6743
## 3rd Qu.:1.0000
## Max. :2.0000
## Raza_Cat AreaResid Area Municipio
## Min. :0.00000 Length:1443 Min. :1.000 Length:1443
## 1st Qu.:0.00000 Class :character 1st Qu.:1.000 Class :character
## Median :0.00000 Mode :character Median :1.000 Mode :character
## Mean :0.09286 Mean :1.085
## 3rd Qu.:0.00000 3rd Qu.:1.000
## Max. :1.00000 Max. :3.000
## MunicipioCat Barrio_resid Comuna Estrato
## Min. :0.000 Length:1443 Length:1443 Length:1443
## 1st Qu.:1.000 Class :character Class :character Class :character
## Median :1.000 Mode :character Mode :character Mode :character
## Mean :1.386
## 3rd Qu.:2.000
## Max. :2.000
## cat_estrato Educacion Ingresos Ingresos_Cat
## Min. :1.000 Length:1443 Length:1443 Min. :0.0000
## 1st Qu.:1.000 Class :character Class :character 1st Qu.:0.0000
## Median :2.000 Mode :character Mode :character Median :1.0000
## Mean :1.955 Mean :0.9785
## 3rd Qu.:2.000 3rd Qu.:2.0000
## Max. :3.000 Max. :2.0000
## Migrante Ocupacion Ocupacion_Cat ActividadLaboral
## Length:1443 Length:1443 Min. :1.000 Length:1443
## Class :character Class :character 1st Qu.:2.000 Class :character
## Mode :character Mode :character Median :4.000 Mode :character
## Mean :3.034
## 3rd Qu.:4.000
## Max. :4.000
## sec_ocupa2 Regimen Regimen_Cat Internet
## Min. : 0.000 Length:1443 Min. :0.0000 Length:1443
## 1st Qu.: 2.000 Class :character 1st Qu.:0.0000 Class :character
## Median : 5.000 Mode :character Median :0.0000 Mode :character
## Mean : 8.643 Mean :0.6383
## 3rd Qu.:16.000 3rd Qu.:1.0000
## Max. :26.000 Max. :2.0000
## Internet_Cat Agua Personas_casa conv10anos
## Min. :1.000 Length:1443 Min. : 1.000 Min. :0.0000
## 1st Qu.:3.000 Class :character 1st Qu.: 2.000 1st Qu.:0.0000
## Median :4.000 Mode :character Median : 3.000 Median :0.0000
## Mean :3.568 Mean : 3.484 Mean :0.3035
## 3rd Qu.:4.000 3rd Qu.: 4.000 3rd Qu.:1.0000
## Max. :5.000 Max. :49.000 Max. :1.0000
## conv1117anos conv1830anos conv3159anos conv60anos
## Min. :0.0000 Min. :0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.000 Median :1.0000 Median :0.0000
## Mean :0.2183 Mean :0.377 Mean :0.6556 Mean :0.4109
## 3rd Qu.:0.0000 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.000 Max. :1.0000 Max. :1.0000
## conv_enfercronicas Vivesolo
## Min. :0.0000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.0000 Median :0.00000
## Mean :0.2994 Mean :0.09702
## 3rd Qu.:1.0000 3rd Qu.:0.00000
## Max. :1.0000 Max. :3.00000#View(C9) #base de datos cali y palmira con la columna ?ndice
#edad
summary(C9$Edad)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 18.00 28.00 36.00 37.74 46.00 75.00#C9[,"Edad_grupos"] <- cut(C9$Edad, breaks = c(18,29,59,76),labels = c("De 18 a 29 años", "De 30 a 59 años", "Más de 60 años"))
head(C9)## C8$Ind_2_severidad C8$cluster ID acep_participa acep_nuevainv mayoredad
## 1 35.88 1.Bajo 1 Si Si Si
## 2 31.53 1.Bajo 2 Si Si Si
## 3 50.10 2.Medio 3 Si Si Si
## 4 46.17 2.Medio 4 Si Si Si
## 5 56.30 2.Medio 5 Si Si Si
## 6 69.42 3.Alto 6 Si Si Si
## vivtrab Edad Edad_g Sexo Sexo_Cat Raza Raza_Cat AreaResid Area
## 1 1 33 30-59 Hombre 0 Blanco 0 Urbana 1
## 2 1 35 30-59 Mujer 1 Mestizo 0 Urbana 1
## 3 1 21 18-29 Mujer 1 Mestizo 0 Urbana 1
## 4 1 20 18-29 Mujer 1 Mestizo 0 Urbana 1
## 5 1 48 30-59 Hombre 0 Blanco 0 Urbana 1
## 6 1 44 30-59 Mujer 1 Mestizo 0 Rural 2
## Municipio MunicipioCat Barrio_resid
## 1 Cali 1 Departamental
## 2 Cali 1 Quintas de Don Simón
## 3 Cali 1 Villa del Sol
## 4 Cali 1 Seminario
## 5 Cali 1 Normandía
## 6 Otro municipio del Valle del Cauca 0 NA
## Comuna Estrato cat_estrato Educacion Ingresos
## 1 Comuna 10 Estrato 4 2 Universitario Más de
## 2 Comuna 17 Estrato 5 3 Esp/Maestría/Doctorado Más de
## 3 Comuna 5 Estrato 4 2 Técnico/Bachillerato o menos Sin ingr
## 4 Comuna 19 Estrato 5 3 Técnico/Bachillerato o menos Sin ingr
## 5 Comuna 2 Estrato 6 3 Esp/Maestría/Doctorado Más de
## 6 No Aplica Estrato 5 3 Esp/Maestría/Doctorado Más de
## Ingresos_Cat Migrante Ocupacion Ocupacion_Cat
## 1 0 No es migrante Empleado 4
## 2 0 No es migrante Empleado 4
## 3 1 No es migrante Estudiante 3
## 4 1 No es migrante Estudiante 3
## 5 0 No es migrante Empleado 4
## 6 0 No es migrante Empleado 4
## ActividadLaboral sec_ocupa2 Regimen
## 1 Educativo 23 Contribu
## 2 Actividades intelectuales y científicas /Docente 3 Contribu
## 3 Estudiante instituto o universidad 2 No sabe
## 4 Estudiante instituto o universidad 2 Contribu
## 5 Actividades intelectuales y científicas /Docente 3 Contribu
## 6 Actividades intelectuales y científicas /Docente 3 Contribu
## Regimen_Cat Internet Internet_Cat Agua Personas_casa
## 1 0 Datos móviles – wifi vivien 4 Si 4
## 2 0 Datos móviles – wifi vivien 4 Si 1
## 3 2 Datos móviles – wifi vivien 4 Si 3
## 4 0 Datos móviles – wifi vivien 4 Si 4
## 5 0 Datos móviles – wifi vivien 4 Si 3
## 6 0 Wifi -vivienda 3 Si 2
## conv10anos conv1117anos conv1830anos conv3159anos conv60anos
## 1 1 0 0 1 1
## 2 0 0 0 1 1
## 3 0 0 0 1 0
## 4 0 0 1 1 1
## 5 0 0 0 1 1
## 6 0 1 0 0 0
## conv_enfercronicas Vivesolo
## 1 0 0
## 2 0 0
## 3 0 0
## 4 1 0
## 5 0 0
## 6 0 0summary(C9$`C8$cluster`)## 1.Bajo 2.Medio 3.Alto
## 433 646 364##Funci?n para generar las tablas
CualiG<-function(Variable,var2){
T_1<-table(Variable,var2)
p_1<-prop.table(T_1,1)*100
p_F<-round(chisq.test(T_1)$p.value,3)
#T_2<-table(Variable)
#pp_2<-prop.table(T_2)*100
Cual<-cbind(T_1,#T_2,
p_1,p_F
#,pp_2)
)
colnames(Cual)<-c("Bajo","Medio","Alto","%Bajo","%Medio","%Alto","p-valor")
Cual
}
### Creaci?n de todas las tablas
Tablas_<-function(C9,Var_r){
Edad<-CualiG(C9$Edad_g,Var_r)
Sexo<-CualiG(C9$Sexo,Var_r)
Raza<-CualiG(C9$Raza_Cat,Var_r)
Residencia<-CualiG(C9$AreaResid,Var_r)
Estrato<-CualiG(C9$cat_estrato,Var_r)
Educacion<-CualiG(C9$Educacion,Var_r)
Ingresos<-CualiG(C9$Ingresos_Cat,Var_r)
Ocupacion<-CualiG(C9$Ocupacion_Cat,Var_r)
rbind(Edad,Sexo, Raza,Residencia,Estrato,Educacion,Ingresos,Ocupacion)
}
C9$Grupo=C8$cluster;dim(C9)## [1] 1443 43#C9<-na.omit(C9)
TablasFinal<-Tablas_(C9,C9$Grupo);TablasFinal## Bajo Medio Alto %Bajo %Medio %Alto
## >60 15 42 35 16.30435 45.65217 38.043478
## 18-29 171 175 83 39.86014 40.79254 19.347319
## 30-59 247 429 246 26.78959 46.52928 26.681128
## Hombre 163 204 107 34.38819 43.03797 22.573840
## Mujer 269 439 257 27.87565 45.49223 26.632124
## 0 394 593 322 30.09931 45.30176 24.598930
## 1 39 53 42 29.10448 39.55224 31.343284
## Rural 25 52 35 22.32143 46.42857 31.250000
## Urbana 408 594 329 30.65364 44.62810 24.718257
## 1 101 176 127 25.00000 43.56436 31.435644
## 2 219 309 172 31.28571 44.14286 24.571429
## 3 113 161 65 33.33333 47.49263 19.174041
## Esp/Maestría/Doctorado 144 218 86 32.14286 48.66071 19.196429
## Técnico/Bachillerato o menos 124 180 140 27.92793 40.54054 31.531532
## Universitario 165 248 138 29.94555 45.00907 25.045372
## 0 174 253 109 32.46269 47.20149 20.335821
## 1 141 166 95 35.07463 41.29353 23.631841
## 2 118 227 160 23.36634 44.95050 31.683168
## 1 54 93 73 24.54545 42.27273 33.181818
## 2 89 113 63 33.58491 42.64151 23.773585
## 3 89 95 20 43.62745 46.56863 9.803922
## 4 201 345 208 26.65782 45.75597 27.586207
## p-valor
## >60 0.000
## 18-29 0.000
## 30-59 0.000
## Hombre 0.030
## Mujer 0.030
## 0 0.209
## 1 0.209
## Rural 0.122
## Urbana 0.122
## 1 0.002
## 2 0.002
## 3 0.002
## Esp/Maestría/Doctorado 0.001
## Técnico/Bachillerato o menos 0.001
## Universitario 0.001
## 0 0.000
## 1 0.000
## 2 0.000
## 1 0.000
## 2 0.000
## 3 0.000
## 4 0.000setwd("C:/LAURA LUCIA/U/9/Tesis/MARZO")
write.csv2(TablasFinal,"Tablas2.csv")
#LATEX
#TablasF2 <- read.csv2("C:/LAURA LUCIA/U/9/Tesis/MARZO/TablasF2.csv")
#View(TablasF2)
#print(xtable(TablasF2), include.rownames = FALSE)
#summary(C8$Ind_2_severidad)
#round(sd(C8$Ind_2_severidad),2)ACM 11-julio-2022 OCHOA
lib_req<-c("randomForest","knitr","pROC","dplyr","MASS","visdat","doBy","FactoMineR","factoextra","caret","e1071","pROC","class","rpart","rpart.plot")# Listado de librerias requeridas por el script
easypackages::packages(lib_req) # Verificación, instalación y carga de librerias.## Loading required package: randomForest## Warning: package 'randomForest' was built under R version 4.2.1## randomForest 4.7-1.1## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
##
## margin## Loading required package: knitr## Loading required package: pROC## Type 'citation("pROC")' for a citation.##
## Attaching package: 'pROC'## The following objects are masked from 'package:stats':
##
## cov, smooth, var## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following object is masked from 'package:randomForest':
##
## combine## The following object is masked from 'package:car':
##
## recode## The following objects are masked from 'package:data.table':
##
## between, first, last## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union## Loading required package: MASS##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## select## Loading required package: visdat## Loading required package: doBy##
## Attaching package: 'doBy'## The following object is masked from 'package:dplyr':
##
## order_by## Loading required package: caret## Loading required package: lattice## Loading required package: e1071## Loading required package: class## Loading required package: rpart## Loading required package: rpart.plot## All packages loaded successfullylibrary(readxl)
acm <- read_excel("C:/LAURA LUCIA/U/9/Tesis/MARZO/exploratorio/ACM.xlsx")
dim(acm)## [1] 1443 9IPRG<-C8$cluster
Datos<-cbind(acm,
#C8$Ind_2_severidad,
IPRG);dim(Datos);summary(Datos)## [1] 1443 10## ID Edad_g Sexo Municipio
## Min. : 1.0 Length:1443 Length:1443 Min. :1.000
## 1st Qu.: 368.5 Class :character Class :character 1st Qu.:1.000
## Median : 736.0 Mode :character Mode :character Median :1.000
## Mean : 734.0 Mean :1.448
## 3rd Qu.:1099.5 3rd Qu.:2.000
## Max. :1460.0 Max. :2.000
## Estrato Educacion Ingresos Ocu
## Length:1443 Length:1443 Min. :0.0000 Min. :1.000
## Class :character Class :character 1st Qu.:0.0000 1st Qu.:2.000
## Mode :character Mode :character Median :1.0000 Median :4.000
## Mean :0.9785 Mean :3.032
## 3rd Qu.:2.0000 3rd Qu.:4.000
## Max. :2.0000 Max. :4.000
## ViveConEnferCronicas IPRG
## Length:1443 1.Bajo :433
## Class :character 2.Medio:646
## Mode :character 3.Alto :364
##
##
## #estructura
Datos = Datos %>% mutate_if(is.character,as.factor) ##Convertir cadenas de texto a caracteres
Datos = Datos %>% mutate_all(na_if," ") ##Datos vacíos a NA
## Nombres de las variables
level_EDAD=c("18-29"="18-29","30-59"="30-59",">60"=">60")
level_GENERO=c("Mujer"="M","Hombre"="H")
level_Municipio=c("1"="Cali","2"="Palmira")
level_ESTRATO=c("E.1-2"="Estrato 1-2","E.3-4"="Estrato 3-4","E.5-6"="Estrato 5-6")
level_EDUCACION=c("Universitario"="Univ","Esp/Maestría/Doctorado"="Mst/Doc","Técnico/Bachillerato o menos"="Téc/Bach")
level_INGRESOS=c("0"=">3smlv","1"="<1smlv","2"="1-3smlv")
level_OCUPACION=c("1"="Jubilado/Desempleado","2"="Independiente","3"="Estudiante","4"="Empleado")
level_VIVE=c("1"="Si","0"="No")
level_IPRG=c("1.Bajo"="Bajo","2.Medio"="Medio","3.Alto"="Alto")#ejemplo ochoa
dataACM<-Datos[,-1]
res.mca<-MCA(dataACM);res.mca
## **Results of the Multiple Correspondence Analysis (MCA)**
## The analysis was performed on 1443 individuals, described by 9 variables
## *The results are available in the following objects:
##
## name description
## 1 "$eig" "eigenvalues"
## 2 "$var" "results for the variables"
## 3 "$var$coord" "coord. of the categories"
## 4 "$var$cos2" "cos2 for the categories"
## 5 "$var$contrib" "contributions of the categories"
## 6 "$var$v.test" "v-test for the categories"
## 7 "$ind" "results for the individuals"
## 8 "$ind$coord" "coord. for the individuals"
## 9 "$ind$cos2" "cos2 for the individuals"
## 10 "$ind$contrib" "contributions of the individuals"
## 11 "$call" "intermediate results"
## 12 "$call$marge.col" "weights of columns"
## 13 "$call$marge.li" "weights of rows"library(ggrepel)
options(ggrepel.max.overlaps = Inf)
plot.MCA(res.mca,choix="var",invisible=NULL,title="Graph of the variables on the MCA map",axes=c(1,2),cex=0.6)
library(ggrepel)
options(ggrepel.max.overlaps = Inf)
plot.MCA(res.mca,choix="ind",invisible=NULL,axes=c(1,2),
selectMod=NULL,selec=NULL,habillage='none',title="MCA factor map",col.quali="magenta",cex=0.6)
res.mca$call$Xtot ## Tabla disjuntiva completa## 18-29 30-59 >60 H M Otro Cali Palmira Estrato 1-2 Estrato 3-4 Estrato 5-6
## 1 0 1 0 1 0 0 1 0 0 1 0
## 2 0 1 0 0 1 0 1 0 0 0 1
## 3 1 0 0 0 1 0 1 0 0 1 0
## 4 1 0 0 0 1 0 1 0 0 0 1
## 5 0 1 0 1 0 0 1 0 0 0 1
## 6 0 1 0 0 1 0 1 0 0 0 1
## 7 0 1 0 0 1 0 1 0 0 1 0
## 8 0 1 0 0 1 0 1 0 0 0 1
## 9 1 0 0 0 1 0 1 0 0 1 0
## 10 1 0 0 0 1 0 1 0 0 0 1
## 11 0 1 0 0 1 0 1 0 0 0 1
## 12 1 0 0 0 1 0 1 0 0 0 1
## 13 1 0 0 0 1 0 1 0 1 0 0
## 14 0 1 0 0 1 0 1 0 0 1 0
## 15 1 0 0 0 1 0 1 0 0 0 1
## 16 0 1 0 0 1 0 1 0 0 1 0
## 17 1 0 0 0 1 0 1 0 0 0 1
## 18 0 1 0 0 1 0 1 0 0 1 0
## 19 1 0 0 0 1 0 1 0 0 1 0
## 20 0 1 0 1 0 0 1 0 0 1 0
## 21 1 0 0 0 1 0 1 0 0 0 1
## 22 0 1 0 1 0 0 1 0 0 1 0
## 23 1 0 0 1 0 0 1 0 0 1 0
## 24 0 1 0 0 1 0 1 0 0 0 1
## 25 1 0 0 0 1 0 1 0 1 0 0
## 26 1 0 0 1 0 0 1 0 0 0 1
## 27 1 0 0 1 0 0 1 0 0 0 1
## 28 0 1 0 1 0 0 1 0 0 0 1
## 29 1 0 0 0 1 0 1 0 0 0 1
## 30 0 1 0 0 1 0 1 0 0 0 1
## 31 0 1 0 1 0 0 1 0 0 0 1
## 32 1 0 0 0 1 0 1 0 0 0 1
## 33 0 1 0 1 0 0 1 0 0 1 0
## 34 0 1 0 1 0 0 1 0 0 1 0
## 35 0 1 0 0 1 0 1 0 0 0 1
## 36 1 0 0 0 1 0 1 0 0 1 0
## 37 0 0 1 1 0 0 1 0 0 0 1
## 38 0 1 0 0 1 0 1 0 0 0 1
## 39 0 1 0 0 1 0 1 0 0 1 0
## 40 0 0 1 0 1 0 1 0 0 0 1
## 41 0 1 0 0 1 0 1 0 0 1 0
## 42 1 0 0 1 0 0 1 0 0 1 0
## 43 0 1 0 1 0 0 1 0 0 0 1
## 44 0 1 0 1 0 0 1 0 0 0 1
## 45 1 0 0 0 1 0 1 0 0 0 1
## 46 0 1 0 1 0 0 1 0 0 1 0
## 47 0 1 0 1 0 0 1 0 0 0 1
## 48 0 1 0 0 1 0 1 0 0 0 1
## 49 0 1 0 1 0 0 1 0 0 1 0
## 50 1 0 0 0 1 0 1 0 0 1 0
## 51 0 1 0 0 1 0 1 0 0 1 0
## 52 0 1 0 0 1 0 1 0 0 0 1
## 53 0 1 0 0 1 0 1 0 0 0 1
## 54 0 1 0 0 1 0 1 0 0 1 0
## 55 0 1 0 0 1 0 1 0 0 0 1
## 56 1 0 0 0 1 0 1 0 0 0 1
## 57 0 1 0 0 1 0 1 0 0 1 0
## 58 0 1 0 0 1 0 1 0 0 0 1
## 59 1 0 0 0 1 0 1 0 0 1 0
## 60 1 0 0 0 1 0 1 0 0 0 1
## 61 0 0 1 1 0 0 1 0 0 1 0
## 62 0 1 0 0 1 0 1 0 0 1 0
## 63 0 1 0 0 1 0 1 0 0 0 1
## 64 0 1 0 0 1 0 1 0 0 1 0
## 65 0 1 0 1 0 0 1 0 0 0 1
## 66 0 1 0 1 0 0 1 0 0 0 1
## 67 0 1 0 0 1 0 1 0 0 0 1
## 68 0 1 0 1 0 0 1 0 0 1 0
## 69 1 0 0 0 1 0 1 0 0 1 0
## 70 0 0 1 0 1 0 1 0 0 0 1
## 71 0 1 0 0 1 0 1 0 0 1 0
## 72 1 0 0 0 1 0 1 0 0 0 1
## 73 0 1 0 0 1 0 1 0 0 1 0
## 74 1 0 0 1 0 0 1 0 0 1 0
## 75 0 1 0 0 1 0 1 0 0 0 1
## 76 1 0 0 0 1 0 1 0 1 0 0
## 77 0 1 0 0 1 0 1 0 0 0 1
## 78 0 0 1 1 0 0 1 0 0 1 0
## 79 0 1 0 0 1 0 1 0 0 1 0
## 80 1 0 0 1 0 0 1 0 1 0 0
## 81 0 1 0 0 1 0 1 0 0 1 0
## 82 0 1 0 1 0 0 1 0 0 0 1
## 83 0 1 0 1 0 0 1 0 0 1 0
## 84 0 0 1 0 1 0 1 0 0 0 1
## 85 0 1 0 1 0 0 1 0 0 1 0
## 86 0 1 0 0 1 0 1 0 0 0 1
## 87 1 0 0 1 0 0 1 0 0 1 0
## 88 1 0 0 0 1 0 1 0 0 0 1
## 89 1 0 0 0 1 0 1 0 0 1 0
## 90 0 1 0 0 1 0 1 0 0 0 1
## 91 1 0 0 0 1 0 1 0 1 0 0
## 92 0 1 0 0 1 0 1 0 0 0 1
## 93 0 1 0 0 1 0 1 0 0 1 0
## 94 0 0 1 1 0 0 1 0 0 0 1
## 95 0 1 0 0 1 0 1 0 0 0 1
## 96 0 1 0 1 0 0 1 0 0 1 0
## 97 0 1 0 0 1 0 1 0 0 1 0
## 98 0 1 0 0 1 0 1 0 0 1 0
## 99 0 1 0 0 1 0 1 0 0 0 1
## 100 0 1 0 0 1 0 1 0 0 0 1
## 101 0 1 0 0 1 0 1 0 0 0 1
## 102 0 1 0 0 1 0 1 0 0 1 0
## 103 1 0 0 1 0 0 1 0 0 1 0
## 104 0 1 0 0 1 0 1 0 0 1 0
## 105 0 1 0 0 1 0 1 0 0 1 0
## 106 0 1 0 0 1 0 1 0 0 0 1
## 107 0 1 0 1 0 0 1 0 0 1 0
## 108 0 0 1 0 1 0 1 0 0 1 0
## 109 1 0 0 1 0 0 1 0 0 1 0
## 110 0 1 0 0 1 0 1 0 0 1 0
## 111 0 1 0 1 0 0 1 0 0 1 0
## 112 0 1 0 0 1 0 1 0 0 0 1
## 113 0 1 0 1 0 0 1 0 0 1 0
## 114 0 1 0 0 1 0 1 0 0 1 0
## 115 0 1 0 0 1 0 1 0 0 0 1
## 116 0 1 0 0 1 0 1 0 0 0 1
## 117 1 0 0 0 1 0 1 0 1 0 0
## 118 0 1 0 0 1 0 1 0 0 0 1
## 119 0 1 0 0 1 0 1 0 0 1 0
## 120 0 1 0 0 1 0 1 0 0 1 0
## 121 1 0 0 0 1 0 1 0 0 1 0
## 122 0 1 0 0 1 0 1 0 1 0 0
## 123 0 1 0 0 1 0 1 0 0 1 0
## 124 0 1 0 1 0 0 1 0 0 0 1
## 125 0 1 0 0 1 0 1 0 0 1 0
## 126 0 1 0 0 1 0 1 0 0 0 1
## 127 0 1 0 0 1 0 1 0 0 1 0
## 128 1 0 0 0 1 0 1 0 0 1 0
## 129 0 1 0 0 1 0 1 0 0 0 1
## 130 0 1 0 0 1 0 1 0 0 1 0
## 131 0 1 0 0 1 0 1 0 0 0 1
## 132 0 0 1 0 1 0 1 0 0 1 0
## 133 0 1 0 1 0 0 1 0 0 0 1
## 134 0 1 0 1 0 0 1 0 0 0 1
## 135 0 1 0 0 1 0 1 0 0 1 0
## 136 1 0 0 0 1 0 1 0 0 1 0
## 137 0 1 0 0 1 0 1 0 0 1 0
## 138 0 0 1 1 0 0 1 0 0 0 1
## 139 1 0 0 1 0 0 1 0 0 1 0
## 140 1 0 0 1 0 0 1 0 0 1 0
## 141 0 1 0 0 1 0 1 0 1 0 0
## 142 0 1 0 1 0 0 1 0 0 1 0
## 143 0 0 1 1 0 0 1 0 0 1 0
## 144 0 1 0 0 1 0 1 0 0 1 0
## 145 0 1 0 0 1 0 1 0 0 1 0
## 146 0 1 0 1 0 0 1 0 0 1 0
## 147 0 1 0 1 0 0 1 0 0 0 1
## 148 1 0 0 1 0 0 1 0 0 1 0
## 149 0 1 0 0 1 0 1 0 1 0 0
## 150 1 0 0 0 1 0 1 0 0 1 0
## 151 1 0 0 1 0 0 1 0 0 1 0
## 152 0 1 0 0 1 0 1 0 0 0 1
## 153 1 0 0 0 1 0 1 0 0 1 0
## 154 0 1 0 1 0 0 1 0 0 0 1
## 155 0 1 0 1 0 0 1 0 0 1 0
## 156 1 0 0 1 0 0 1 0 0 0 1
## 157 0 1 0 0 1 0 1 0 0 1 0
## 158 0 1 0 0 1 0 1 0 0 0 1
## 159 0 1 0 0 1 0 1 0 0 1 0
## 160 0 0 1 1 0 0 1 0 0 0 1
## 161 1 0 0 0 1 0 1 0 0 1 0
## 162 0 1 0 0 1 0 1 0 0 0 1
## 163 0 1 0 0 1 0 1 0 0 0 1
## 164 0 1 0 0 1 0 1 0 0 0 1
## 165 0 1 0 1 0 0 1 0 0 1 0
## 166 0 1 0 0 1 0 1 0 0 1 0
## 167 0 1 0 0 1 0 1 0 0 0 1
## 168 0 1 0 0 1 0 1 0 0 1 0
## 169 1 0 0 0 1 0 1 0 1 0 0
## 170 0 1 0 0 1 0 1 0 0 1 0
## 171 1 0 0 1 0 0 1 0 1 0 0
## 172 0 0 1 0 1 0 1 0 0 0 1
## 173 0 1 0 1 0 0 1 0 0 1 0
## 174 0 1 0 1 0 0 1 0 0 1 0
## 175 1 0 0 0 1 0 1 0 0 1 0
## 176 0 1 0 0 1 0 1 0 0 1 0
## 177 0 1 0 0 1 0 1 0 0 0 1
## 178 1 0 0 0 1 0 1 0 0 1 0
## 179 0 1 0 0 1 0 1 0 0 0 1
## 180 0 0 1 0 1 0 1 0 0 0 1
## 181 1 0 0 1 0 0 1 0 1 0 0
## 182 0 1 0 0 1 0 1 0 1 0 0
## 183 1 0 0 0 1 0 1 0 1 0 0
## 184 0 0 1 1 0 0 1 0 0 1 0
## 185 1 0 0 0 1 0 1 0 1 0 0
## 186 0 1 0 0 1 0 1 0 0 0 1
## 187 0 1 0 0 1 0 1 0 0 1 0
## 188 0 1 0 0 1 0 1 0 0 1 0
## 189 0 1 0 0 1 0 1 0 0 0 1
## 190 1 0 0 0 1 0 1 0 0 0 1
## 191 1 0 0 0 1 0 1 0 0 0 1
## 192 0 1 0 0 1 0 1 0 0 1 0
## 193 0 1 0 1 0 0 1 0 0 1 0
## 194 0 1 0 0 1 0 1 0 0 1 0
## 195 0 0 1 1 0 0 1 0 0 0 1
## 196 1 0 0 0 1 0 1 0 0 0 1
## 197 0 1 0 0 1 0 1 0 1 0 0
## 198 1 0 0 1 0 0 1 0 1 0 0
## 199 1 0 0 1 0 0 1 0 1 0 0
## 200 0 1 0 1 0 0 1 0 0 1 0
## 201 1 0 0 1 0 0 1 0 0 0 1
## 202 0 0 1 0 1 0 1 0 0 0 1
## 203 0 1 0 0 1 0 1 0 0 0 1
## 204 0 1 0 0 1 0 1 0 0 1 0
## 205 1 0 0 0 1 0 1 0 1 0 0
## 206 0 1 0 0 1 0 1 0 0 1 0
## 207 1 0 0 0 1 0 1 0 0 0 1
## 208 0 1 0 0 1 0 1 0 0 0 1
## 209 1 0 0 1 0 0 1 0 1 0 0
## 210 0 1 0 0 1 0 1 0 0 0 1
## 211 0 1 0 1 0 0 1 0 0 0 1
## 212 0 0 1 0 1 0 1 0 0 1 0
## 213 1 0 0 1 0 0 1 0 0 0 1
## 214 0 1 0 0 1 0 1 0 0 1 0
## 215 0 1 0 0 1 0 1 0 1 0 0
## 216 0 0 1 0 1 0 1 0 0 1 0
## 217 0 1 0 0 1 0 1 0 0 1 0
## 218 0 1 0 0 1 0 1 0 0 1 0
## 219 1 0 0 0 1 0 1 0 0 1 0
## 220 1 0 0 0 1 0 1 0 0 1 0
## 221 0 1 0 1 0 0 1 0 0 0 1
## 222 0 1 0 0 1 0 1 0 0 1 0
## 223 0 1 0 0 1 0 1 0 0 1 0
## 224 0 1 0 0 1 0 1 0 0 0 1
## 225 0 1 0 0 1 0 1 0 0 0 1
## 226 1 0 0 1 0 0 1 0 0 0 1
## 227 0 1 0 0 1 0 1 0 0 1 0
## 228 0 1 0 0 1 0 1 0 0 0 1
## 229 0 1 0 1 0 0 1 0 1 0 0
## 230 0 1 0 0 1 0 1 0 0 1 0
## 231 0 1 0 0 1 0 1 0 0 1 0
## 232 0 0 1 1 0 0 1 0 0 1 0
## 233 0 0 1 1 0 0 1 0 0 1 0
## 234 0 1 0 0 1 0 1 0 0 0 1
## 235 0 1 0 1 0 0 1 0 0 0 1
## 236 0 1 0 0 1 0 1 0 0 1 0
## 237 1 0 0 1 0 0 1 0 0 0 1
## 238 0 0 1 0 1 0 1 0 0 0 1
## 239 0 1 0 1 0 0 1 0 0 1 0
## 240 0 1 0 0 1 0 1 0 0 1 0
## 241 0 0 1 0 1 0 1 0 0 0 1
## 242 1 0 0 0 1 0 1 0 0 1 0
## 243 0 1 0 0 1 0 1 0 0 1 0
## 244 1 0 0 1 0 0 1 0 0 0 1
## 245 0 1 0 1 0 0 1 0 0 0 1
## 246 0 1 0 0 1 0 1 0 0 0 1
## 247 1 0 0 0 1 0 1 0 0 1 0
## 248 0 1 0 1 0 0 1 0 0 1 0
## 249 0 1 0 1 0 0 1 0 0 1 0
## 250 0 1 0 1 0 0 1 0 0 1 0
## 251 0 1 0 1 0 0 1 0 0 1 0
## 252 1 0 0 1 0 0 1 0 0 1 0
## 253 0 1 0 0 1 0 1 0 0 1 0
## 254 0 1 0 1 0 0 1 0 0 1 0
## 255 0 1 0 0 1 0 1 0 0 1 0
## 256 0 1 0 1 0 0 1 0 0 0 1
## 257 1 0 0 1 0 0 1 0 0 1 0
## 258 0 1 0 1 0 0 1 0 0 1 0
## 259 0 1 0 1 0 0 1 0 0 1 0
## 260 0 1 0 0 1 0 1 0 1 0 0
## 261 0 1 0 1 0 0 1 0 0 1 0
## 262 0 1 0 0 1 0 1 0 1 0 0
## 263 1 0 0 0 1 0 1 0 0 0 1
## 264 0 1 0 0 1 0 1 0 0 0 1
## 265 0 1 0 1 0 0 1 0 0 0 1
## 266 1 0 0 0 1 0 1 0 0 1 0
## 267 0 1 0 0 1 0 1 0 0 0 1
## 268 1 0 0 1 0 0 1 0 1 0 0
## 269 0 1 0 0 1 0 1 0 0 0 1
## 270 0 1 0 0 1 0 1 0 1 0 0
## 271 0 1 0 1 0 0 1 0 0 1 0
## 272 0 1 0 0 1 0 1 0 0 1 0
## 273 0 1 0 0 1 0 1 0 0 0 1
## 274 0 1 0 1 0 0 1 0 0 1 0
## 275 0 1 0 0 1 0 1 0 0 1 0
## 276 0 1 0 0 1 0 1 0 0 0 1
## 277 0 1 0 1 0 0 1 0 0 0 1
## 278 0 1 0 0 1 0 1 0 0 0 1
## 279 1 0 0 0 1 0 1 0 0 0 1
## 280 0 1 0 0 1 0 1 0 1 0 0
## 281 0 1 0 1 0 0 1 0 0 0 1
## 282 0 1 0 0 1 0 1 0 0 1 0
## 283 0 1 0 0 1 0 1 0 0 1 0
## 284 0 0 1 1 0 0 1 0 0 0 1
## 285 0 1 0 0 1 0 1 0 0 1 0
## 286 0 0 1 1 0 0 1 0 0 1 0
## 287 0 1 0 1 0 0 1 0 0 1 0
## 288 0 1 0 1 0 0 1 0 0 1 0
## 289 0 1 0 0 1 0 1 0 0 0 1
## 290 1 0 0 0 1 0 1 0 0 0 1
## 291 0 1 0 1 0 0 1 0 0 1 0
## 292 0 1 0 0 1 0 1 0 1 0 0
## 293 0 1 0 1 0 0 1 0 0 0 1
## 294 0 1 0 0 1 0 1 0 0 1 0
## 295 0 1 0 0 1 0 1 0 0 1 0
## 296 1 0 0 0 1 0 1 0 0 0 1
## 297 0 1 0 1 0 0 1 0 0 1 0
## 298 1 0 0 0 1 0 1 0 0 1 0
## 299 0 1 0 0 1 0 1 0 1 0 0
## 300 0 1 0 1 0 0 1 0 0 1 0
## 301 1 0 0 0 1 0 1 0 1 0 0
## 302 0 1 0 1 0 0 1 0 0 0 1
## 303 0 1 0 1 0 0 1 0 1 0 0
## 304 0 1 0 0 1 0 1 0 0 0 1
## 305 0 1 0 1 0 0 1 0 0 1 0
## 306 1 0 0 0 1 0 1 0 0 0 1
## 307 0 1 0 1 0 0 1 0 0 1 0
## 308 1 0 0 0 1 0 1 0 0 1 0
## 309 0 1 0 0 1 0 1 0 0 1 0
## 310 0 1 0 0 1 0 1 0 0 1 0
## 311 0 1 0 0 1 0 1 0 0 1 0
## 312 0 1 0 1 0 0 1 0 0 1 0
## 313 0 1 0 1 0 0 1 0 0 0 1
## 314 0 1 0 0 1 0 1 0 0 0 1
## 315 0 0 1 0 1 0 1 0 0 0 1
## 316 0 1 0 0 1 0 1 0 0 1 0
## 317 0 1 0 0 1 0 1 0 0 1 0
## 318 0 0 1 0 1 0 1 0 0 1 0
## 319 0 1 0 0 1 0 1 0 1 0 0
## 320 1 0 0 0 1 0 1 0 0 0 1
## 321 0 1 0 0 1 0 1 0 0 1 0
## 322 0 1 0 1 0 0 1 0 0 0 1
## 323 0 1 0 1 0 0 1 0 1 0 0
## 324 0 1 0 1 0 0 1 0 0 1 0
## 325 1 0 0 0 1 0 1 0 0 0 1
## 326 0 1 0 0 1 0 1 0 1 0 0
## 327 0 1 0 1 0 0 1 0 0 1 0
## 328 1 0 0 0 1 0 1 0 0 1 0
## 329 0 0 1 1 0 0 1 0 0 1 0
## 330 0 1 0 0 1 0 1 0 1 0 0
## 331 0 1 0 0 1 0 1 0 0 1 0
## 332 0 1 0 1 0 0 1 0 0 1 0
## 333 0 0 1 0 1 0 1 0 1 0 0
## 334 0 1 0 1 0 0 1 0 0 1 0
## 335 0 1 0 0 1 0 1 0 0 1 0
## 336 1 0 0 0 0 1 1 0 0 0 1
## 337 1 0 0 0 1 0 1 0 0 1 0
## 338 0 1 0 0 1 0 1 0 0 0 1
## 339 0 1 0 0 1 0 1 0 1 0 0
## 340 0 1 0 0 1 0 1 0 1 0 0
## 341 0 1 0 0 1 0 1 0 0 1 0
## 342 1 0 0 0 1 0 1 0 0 1 0
## 343 0 1 0 0 1 0 1 0 0 0 1
## 344 0 1 0 0 1 0 1 0 0 0 1
## 345 0 0 1 1 0 0 1 0 0 0 1
## 346 1 0 0 0 1 0 1 0 1 0 0
## 347 0 1 0 1 0 0 1 0 0 1 0
## 348 0 1 0 1 0 0 1 0 0 0 1
## 349 1 0 0 0 1 0 1 0 0 0 1
## 350 0 1 0 0 1 0 1 0 0 0 1
## 351 0 1 0 0 1 0 1 0 1 0 0
## 352 1 0 0 0 1 0 1 0 0 1 0
## 353 0 1 0 0 1 0 1 0 0 1 0
## 354 1 0 0 0 1 0 1 0 0 1 0
## 355 0 1 0 1 0 0 1 0 0 0 1
## 356 0 1 0 1 0 0 1 0 0 1 0
## 357 0 1 0 0 1 0 1 0 0 0 1
## 358 1 0 0 1 0 0 1 0 0 1 0
## 359 1 0 0 0 1 0 1 0 0 1 0
## 360 0 1 0 0 1 0 1 0 1 0 0
## 361 0 1 0 0 1 0 1 0 0 1 0
## 362 0 0 1 0 1 0 1 0 0 0 1
## 363 0 1 0 0 1 0 1 0 0 1 0
## 364 0 1 0 0 1 0 1 0 0 1 0
## 365 0 1 0 0 1 0 1 0 0 0 1
## 366 0 1 0 1 0 0 1 0 0 1 0
## 367 0 1 0 0 1 0 1 0 0 1 0
## 368 0 1 0 1 0 0 1 0 0 1 0
## 369 0 1 0 1 0 0 1 0 0 1 0
## 370 0 1 0 0 1 0 1 0 0 1 0
## 371 0 1 0 0 1 0 1 0 0 1 0
## 372 0 1 0 0 1 0 1 0 0 0 1
## 373 0 0 1 0 1 0 1 0 0 0 1
## 374 0 1 0 1 0 0 1 0 0 0 1
## 375 0 0 1 0 1 0 1 0 0 1 0
## 376 0 1 0 0 1 0 1 0 0 1 0
## 377 0 1 0 0 1 0 1 0 0 1 0
## 378 1 0 0 1 0 0 1 0 0 1 0
## 379 0 1 0 0 1 0 1 0 0 0 1
## 380 0 1 0 0 1 0 1 0 0 0 1
## 381 0 1 0 0 1 0 1 0 0 0 1
## 382 0 0 1 0 1 0 1 0 0 0 1
## 383 1 0 0 0 1 0 1 0 0 0 1
## 384 0 1 0 0 1 0 1 0 0 0 1
## 385 0 1 0 0 1 0 1 0 0 1 0
## 386 0 1 0 1 0 0 1 0 0 0 1
## 387 0 0 1 1 0 0 1 0 0 1 0
## 388 0 1 0 0 1 0 1 0 0 0 1
## 389 0 0 1 0 1 0 1 0 0 0 1
## 390 0 1 0 0 1 0 1 0 0 0 1
## 391 1 0 0 0 1 0 1 0 0 0 1
## 392 1 0 0 0 1 0 1 0 0 1 0
## 393 1 0 0 0 1 0 1 0 1 0 0
## 394 1 0 0 0 1 0 1 0 0 1 0
## 395 0 1 0 1 0 0 1 0 0 0 1
## 396 0 0 1 1 0 0 1 0 0 1 0
## 397 0 1 0 0 1 0 1 0 0 0 1
## 398 0 1 0 1 0 0 1 0 0 0 1
## 399 1 0 0 0 1 0 1 0 0 0 1
## 400 0 1 0 0 1 0 1 0 0 1 0
## 401 1 0 0 0 1 0 1 0 0 0 1
## 402 0 1 0 0 1 0 1 0 1 0 0
## 403 1 0 0 0 1 0 1 0 0 1 0
## 404 0 1 0 0 1 0 1 0 0 0 1
## 405 0 1 0 1 0 0 1 0 0 0 1
## 406 0 1 0 0 1 0 1 0 0 1 0
## 407 1 0 0 1 0 0 1 0 0 1 0
## 408 1 0 0 0 1 0 1 0 0 1 0
## 409 1 0 0 0 1 0 1 0 0 1 0
## 410 0 1 0 0 1 0 1 0 0 1 0
## 411 1 0 0 1 0 0 1 0 0 1 0
## 412 1 0 0 0 1 0 1 0 0 1 0
## 413 1 0 0 0 1 0 1 0 1 0 0
## 414 1 0 0 0 1 0 1 0 1 0 0
## 415 0 1 0 0 1 0 1 0 1 0 0
## 416 0 1 0 0 1 0 1 0 1 0 0
## 417 1 0 0 0 1 0 1 0 1 0 0
## 418 0 1 0 0 1 0 1 0 0 0 1
## 419 1 0 0 0 1 0 1 0 0 1 0
## 420 0 0 1 0 1 0 1 0 1 0 0
## 421 0 1 0 0 1 0 1 0 1 0 0
## 422 1 0 0 0 1 0 1 0 0 1 0
## 423 1 0 0 0 1 0 1 0 1 0 0
## 424 0 1 0 0 1 0 1 0 1 0 0
## 425 1 0 0 0 1 0 1 0 1 0 0
## 426 0 1 0 0 1 0 1 0 1 0 0
## 427 0 1 0 0 1 0 1 0 1 0 0
## 428 0 1 0 0 1 0 1 0 1 0 0
## 429 0 1 0 0 1 0 1 0 1 0 0
## 430 1 0 0 0 1 0 1 0 1 0 0
## 431 1 0 0 1 0 0 1 0 1 0 0
## 432 0 1 0 0 1 0 1 0 1 0 0
## 433 0 1 0 1 0 0 1 0 0 1 0
## 434 1 0 0 1 0 0 1 0 0 1 0
## 435 0 0 1 1 0 0 1 0 0 1 0
## 436 0 1 0 0 1 0 1 0 1 0 0
## 437 0 1 0 0 1 0 1 0 1 0 0
## 438 1 0 0 0 1 0 1 0 0 1 0
## 439 0 1 0 1 0 0 1 0 0 1 0
## 440 1 0 0 0 1 0 1 0 0 1 0
## 441 1 0 0 0 1 0 1 0 0 0 1
## 442 0 1 0 1 0 0 1 0 0 0 1
## 443 0 1 0 0 1 0 1 0 0 1 0
## 444 0 0 1 0 1 0 1 0 1 0 0
## 445 0 1 0 1 0 0 1 0 0 0 1
## 446 0 1 0 0 1 0 1 0 0 1 0
## 447 0 0 1 0 1 0 1 0 0 1 0
## 448 0 1 0 0 1 0 1 0 0 1 0
## 449 0 1 0 0 1 0 1 0 0 1 0
## 450 0 1 0 1 0 0 1 0 0 1 0
## 451 0 1 0 1 0 0 1 0 0 1 0
## 452 0 1 0 0 1 0 1 0 0 1 0
## 453 0 0 1 0 1 0 1 0 0 0 1
## 454 0 1 0 1 0 0 1 0 0 1 0
## 455 0 0 1 0 1 0 1 0 0 0 1
## 456 0 1 0 0 1 0 1 0 1 0 0
## 457 0 1 0 0 1 0 1 0 0 0 1
## 458 0 1 0 1 0 0 1 0 0 1 0
## 459 1 0 0 0 1 0 1 0 0 1 0
## 460 0 1 0 0 1 0 1 0 0 0 1
## 461 0 0 1 0 1 0 1 0 0 1 0
## 462 0 1 0 0 1 0 1 0 0 1 0
## 463 1 0 0 0 1 0 1 0 1 0 0
## 464 1 0 0 0 1 0 1 0 0 1 0
## 465 1 0 0 0 1 0 1 0 0 1 0
## 466 1 0 0 1 0 0 1 0 0 1 0
## 467 1 0 0 0 1 0 1 0 0 0 1
## 468 1 0 0 0 1 0 1 0 0 0 1
## 469 0 1 0 1 0 0 1 0 0 0 1
## 470 1 0 0 1 0 0 1 0 0 1 0
## 471 0 1 0 1 0 0 1 0 1 0 0
## 472 1 0 0 1 0 0 1 0 0 1 0
## 473 0 1 0 0 1 0 1 0 0 1 0
## 474 0 1 0 1 0 0 1 0 0 1 0
## 475 0 1 0 0 1 0 1 0 0 1 0
## 476 0 1 0 0 1 0 1 0 0 1 0
## 477 0 1 0 1 0 0 1 0 0 0 1
## 478 1 0 0 0 1 0 1 0 0 1 0
## 479 1 0 0 0 1 0 1 0 0 0 1
## 480 0 1 0 0 1 0 1 0 0 0 1
## 481 0 1 0 0 1 0 1 0 0 1 0
## 482 0 1 0 1 0 0 1 0 0 0 1
## 483 0 1 0 1 0 0 1 0 0 0 1
## 484 1 0 0 1 0 0 1 0 0 1 0
## 485 0 0 1 0 1 0 1 0 0 1 0
## 486 0 1 0 1 0 0 1 0 0 0 1
## 487 0 1 0 0 1 0 1 0 0 0 1
## 488 0 0 1 1 0 0 1 0 0 0 1
## 489 0 0 1 1 0 0 1 0 0 0 1
## 490 0 1 0 0 1 0 1 0 0 1 0
## 491 1 0 0 1 0 0 1 0 0 0 1
## 492 0 1 0 1 0 0 1 0 0 1 0
## 493 0 1 0 1 0 0 1 0 0 0 1
## 494 1 0 0 1 0 0 1 0 0 1 0
## 495 1 0 0 1 0 0 1 0 0 1 0
## 496 0 1 0 1 0 0 1 0 0 1 0
## 497 0 1 0 0 1 0 1 0 0 1 0
## 498 0 1 0 1 0 0 1 0 0 1 0
## 499 0 1 0 0 1 0 1 0 0 0 1
## 500 0 1 0 0 1 0 1 0 0 1 0
## 501 0 1 0 0 1 0 1 0 0 1 0
## 502 0 1 0 1 0 0 1 0 1 0 0
## 503 0 1 0 0 1 0 1 0 0 1 0
## 504 0 1 0 0 1 0 1 0 0 0 1
## 505 0 1 0 1 0 0 1 0 0 0 1
## 506 0 0 1 0 1 0 1 0 0 1 0
## 507 0 1 0 1 0 0 1 0 0 1 0
## 508 0 1 0 0 1 0 1 0 0 1 0
## 509 0 1 0 1 0 0 1 0 0 0 1
## 510 1 0 0 1 0 0 1 0 0 0 1
## 511 0 1 0 0 1 0 1 0 0 1 0
## 512 0 1 0 0 1 0 1 0 0 0 1
## 513 1 0 0 0 1 0 1 0 0 1 0
## 514 0 1 0 0 1 0 1 0 0 1 0
## 515 1 0 0 1 0 0 1 0 0 1 0
## 516 1 0 0 0 1 0 1 0 0 1 0
## 517 1 0 0 0 1 0 1 0 0 1 0
## 518 0 1 0 0 1 0 1 0 0 1 0
## 519 0 1 0 0 1 0 1 0 0 0 1
## 520 1 0 0 1 0 0 1 0 0 1 0
## 521 1 0 0 1 0 0 1 0 0 1 0
## 522 0 1 0 0 1 0 1 0 1 0 0
## 523 0 1 0 0 1 0 1 0 0 0 1
## 524 1 0 0 1 0 0 1 0 0 0 1
## 525 1 0 0 0 1 0 1 0 1 0 0
## 526 1 0 0 0 1 0 1 0 0 1 0
## 527 0 1 0 0 1 0 1 0 0 1 0
## 528 1 0 0 1 0 0 1 0 0 1 0
## 529 1 0 0 0 1 0 1 0 0 1 0
## 530 0 0 1 0 1 0 1 0 0 0 1
## 531 1 0 0 0 1 0 1 0 0 1 0
## 532 1 0 0 0 1 0 1 0 0 1 0
## 533 1 0 0 1 0 0 1 0 0 1 0
## 534 1 0 0 1 0 0 1 0 0 1 0
## 535 1 0 0 0 1 0 1 0 0 1 0
## 536 1 0 0 1 0 0 1 0 0 0 1
## 537 1 0 0 0 1 0 1 0 1 0 0
## 538 0 1 0 1 0 0 1 0 0 0 1
## 539 1 0 0 1 0 0 1 0 1 0 0
## 540 1 0 0 0 1 0 1 0 1 0 0
## 541 0 1 0 1 0 0 1 0 0 0 1
## 542 1 0 0 0 1 0 1 0 0 1 0
## 543 0 0 1 0 1 0 1 0 0 1 0
## 544 1 0 0 1 0 0 1 0 0 1 0
## 545 0 1 0 0 1 0 1 0 0 1 0
## 546 0 1 0 1 0 0 1 0 0 1 0
## 547 1 0 0 1 0 0 1 0 0 1 0
## 548 0 1 0 0 1 0 1 0 0 1 0
## 549 1 0 0 0 1 0 1 0 1 0 0
## 550 0 1 0 0 1 0 1 0 0 0 1
## 551 0 1 0 1 0 0 1 0 0 1 0
## 552 0 1 0 0 1 0 1 0 0 1 0
## 553 1 0 0 1 0 0 1 0 0 1 0
## 554 0 1 0 0 1 0 1 0 0 0 1
## 555 0 1 0 0 1 0 1 0 0 1 0
## 556 0 1 0 0 1 0 1 0 0 1 0
## 557 1 0 0 1 0 0 1 0 0 1 0
## 558 1 0 0 0 1 0 1 0 1 0 0
## 559 0 1 0 1 0 0 1 0 0 1 0
## 560 0 1 0 0 1 0 1 0 1 0 0
## 561 0 1 0 0 1 0 1 0 0 0 1
## 562 0 1 0 0 1 0 1 0 0 0 1
## 563 0 1 0 0 1 0 1 0 0 0 1
## 564 1 0 0 0 1 0 1 0 0 1 0
## 565 0 1 0 0 1 0 1 0 0 0 1
## 566 0 1 0 0 1 0 1 0 0 0 1
## 567 0 1 0 0 1 0 1 0 0 1 0
## 568 0 1 0 0 1 0 1 0 0 0 1
## 569 1 0 0 0 1 0 1 0 0 1 0
## 570 1 0 0 0 1 0 1 0 0 0 1
## 571 1 0 0 1 0 0 1 0 0 0 1
## 572 1 0 0 1 0 0 1 0 0 0 1
## 573 0 1 0 0 1 0 1 0 1 0 0
## 574 0 1 0 1 0 0 1 0 0 1 0
## 575 0 1 0 0 1 0 1 0 1 0 0
## 576 1 0 0 0 1 0 1 0 0 0 1
## 577 0 0 1 1 0 0 1 0 0 0 1
## 578 1 0 0 0 1 0 1 0 1 0 0
## 579 0 1 0 0 1 0 1 0 0 1 0
## 580 1 0 0 0 1 0 1 0 0 0 1
## 581 0 1 0 1 0 0 1 0 0 1 0
## 582 1 0 0 0 1 0 1 0 1 0 0
## 583 0 1 0 0 1 0 1 0 0 1 0
## 584 0 1 0 0 1 0 1 0 0 1 0
## 585 0 1 0 0 1 0 1 0 0 1 0
## 586 1 0 0 0 1 0 1 0 0 0 1
## 587 1 0 0 0 1 0 1 0 0 1 0
## 588 0 1 0 0 1 0 1 0 0 1 0
## 589 1 0 0 0 1 0 1 0 0 1 0
## 590 1 0 0 0 1 0 1 0 1 0 0
## 591 0 1 0 1 0 0 1 0 0 1 0
## 592 1 0 0 0 1 0 1 0 0 0 1
## 593 1 0 0 1 0 0 1 0 0 1 0
## 594 1 0 0 0 1 0 1 0 0 1 0
## 595 0 1 0 0 1 0 1 0 0 1 0
## 596 0 0 1 0 1 0 1 0 0 0 1
## 597 0 1 0 0 1 0 1 0 0 0 1
## 598 0 1 0 0 1 0 1 0 0 0 1
## 599 0 1 0 0 1 0 1 0 0 0 1
## 600 0 1 0 1 0 0 1 0 1 0 0
## 601 0 0 1 1 0 0 1 0 0 1 0
## 602 0 1 0 0 1 0 1 0 1 0 0
## 603 1 0 0 0 1 0 1 0 0 0 1
## 604 1 0 0 0 1 0 1 0 1 0 0
## 605 0 1 0 0 1 0 1 0 0 1 0
## 606 1 0 0 0 1 0 1 0 0 1 0
## 607 0 1 0 0 1 0 1 0 0 0 1
## 608 1 0 0 1 0 0 1 0 0 1 0
## 609 0 1 0 1 0 0 1 0 0 1 0
## 610 0 0 1 1 0 0 1 0 0 0 1
## 611 1 0 0 0 1 0 1 0 1 0 0
## 612 0 1 0 1 0 0 1 0 0 0 1
## 613 1 0 0 0 1 0 1 0 0 1 0
## 614 1 0 0 0 1 0 1 0 0 1 0
## 615 0 1 0 0 1 0 1 0 0 0 1
## 616 0 0 1 1 0 0 1 0 0 0 1
## 617 0 1 0 0 1 0 1 0 0 1 0
## 618 0 0 1 0 1 0 1 0 0 0 1
## 619 1 0 0 0 1 0 1 0 0 1 0
## 620 0 1 0 1 0 0 1 0 0 1 0
## 621 0 0 1 0 1 0 1 0 0 1 0
## 622 0 0 1 0 1 0 1 0 0 0 1
## 623 1 0 0 0 1 0 1 0 0 1 0
## 624 0 1 0 1 0 0 1 0 0 0 1
## 625 1 0 0 1 0 0 1 0 0 0 1
## 626 0 0 1 0 1 0 1 0 0 1 0
## 627 1 0 0 0 1 0 1 0 0 1 0
## 628 1 0 0 0 1 0 1 0 0 1 0
## 629 0 0 1 1 0 0 1 0 0 0 1
## 630 0 1 0 0 1 0 1 0 0 1 0
## 631 1 0 0 1 0 0 1 0 0 1 0
## 632 0 1 0 0 1 0 1 0 0 1 0
## 633 0 1 0 0 1 0 1 0 0 1 0
## 634 1 0 0 0 1 0 1 0 0 1 0
## 635 0 1 0 0 1 0 1 0 0 0 1
## 636 0 1 0 0 1 0 1 0 0 0 1
## 637 0 1 0 0 1 0 1 0 0 0 1
## 638 0 1 0 1 0 0 1 0 0 1 0
## 639 0 1 0 0 1 0 1 0 0 0 1
## 640 0 1 0 0 1 0 1 0 0 1 0
## 641 0 1 0 0 1 0 1 0 0 0 1
## 642 1 0 0 0 1 0 1 0 0 1 0
## 643 0 1 0 1 0 0 1 0 0 0 1
## 644 0 1 0 1 0 0 1 0 0 0 1
## 645 0 0 1 0 1 0 1 0 0 0 1
## 646 0 1 0 1 0 0 1 0 0 1 0
## 647 0 1 0 0 1 0 1 0 0 0 1
## 648 0 1 0 0 1 0 1 0 0 0 1
## 649 0 1 0 1 0 0 1 0 0 0 1
## 650 0 1 0 0 1 0 1 0 0 1 0
## 651 0 1 0 1 0 0 1 0 0 0 1
## 652 0 1 0 0 1 0 1 0 0 0 1
## 653 1 0 0 1 0 0 1 0 0 1 0
## 654 0 1 0 1 0 0 1 0 0 0 1
## 655 0 1 0 0 0 1 1 0 1 0 0
## 656 0 1 0 0 1 0 1 0 0 1 0
## 657 0 1 0 1 0 0 1 0 0 0 1
## 658 0 1 0 0 1 0 1 0 1 0 0
## 659 0 1 0 1 0 0 1 0 0 1 0
## 660 1 0 0 0 1 0 1 0 1 0 0
## 661 0 1 0 0 1 0 1 0 0 0 1
## 662 0 1 0 0 1 0 1 0 0 1 0
## 663 0 1 0 0 1 0 1 0 1 0 0
## 664 1 0 0 1 0 0 1 0 0 1 0
## 665 0 1 0 0 1 0 1 0 0 0 1
## 666 1 0 0 0 1 0 1 0 0 0 1
## 667 1 0 0 1 0 0 1 0 0 0 1
## 668 0 1 0 0 1 0 1 0 0 1 0
## 669 0 1 0 1 0 0 1 0 0 1 0
## 670 0 1 0 1 0 0 1 0 1 0 0
## 671 0 1 0 1 0 0 1 0 0 1 0
## 672 0 1 0 1 0 0 1 0 0 1 0
## 673 0 1 0 0 1 0 1 0 1 0 0
## 674 0 1 0 0 1 0 1 0 0 1 0
## 675 1 0 0 0 1 0 1 0 0 1 0
## 676 0 1 0 0 1 0 1 0 1 0 0
## 677 0 1 0 0 1 0 1 0 0 1 0
## 678 0 1 0 0 1 0 1 0 0 1 0
## 679 0 1 0 1 0 0 1 0 1 0 0
## 680 0 1 0 0 1 0 1 0 1 0 0
## 681 1 0 0 1 0 0 1 0 0 1 0
## 682 0 1 0 0 1 0 1 0 1 0 0
## 683 0 1 0 0 1 0 1 0 0 1 0
## 684 0 0 1 1 0 0 1 0 0 1 0
## 685 0 1 0 1 0 0 1 0 0 1 0
## 686 0 1 0 0 1 0 1 0 1 0 0
## 687 1 0 0 1 0 0 1 0 0 1 0
## 688 1 0 0 0 1 0 1 0 1 0 0
## 689 0 1 0 0 1 0 1 0 0 0 1
## 690 0 1 0 0 1 0 1 0 0 0 1
## 691 0 1 0 0 1 0 1 0 1 0 0
## 692 0 1 0 0 1 0 1 0 1 0 0
## 693 1 0 0 0 1 0 1 0 0 0 1
## 694 1 0 0 1 0 0 1 0 0 1 0
## 695 0 1 0 0 1 0 1 0 0 1 0
## 696 0 1 0 0 1 0 1 0 0 0 1
## 697 0 0 1 1 0 0 1 0 1 0 0
## 698 0 1 0 1 0 0 1 0 1 0 0
## 699 0 1 0 0 1 0 1 0 0 1 0
## 700 0 1 0 0 1 0 1 0 1 0 0
## 701 0 1 0 0 1 0 1 0 1 0 0
## 702 0 1 0 0 1 0 1 0 0 0 1
## 703 0 1 0 0 1 0 1 0 0 1 0
## 704 0 1 0 0 1 0 1 0 0 0 1
## 705 0 0 1 0 1 0 1 0 0 1 0
## 706 0 0 1 1 0 0 1 0 0 1 0
## 707 0 1 0 0 1 0 1 0 0 1 0
## 708 0 1 0 0 1 0 1 0 0 0 1
## 709 0 1 0 1 0 0 1 0 0 1 0
## 710 0 1 0 0 1 0 1 0 0 1 0
## 711 0 1 0 0 1 0 1 0 0 1 0
## 712 0 1 0 1 0 0 1 0 1 0 0
## 713 0 1 0 0 1 0 1 0 1 0 0
## 714 0 1 0 1 0 0 1 0 0 1 0
## 715 0 1 0 1 0 0 1 0 0 0 1
## 716 0 1 0 1 0 0 1 0 0 1 0
## 717 0 1 0 0 1 0 1 0 0 0 1
## 718 0 1 0 0 1 0 1 0 0 0 1
## 719 0 1 0 1 0 0 1 0 0 0 1
## 720 0 0 1 0 1 0 1 0 0 0 1
## 721 1 0 0 0 1 0 1 0 0 0 1
## 722 1 0 0 1 0 0 1 0 0 1 0
## 723 0 1 0 0 1 0 1 0 0 1 0
## 724 1 0 0 1 0 0 1 0 0 1 0
## 725 0 1 0 1 0 0 1 0 0 1 0
## 726 0 1 0 0 1 0 1 0 1 0 0
## 727 1 0 0 1 0 0 1 0 0 0 1
## 728 0 1 0 0 1 0 1 0 0 1 0
## 729 0 1 0 0 1 0 1 0 0 0 1
## 730 1 0 0 1 0 0 1 0 0 1 0
## 731 1 0 0 0 1 0 1 0 0 1 0
## 732 0 1 0 0 1 0 1 0 1 0 0
## 733 0 1 0 0 1 0 1 0 1 0 0
## 734 0 1 0 0 1 0 1 0 0 0 1
## 735 0 1 0 0 1 0 1 0 0 0 1
## 736 1 0 0 1 0 0 1 0 0 1 0
## 737 1 0 0 0 1 0 1 0 1 0 0
## 738 0 1 0 0 1 0 1 0 0 1 0
## 739 0 1 0 0 1 0 1 0 0 1 0
## 740 0 1 0 1 0 0 1 0 0 1 0
## 741 0 1 0 0 1 0 1 0 0 1 0
## 742 0 1 0 1 0 0 1 0 0 0 1
## 743 0 1 0 0 1 0 1 0 0 1 0
## 744 0 1 0 0 1 0 1 0 0 0 1
## 745 0 1 0 0 1 0 1 0 0 1 0
## 746 0 1 0 1 0 0 1 0 0 0 1
## 747 0 0 1 0 1 0 1 0 1 0 0
## 748 0 1 0 0 1 0 1 0 1 0 0
## 749 0 1 0 1 0 0 1 0 0 1 0
## 750 0 1 0 1 0 0 1 0 0 1 0
## 751 1 0 0 1 0 0 1 0 1 0 0
## 752 0 1 0 0 1 0 1 0 0 0 1
## 753 0 1 0 0 1 0 1 0 1 0 0
## 754 0 1 0 1 0 0 1 0 0 1 0
## 755 0 1 0 0 1 0 1 0 0 1 0
## 756 1 0 0 0 1 0 1 0 0 1 0
## 757 0 1 0 0 1 0 1 0 1 0 0
## 758 0 1 0 1 0 0 1 0 0 1 0
## 759 0 1 0 1 0 0 1 0 0 1 0
## 760 1 0 0 1 0 0 1 0 0 1 0
## 761 0 1 0 0 1 0 1 0 1 0 0
## 762 0 1 0 1 0 0 1 0 0 1 0
## 763 1 0 0 1 0 0 1 0 0 1 0
## 764 1 0 0 1 0 0 1 0 1 0 0
## 765 0 0 1 0 1 0 1 0 0 0 1
## 766 0 1 0 0 1 0 1 0 0 1 0
## 767 0 1 0 0 1 0 1 0 1 0 0
## 768 0 1 0 1 0 0 1 0 1 0 0
## 769 0 1 0 1 0 0 1 0 1 0 0
## 770 0 1 0 0 1 0 1 0 0 1 0
## 771 1 0 0 0 1 0 1 0 1 0 0
## 772 0 0 1 1 0 0 1 0 0 0 1
## 773 0 1 0 0 1 0 1 0 0 0 1
## 774 0 1 0 0 1 0 1 0 0 1 0
## 775 0 0 1 1 0 0 1 0 0 1 0
## 776 0 1 0 0 1 0 1 0 0 0 1
## 777 1 0 0 1 0 0 1 0 0 1 0
## 778 0 1 0 1 0 0 1 0 0 1 0
## 779 0 1 0 0 1 0 1 0 0 0 1
## 780 0 1 0 0 1 0 1 0 0 1 0
## 781 1 0 0 0 1 0 1 0 0 0 1
## 782 0 1 0 0 1 0 1 0 0 0 1
## 783 0 1 0 0 1 0 1 0 0 0 1
## 784 0 1 0 1 0 0 1 0 0 1 0
## 785 0 1 0 0 1 0 1 0 1 0 0
## 786 0 1 0 0 1 0 1 0 0 1 0
## 787 0 1 0 1 0 0 1 0 0 1 0
## 788 0 1 0 0 1 0 1 0 0 0 1
## 789 1 0 0 0 1 0 1 0 1 0 0
## 790 0 1 0 0 1 0 1 0 0 1 0
## 791 0 0 1 0 1 0 1 0 0 0 1
## 792 1 0 0 0 1 0 1 0 0 1 0
## 793 0 1 0 1 0 0 1 0 1 0 0
## 794 0 1 0 0 1 0 1 0 0 1 0
## 795 1 0 0 0 1 0 1 0 0 0 1
## 796 0 1 0 0 1 0 1 0 0 1 0
## 797 0 1 0 0 1 0 1 0 0 0 1
## 798 0 1 0 0 1 0 0 1 0 1 0
## 799 0 1 0 0 1 0 0 1 0 1 0
## 800 0 1 0 1 0 0 0 1 0 1 0
## 801 0 1 0 0 1 0 0 1 0 1 0
## 802 0 1 0 0 1 0 0 1 1 0 0
## 803 0 1 0 1 0 0 0 1 0 1 0
## 804 1 0 0 0 1 0 0 1 0 1 0
## 805 0 0 1 1 0 0 0 1 0 0 1
## 806 0 1 0 1 0 0 0 1 0 1 0
## 807 1 0 0 0 1 0 0 1 1 0 0
## 808 1 0 0 0 1 0 0 1 1 0 0
## 809 0 1 0 0 1 0 0 1 0 0 1
## 810 0 1 0 1 0 0 0 1 1 0 0
## 811 0 1 0 0 1 0 0 1 1 0 0
## 812 0 1 0 1 0 0 0 1 0 1 0
## 813 1 0 0 0 1 0 0 1 0 1 0
## 814 0 1 0 0 1 0 0 1 0 1 0
## 815 1 0 0 0 1 0 0 1 1 0 0
## 816 1 0 0 0 1 0 0 1 0 0 1
## 817 0 1 0 0 1 0 0 1 0 0 1
## 818 0 1 0 1 0 0 0 1 0 1 0
## 819 0 1 0 0 1 0 0 1 0 1 0
## 820 0 1 0 0 1 0 0 1 1 0 0
## 821 1 0 0 1 0 0 0 1 1 0 0
## 822 0 1 0 0 1 0 0 1 1 0 0
## 823 1 0 0 1 0 0 0 1 0 0 1
## 824 0 1 0 0 1 0 0 1 0 1 0
## 825 0 1 0 0 1 0 0 1 0 0 1
## 826 1 0 0 0 1 0 0 1 0 1 0
## 827 0 1 0 1 0 0 0 1 0 1 0
## 828 1 0 0 0 1 0 0 1 0 1 0
## 829 0 0 1 1 0 0 0 1 0 0 1
## 830 0 1 0 0 1 0 0 1 0 1 0
## 831 1 0 0 0 1 0 0 1 0 0 1
## 832 0 1 0 0 1 0 0 1 1 0 0
## 833 0 1 0 0 1 0 0 1 0 1 0
## 834 1 0 0 1 0 0 0 1 0 0 1
## 835 0 1 0 0 1 0 0 1 0 1 0
## 836 0 1 0 0 1 0 0 1 1 0 0
## 837 0 1 0 1 0 0 0 1 0 0 1
## 838 0 1 0 0 1 0 0 1 0 1 0
## 839 0 1 0 0 1 0 0 1 0 0 1
## 840 0 1 0 0 1 0 0 1 0 1 0
## 841 1 0 0 1 0 0 0 1 1 0 0
## 842 0 1 0 0 1 0 0 1 1 0 0
## 843 1 0 0 0 1 0 0 1 1 0 0
## 844 0 1 0 0 1 0 0 1 0 0 1
## 845 0 1 0 1 0 0 0 1 0 1 0
## 846 0 1 0 0 1 0 0 1 0 1 0
## 847 1 0 0 1 0 0 0 1 1 0 0
## 848 1 0 0 0 1 0 0 1 1 0 0
## 849 1 0 0 1 0 0 0 1 0 0 1
## 850 0 1 0 0 1 0 0 1 1 0 0
## 851 0 1 0 1 0 0 0 1 0 1 0
## 852 1 0 0 0 1 0 0 1 0 1 0
## 853 0 1 0 1 0 0 0 1 0 1 0
## 854 0 1 0 1 0 0 0 1 1 0 0
## 855 1 0 0 1 0 0 0 1 0 1 0
## 856 1 0 0 0 1 0 0 1 1 0 0
## 857 1 0 0 0 1 0 0 1 1 0 0
## 858 0 1 0 0 1 0 0 1 0 1 0
## 859 0 1 0 0 1 0 0 1 0 1 0
## 860 1 0 0 1 0 0 0 1 0 0 1
## 861 0 1 0 0 1 0 0 1 0 1 0
## 862 0 1 0 0 1 0 0 1 0 1 0
## 863 0 1 0 0 1 0 0 1 0 0 1
## 864 0 1 0 0 1 0 0 1 0 1 0
## 865 0 1 0 0 1 0 0 1 0 1 0
## 866 1 0 0 1 0 0 0 1 0 1 0
## 867 1 0 0 0 1 0 0 1 1 0 0
## 868 0 1 0 0 1 0 0 1 0 1 0
## 869 0 1 0 0 1 0 0 1 0 1 0
## 870 1 0 0 1 0 0 0 1 0 1 0
## 871 0 1 0 1 0 0 0 1 1 0 0
## 872 0 0 1 1 0 0 0 1 0 1 0
## 873 0 1 0 1 0 0 0 1 1 0 0
## 874 0 1 0 1 0 0 0 1 1 0 0
## 875 1 0 0 0 1 0 0 1 0 1 0
## 876 0 0 1 1 0 0 0 1 0 1 0
## 877 1 0 0 0 1 0 0 1 0 0 1
## 878 1 0 0 1 0 0 0 1 1 0 0
## 879 1 0 0 0 1 0 0 1 1 0 0
## 880 0 1 0 1 0 0 0 1 0 1 0
## 881 0 1 0 1 0 0 0 1 0 1 0
## 882 0 1 0 1 0 0 0 1 0 0 1
## 883 0 1 0 1 0 0 0 1 1 0 0
## 884 0 1 0 0 1 0 0 1 1 0 0
## 885 0 1 0 0 1 0 0 1 1 0 0
## 886 0 1 0 1 0 0 0 1 1 0 0
## 887 0 1 0 0 1 0 0 1 0 1 0
## 888 0 1 0 1 0 0 0 1 0 1 0
## 889 1 0 0 0 1 0 0 1 1 0 0
## 890 0 1 0 1 0 0 0 1 1 0 0
## 891 0 0 1 1 0 0 0 1 1 0 0
## 892 0 1 0 0 1 0 0 1 0 1 0
## 893 0 1 0 0 1 0 0 1 0 1 0
## 894 1 0 0 0 1 0 0 1 0 0 1
## 895 0 1 0 1 0 0 0 1 1 0 0
## 896 0 1 0 1 0 0 0 1 1 0 0
## 897 1 0 0 0 1 0 0 1 0 0 1
## 898 1 0 0 1 0 0 0 1 1 0 0
## 899 1 0 0 0 1 0 0 1 0 1 0
## 900 0 1 0 0 1 0 0 1 1 0 0
## 901 0 1 0 0 1 0 0 1 1 0 0
## 902 0 1 0 0 1 0 0 1 0 0 1
## 903 1 0 0 0 1 0 0 1 0 0 1
## 904 1 0 0 1 0 0 0 1 0 0 1
## 905 0 1 0 0 1 0 0 1 0 0 1
## 906 0 1 0 1 0 0 0 1 0 1 0
## 907 0 1 0 1 0 0 0 1 0 1 0
## 908 0 1 0 0 1 0 0 1 0 1 0
## 909 0 1 0 0 1 0 0 1 1 0 0
## 910 0 1 0 0 1 0 0 1 1 0 0
## 911 0 1 0 1 0 0 0 1 0 1 0
## 912 1 0 0 1 0 0 0 1 0 1 0
## 913 0 1 0 1 0 0 0 1 0 1 0
## 914 0 1 0 0 1 0 0 1 1 0 0
## 915 0 1 0 0 1 0 0 1 1 0 0
## 916 0 1 0 1 0 0 0 1 0 0 1
## 917 0 1 0 0 1 0 0 1 1 0 0
## 918 0 1 0 1 0 0 0 1 0 1 0
## 919 0 1 0 0 1 0 0 1 1 0 0
## 920 1 0 0 0 1 0 0 1 0 1 0
## 921 0 1 0 0 1 0 0 1 0 1 0
## 922 0 1 0 0 1 0 0 1 0 1 0
## 923 1 0 0 1 0 0 0 1 0 1 0
## 924 0 1 0 0 1 0 0 1 0 1 0
## 925 0 1 0 0 1 0 0 1 1 0 0
## 926 1 0 0 1 0 0 0 1 0 1 0
## 927 1 0 0 0 1 0 0 1 0 1 0
## 928 0 1 0 1 0 0 0 1 1 0 0
## 929 0 1 0 0 1 0 0 1 1 0 0
## 930 0 1 0 1 0 0 0 1 0 0 1
## 931 1 0 0 1 0 0 0 1 0 1 0
## 932 1 0 0 0 1 0 0 1 1 0 0
## 933 1 0 0 1 0 0 0 1 1 0 0
## 934 1 0 0 0 1 0 0 1 1 0 0
## 935 1 0 0 1 0 0 0 1 0 1 0
## 936 1 0 0 1 0 0 0 1 0 1 0
## 937 0 1 0 1 0 0 0 1 0 1 0
## 938 1 0 0 0 1 0 0 1 0 1 0
## 939 0 1 0 1 0 0 0 1 0 1 0
## 940 1 0 0 0 1 0 0 1 1 0 0
## 941 1 0 0 0 1 0 0 1 0 1 0
## 942 1 0 0 0 1 0 0 1 1 0 0
## 943 0 1 0 0 1 0 0 1 1 0 0
## 944 0 1 0 1 0 0 0 1 0 1 0
## 945 0 1 0 0 1 0 0 1 1 0 0
## 946 1 0 0 0 1 0 0 1 1 0 0
## 947 0 1 0 0 1 0 0 1 0 1 0
## 948 0 1 0 0 1 0 0 1 1 0 0
## 949 0 1 0 1 0 0 0 1 0 1 0
## 950 0 1 0 0 1 0 0 1 0 1 0
## 951 1 0 0 0 1 0 0 1 1 0 0
## 952 1 0 0 0 1 0 0 1 0 1 0
## 953 0 1 0 0 1 0 0 1 1 0 0
## 954 1 0 0 1 0 0 0 1 1 0 0
## 955 0 1 0 1 0 0 0 1 0 1 0
## 956 0 1 0 0 1 0 0 1 1 0 0
## 957 1 0 0 1 0 0 0 1 1 0 0
## 958 0 1 0 0 1 0 0 1 0 0 1
## 959 1 0 0 1 0 0 0 1 1 0 0
## 960 0 1 0 0 1 0 0 1 1 0 0
## 961 1 0 0 0 1 0 0 1 0 1 0
## 962 0 1 0 0 1 0 0 1 0 1 0
## 963 1 0 0 1 0 0 0 1 0 1 0
## 964 1 0 0 0 1 0 0 1 1 0 0
## 965 0 1 0 0 1 0 0 1 0 1 0
## 966 0 1 0 0 1 0 0 1 0 1 0
## 967 0 1 0 0 1 0 0 1 0 1 0
## 968 0 1 0 1 0 0 0 1 0 1 0
## 969 0 1 0 0 1 0 0 1 1 0 0
## 970 1 0 0 0 1 0 0 1 1 0 0
## 971 0 1 0 0 1 0 0 1 1 0 0
## 972 0 1 0 1 0 0 0 1 1 0 0
## 973 0 1 0 0 1 0 0 1 1 0 0
## 974 1 0 0 0 1 0 0 1 0 1 0
## 975 0 1 0 1 0 0 0 1 0 1 0
## 976 1 0 0 0 1 0 0 1 1 0 0
## 977 0 1 0 0 1 0 0 1 1 0 0
## 978 0 1 0 0 1 0 0 1 1 0 0
## 979 0 0 1 1 0 0 0 1 1 0 0
## 980 0 1 0 0 1 0 0 1 0 1 0
## 981 0 1 0 0 1 0 0 1 1 0 0
## 982 0 1 0 0 1 0 0 1 1 0 0
## 983 0 1 0 0 1 0 0 1 1 0 0
## 984 0 1 0 1 0 0 0 1 1 0 0
## 985 1 0 0 0 1 0 0 1 1 0 0
## 986 1 0 0 1 0 0 0 1 0 1 0
## 987 0 1 0 0 1 0 0 1 0 1 0
## 988 1 0 0 0 1 0 0 1 0 1 0
## 989 0 0 1 0 1 0 0 1 1 0 0
## 990 0 1 0 0 1 0 0 1 1 0 0
## 991 0 1 0 0 1 0 0 1 1 0 0
## 992 1 0 0 0 1 0 0 1 0 1 0
## 993 0 1 0 0 1 0 0 1 0 0 1
## 994 1 0 0 0 1 0 0 1 1 0 0
## 995 0 1 0 1 0 0 0 1 1 0 0
## 996 0 1 0 1 0 0 0 1 1 0 0
## 997 0 1 0 0 1 0 0 1 0 1 0
## 998 0 1 0 0 1 0 0 1 0 0 1
## 999 0 1 0 1 0 0 0 1 0 1 0
## 1000 0 1 0 1 0 0 0 1 0 1 0
## 1001 0 1 0 0 1 0 0 1 1 0 0
## 1002 0 1 0 0 1 0 0 1 0 1 0
## 1003 0 1 0 0 1 0 0 1 0 1 0
## 1004 0 1 0 0 1 0 0 1 1 0 0
## 1005 0 1 0 0 1 0 0 1 1 0 0
## 1006 0 1 0 0 1 0 0 1 1 0 0
## 1007 1 0 0 0 1 0 0 1 1 0 0
## 1008 0 1 0 0 1 0 0 1 0 1 0
## 1009 0 1 0 0 1 0 0 1 1 0 0
## 1010 0 1 0 0 1 0 0 1 1 0 0
## 1011 0 1 0 0 1 0 0 1 0 1 0
## 1012 0 1 0 0 1 0 0 1 1 0 0
## 1013 0 1 0 0 1 0 0 1 0 0 1
## 1014 1 0 0 0 1 0 0 1 1 0 0
## 1015 0 1 0 0 1 0 0 1 0 1 0
## 1016 0 1 0 0 1 0 0 1 1 0 0
## 1017 1 0 0 1 0 0 0 1 1 0 0
## 1018 1 0 0 1 0 0 0 1 1 0 0
## 1019 1 0 0 0 1 0 0 1 0 1 0
## 1020 0 1 0 0 1 0 0 1 1 0 0
## 1021 0 1 0 0 1 0 0 1 0 1 0
## 1022 0 1 0 0 1 0 0 1 1 0 0
## 1023 1 0 0 0 1 0 0 1 1 0 0
## 1024 1 0 0 1 0 0 0 1 1 0 0
## 1025 1 0 0 1 0 0 0 1 1 0 0
## 1026 0 1 0 0 1 0 0 1 0 1 0
## 1027 0 1 0 0 1 0 0 1 0 1 0
## 1028 0 1 0 0 1 0 0 1 0 1 0
## 1029 0 1 0 0 1 0 0 1 1 0 0
## 1030 0 1 0 0 1 0 0 1 1 0 0
## 1031 0 1 0 0 1 0 0 1 1 0 0
## 1032 1 0 0 0 1 0 0 1 1 0 0
## 1033 0 1 0 0 1 0 0 1 1 0 0
## 1034 1 0 0 0 1 0 0 1 0 1 0
## 1035 0 1 0 0 1 0 0 1 1 0 0
## 1036 0 1 0 0 1 0 0 1 0 1 0
## 1037 1 0 0 1 0 0 0 1 0 0 1
## 1038 1 0 0 1 0 0 0 1 0 1 0
## 1039 1 0 0 0 1 0 0 1 1 0 0
## 1040 1 0 0 0 1 0 0 1 0 1 0
## 1041 0 1 0 1 0 0 0 1 0 1 0
## 1042 1 0 0 0 1 0 0 1 0 1 0
## 1043 0 1 0 0 1 0 0 1 0 1 0
## 1044 1 0 0 0 1 0 0 1 1 0 0
## 1045 1 0 0 0 1 0 0 1 1 0 0
## 1046 1 0 0 1 0 0 0 1 1 0 0
## 1047 0 1 0 1 0 0 0 1 1 0 0
## 1048 1 0 0 0 1 0 0 1 1 0 0
## 1049 1 0 0 0 1 0 0 1 0 1 0
## 1050 1 0 0 0 1 0 0 1 0 1 0
## 1051 1 0 0 0 1 0 0 1 0 1 0
## 1052 0 1 0 1 0 0 0 1 0 1 0
## 1053 0 1 0 1 0 0 0 1 0 0 1
## 1054 0 1 0 1 0 0 0 1 0 1 0
## 1055 1 0 0 1 0 0 0 1 1 0 0
## 1056 1 0 0 0 1 0 0 1 0 1 0
## 1057 0 1 0 0 1 0 0 1 0 1 0
## 1058 0 0 1 1 0 0 0 1 1 0 0
## 1059 1 0 0 0 1 0 0 1 1 0 0
## 1060 1 0 0 1 0 0 0 1 0 1 0
## 1061 1 0 0 0 1 0 0 1 0 1 0
## 1062 0 1 0 0 1 0 0 1 0 1 0
## 1063 0 1 0 1 0 0 0 1 1 0 0
## 1064 1 0 0 0 1 0 0 1 0 1 0
## 1065 0 1 0 0 1 0 0 1 1 0 0
## 1066 1 0 0 1 0 0 0 1 1 0 0
## 1067 0 1 0 1 0 0 0 1 1 0 0
## 1068 0 1 0 0 1 0 0 1 1 0 0
## 1069 0 1 0 0 1 0 0 1 0 1 0
## 1070 0 1 0 0 1 0 0 1 0 1 0
## 1071 1 0 0 0 1 0 0 1 1 0 0
## 1072 1 0 0 0 1 0 0 1 1 0 0
## 1073 0 1 0 1 0 0 0 1 0 1 0
## 1074 1 0 0 1 0 0 0 1 1 0 0
## 1075 0 1 0 0 1 0 0 1 0 1 0
## 1076 1 0 0 0 1 0 0 1 1 0 0
## 1077 1 0 0 0 1 0 0 1 0 1 0
## 1078 0 1 0 1 0 0 0 1 0 1 0
## 1079 1 0 0 0 1 0 0 1 1 0 0
## 1080 0 1 0 0 1 0 0 1 0 0 1
## 1081 0 1 0 0 1 0 0 1 1 0 0
## 1082 0 1 0 1 0 0 0 1 0 1 0
## 1083 1 0 0 0 1 0 0 1 1 0 0
## 1084 0 1 0 0 1 0 0 1 1 0 0
## 1085 0 1 0 0 1 0 0 1 0 1 0
## 1086 0 1 0 0 1 0 0 1 0 1 0
## 1087 0 1 0 0 1 0 0 1 0 1 0
## 1088 1 0 0 1 0 0 0 1 1 0 0
## 1089 1 0 0 1 0 0 0 1 0 1 0
## 1090 1 0 0 1 0 0 0 1 1 0 0
## 1091 0 1 0 0 1 0 0 1 1 0 0
## 1092 0 1 0 1 0 0 0 1 0 1 0
## 1093 0 1 0 0 1 0 0 1 1 0 0
## 1094 0 1 0 0 1 0 0 1 0 0 1
## 1095 1 0 0 1 0 0 0 1 0 1 0
## 1096 1 0 0 0 1 0 0 1 0 1 0
## 1097 1 0 0 0 1 0 0 1 1 0 0
## 1098 1 0 0 1 0 0 0 1 0 1 0
## 1099 1 0 0 0 1 0 0 1 0 1 0
## 1100 0 1 0 1 0 0 0 1 1 0 0
## 1101 1 0 0 1 0 0 0 1 0 1 0
## 1102 0 1 0 0 1 0 0 1 1 0 0
## 1103 0 1 0 0 1 0 0 1 0 1 0
## 1104 0 1 0 1 0 0 0 1 0 1 0
## 1105 0 1 0 0 1 0 0 1 1 0 0
## 1106 0 0 1 1 0 0 0 1 0 1 0
## 1107 0 0 1 1 0 0 0 1 0 1 0
## 1108 0 1 0 0 1 0 0 1 0 1 0
## 1109 0 1 0 0 1 0 0 1 0 1 0
## 1110 0 1 0 0 1 0 0 1 1 0 0
## 1111 0 1 0 0 1 0 0 1 0 1 0
## 1112 0 1 0 0 1 0 0 1 1 0 0
## 1113 0 1 0 1 0 0 0 1 0 1 0
## 1114 0 1 0 1 0 0 0 1 0 0 1
## 1115 0 1 0 0 1 0 0 1 1 0 0
## 1116 0 1 0 0 1 0 0 1 1 0 0
## 1117 0 1 0 0 1 0 0 1 0 1 0
## 1118 0 1 0 1 0 0 0 1 0 1 0
## 1119 0 1 0 0 1 0 0 1 0 1 0
## 1120 0 1 0 0 1 0 0 1 0 1 0
## 1121 1 0 0 1 0 0 0 1 1 0 0
## 1122 0 1 0 1 0 0 0 1 1 0 0
## 1123 0 1 0 0 1 0 0 1 0 1 0
## 1124 0 1 0 0 1 0 0 1 0 1 0
## 1125 0 1 0 0 1 0 0 1 1 0 0
## 1126 0 1 0 0 1 0 0 1 0 0 1
## 1127 0 1 0 0 1 0 0 1 0 1 0
## 1128 0 1 0 0 1 0 0 1 0 1 0
## 1129 0 1 0 0 1 0 0 1 0 1 0
## 1130 0 0 1 1 0 0 0 1 0 1 0
## 1131 1 0 0 1 0 0 0 1 0 1 0
## 1132 0 1 0 0 1 0 0 1 1 0 0
## 1133 0 1 0 0 1 0 0 1 0 1 0
## 1134 0 1 0 0 1 0 0 1 0 1 0
## 1135 0 1 0 0 1 0 0 1 0 1 0
## 1136 1 0 0 1 0 0 0 1 1 0 0
## 1137 0 1 0 0 1 0 0 1 0 1 0
## 1138 0 1 0 1 0 0 0 1 1 0 0
## 1139 0 1 0 0 1 0 0 1 1 0 0
## 1140 0 1 0 0 1 0 0 1 1 0 0
## 1141 0 1 0 1 0 0 0 1 0 1 0
## 1142 0 1 0 0 1 0 0 1 0 0 1
## 1143 0 1 0 0 1 0 0 1 1 0 0
## 1144 0 1 0 0 1 0 0 1 1 0 0
## 1145 1 0 0 0 1 0 0 1 0 1 0
## 1146 0 1 0 0 1 0 0 1 1 0 0
## 1147 0 1 0 0 1 0 0 1 1 0 0
## 1148 0 1 0 0 1 0 0 1 0 1 0
## 1149 0 1 0 0 1 0 0 1 0 1 0
## 1150 0 1 0 0 1 0 0 1 0 0 1
## 1151 0 1 0 0 1 0 0 1 0 1 0
## 1152 0 1 0 0 1 0 0 1 0 1 0
## 1153 1 0 0 0 1 0 0 1 1 0 0
## 1154 0 1 0 0 1 0 0 1 0 1 0
## 1155 0 1 0 0 1 0 0 1 1 0 0
## 1156 1 0 0 1 0 0 0 1 1 0 0
## 1157 1 0 0 1 0 0 0 1 0 0 1
## 1158 1 0 0 0 1 0 0 1 1 0 0
## 1159 0 1 0 0 1 0 0 1 0 1 0
## 1160 0 0 1 1 0 0 0 1 0 0 1
## 1161 0 1 0 0 1 0 0 1 0 0 1
## 1162 0 0 1 1 0 0 0 1 1 0 0
## 1163 0 0 1 1 0 0 0 1 0 1 0
## 1164 0 1 0 1 0 0 0 1 0 1 0
## 1165 0 1 0 0 1 0 0 1 1 0 0
## 1166 0 1 0 1 0 0 0 1 0 0 1
## 1167 0 0 1 1 0 0 0 1 1 0 0
## 1168 0 1 0 0 1 0 0 1 0 1 0
## 1169 0 1 0 1 0 0 0 1 1 0 0
## 1170 0 1 0 1 0 0 0 1 1 0 0
## 1171 0 1 0 1 0 0 0 1 0 1 0
## 1172 0 0 1 0 1 0 0 1 1 0 0
## 1173 1 0 0 0 1 0 0 1 1 0 0
## 1174 0 1 0 0 1 0 0 1 0 1 0
## 1175 0 1 0 0 1 0 0 1 0 1 0
## 1176 1 0 0 0 1 0 0 1 1 0 0
## 1177 0 1 0 1 0 0 0 1 1 0 0
## 1178 0 1 0 1 0 0 0 1 0 0 1
## 1179 0 1 0 0 1 0 0 1 0 1 0
## 1180 0 1 0 0 1 0 0 1 0 1 0
## 1181 0 1 0 0 1 0 0 1 1 0 0
## 1182 0 1 0 0 1 0 0 1 0 0 1
## 1183 0 1 0 0 1 0 0 1 0 1 0
## 1184 0 1 0 0 1 0 0 1 0 1 0
## 1185 0 0 1 1 0 0 0 1 0 1 0
## 1186 0 1 0 1 0 0 0 1 1 0 0
## 1187 1 0 0 0 1 0 0 1 0 1 0
## 1188 0 1 0 0 1 0 0 1 0 1 0
## 1189 0 1 0 0 1 0 0 1 0 0 1
## 1190 0 1 0 0 1 0 0 1 0 1 0
## 1191 0 1 0 0 1 0 0 1 0 0 1
## 1192 0 1 0 0 1 0 0 1 0 1 0
## 1193 0 1 0 0 1 0 0 1 0 1 0
## 1194 0 1 0 0 1 0 0 1 0 1 0
## 1195 0 1 0 0 1 0 0 1 0 1 0
## 1196 0 0 1 1 0 0 0 1 0 1 0
## 1197 0 1 0 0 1 0 0 1 1 0 0
## 1198 1 0 0 1 0 0 0 1 0 1 0
## 1199 0 1 0 1 0 0 0 1 1 0 0
## 1200 0 1 0 1 0 0 0 1 1 0 0
## 1201 0 1 0 1 0 0 0 1 1 0 0
## 1202 0 1 0 0 1 0 0 1 0 1 0
## 1203 1 0 0 1 0 0 0 1 0 1 0
## 1204 0 1 0 0 1 0 0 1 0 1 0
## 1205 1 0 0 1 0 0 0 1 1 0 0
## 1206 0 1 0 0 1 0 0 1 1 0 0
## 1207 0 1 0 0 1 0 0 1 0 1 0
## 1208 0 1 0 1 0 0 0 1 0 1 0
## 1209 0 1 0 0 1 0 0 1 0 1 0
## 1210 1 0 0 0 1 0 0 1 0 1 0
## 1211 0 1 0 1 0 0 0 1 1 0 0
## 1212 1 0 0 0 1 0 0 1 1 0 0
## 1213 0 1 0 0 1 0 0 1 0 1 0
## 1214 0 1 0 0 1 0 0 1 0 1 0
## 1215 0 1 0 0 1 0 0 1 0 1 0
## 1216 1 0 0 1 0 0 0 1 0 0 1
## 1217 0 0 1 1 0 0 0 1 0 1 0
## 1218 0 1 0 0 1 0 0 1 1 0 0
## 1219 1 0 0 0 1 0 0 1 1 0 0
## 1220 0 1 0 0 1 0 0 1 1 0 0
## 1221 0 1 0 1 0 0 0 1 0 0 1
## 1222 0 1 0 0 1 0 0 1 0 1 0
## 1223 1 0 0 0 1 0 0 1 0 1 0
## 1224 1 0 0 0 1 0 0 1 0 1 0
## 1225 1 0 0 0 1 0 0 1 0 1 0
## 1226 1 0 0 1 0 0 0 1 1 0 0
## 1227 0 1 0 1 0 0 0 1 0 1 0
## 1228 1 0 0 0 1 0 0 1 0 1 0
## 1229 0 1 0 0 1 0 0 1 1 0 0
## 1230 0 1 0 1 0 0 0 1 0 1 0
## 1231 0 1 0 0 1 0 0 1 0 1 0
## 1232 0 1 0 0 1 0 0 1 0 1 0
## 1233 0 1 0 0 1 0 0 1 0 1 0
## 1234 0 1 0 0 1 0 0 1 1 0 0
## 1235 0 1 0 0 1 0 0 1 1 0 0
## 1236 0 1 0 1 0 0 0 1 0 1 0
## 1237 0 0 1 1 0 0 0 1 1 0 0
## 1238 1 0 0 0 1 0 0 1 0 1 0
## 1239 1 0 0 0 1 0 0 1 0 0 1
## 1240 0 1 0 0 1 0 0 1 1 0 0
## 1241 1 0 0 1 0 0 0 1 1 0 0
## 1242 0 1 0 0 1 0 0 1 0 1 0
## 1243 0 1 0 1 0 0 0 1 1 0 0
## 1244 0 1 0 0 1 0 0 1 0 0 1
## 1245 0 1 0 1 0 0 0 1 0 1 0
## 1246 0 1 0 0 1 0 0 1 1 0 0
## 1247 0 1 0 0 1 0 0 1 0 1 0
## 1248 0 1 0 0 1 0 0 1 0 1 0
## 1249 0 1 0 0 1 0 0 1 0 0 1
## 1250 0 1 0 0 1 0 0 1 0 1 0
## 1251 0 1 0 0 1 0 0 1 1 0 0
## 1252 1 0 0 0 1 0 0 1 0 1 0
## 1253 1 0 0 0 1 0 0 1 0 1 0
## 1254 0 1 0 1 0 0 0 1 0 1 0
## 1255 1 0 0 1 0 0 0 1 1 0 0
## 1256 0 1 0 0 1 0 0 1 0 1 0
## 1257 1 0 0 0 1 0 0 1 1 0 0
## 1258 0 1 0 0 1 0 0 1 0 0 1
## 1259 0 1 0 1 0 0 0 1 1 0 0
## 1260 1 0 0 1 0 0 0 1 1 0 0
## 1261 0 1 0 1 0 0 0 1 1 0 0
## 1262 0 1 0 0 1 0 0 1 0 1 0
## 1263 0 1 0 0 1 0 0 1 0 1 0
## 1264 0 1 0 1 0 0 0 1 0 0 1
## 1265 0 1 0 1 0 0 0 1 0 1 0
## 1266 1 0 0 0 1 0 0 1 0 1 0
## 1267 0 1 0 0 1 0 0 1 0 1 0
## 1268 0 1 0 1 0 0 0 1 1 0 0
## 1269 1 0 0 0 1 0 0 1 0 1 0
## 1270 0 1 0 1 0 0 0 1 0 1 0
## 1271 0 1 0 0 1 0 0 1 0 1 0
## 1272 0 1 0 1 0 0 0 1 0 1 0
## 1273 0 1 0 0 1 0 0 1 1 0 0
## 1274 0 1 0 1 0 0 0 1 1 0 0
## 1275 1 0 0 1 0 0 0 1 1 0 0
## 1276 1 0 0 0 1 0 0 1 0 1 0
## 1277 0 1 0 0 1 0 0 1 0 1 0
## 1278 0 1 0 1 0 0 0 1 1 0 0
## 1279 1 0 0 0 1 0 0 1 0 1 0
## 1280 0 1 0 0 1 0 0 1 0 1 0
## 1281 1 0 0 0 1 0 0 1 0 1 0
## 1282 0 1 0 1 0 0 0 1 0 1 0
## 1283 0 1 0 0 1 0 0 1 0 1 0
## 1284 1 0 0 1 0 0 0 1 1 0 0
## 1285 0 0 1 0 1 0 0 1 1 0 0
## 1286 0 1 0 1 0 0 0 1 0 1 0
## 1287 1 0 0 0 1 0 0 1 1 0 0
## 1288 0 1 0 0 1 0 0 1 1 0 0
## 1289 0 1 0 0 1 0 0 1 0 1 0
## 1290 0 1 0 0 1 0 0 1 0 1 0
## 1291 0 1 0 0 1 0 0 1 1 0 0
## 1292 0 1 0 0 1 0 0 1 0 1 0
## 1293 1 0 0 0 1 0 0 1 0 1 0
## 1294 0 1 0 1 0 0 0 1 1 0 0
## 1295 1 0 0 1 0 0 0 1 0 1 0
## 1296 0 1 0 0 1 0 0 1 1 0 0
## 1297 0 1 0 1 0 0 0 1 0 1 0
## 1298 0 1 0 0 1 0 0 1 0 1 0
## 1299 1 0 0 0 1 0 0 1 1 0 0
## 1300 0 1 0 0 1 0 0 1 0 1 0
## 1301 0 1 0 0 1 0 0 1 1 0 0
## 1302 1 0 0 0 1 0 0 1 0 1 0
## 1303 1 0 0 0 1 0 0 1 0 1 0
## 1304 0 1 0 0 1 0 0 1 1 0 0
## 1305 0 1 0 1 0 0 0 1 1 0 0
## 1306 0 1 0 0 1 0 0 1 1 0 0
## 1307 1 0 0 0 1 0 0 1 1 0 0
## 1308 1 0 0 0 1 0 0 1 0 1 0
## 1309 0 1 0 0 1 0 0 1 0 1 0
## 1310 1 0 0 0 1 0 0 1 1 0 0
## 1311 1 0 0 0 1 0 0 1 0 1 0
## 1312 0 1 0 0 1 0 0 1 1 0 0
## 1313 0 1 0 0 1 0 0 1 0 1 0
## 1314 0 1 0 0 1 0 0 1 0 1 0
## 1315 0 1 0 1 0 0 0 1 1 0 0
## 1316 1 0 0 0 1 0 0 1 1 0 0
## 1317 1 0 0 0 1 0 0 1 0 1 0
## 1318 0 1 0 0 1 0 0 1 0 1 0
## 1319 1 0 0 1 0 0 0 1 1 0 0
## 1320 0 1 0 1 0 0 0 1 0 1 0
## 1321 0 1 0 0 1 0 0 1 1 0 0
## 1322 0 1 0 1 0 0 0 1 1 0 0
## 1323 1 0 0 0 1 0 0 1 0 1 0
## 1324 0 1 0 1 0 0 0 1 1 0 0
## 1325 1 0 0 0 1 0 0 1 0 1 0
## 1326 0 1 0 0 1 0 0 1 1 0 0
## 1327 1 0 0 0 1 0 0 1 0 1 0
## 1328 0 1 0 0 1 0 0 1 0 1 0
## 1329 0 1 0 1 0 0 0 1 1 0 0
## 1330 0 1 0 0 1 0 0 1 1 0 0
## 1331 0 1 0 1 0 0 0 1 0 1 0
## 1332 0 1 0 0 1 0 0 1 0 0 1
## 1333 0 1 0 0 1 0 0 1 0 1 0
## 1334 1 0 0 0 1 0 0 1 1 0 0
## 1335 0 1 0 0 1 0 0 1 1 0 0
## 1336 0 1 0 0 1 0 0 1 1 0 0
## 1337 1 0 0 0 1 0 0 1 0 1 0
## 1338 1 0 0 0 1 0 0 1 1 0 0
## 1339 0 1 0 1 0 0 0 1 1 0 0
## 1340 0 1 0 0 1 0 0 1 1 0 0
## 1341 1 0 0 1 0 0 0 1 0 1 0
## 1342 0 1 0 0 1 0 0 1 1 0 0
## 1343 1 0 0 0 1 0 0 1 0 0 1
## 1344 1 0 0 1 0 0 0 1 1 0 0
## 1345 0 1 0 0 1 0 0 1 0 1 0
## 1346 0 1 0 0 1 0 0 1 0 1 0
## 1347 0 1 0 0 1 0 0 1 0 1 0
## 1348 1 0 0 0 1 0 0 1 1 0 0
## 1349 0 1 0 0 1 0 0 1 1 0 0
## 1350 1 0 0 0 1 0 0 1 1 0 0
## 1351 1 0 0 0 1 0 0 1 1 0 0
## 1352 0 1 0 0 1 0 0 1 0 1 0
## 1353 1 0 0 0 1 0 0 1 0 1 0
## 1354 0 1 0 0 1 0 0 1 0 1 0
## 1355 0 1 0 0 1 0 0 1 0 1 0
## 1356 0 1 0 0 1 0 0 1 1 0 0
## 1357 0 1 0 1 0 0 0 1 0 1 0
## 1358 0 1 0 1 0 0 0 1 1 0 0
## 1359 0 1 0 0 1 0 0 1 1 0 0
## 1360 0 1 0 1 0 0 0 1 0 1 0
## 1361 1 0 0 0 1 0 0 1 1 0 0
## 1362 1 0 0 1 0 0 0 1 0 1 0
## 1363 0 1 0 0 1 0 0 1 1 0 0
## 1364 0 1 0 0 1 0 0 1 0 0 1
## 1365 0 1 0 1 0 0 0 1 0 1 0
## 1366 1 0 0 0 1 0 0 1 0 1 0
## 1367 1 0 0 0 1 0 0 1 1 0 0
## 1368 0 1 0 0 1 0 0 1 1 0 0
## 1369 0 1 0 0 1 0 0 1 0 0 1
## 1370 0 1 0 0 1 0 0 1 1 0 0
## 1371 0 1 0 1 0 0 0 1 0 1 0
## 1372 0 1 0 0 1 0 0 1 0 1 0
## 1373 0 1 0 0 1 0 0 1 1 0 0
## 1374 0 1 0 1 0 0 0 1 1 0 0
## 1375 0 1 0 0 1 0 0 1 1 0 0
## 1376 0 1 0 0 1 0 0 1 0 1 0
## 1377 0 1 0 1 0 0 0 1 1 0 0
## 1378 1 0 0 0 1 0 0 1 1 0 0
## 1379 0 1 0 0 1 0 0 1 1 0 0
## 1380 0 1 0 1 0 0 0 1 0 1 0
## 1381 0 1 0 0 1 0 0 1 0 1 0
## 1382 0 1 0 0 1 0 0 1 1 0 0
## 1383 0 1 0 0 1 0 0 1 1 0 0
## 1384 1 0 0 0 1 0 0 1 0 1 0
## 1385 0 1 0 0 1 0 0 1 0 1 0
## 1386 0 1 0 0 1 0 0 1 1 0 0
## 1387 1 0 0 0 1 0 0 1 0 1 0
## 1388 0 1 0 1 0 0 0 1 1 0 0
## 1389 0 1 0 0 1 0 0 1 1 0 0
## 1390 1 0 0 0 1 0 0 1 0 1 0
## 1391 1 0 0 0 1 0 0 1 1 0 0
## 1392 0 1 0 1 0 0 0 1 0 1 0
## 1393 0 1 0 0 1 0 0 1 1 0 0
## 1394 0 1 0 1 0 0 0 1 0 1 0
## 1395 1 0 0 0 1 0 0 1 0 1 0
## 1396 1 0 0 0 1 0 0 1 0 1 0
## 1397 0 1 0 0 1 0 0 1 0 1 0
## 1398 0 1 0 0 1 0 0 1 0 1 0
## 1399 0 1 0 1 0 0 0 1 1 0 0
## 1400 0 1 0 1 0 0 0 1 0 1 0
## 1401 0 1 0 0 1 0 0 1 0 1 0
## 1402 0 1 0 0 1 0 0 1 1 0 0
## 1403 1 0 0 1 0 0 0 1 0 1 0
## 1404 1 0 0 0 1 0 0 1 1 0 0
## 1405 1 0 0 0 1 0 0 1 0 1 0
## 1406 0 1 0 0 1 0 0 1 0 1 0
## 1407 0 1 0 0 1 0 0 1 1 0 0
## 1408 0 1 0 0 1 0 0 1 0 1 0
## 1409 1 0 0 0 1 0 0 1 1 0 0
## 1410 0 1 0 0 0 1 0 1 0 1 0
## 1411 0 1 0 0 1 0 0 1 1 0 0
## 1412 0 1 0 0 1 0 0 1 1 0 0
## 1413 1 0 0 0 1 0 0 1 1 0 0
## 1414 1 0 0 1 0 0 0 1 1 0 0
## 1415 0 1 0 0 1 0 0 1 0 1 0
## 1416 0 1 0 0 1 0 0 1 0 1 0
## 1417 0 1 0 1 0 0 0 1 1 0 0
## 1418 1 0 0 0 1 0 0 1 1 0 0
## 1419 1 0 0 0 1 0 0 1 0 1 0
## 1420 1 0 0 1 0 0 0 1 1 0 0
## 1421 0 1 0 0 1 0 0 1 1 0 0
## 1422 0 1 0 1 0 0 0 1 0 1 0
## 1423 0 1 0 1 0 0 0 1 0 1 0
## 1424 0 1 0 0 1 0 0 1 1 0 0
## 1425 0 1 0 0 1 0 0 1 1 0 0
## 1426 0 1 0 1 0 0 0 1 1 0 0
## 1427 0 1 0 1 0 0 0 1 1 0 0
## 1428 1 0 0 0 1 0 0 1 1 0 0
## 1429 1 0 0 0 1 0 0 1 1 0 0
## 1430 0 1 0 1 0 0 0 1 0 1 0
## 1431 1 0 0 0 1 0 0 1 1 0 0
## 1432 0 1 0 0 0 1 0 1 0 1 0
## 1433 0 1 0 1 0 0 0 1 0 1 0
## 1434 1 0 0 0 1 0 0 1 1 0 0
## 1435 0 1 0 0 1 0 0 1 1 0 0
## 1436 1 0 0 0 1 0 0 1 0 1 0
## 1437 1 0 0 0 1 0 0 1 1 0 0
## 1438 0 1 0 0 1 0 0 1 0 1 0
## 1439 0 1 0 0 1 0 0 1 1 0 0
## 1440 0 1 0 0 1 0 0 1 1 0 0
## 1441 0 1 0 0 1 0 0 1 0 1 0
## 1442 1 0 0 0 1 0 0 1 1 0 0
## 1443 0 1 0 0 1 0 0 1 0 1 0
## Téc/Bach Univ Mst/Doc <1smlv 1-3smlv >3smlv Empleado Estudiante
## 1 0 1 0 0 0 1 0 0
## 2 0 0 1 0 0 1 1 0
## 3 1 0 0 1 0 0 0 1
## 4 1 0 0 1 0 0 0 1
## 5 0 0 1 0 0 1 1 0
## 6 0 0 1 0 0 1 1 0
## 7 0 1 0 0 0 1 1 0
## 8 0 1 0 0 0 1 1 0
## 9 0 1 0 0 1 0 1 0
## 10 1 0 0 1 0 0 0 1
## 11 0 0 1 0 0 1 1 0
## 12 1 0 0 1 0 0 0 1
## 13 0 1 0 0 1 0 1 0
## 14 0 0 1 0 0 1 1 0
## 15 0 1 0 0 1 0 1 0
## 16 0 1 0 0 0 1 1 0
## 17 1 0 0 1 0 0 0 1
## 18 0 0 1 0 0 1 1 0
## 19 0 1 0 0 1 0 1 0
## 20 0 0 1 0 0 1 1 0
## 21 1 0 0 1 0 0 0 1
## 22 0 0 1 0 0 1 1 0
## 23 0 1 0 0 1 0 1 0
## 24 0 1 0 0 0 1 1 0
## 25 0 1 0 1 0 0 0 1
## 26 1 0 0 0 0 1 0 1
## 27 0 1 0 0 1 0 0 1
## 28 0 0 1 0 0 1 1 0
## 29 0 1 0 1 0 0 0 1
## 30 0 1 0 0 0 1 1 0
## 31 0 0 1 0 0 1 1 0
## 32 0 1 0 0 1 0 1 0
## 33 0 0 1 0 0 1 1 0
## 34 1 0 0 0 1 0 1 0
## 35 0 0 1 0 0 1 1 0
## 36 1 0 0 1 0 0 0 1
## 37 0 0 1 0 0 1 1 0
## 38 0 1 0 0 0 1 1 0
## 39 0 1 0 0 1 0 1 0
## 40 0 0 1 0 0 1 0 0
## 41 0 1 0 0 1 0 1 0
## 42 0 1 0 0 1 0 1 0
## 43 0 1 0 0 0 1 1 0
## 44 0 0 1 0 0 1 1 0
## 45 1 0 0 1 0 0 0 1
## 46 0 1 0 0 0 1 1 0
## 47 0 0 1 0 0 1 1 0
## 48 0 0 1 0 0 1 1 0
## 49 0 0 1 0 0 1 1 0
## 50 0 1 0 0 1 0 1 0
## 51 0 1 0 0 1 0 1 0
## 52 0 0 1 0 0 1 1 0
## 53 0 0 1 0 0 1 1 0
## 54 1 0 0 0 1 0 1 0
## 55 0 0 1 0 0 1 1 0
## 56 0 1 0 0 1 0 0 1
## 57 0 0 1 0 0 1 0 0
## 58 0 0 1 0 0 1 1 0
## 59 1 0 0 1 0 0 0 1
## 60 0 1 0 0 0 1 1 0
## 61 0 1 0 0 0 1 0 0
## 62 0 0 1 0 0 1 1 0
## 63 0 0 1 0 1 0 0 0
## 64 0 0 1 0 1 0 1 0
## 65 0 0 1 0 0 1 1 0
## 66 0 0 1 0 0 1 1 0
## 67 0 0 1 0 0 1 0 0
## 68 0 0 1 0 0 1 1 0
## 69 0 1 0 0 0 1 1 0
## 70 0 0 1 0 0 1 0 0
## 71 0 0 1 0 0 1 1 0
## 72 0 1 0 1 0 0 0 1
## 73 0 0 1 0 0 1 1 0
## 74 0 0 1 0 0 1 1 0
## 75 0 0 1 0 0 1 0 0
## 76 1 0 0 0 1 0 1 0
## 77 0 0 1 0 0 1 1 0
## 78 0 0 1 0 0 1 1 0
## 79 1 0 0 1 0 0 0 0
## 80 1 0 0 0 1 0 1 0
## 81 1 0 0 0 1 0 1 0
## 82 0 1 0 0 0 1 0 0
## 83 0 1 0 0 0 1 1 0
## 84 0 0 1 0 0 1 0 0
## 85 0 1 0 0 1 0 1 0
## 86 0 0 1 0 0 1 0 0
## 87 1 0 0 1 0 0 0 1
## 88 1 0 0 1 0 0 0 1
## 89 1 0 0 0 1 0 0 1
## 90 0 0 1 0 0 1 1 0
## 91 0 1 0 0 1 0 1 0
## 92 0 1 0 0 0 1 1 0
## 93 0 0 1 0 0 1 1 0
## 94 0 0 1 0 0 1 1 0
## 95 0 1 0 0 0 1 1 0
## 96 0 1 0 0 0 1 1 0
## 97 0 1 0 0 1 0 1 0
## 98 0 0 1 0 0 1 1 0
## 99 0 0 1 0 0 1 1 0
## 100 0 1 0 0 0 1 1 0
## 101 0 0 1 0 0 1 0 0
## 102 0 0 1 0 0 1 1 0
## 103 0 1 0 0 1 0 1 0
## 104 0 1 0 0 0 1 1 0
## 105 0 1 0 0 0 1 1 0
## 106 0 1 0 0 1 0 1 0
## 107 1 0 0 0 1 0 1 0
## 108 0 1 0 0 1 0 0 0
## 109 0 0 1 0 0 1 0 0
## 110 0 1 0 0 1 0 1 0
## 111 1 0 0 0 1 0 1 0
## 112 0 1 0 0 0 1 1 0
## 113 0 1 0 0 0 1 1 0
## 114 0 1 0 0 1 0 1 0
## 115 0 0 1 0 0 1 1 0
## 116 0 1 0 0 0 1 1 0
## 117 0 1 0 0 0 1 0 0
## 118 0 1 0 0 0 1 1 0
## 119 0 0 1 0 0 1 1 0
## 120 0 1 0 0 0 1 1 0
## 121 0 1 0 0 1 0 0 1
## 122 0 1 0 0 1 0 1 0
## 123 1 0 0 1 0 0 0 1
## 124 0 0 1 0 0 1 1 0
## 125 0 0 1 0 1 0 1 0
## 126 1 0 0 0 1 0 1 0
## 127 0 1 0 0 0 1 0 0
## 128 0 1 0 1 0 0 0 1
## 129 0 1 0 0 0 1 1 0
## 130 1 0 0 0 1 0 0 0
## 131 0 0 1 0 0 1 1 0
## 132 0 0 1 0 0 1 0 0
## 133 0 0 1 1 0 0 0 0
## 134 0 1 0 0 0 1 1 0
## 135 1 0 0 0 1 0 1 0
## 136 1 0 0 1 0 0 0 1
## 137 0 0 1 0 0 1 1 0
## 138 0 1 0 0 0 1 0 0
## 139 0 1 0 0 0 1 1 0
## 140 1 0 0 1 0 0 0 1
## 141 0 1 0 0 0 1 1 0
## 142 0 0 1 0 0 1 1 0
## 143 0 1 0 0 0 1 0 0
## 144 0 1 0 0 0 1 1 0
## 145 1 0 0 0 1 0 1 0
## 146 0 1 0 0 0 1 1 0
## 147 0 0 1 0 0 1 0 0
## 148 0 0 1 0 0 1 1 0
## 149 0 1 0 0 0 1 1 0
## 150 1 0 0 1 0 0 0 0
## 151 1 0 0 1 0 0 0 1
## 152 0 1 0 1 0 0 0 1
## 153 0 1 0 0 1 0 1 0
## 154 0 0 1 0 1 0 0 1
## 155 1 0 0 0 1 0 1 0
## 156 1 0 0 1 0 0 0 1
## 157 0 1 0 0 0 1 0 0
## 158 0 0 1 0 0 1 0 0
## 159 0 1 0 0 1 0 1 0
## 160 0 0 1 0 0 1 0 0
## 161 1 0 0 1 0 0 0 1
## 162 0 1 0 0 0 1 1 0
## 163 0 1 0 0 1 0 0 0
## 164 0 0 1 0 0 1 1 0
## 165 0 0 1 0 0 1 0 1
## 166 0 1 0 0 1 0 0 0
## 167 0 0 1 0 0 1 1 0
## 168 0 1 0 0 0 1 1 0
## 169 0 1 0 0 1 0 1 0
## 170 0 0 1 0 0 1 0 0
## 171 1 0 0 0 1 0 1 0
## 172 0 0 1 0 0 1 0 0
## 173 0 0 1 0 0 1 1 0
## 174 0 1 0 0 0 1 1 0
## 175 0 1 0 0 0 1 1 0
## 176 0 0 1 0 0 1 1 0
## 177 0 1 0 0 0 1 1 0
## 178 1 0 0 1 0 0 0 0
## 179 0 0 1 0 0 1 1 0
## 180 0 0 1 0 0 1 1 0
## 181 1 0 0 1 0 0 0 1
## 182 0 1 0 0 0 1 1 0
## 183 0 1 0 0 1 0 0 0
## 184 0 0 1 0 0 1 1 0
## 185 0 0 1 0 0 1 1 0
## 186 0 0 1 0 0 1 1 0
## 187 0 1 0 0 0 1 1 0
## 188 0 0 1 0 1 0 1 0
## 189 0 1 0 0 1 0 0 0
## 190 0 1 0 1 0 0 0 1
## 191 0 1 0 0 0 1 1 0
## 192 0 1 0 0 1 0 0 1
## 193 1 0 0 0 1 0 1 0
## 194 0 0 1 1 0 0 0 0
## 195 0 1 0 0 0 1 1 0
## 196 0 1 0 1 0 0 0 1
## 197 0 1 0 0 0 1 1 0
## 198 1 0 0 1 0 0 0 1
## 199 1 0 0 1 0 0 0 1
## 200 0 1 0 0 1 0 1 0
## 201 1 0 0 1 0 0 0 1
## 202 0 0 1 0 0 1 0 0
## 203 0 1 0 0 0 1 0 1
## 204 1 0 0 0 1 0 0 0
## 205 0 1 0 0 1 0 1 0
## 206 0 1 0 0 0 1 1 0
## 207 0 1 0 1 0 0 0 1
## 208 0 1 0 0 0 1 1 0
## 209 1 0 0 1 0 0 0 1
## 210 0 0 1 0 0 1 0 0
## 211 0 0 1 0 1 0 1 0
## 212 0 0 1 0 0 1 0 0
## 213 1 0 0 1 0 0 0 1
## 214 0 1 0 0 0 1 0 0
## 215 0 1 0 0 0 1 1 0
## 216 0 0 1 0 0 1 0 0
## 217 0 1 0 0 0 1 1 0
## 218 0 0 1 0 1 0 1 0
## 219 0 1 0 1 0 0 0 0
## 220 0 1 0 0 1 0 1 0
## 221 0 0 1 0 0 1 1 0
## 222 0 0 1 0 0 1 1 0
## 223 0 0 1 0 0 1 0 0
## 224 0 0 1 0 0 1 1 0
## 225 0 0 1 0 0 1 1 0
## 226 1 0 0 1 0 0 0 1
## 227 0 0 1 0 0 1 1 0
## 228 0 1 0 0 0 1 1 0
## 229 1 0 0 0 1 0 1 0
## 230 0 1 0 0 1 0 1 0
## 231 0 1 0 0 0 1 0 0
## 232 1 0 0 0 0 1 1 0
## 233 0 1 0 0 1 0 0 0
## 234 1 0 0 0 1 0 1 0
## 235 0 1 0 0 0 1 0 0
## 236 0 1 0 1 0 0 0 0
## 237 1 0 0 1 0 0 0 1
## 238 0 1 0 0 1 0 0 0
## 239 0 0 1 0 0 1 1 0
## 240 0 0 1 0 0 1 0 0
## 241 0 0 1 0 0 1 1 0
## 242 1 0 0 1 0 0 0 1
## 243 0 0 1 0 0 1 1 0
## 244 1 0 0 1 0 0 0 1
## 245 0 1 0 0 1 0 0 0
## 246 0 0 1 0 1 0 0 0
## 247 0 1 0 1 0 0 0 1
## 248 0 1 0 0 0 1 1 0
## 249 0 0 1 0 0 1 1 0
## 250 1 0 0 0 1 0 1 0
## 251 0 1 0 0 0 1 1 0
## 252 1 0 0 1 0 0 0 1
## 253 0 0 1 0 1 0 1 0
## 254 0 0 1 0 0 1 1 0
## 255 0 1 0 0 1 0 1 0
## 256 0 1 0 0 0 1 1 0
## 257 1 0 0 1 0 0 0 1
## 258 0 1 0 0 0 1 1 0
## 259 1 0 0 0 1 0 0 0
## 260 0 0 1 0 0 1 1 0
## 261 0 1 0 0 1 0 1 0
## 262 0 0 1 0 1 0 1 0
## 263 1 0 0 1 0 0 0 1
## 264 0 1 0 0 0 1 0 0
## 265 0 0 1 0 0 1 1 0
## 266 0 0 1 0 0 1 1 0
## 267 0 0 1 0 0 1 1 0
## 268 1 0 0 0 1 0 1 0
## 269 0 0 1 0 0 1 1 0
## 270 1 0 0 1 0 0 0 0
## 271 1 0 0 0 1 0 1 0
## 272 0 0 1 0 0 1 1 0
## 273 0 0 1 0 0 1 1 0
## 274 0 1 0 0 1 0 1 0
## 275 0 0 1 0 1 0 0 0
## 276 0 0 1 0 0 1 1 0
## 277 0 0 1 0 0 1 0 0
## 278 0 0 1 0 0 1 1 0
## 279 0 1 0 0 1 0 1 0
## 280 0 0 1 0 0 1 1 0
## 281 0 0 1 0 0 1 0 0
## 282 0 0 1 0 0 1 1 0
## 283 0 0 1 0 0 1 1 0
## 284 0 0 1 0 0 1 0 0
## 285 0 0 1 0 0 1 1 0
## 286 0 0 1 0 0 1 0 0
## 287 0 0 1 0 0 1 1 0
## 288 0 0 1 0 1 0 0 1
## 289 0 0 1 0 0 1 1 0
## 290 0 1 0 0 1 0 0 1
## 291 0 0 1 0 0 1 1 0
## 292 0 0 1 0 1 0 0 1
## 293 0 0 1 0 0 1 1 0
## 294 0 0 1 0 0 1 1 0
## 295 0 1 0 0 1 0 0 0
## 296 1 0 0 1 0 0 0 1
## 297 0 0 1 0 0 1 1 0
## 298 0 1 0 0 0 1 0 1
## 299 0 1 0 1 0 0 0 0
## 300 1 0 0 1 0 0 1 0
## 301 0 1 0 1 0 0 0 1
## 302 0 0 1 0 0 1 1 0
## 303 0 0 1 0 0 1 1 0
## 304 0 1 0 0 1 0 1 0
## 305 0 0 1 0 0 1 1 0
## 306 0 0 1 0 1 0 0 1
## 307 0 0 1 0 0 1 1 0
## 308 1 0 0 1 0 0 0 1
## 309 0 0 1 0 0 1 1 0
## 310 0 1 0 0 1 0 1 0
## 311 0 1 0 0 1 0 1 0
## 312 0 0 1 0 1 0 1 0
## 313 0 0 1 0 0 1 1 0
## 314 0 0 1 0 0 1 0 0
## 315 0 0 1 0 0 1 0 0
## 316 1 0 0 0 1 0 1 0
## 317 0 0 1 0 0 1 1 0
## 318 0 0 1 0 0 1 0 0
## 319 1 0 0 0 1 0 1 0
## 320 1 0 0 1 0 0 0 1
## 321 0 0 1 0 0 1 1 0
## 322 0 0 1 0 0 1 1 0
## 323 0 0 1 0 0 1 1 0
## 324 0 0 1 0 0 1 1 0
## 325 0 1 0 1 0 0 0 0
## 326 1 0 0 0 1 0 1 0
## 327 0 0 1 0 0 1 1 0
## 328 0 1 0 0 1 0 1 0
## 329 1 0 0 0 1 0 1 0
## 330 0 1 0 0 0 1 1 0
## 331 0 1 0 0 1 0 1 0
## 332 0 0 1 0 0 1 1 0
## 333 1 0 0 1 0 0 0 0
## 334 0 0 1 0 0 1 1 0
## 335 0 1 0 0 0 1 1 0
## 336 0 1 0 0 0 1 0 1
## 337 0 1 0 0 1 0 0 1
## 338 0 1 0 0 0 1 1 0
## 339 0 0 1 0 1 0 1 0
## 340 1 0 0 0 1 0 1 0
## 341 0 0 1 0 0 1 1 0
## 342 0 1 0 0 1 0 0 1
## 343 0 0 1 0 1 0 0 1
## 344 0 0 1 0 0 1 1 0
## 345 0 0 1 0 0 1 0 0
## 346 0 1 0 0 1 0 0 0
## 347 1 0 0 0 1 0 1 0
## 348 0 1 0 0 0 1 1 0
## 349 0 1 0 0 1 0 0 1
## 350 1 0 0 1 0 0 0 0
## 351 1 0 0 1 0 0 0 0
## 352 0 1 0 1 0 0 0 1
## 353 0 0 1 0 0 1 1 0
## 354 0 1 0 0 0 1 0 0
## 355 0 0 1 0 0 1 0 0
## 356 0 0 1 0 0 1 1 0
## 357 0 1 0 0 1 0 1 0
## 358 0 1 0 0 0 1 1 0
## 359 0 1 0 0 1 0 0 1
## 360 1 0 0 1 0 0 0 0
## 361 0 0 1 0 1 0 1 0
## 362 0 0 1 0 0 1 0 0
## 363 1 0 0 0 1 0 0 0
## 364 0 0 1 0 0 1 1 0
## 365 0 1 0 0 0 1 1 0
## 366 0 0 1 0 0 1 1 0
## 367 0 0 1 0 0 1 1 0
## 368 0 1 0 0 0 1 1 0
## 369 0 1 0 0 0 1 1 0
## 370 0 0 1 0 0 1 0 0
## 371 0 1 0 0 1 0 0 0
## 372 0 0 1 0 0 1 0 0
## 373 0 0 1 0 0 1 0 0
## 374 0 1 0 0 0 1 1 0
## 375 0 1 0 0 1 0 1 0
## 376 0 0 1 0 0 1 0 0
## 377 1 0 0 0 1 0 1 0
## 378 1 0 0 1 0 0 0 1
## 379 0 0 1 0 0 1 1 0
## 380 0 0 1 0 0 1 1 0
## 381 0 0 1 0 0 1 1 0
## 382 0 0 1 0 0 1 0 0
## 383 0 1 0 0 1 0 0 1
## 384 0 0 1 0 0 1 1 0
## 385 1 0 0 0 1 0 1 0
## 386 0 0 1 0 0 1 0 0
## 387 0 0 1 0 0 1 0 0
## 388 0 0 1 0 0 1 1 0
## 389 0 0 1 0 0 1 1 0
## 390 0 0 1 0 0 1 0 0
## 391 1 0 0 1 0 0 0 1
## 392 0 1 0 0 1 0 0 1
## 393 1 0 0 1 0 0 0 1
## 394 1 0 0 1 0 0 0 1
## 395 0 0 1 0 0 1 1 0
## 396 1 0 0 0 1 0 0 0
## 397 0 1 0 0 0 1 1 0
## 398 0 1 0 0 1 0 0 1
## 399 0 1 0 0 1 0 1 0
## 400 0 0 1 0 0 1 0 0
## 401 0 0 1 0 0 1 0 0
## 402 0 0 1 0 0 1 1 0
## 403 0 1 0 1 0 0 0 1
## 404 0 0 1 0 0 1 1 0
## 405 0 0 1 0 0 1 1 0
## 406 1 0 0 1 0 0 0 0
## 407 1 0 0 1 0 0 0 0
## 408 1 0 0 1 0 0 0 1
## 409 1 0 0 1 0 0 0 1
## 410 0 1 0 0 1 0 0 0
## 411 1 0 0 0 1 0 1 0
## 412 1 0 0 1 0 0 0 1
## 413 1 0 0 1 0 0 0 0
## 414 1 0 0 1 0 0 0 0
## 415 1 0 0 1 0 0 0 0
## 416 1 0 0 1 0 0 0 0
## 417 1 0 0 1 0 0 0 0
## 418 0 0 1 0 0 1 1 0
## 419 1 0 0 1 0 0 0 0
## 420 1 0 0 1 0 0 0 0
## 421 1 0 0 1 0 0 0 0
## 422 1 0 0 0 1 0 1 0
## 423 1 0 0 1 0 0 1 0
## 424 1 0 0 1 0 0 0 0
## 425 1 0 0 1 0 0 1 0
## 426 1 0 0 1 0 0 0 0
## 427 1 0 0 1 0 0 0 0
## 428 1 0 0 1 0 0 0 0
## 429 1 0 0 1 0 0 0 0
## 430 1 0 0 1 0 0 0 1
## 431 1 0 0 1 0 0 1 0
## 432 1 0 0 1 0 0 0 0
## 433 1 0 0 0 1 0 0 0
## 434 1 0 0 0 1 0 1 0
## 435 0 1 0 0 1 0 1 0
## 436 1 0 0 0 1 0 1 0
## 437 1 0 0 1 0 0 1 0
## 438 0 1 0 0 1 0 1 0
## 439 0 0 1 0 1 0 0 0
## 440 0 1 0 1 0 0 0 1
## 441 0 1 0 1 0 0 0 1
## 442 0 0 1 0 1 0 0 1
## 443 0 0 1 0 0 1 1 0
## 444 1 0 0 0 1 0 0 0
## 445 0 0 1 0 0 1 0 0
## 446 0 0 1 0 1 0 1 0
## 447 0 0 1 0 0 1 1 0
## 448 0 0 1 0 0 1 1 0
## 449 1 0 0 0 1 0 1 0
## 450 1 0 0 1 0 0 0 0
## 451 0 0 1 0 0 1 1 0
## 452 0 0 1 0 0 1 0 0
## 453 1 0 0 0 1 0 0 0
## 454 1 0 0 1 0 0 0 1
## 455 0 1 0 0 1 0 0 0
## 456 0 0 1 0 1 0 1 0
## 457 0 0 1 0 0 1 1 0
## 458 0 0 1 0 0 1 1 0
## 459 0 1 0 1 0 0 0 1
## 460 0 0 1 0 0 1 1 0
## 461 0 0 1 0 0 1 1 0
## 462 1 0 0 0 1 0 1 0
## 463 0 1 0 1 0 0 0 0
## 464 0 1 0 1 0 0 0 1
## 465 0 1 0 1 0 0 0 1
## 466 1 0 0 1 0 0 0 1
## 467 1 0 0 1 0 0 0 1
## 468 0 1 0 0 0 1 1 0
## 469 0 0 1 0 1 0 0 0
## 470 0 1 0 0 1 0 1 0
## 471 1 0 0 1 0 0 0 0
## 472 0 1 0 1 0 0 0 1
## 473 0 1 0 0 0 1 0 0
## 474 0 0 1 0 0 1 1 0
## 475 0 0 1 0 0 1 1 0
## 476 0 0 1 0 0 1 0 0
## 477 0 1 0 0 1 0 0 0
## 478 0 1 0 0 0 1 1 0
## 479 0 1 0 0 1 0 0 1
## 480 0 0 1 0 0 1 1 0
## 481 0 0 1 0 0 1 1 0
## 482 0 0 1 0 0 1 0 0
## 483 0 0 1 0 0 1 1 0
## 484 0 1 0 0 1 0 1 0
## 485 0 1 0 0 0 1 1 0
## 486 0 0 1 0 0 1 1 0
## 487 0 1 0 0 0 1 1 0
## 488 0 1 0 0 0 1 0 0
## 489 0 1 0 0 0 1 0 0
## 490 0 1 0 0 0 1 1 0
## 491 1 0 0 0 1 0 0 1
## 492 0 1 0 0 1 0 1 0
## 493 0 0 1 0 0 1 0 0
## 494 0 1 0 0 0 1 1 0
## 495 0 1 0 0 0 1 1 0
## 496 0 1 0 0 0 1 1 0
## 497 0 1 0 0 1 0 1 0
## 498 1 0 0 0 1 0 1 0
## 499 0 0 1 1 0 0 0 0
## 500 0 1 0 0 1 0 1 0
## 501 0 1 0 0 0 1 0 0
## 502 1 0 0 0 1 0 1 0
## 503 0 0 1 0 1 0 1 0
## 504 0 0 1 0 0 1 1 0
## 505 0 0 1 0 0 1 0 0
## 506 1 0 0 0 1 0 0 0
## 507 0 0 1 0 1 0 0 0
## 508 0 1 0 0 1 0 0 0
## 509 0 0 1 0 0 1 0 0
## 510 0 0 1 0 0 1 1 0
## 511 0 0 1 1 0 0 0 0
## 512 0 0 1 0 0 1 1 0
## 513 1 0 0 1 0 0 0 1
## 514 1 0 0 1 0 0 0 0
## 515 1 0 0 1 0 0 0 1
## 516 1 0 0 0 1 0 0 1
## 517 0 1 0 0 1 0 1 0
## 518 0 0 1 0 0 1 0 0
## 519 0 0 1 0 0 1 0 0
## 520 0 1 0 0 1 0 1 0
## 521 0 1 0 0 1 0 1 0
## 522 0 0 1 0 0 1 1 0
## 523 1 0 0 0 0 1 0 0
## 524 0 1 0 0 1 0 1 0
## 525 1 0 0 1 0 0 0 0
## 526 0 0 1 0 0 1 1 0
## 527 0 0 1 0 0 1 1 0
## 528 1 0 0 0 1 0 0 1
## 529 0 1 0 1 0 0 0 1
## 530 0 0 1 0 0 1 1 0
## 531 0 1 0 1 0 0 0 1
## 532 1 0 0 1 0 0 0 1
## 533 1 0 0 1 0 0 0 1
## 534 0 1 0 1 0 0 0 1
## 535 1 0 0 0 1 0 0 1
## 536 1 0 0 1 0 0 0 1
## 537 0 1 0 0 1 0 0 0
## 538 0 0 1 0 0 1 1 0
## 539 1 0 0 1 0 0 0 1
## 540 0 1 0 0 1 0 0 0
## 541 0 0 1 0 0 1 1 0
## 542 0 1 0 1 0 0 0 1
## 543 0 1 0 0 1 0 1 0
## 544 0 1 0 1 0 0 0 1
## 545 0 1 0 0 1 0 0 0
## 546 0 0 1 0 0 1 1 0
## 547 0 0 1 0 1 0 1 0
## 548 0 1 0 0 0 1 1 0
## 549 0 1 0 1 0 0 0 1
## 550 0 0 1 0 0 1 0 0
## 551 0 0 1 0 1 0 0 0
## 552 0 1 0 0 1 0 1 0
## 553 1 0 0 0 1 0 1 0
## 554 0 0 1 0 0 1 1 0
## 555 0 1 0 0 1 0 0 0
## 556 0 0 1 0 0 1 1 0
## 557 0 1 0 0 0 1 1 0
## 558 1 0 0 1 0 0 0 1
## 559 0 1 0 0 0 1 0 0
## 560 1 0 0 0 1 0 1 0
## 561 0 0 1 0 0 1 0 0
## 562 0 1 0 0 1 0 1 0
## 563 0 0 1 1 0 0 0 0
## 564 0 1 0 1 0 0 0 1
## 565 0 1 0 0 0 1 0 0
## 566 0 0 1 0 0 1 0 0
## 567 0 1 0 0 1 0 0 0
## 568 0 0 1 0 0 1 0 0
## 569 1 0 0 1 0 0 0 1
## 570 1 0 0 1 0 0 0 1
## 571 0 1 0 0 0 1 1 0
## 572 0 1 0 1 0 0 0 1
## 573 0 0 1 0 0 1 0 0
## 574 0 1 0 0 0 1 0 0
## 575 1 0 0 1 0 0 0 0
## 576 0 1 0 0 0 1 1 0
## 577 0 1 0 0 0 1 1 0
## 578 0 0 1 0 1 0 1 0
## 579 0 1 0 0 1 0 1 0
## 580 0 0 1 0 0 1 0 1
## 581 0 1 0 0 1 0 1 0
## 582 0 1 0 0 1 0 1 0
## 583 0 0 1 0 0 1 1 0
## 584 1 0 0 0 1 0 0 0
## 585 0 1 0 0 1 0 1 0
## 586 0 1 0 1 0 0 0 0
## 587 0 1 0 0 1 0 0 1
## 588 0 1 0 0 0 1 0 0
## 589 1 0 0 1 0 0 0 1
## 590 0 1 0 0 1 0 1 0
## 591 1 0 0 0 1 0 0 0
## 592 1 0 0 1 0 0 0 1
## 593 0 1 0 0 1 0 1 0
## 594 0 1 0 1 0 0 0 1
## 595 0 1 0 0 1 0 1 0
## 596 0 0 1 0 0 1 0 0
## 597 0 0 1 0 1 0 0 1
## 598 0 0 1 0 0 1 0 1
## 599 0 0 1 0 0 1 1 0
## 600 0 0 1 0 1 0 1 0
## 601 0 0 1 0 0 1 1 0
## 602 1 0 0 1 0 0 0 0
## 603 0 1 0 0 1 0 0 0
## 604 1 0 0 0 1 0 1 0
## 605 0 1 0 0 1 0 0 0
## 606 0 1 0 0 1 0 1 0
## 607 0 0 1 0 0 1 1 0
## 608 0 1 0 0 0 1 1 0
## 609 0 0 1 0 0 1 1 0
## 610 0 0 1 0 0 1 0 0
## 611 0 1 0 1 0 0 0 1
## 612 0 1 0 0 0 1 1 0
## 613 1 0 0 1 0 0 0 1
## 614 1 0 0 1 0 0 0 1
## 615 0 0 1 0 0 1 1 0
## 616 0 1 0 0 0 1 0 0
## 617 0 0 1 0 0 1 1 0
## 618 0 0 1 0 0 1 0 0
## 619 1 0 0 0 0 1 0 0
## 620 0 0 1 0 0 1 0 0
## 621 0 1 0 0 1 0 0 0
## 622 1 0 0 0 1 0 0 0
## 623 1 0 0 1 0 0 0 1
## 624 0 1 0 0 1 0 0 1
## 625 0 0 1 0 0 1 0 1
## 626 0 1 0 0 1 0 0 0
## 627 1 0 0 1 0 0 1 0
## 628 0 1 0 0 0 1 1 0
## 629 0 0 1 0 0 1 0 0
## 630 0 0 1 0 0 1 1 0
## 631 0 1 0 1 0 0 0 1
## 632 1 0 0 1 0 0 0 0
## 633 0 0 1 0 0 1 1 0
## 634 0 1 0 0 0 1 1 0
## 635 0 1 0 0 0 1 0 0
## 636 0 0 1 0 0 1 1 0
## 637 0 1 0 1 0 0 0 0
## 638 0 1 0 0 1 0 1 0
## 639 0 0 1 0 0 1 0 0
## 640 0 0 1 0 0 1 0 0
## 641 0 1 0 0 0 1 0 0
## 642 1 0 0 1 0 0 0 1
## 643 0 0 1 0 0 1 0 0
## 644 0 1 0 0 0 1 1 0
## 645 0 0 1 0 0 1 0 0
## 646 0 1 0 0 0 1 1 0
## 647 0 0 1 0 0 1 1 0
## 648 1 0 0 0 1 0 0 0
## 649 0 0 1 0 0 1 1 0
## 650 1 0 0 1 0 0 0 0
## 651 0 0 1 0 0 1 1 0
## 652 0 0 1 0 0 1 1 0
## 653 0 1 0 1 0 0 0 1
## 654 0 1 0 0 1 0 1 0
## 655 0 1 0 1 0 0 0 0
## 656 0 0 1 0 1 0 0 0
## 657 0 1 0 0 1 0 0 0
## 658 0 0 1 0 0 1 1 0
## 659 0 0 1 0 0 1 1 0
## 660 1 0 0 0 1 0 0 1
## 661 0 1 0 0 1 0 1 0
## 662 0 1 0 0 0 1 1 0
## 663 0 1 0 0 1 0 1 0
## 664 0 1 0 0 1 0 1 0
## 665 0 1 0 0 1 0 1 0
## 666 1 0 0 1 0 0 0 1
## 667 1 0 0 1 0 0 0 1
## 668 0 1 0 0 1 0 1 0
## 669 0 0 1 0 1 0 1 0
## 670 1 0 0 0 1 0 1 0
## 671 0 1 0 0 1 0 1 0
## 672 0 1 0 0 1 0 1 0
## 673 0 0 1 0 1 0 1 0
## 674 0 1 0 0 1 0 1 0
## 675 0 1 0 0 1 0 1 0
## 676 0 1 0 0 1 0 1 0
## 677 0 1 0 0 1 0 1 0
## 678 0 1 0 0 1 0 1 0
## 679 0 1 0 0 1 0 1 0
## 680 1 0 0 0 1 0 1 0
## 681 0 1 0 1 0 0 0 1
## 682 1 0 0 0 1 0 1 0
## 683 0 1 0 0 1 0 1 0
## 684 1 0 0 0 1 0 0 0
## 685 0 1 0 0 0 1 0 0
## 686 0 0 1 0 1 0 1 0
## 687 1 0 0 1 0 0 0 1
## 688 0 1 0 1 0 0 0 1
## 689 0 1 0 1 0 0 0 0
## 690 0 0 1 0 0 1 1 0
## 691 0 0 1 0 0 1 0 0
## 692 0 1 0 1 0 0 0 1
## 693 0 1 0 1 0 0 0 1
## 694 0 1 0 0 1 0 1 0
## 695 1 0 0 0 1 0 1 0
## 696 0 0 1 0 0 1 1 0
## 697 1 0 0 0 1 0 0 0
## 698 1 0 0 0 1 0 1 0
## 699 0 0 1 0 1 0 1 0
## 700 1 0 0 1 0 0 0 0
## 701 1 0 0 1 0 0 0 0
## 702 0 0 1 0 0 1 1 0
## 703 0 1 0 0 0 1 1 0
## 704 1 0 0 0 1 0 1 0
## 705 1 0 0 1 0 0 0 0
## 706 1 0 0 0 1 0 0 0
## 707 0 0 1 0 0 1 1 0
## 708 0 0 1 0 0 1 1 0
## 709 0 1 0 0 0 1 1 0
## 710 0 0 1 0 0 1 1 0
## 711 0 1 0 0 1 0 0 1
## 712 0 1 0 0 1 0 1 0
## 713 0 1 0 1 0 0 0 0
## 714 0 0 1 0 0 1 1 0
## 715 0 0 1 0 0 1 0 0
## 716 0 0 1 0 0 1 1 0
## 717 0 1 0 0 0 1 0 0
## 718 0 0 1 0 0 1 1 0
## 719 0 0 1 0 1 0 1 0
## 720 0 1 0 1 0 0 0 0
## 721 1 0 0 1 0 0 0 1
## 722 1 0 0 1 0 0 0 1
## 723 0 0 1 0 1 0 1 0
## 724 0 1 0 0 1 0 0 1
## 725 0 0 1 0 0 1 1 0
## 726 1 0 0 0 1 0 1 0
## 727 1 0 0 1 0 0 0 1
## 728 0 1 0 0 1 0 1 0
## 729 0 0 1 0 0 1 0 0
## 730 0 0 1 0 0 1 1 0
## 731 1 0 0 1 0 0 0 1
## 732 1 0 0 1 0 0 1 0
## 733 1 0 0 1 0 0 0 0
## 734 0 0 1 0 0 1 1 0
## 735 0 0 1 0 0 1 1 0
## 736 0 1 0 1 0 0 0 0
## 737 0 1 0 0 1 0 1 0
## 738 0 0 1 0 0 1 0 0
## 739 0 0 1 0 0 1 0 0
## 740 0 0 1 0 0 1 1 0
## 741 0 0 1 0 0 1 1 0
## 742 0 1 0 0 0 1 1 0
## 743 0 1 0 0 1 0 0 0
## 744 0 1 0 0 0 1 0 0
## 745 0 1 0 0 1 0 0 0
## 746 0 1 0 1 0 0 0 0
## 747 1 0 0 1 0 0 1 0
## 748 1 0 0 0 1 0 1 0
## 749 0 1 0 0 0 1 1 0
## 750 0 0 1 0 0 1 1 0
## 751 0 1 0 1 0 0 0 1
## 752 0 0 1 0 0 1 0 0
## 753 1 0 0 1 0 0 0 0
## 754 0 0 1 0 0 1 1 0
## 755 0 0 1 0 0 1 1 0
## 756 0 0 1 0 1 0 0 1
## 757 0 1 0 1 0 0 0 1
## 758 1 0 0 0 1 0 1 0
## 759 0 0 1 0 0 1 1 0
## 760 0 0 1 0 1 0 1 0
## 761 0 1 0 0 1 0 1 0
## 762 0 1 0 0 0 1 1 0
## 763 0 1 0 1 0 0 0 0
## 764 0 1 0 1 0 0 0 1
## 765 0 0 1 0 0 1 1 0
## 766 0 0 1 0 0 1 1 0
## 767 0 1 0 0 1 0 1 0
## 768 0 1 0 0 0 1 0 0
## 769 1 0 0 0 1 0 0 0
## 770 0 0 1 0 1 0 1 0
## 771 0 1 0 0 1 0 0 0
## 772 0 1 0 0 0 1 1 0
## 773 0 1 0 0 1 0 0 0
## 774 1 0 0 1 0 0 1 0
## 775 0 1 0 0 0 1 0 0
## 776 0 0 1 0 0 1 1 0
## 777 1 0 0 1 0 0 0 1
## 778 0 0 1 0 0 1 1 0
## 779 0 1 0 0 0 1 1 0
## 780 1 0 0 0 1 0 1 0
## 781 0 0 1 0 0 1 0 0
## 782 0 1 0 1 0 0 0 1
## 783 0 1 0 0 0 1 1 0
## 784 0 1 0 0 1 0 0 0
## 785 0 1 0 0 0 1 1 0
## 786 0 1 0 0 0 1 1 0
## 787 0 1 0 0 0 1 1 0
## 788 0 0 1 0 0 1 1 0
## 789 1 0 0 1 0 0 0 1
## 790 1 0 0 0 1 0 1 0
## 791 0 0 1 0 0 1 0 0
## 792 1 0 0 0 1 0 1 0
## 793 1 0 0 0 1 0 0 0
## 794 0 1 0 0 0 1 0 0
## 795 0 0 1 0 0 1 0 0
## 796 1 0 0 0 1 0 0 0
## 797 0 0 1 0 0 1 0 0
## 798 0 1 0 0 0 1 1 0
## 799 0 0 1 0 0 1 1 0
## 800 0 0 1 0 0 1 1 0
## 801 0 1 0 0 1 0 1 0
## 802 0 0 1 0 0 1 1 0
## 803 0 0 1 0 0 1 1 0
## 804 0 1 0 0 1 0 0 0
## 805 0 1 0 0 0 1 0 0
## 806 0 0 1 0 0 1 0 0
## 807 1 0 0 0 1 0 0 0
## 808 0 1 0 0 1 0 1 0
## 809 0 0 1 0 0 1 1 0
## 810 0 1 0 0 0 1 1 0
## 811 0 1 0 1 0 0 0 0
## 812 0 1 0 0 1 0 0 0
## 813 1 0 0 0 1 0 1 0
## 814 0 0 1 0 0 1 1 0
## 815 1 0 0 0 1 0 1 0
## 816 0 0 1 0 0 1 1 0
## 817 0 0 1 0 0 1 1 0
## 818 0 1 0 0 0 1 1 0
## 819 0 0 1 0 0 1 0 0
## 820 1 0 0 1 0 0 0 0
## 821 0 1 0 0 1 0 1 0
## 822 0 1 0 0 1 0 0 0
## 823 0 1 0 0 0 1 0 0
## 824 0 0 1 0 0 1 0 0
## 825 0 0 1 0 0 1 0 0
## 826 1 0 0 0 1 0 0 0
## 827 0 1 0 0 1 0 0 0
## 828 0 0 1 0 0 1 1 0
## 829 1 0 0 1 0 0 0 0
## 830 0 1 0 0 1 0 0 0
## 831 1 0 0 1 0 0 0 1
## 832 0 0 1 1 0 0 0 0
## 833 0 0 1 0 0 1 0 0
## 834 0 1 0 0 0 1 1 0
## 835 0 0 1 0 0 1 1 0
## 836 1 0 0 1 0 0 0 0
## 837 0 0 1 0 0 1 0 0
## 838 0 0 1 0 0 1 1 0
## 839 0 1 0 0 1 0 0 0
## 840 0 1 0 1 0 0 0 0
## 841 1 0 0 0 1 0 0 0
## 842 0 1 0 0 0 1 0 0
## 843 1 0 0 1 0 0 1 0
## 844 0 0 1 0 0 1 1 0
## 845 0 0 1 0 1 0 0 0
## 846 0 0 1 0 0 1 1 0
## 847 0 1 0 0 1 0 1 0
## 848 1 0 0 1 0 0 0 1
## 849 0 1 0 0 1 0 1 0
## 850 0 0 1 0 1 0 1 0
## 851 0 1 0 0 0 1 1 0
## 852 1 0 0 1 0 0 0 0
## 853 0 0 1 0 0 1 1 0
## 854 1 0 0 0 1 0 1 0
## 855 0 1 0 0 1 0 0 0
## 856 1 0 0 1 0 0 0 0
## 857 0 1 0 1 0 0 0 0
## 858 1 0 0 0 1 0 1 0
## 859 0 0 1 1 0 0 0 0
## 860 1 0 0 1 0 0 0 1
## 861 0 0 1 0 0 1 1 0
## 862 0 1 0 0 1 0 0 0
## 863 0 0 1 0 0 1 1 0
## 864 0 0 1 1 0 0 0 0
## 865 0 1 0 1 0 0 0 0
## 866 1 0 0 1 0 0 0 1
## 867 1 0 0 1 0 0 0 1
## 868 0 0 1 0 1 0 1 0
## 869 0 0 1 1 0 0 0 0
## 870 1 0 0 0 1 0 0 1
## 871 1 0 0 0 1 0 1 0
## 872 0 0 1 1 0 0 0 0
## 873 0 1 0 0 1 0 1 0
## 874 0 0 1 0 0 1 0 0
## 875 0 1 0 0 0 1 0 0
## 876 0 1 0 0 1 0 0 0
## 877 0 1 0 0 1 0 1 0
## 878 1 0 0 0 1 0 1 0
## 879 1 0 0 1 0 0 0 0
## 880 0 0 1 0 0 1 0 0
## 881 0 0 1 0 0 1 1 0
## 882 1 0 0 1 0 0 0 0
## 883 0 1 0 0 1 0 1 0
## 884 1 0 0 0 1 0 1 0
## 885 0 0 1 0 1 0 1 0
## 886 1 0 0 0 1 0 0 0
## 887 0 0 1 0 0 1 0 0
## 888 0 1 0 0 0 1 1 0
## 889 1 0 0 1 0 0 0 0
## 890 0 1 0 0 1 0 1 0
## 891 1 0 0 1 0 0 0 0
## 892 0 0 1 0 0 1 1 0
## 893 0 1 0 0 1 0 0 0
## 894 0 0 1 0 0 1 0 0
## 895 0 1 0 0 0 1 1 0
## 896 0 1 0 0 1 0 1 0
## 897 0 1 0 1 0 0 0 1
## 898 0 0 1 1 0 0 0 0
## 899 0 1 0 0 1 0 1 0
## 900 0 1 0 0 1 0 1 0
## 901 1 0 0 0 1 0 1 0
## 902 0 0 1 0 0 1 1 0
## 903 0 0 1 0 0 1 0 0
## 904 0 1 0 1 0 0 0 1
## 905 0 1 0 0 0 1 1 0
## 906 0 0 1 0 0 1 1 0
## 907 0 1 0 1 0 0 1 0
## 908 0 1 0 0 1 0 1 0
## 909 0 1 0 0 0 1 0 0
## 910 1 0 0 1 0 0 0 0
## 911 0 1 0 0 0 1 0 0
## 912 1 0 0 1 0 0 0 1
## 913 1 0 0 1 0 0 0 0
## 914 1 0 0 1 0 0 0 0
## 915 0 1 0 0 1 0 0 0
## 916 0 0 1 0 0 1 1 0
## 917 0 1 0 1 0 0 0 0
## 918 0 0 1 0 0 1 1 0
## 919 1 0 0 1 0 0 0 0
## 920 1 0 0 1 0 0 0 1
## 921 0 1 0 0 0 1 1 0
## 922 0 0 1 0 0 1 1 0
## 923 0 1 0 0 1 0 0 0
## 924 0 1 0 0 1 0 1 0
## 925 1 0 0 1 0 0 1 0
## 926 0 1 0 0 1 0 1 0
## 927 0 0 1 0 0 1 1 0
## 928 0 1 0 0 1 0 0 1
## 929 1 0 0 1 0 0 0 0
## 930 0 0 1 0 0 1 1 0
## 931 1 0 0 1 0 0 0 1
## 932 0 1 0 0 1 0 1 0
## 933 0 1 0 0 1 0 0 1
## 934 1 0 0 1 0 0 0 0
## 935 0 1 0 1 0 0 0 1
## 936 1 0 0 1 0 0 0 1
## 937 0 0 1 0 0 1 1 0
## 938 0 1 0 0 1 0 1 0
## 939 1 0 0 0 0 1 1 0
## 940 1 0 0 0 1 0 1 0
## 941 0 1 0 0 1 0 1 0
## 942 1 0 0 1 0 0 0 0
## 943 1 0 0 1 0 0 0 0
## 944 0 0 1 0 0 1 1 0
## 945 1 0 0 1 0 0 0 0
## 946 1 0 0 1 0 0 0 0
## 947 0 0 1 0 0 1 1 0
## 948 0 1 0 0 1 0 1 0
## 949 0 0 1 0 0 1 1 0
## 950 0 1 0 0 1 0 1 0
## 951 1 0 0 1 0 0 0 0
## 952 0 1 0 0 1 0 0 0
## 953 0 1 0 0 1 0 1 0
## 954 0 1 0 0 1 0 1 0
## 955 0 0 1 0 0 1 1 0
## 956 1 0 0 0 1 0 1 0
## 957 1 0 0 1 0 0 0 0
## 958 0 0 1 0 0 1 1 0
## 959 1 0 0 1 0 0 0 1
## 960 1 0 0 1 0 0 0 0
## 961 0 1 0 0 1 0 1 0
## 962 1 0 0 0 1 0 0 0
## 963 1 0 0 1 0 0 0 1
## 964 1 0 0 1 0 0 0 0
## 965 0 0 1 0 1 0 1 0
## 966 0 0 1 1 0 0 0 0
## 967 0 1 0 0 1 0 1 0
## 968 1 0 0 1 0 0 0 0
## 969 1 0 0 1 0 0 0 0
## 970 1 0 0 1 0 0 0 0
## 971 0 1 0 0 1 0 1 0
## 972 1 0 0 0 1 0 1 0
## 973 1 0 0 1 0 0 0 0
## 974 0 1 0 0 1 0 0 0
## 975 1 0 0 0 1 0 1 0
## 976 1 0 0 1 0 0 0 1
## 977 1 0 0 1 0 0 0 0
## 978 1 0 0 0 1 0 1 0
## 979 1 0 0 0 1 0 0 0
## 980 0 0 1 0 0 1 1 0
## 981 1 0 0 1 0 0 0 0
## 982 1 0 0 0 0 1 1 0
## 983 0 0 1 0 1 0 1 0
## 984 0 1 0 0 1 0 1 0
## 985 1 0 0 1 0 0 0 0
## 986 1 0 0 1 0 0 0 1
## 987 0 1 0 0 1 0 1 0
## 988 0 1 0 0 1 0 1 0
## 989 1 0 0 1 0 0 0 0
## 990 1 0 0 0 1 0 1 0
## 991 0 1 0 0 1 0 1 0
## 992 1 0 0 1 0 0 0 1
## 993 0 1 0 0 1 0 0 0
## 994 1 0 0 1 0 0 0 0
## 995 1 0 0 0 1 0 1 0
## 996 1 0 0 0 1 0 0 0
## 997 0 1 0 0 1 0 1 0
## 998 0 1 0 0 1 0 0 0
## 999 0 0 1 0 0 1 1 0
## 1000 0 0 1 0 1 0 0 0
## 1001 0 1 0 0 1 0 1 0
## 1002 0 1 0 0 1 0 1 0
## 1003 1 0 0 1 0 0 0 0
## 1004 1 0 0 1 0 0 0 0
## 1005 1 0 0 0 1 0 1 0
## 1006 1 0 0 1 0 0 0 0
## 1007 0 1 0 0 1 0 1 0
## 1008 0 0 1 1 0 0 0 0
## 1009 1 0 0 1 0 0 0 0
## 1010 1 0 0 1 0 0 0 0
## 1011 0 1 0 0 0 1 1 0
## 1012 0 1 0 0 1 0 1 0
## 1013 0 1 0 1 0 0 0 0
## 1014 0 1 0 0 1 0 1 0
## 1015 0 1 0 0 1 0 1 0
## 1016 0 1 0 0 1 0 1 0
## 1017 1 0 0 0 1 0 1 0
## 1018 1 0 0 1 0 0 0 1
## 1019 1 0 0 1 0 0 0 1
## 1020 1 0 0 0 0 1 0 0
## 1021 0 0 1 0 0 1 1 0
## 1022 0 1 0 1 0 0 0 0
## 1023 1 0 0 1 0 0 0 0
## 1024 0 0 1 0 0 1 0 0
## 1025 1 0 0 1 0 0 0 0
## 1026 0 1 0 1 0 0 0 0
## 1027 0 1 0 1 0 0 0 0
## 1028 1 0 0 0 1 0 0 0
## 1029 0 1 0 0 0 1 1 0
## 1030 0 1 0 0 1 0 1 0
## 1031 0 1 0 0 1 0 1 0
## 1032 0 1 0 1 0 0 0 0
## 1033 0 1 0 0 1 0 1 0
## 1034 1 0 0 1 0 0 1 0
## 1035 1 0 0 0 1 0 1 0
## 1036 0 0 1 0 0 1 1 0
## 1037 0 1 0 0 1 0 0 0
## 1038 1 0 0 1 0 0 0 1
## 1039 0 1 0 1 0 0 0 1
## 1040 0 1 0 1 0 0 0 1
## 1041 0 1 0 0 0 1 1 0
## 1042 0 1 0 0 1 0 0 0
## 1043 0 0 1 0 0 1 1 0
## 1044 1 0 0 1 0 0 0 0
## 1045 1 0 0 0 1 0 1 0
## 1046 1 0 0 1 0 0 0 1
## 1047 1 0 0 0 1 0 1 0
## 1048 1 0 0 1 0 0 0 0
## 1049 1 0 0 1 0 0 0 0
## 1050 0 1 0 0 1 0 1 0
## 1051 0 1 0 0 1 0 1 0
## 1052 0 1 0 0 0 1 1 0
## 1053 0 0 1 0 0 1 1 0
## 1054 0 1 0 0 1 0 1 0
## 1055 0 1 0 1 0 0 0 1
## 1056 0 1 0 1 0 0 0 1
## 1057 1 0 0 0 1 0 1 0
## 1058 1 0 0 0 0 1 0 0
## 1059 1 0 0 1 0 0 0 0
## 1060 1 0 0 1 0 0 0 1
## 1061 0 1 0 1 0 0 0 0
## 1062 0 1 0 0 0 1 1 0
## 1063 1 0 0 0 1 0 1 0
## 1064 0 1 0 0 1 0 1 0
## 1065 1 0 0 0 1 0 1 0
## 1066 1 0 0 0 1 0 1 0
## 1067 0 1 0 0 1 0 1 0
## 1068 0 1 0 1 0 0 0 0
## 1069 1 0 0 0 0 1 1 0
## 1070 1 0 0 0 1 0 1 0
## 1071 1 0 0 1 0 0 0 1
## 1072 1 0 0 1 0 0 0 1
## 1073 1 0 0 1 0 0 0 0
## 1074 0 1 0 1 0 0 1 0
## 1075 0 0 1 0 0 1 1 0
## 1076 0 1 0 0 1 0 1 0
## 1077 0 1 0 1 0 0 0 1
## 1078 1 0 0 0 1 0 0 0
## 1079 1 0 0 1 0 0 0 0
## 1080 0 0 1 0 0 1 1 0
## 1081 1 0 0 1 0 0 0 0
## 1082 1 0 0 0 1 0 0 0
## 1083 0 1 0 1 0 0 0 0
## 1084 1 0 0 0 1 0 1 0
## 1085 0 1 0 0 1 0 1 0
## 1086 0 1 0 0 1 0 1 0
## 1087 0 0 1 0 0 1 1 0
## 1088 1 0 0 1 0 0 0 0
## 1089 0 1 0 1 0 0 0 0
## 1090 1 0 0 1 0 0 0 0
## 1091 1 0 0 1 0 0 0 1
## 1092 1 0 0 0 1 0 1 0
## 1093 0 0 1 0 1 0 0 0
## 1094 0 0 1 0 0 1 0 0
## 1095 0 1 0 0 1 0 1 0
## 1096 1 0 0 1 0 0 0 1
## 1097 1 0 0 0 1 0 1 0
## 1098 0 1 0 0 1 0 1 0
## 1099 0 1 0 1 0 0 0 1
## 1100 0 1 0 0 1 0 0 0
## 1101 1 0 0 0 1 0 1 0
## 1102 1 0 0 0 1 0 1 0
## 1103 1 0 0 1 0 0 0 0
## 1104 0 0 1 0 0 1 0 0
## 1105 1 0 0 0 1 0 1 0
## 1106 0 1 0 1 0 0 0 0
## 1107 0 0 1 0 0 1 1 0
## 1108 0 0 1 1 0 0 0 0
## 1109 0 1 0 1 0 0 0 0
## 1110 1 0 0 0 1 0 1 0
## 1111 1 0 0 0 1 0 1 0
## 1112 0 1 0 0 0 1 1 0
## 1113 0 1 0 1 0 0 0 0
## 1114 0 0 1 0 0 1 1 0
## 1115 1 0 0 1 0 0 1 0
## 1116 0 1 0 1 0 0 0 0
## 1117 0 0 1 0 1 0 0 0
## 1118 0 1 0 0 0 1 1 0
## 1119 0 0 1 0 0 1 1 0
## 1120 0 1 0 0 1 0 1 0
## 1121 1 0 0 1 0 0 1 0
## 1122 0 1 0 0 1 0 0 0
## 1123 0 0 1 0 1 0 0 0
## 1124 0 1 0 0 1 0 1 0
## 1125 0 1 0 0 0 1 1 0
## 1126 0 0 1 0 0 1 0 1
## 1127 0 0 1 0 0 1 1 0
## 1128 0 0 1 0 0 1 1 0
## 1129 0 1 0 1 0 0 0 0
## 1130 0 0 1 0 0 1 1 0
## 1131 0 1 0 0 1 0 0 0
## 1132 0 0 1 0 0 1 1 0
## 1133 0 1 0 0 0 1 1 0
## 1134 0 1 0 0 1 0 1 0
## 1135 1 0 0 0 1 0 1 0
## 1136 0 1 0 0 1 0 1 0
## 1137 1 0 0 0 1 0 1 0
## 1138 0 1 0 0 1 0 1 0
## 1139 1 0 0 0 1 0 0 0
## 1140 1 0 0 1 0 0 0 0
## 1141 0 0 1 0 1 0 0 0
## 1142 0 1 0 0 0 1 0 0
## 1143 0 1 0 0 1 0 1 0
## 1144 1 0 0 1 0 0 0 0
## 1145 1 0 0 0 1 0 1 0
## 1146 0 0 1 0 0 1 1 0
## 1147 1 0 0 1 0 0 0 0
## 1148 1 0 0 1 0 0 0 0
## 1149 0 0 1 0 0 1 1 0
## 1150 0 0 1 0 0 1 1 0
## 1151 0 1 0 0 0 1 1 0
## 1152 0 1 0 0 1 0 1 0
## 1153 1 0 0 1 0 0 0 0
## 1154 0 1 0 0 0 1 1 0
## 1155 0 0 1 0 1 0 0 0
## 1156 0 1 0 1 0 0 0 0
## 1157 0 0 1 0 0 1 1 0
## 1158 0 1 0 1 0 0 0 0
## 1159 0 1 0 0 1 0 1 0
## 1160 0 1 0 0 1 0 0 0
## 1161 0 1 0 0 1 0 0 0
## 1162 0 0 1 0 0 1 0 0
## 1163 1 0 0 0 1 0 0 0
## 1164 0 0 1 0 1 0 1 0
## 1165 0 1 0 0 1 0 1 0
## 1166 0 0 1 0 0 1 1 0
## 1167 0 1 0 0 1 0 0 0
## 1168 0 0 1 0 1 0 1 0
## 1169 1 0 0 0 1 0 1 0
## 1170 0 0 1 0 0 1 1 0
## 1171 0 1 0 1 0 0 0 0
## 1172 0 1 0 1 0 0 0 0
## 1173 0 1 0 1 0 0 0 0
## 1174 0 1 0 0 1 0 0 0
## 1175 0 0 1 0 1 0 1 0
## 1176 1 0 0 1 0 0 0 1
## 1177 1 0 0 0 0 1 0 0
## 1178 0 0 1 0 0 1 1 0
## 1179 0 0 1 0 1 0 1 0
## 1180 0 1 0 0 0 1 1 0
## 1181 0 0 1 1 0 0 0 0
## 1182 0 1 0 1 0 0 0 0
## 1183 0 0 1 0 0 1 0 0
## 1184 0 0 1 0 0 1 0 0
## 1185 0 0 1 0 0 1 0 0
## 1186 0 0 1 1 0 0 0 0
## 1187 0 1 0 1 0 0 0 0
## 1188 0 0 1 0 1 0 1 0
## 1189 0 0 1 0 1 0 1 0
## 1190 1 0 0 0 1 0 0 0
## 1191 1 0 0 0 1 0 1 0
## 1192 0 1 0 1 0 0 0 0
## 1193 1 0 0 1 0 0 0 0
## 1194 1 0 0 0 1 0 1 0
## 1195 1 0 0 1 0 0 0 0
## 1196 0 0 1 0 0 1 0 0
## 1197 0 1 0 0 1 0 1 0
## 1198 1 0 0 0 1 0 1 0
## 1199 0 1 0 0 1 0 0 0
## 1200 0 1 0 0 1 0 1 0
## 1201 1 0 0 0 1 0 1 0
## 1202 0 1 0 0 1 0 1 0
## 1203 0 1 0 1 0 0 0 0
## 1204 0 1 0 0 0 1 1 0
## 1205 1 0 0 0 1 0 1 0
## 1206 1 0 0 1 0 0 0 0
## 1207 1 0 0 0 1 0 1 0
## 1208 0 0 1 0 0 1 1 0
## 1209 0 0 1 0 0 1 1 0
## 1210 1 0 0 0 1 0 0 0
## 1211 0 1 0 0 1 0 1 0
## 1212 0 1 0 1 0 0 0 1
## 1213 1 0 0 1 0 0 1 0
## 1214 0 1 0 0 1 0 1 0
## 1215 0 1 0 0 1 0 1 0
## 1216 0 1 0 0 0 1 0 0
## 1217 1 0 0 0 0 1 1 0
## 1218 0 1 0 1 0 0 0 0
## 1219 0 1 0 1 0 0 0 1
## 1220 1 0 0 1 0 0 0 0
## 1221 0 0 1 0 0 1 0 0
## 1222 0 0 1 0 1 0 0 0
## 1223 0 1 0 0 1 0 1 0
## 1224 0 1 0 0 0 1 1 0
## 1225 1 0 0 1 0 0 0 0
## 1226 1 0 0 1 0 0 1 0
## 1227 0 1 0 0 1 0 1 0
## 1228 0 1 0 1 0 0 0 1
## 1229 0 1 0 1 0 0 0 0
## 1230 1 0 0 0 1 0 1 0
## 1231 0 0 1 0 0 1 1 0
## 1232 1 0 0 0 1 0 1 0
## 1233 1 0 0 0 1 0 1 0
## 1234 0 1 0 0 1 0 1 0
## 1235 1 0 0 0 1 0 0 0
## 1236 0 0 1 0 0 1 1 0
## 1237 1 0 0 1 0 0 0 0
## 1238 1 0 0 1 0 0 0 1
## 1239 1 0 0 1 0 0 0 0
## 1240 0 1 0 0 1 0 0 0
## 1241 1 0 0 1 0 0 0 0
## 1242 0 1 0 0 1 0 1 0
## 1243 1 0 0 1 0 0 0 0
## 1244 0 1 0 1 0 0 0 0
## 1245 0 0 1 0 0 1 1 0
## 1246 0 1 0 1 0 0 0 0
## 1247 0 0 1 0 1 0 0 0
## 1248 0 0 1 0 0 1 1 0
## 1249 0 0 1 0 0 1 1 0
## 1250 0 0 1 0 1 0 1 0
## 1251 1 0 0 0 1 0 0 0
## 1252 1 0 0 1 0 0 0 0
## 1253 1 0 0 1 0 0 1 0
## 1254 0 0 1 0 1 0 1 0
## 1255 1 0 0 0 1 0 1 0
## 1256 0 1 0 0 1 0 1 0
## 1257 0 1 0 1 0 0 0 1
## 1258 0 0 1 0 1 0 0 0
## 1259 0 1 0 1 0 0 0 0
## 1260 1 0 0 1 0 0 0 1
## 1261 0 1 0 0 0 1 1 0
## 1262 1 0 0 1 0 0 0 0
## 1263 0 1 0 0 1 0 1 0
## 1264 0 0 1 0 0 1 0 0
## 1265 1 0 0 0 1 0 1 0
## 1266 1 0 0 1 0 0 0 0
## 1267 0 1 0 0 1 0 1 0
## 1268 0 0 1 0 0 1 1 0
## 1269 0 1 0 0 1 0 0 0
## 1270 0 0 1 0 1 0 0 0
## 1271 0 0 1 1 0 0 0 0
## 1272 1 0 0 0 0 1 1 0
## 1273 0 1 0 0 0 1 1 0
## 1274 0 1 0 1 0 0 1 0
## 1275 1 0 0 0 1 0 0 0
## 1276 1 0 0 1 0 0 0 0
## 1277 0 0 1 0 0 1 1 0
## 1278 1 0 0 1 0 0 1 0
## 1279 1 0 0 1 0 0 0 1
## 1280 0 0 1 0 1 0 1 0
## 1281 0 0 1 1 0 0 0 1
## 1282 0 0 1 0 0 1 0 0
## 1283 0 1 0 0 1 0 0 0
## 1284 1 0 0 1 0 0 0 0
## 1285 1 0 0 0 0 1 0 0
## 1286 1 0 0 0 1 0 1 0
## 1287 1 0 0 1 0 0 0 1
## 1288 1 0 0 0 1 0 1 0
## 1289 0 1 0 0 1 0 1 0
## 1290 0 1 0 0 1 0 1 0
## 1291 0 1 0 0 1 0 1 0
## 1292 0 1 0 1 0 0 0 0
## 1293 0 1 0 1 0 0 0 1
## 1294 0 1 0 0 1 0 1 0
## 1295 0 1 0 0 0 1 1 0
## 1296 0 1 0 0 1 0 1 0
## 1297 0 1 0 0 1 0 1 0
## 1298 0 1 0 0 1 0 1 0
## 1299 0 0 1 0 0 1 1 0
## 1300 0 0 1 0 0 1 1 0
## 1301 1 0 0 1 0 0 0 0
## 1302 0 1 0 1 0 0 1 0
## 1303 0 1 0 1 0 0 0 0
## 1304 0 1 0 0 1 0 1 0
## 1305 0 1 0 0 1 0 1 0
## 1306 0 1 0 0 1 0 1 0
## 1307 1 0 0 1 0 0 0 1
## 1308 1 0 0 1 0 0 0 0
## 1309 1 0 0 1 0 0 0 0
## 1310 0 1 0 1 0 0 0 0
## 1311 1 0 0 1 0 0 0 0
## 1312 1 0 0 0 1 0 1 0
## 1313 0 1 0 0 1 0 1 0
## 1314 1 0 0 0 1 0 1 0
## 1315 0 1 0 0 1 0 1 0
## 1316 0 1 0 0 0 1 1 0
## 1317 1 0 0 1 0 0 0 0
## 1318 0 0 1 0 0 1 1 0
## 1319 1 0 0 0 1 0 1 0
## 1320 0 1 0 0 1 0 1 0
## 1321 1 0 0 1 0 0 0 0
## 1322 1 0 0 0 1 0 1 0
## 1323 1 0 0 1 0 0 1 0
## 1324 1 0 0 1 0 0 1 0
## 1325 0 0 1 1 0 0 1 0
## 1326 0 1 0 0 1 0 1 0
## 1327 0 1 0 0 1 0 1 0
## 1328 1 0 0 1 0 0 0 0
## 1329 1 0 0 0 1 0 1 0
## 1330 1 0 0 1 0 0 1 0
## 1331 0 0 1 0 0 1 1 0
## 1332 0 0 1 0 1 0 0 0
## 1333 0 1 0 0 1 0 0 0
## 1334 0 1 0 1 0 0 0 0
## 1335 1 0 0 1 0 0 0 0
## 1336 0 1 0 0 1 0 1 0
## 1337 1 0 0 0 1 0 1 0
## 1338 1 0 0 1 0 0 0 0
## 1339 1 0 0 1 0 0 0 0
## 1340 1 0 0 1 0 0 0 0
## 1341 0 1 0 0 1 0 0 0
## 1342 1 0 0 0 1 0 1 0
## 1343 0 1 0 0 1 0 0 0
## 1344 0 1 0 1 0 0 0 0
## 1345 1 0 0 0 1 0 1 0
## 1346 1 0 0 0 1 0 1 0
## 1347 0 0 1 1 0 0 0 0
## 1348 1 0 0 1 0 0 0 0
## 1349 1 0 0 1 0 0 1 0
## 1350 1 0 0 1 0 0 0 0
## 1351 1 0 0 1 0 0 0 1
## 1352 1 0 0 1 0 0 0 0
## 1353 0 1 0 0 1 0 1 0
## 1354 0 0 1 0 0 1 0 0
## 1355 0 1 0 1 0 0 0 0
## 1356 1 0 0 0 1 0 0 0
## 1357 1 0 0 0 1 0 1 0
## 1358 0 1 0 0 1 0 1 0
## 1359 1 0 0 0 1 0 0 0
## 1360 0 1 0 0 0 1 1 0
## 1361 1 0 0 1 0 0 0 0
## 1362 0 1 0 1 0 0 0 1
## 1363 0 1 0 0 1 0 1 0
## 1364 0 0 1 0 0 1 1 0
## 1365 1 0 0 0 1 0 1 0
## 1366 0 1 0 1 0 0 1 0
## 1367 0 1 0 1 0 0 0 1
## 1368 0 1 0 0 0 1 1 0
## 1369 0 0 1 0 0 1 0 0
## 1370 0 0 1 0 1 0 1 0
## 1371 1 0 0 0 1 0 1 0
## 1372 0 0 1 1 0 0 0 0
## 1373 0 0 1 0 1 0 0 0
## 1374 0 1 0 0 1 0 1 0
## 1375 0 1 0 1 0 0 0 0
## 1376 0 1 0 0 0 1 1 0
## 1377 1 0 0 1 0 0 0 0
## 1378 0 1 0 0 1 0 1 0
## 1379 1 0 0 1 0 0 0 0
## 1380 1 0 0 0 1 0 1 0
## 1381 0 0 1 1 0 0 0 0
## 1382 1 0 0 1 0 0 0 0
## 1383 1 0 0 1 0 0 0 0
## 1384 0 0 1 0 0 1 0 1
## 1385 1 0 0 0 1 0 1 0
## 1386 0 1 0 0 0 1 1 0
## 1387 1 0 0 1 0 0 0 0
## 1388 0 1 0 0 0 1 1 0
## 1389 0 1 0 0 1 0 0 0
## 1390 1 0 0 1 0 0 0 1
## 1391 1 0 0 1 0 0 0 0
## 1392 0 0 1 0 0 1 0 0
## 1393 1 0 0 0 1 0 1 0
## 1394 0 1 0 0 1 0 1 0
## 1395 0 1 0 0 1 0 1 0
## 1396 0 1 0 1 0 0 0 1
## 1397 1 0 0 0 1 0 0 0
## 1398 1 0 0 0 1 0 0 0
## 1399 0 1 0 0 1 0 1 0
## 1400 0 0 1 0 0 1 0 0
## 1401 0 1 0 0 1 0 1 0
## 1402 1 0 0 1 0 0 0 0
## 1403 1 0 0 1 0 0 0 0
## 1404 0 1 0 0 1 0 1 0
## 1405 0 0 1 0 0 1 1 0
## 1406 1 0 0 1 0 0 0 0
## 1407 1 0 0 1 0 0 0 0
## 1408 0 0 1 0 0 1 0 0
## 1409 1 0 0 1 0 0 0 1
## 1410 0 1 0 0 1 0 0 0
## 1411 1 0 0 1 0 0 0 0
## 1412 1 0 0 1 0 0 0 1
## 1413 1 0 0 1 0 0 0 1
## 1414 0 1 0 1 0 0 0 0
## 1415 0 0 1 1 0 0 0 0
## 1416 1 0 0 1 0 0 1 0
## 1417 1 0 0 0 1 0 1 0
## 1418 1 0 0 1 0 0 1 0
## 1419 1 0 0 1 0 0 0 1
## 1420 0 1 0 0 0 1 1 0
## 1421 1 0 0 0 1 0 0 0
## 1422 1 0 0 0 1 0 1 0
## 1423 0 1 0 0 0 1 1 0
## 1424 0 1 0 0 1 0 0 0
## 1425 1 0 0 1 0 0 1 0
## 1426 1 0 0 1 0 0 0 0
## 1427 0 1 0 0 0 1 1 0
## 1428 0 1 0 0 1 0 0 1
## 1429 1 0 0 0 1 0 1 0
## 1430 0 1 0 0 0 1 1 0
## 1431 1 0 0 1 0 0 0 0
## 1432 1 0 0 1 0 0 1 0
## 1433 1 0 0 0 1 0 1 0
## 1434 0 1 0 0 1 0 1 0
## 1435 1 0 0 0 1 0 1 0
## 1436 1 0 0 0 1 0 0 0
## 1437 1 0 0 0 1 0 0 0
## 1438 0 1 0 0 1 0 1 0
## 1439 0 1 0 0 1 0 0 0
## 1440 0 1 0 1 0 0 0 0
## 1441 0 0 1 0 0 1 1 0
## 1442 0 1 0 1 0 0 0 0
## 1443 0 0 1 0 0 1 1 0
## Independiente Jubilado/Desempleado No Si Bajo Medio Alto
## 1 0 1 1 0 1 0 0
## 2 0 0 1 0 1 0 0
## 3 0 0 1 0 0 1 0
## 4 0 0 0 1 0 1 0
## 5 0 0 1 0 0 1 0
## 6 0 0 1 0 0 0 1
## 7 0 0 1 0 1 0 0
## 8 0 0 1 0 0 1 0
## 9 0 0 1 0 0 1 0
## 10 0 0 1 0 1 0 0
## 11 0 0 1 0 0 1 0
## 12 0 0 1 0 1 0 0
## 13 0 0 0 1 0 1 0
## 14 0 0 1 0 0 1 0
## 15 0 0 0 1 0 1 0
## 16 0 0 1 0 0 1 0
## 17 0 0 1 0 1 0 0
## 18 0 0 1 0 0 1 0
## 19 0 0 1 0 0 0 1
## 20 0 0 1 0 0 1 0
## 21 0 0 0 1 0 0 1
## 22 0 0 1 0 1 0 0
## 23 0 0 1 0 1 0 0
## 24 0 0 1 0 1 0 0
## 25 0 0 1 0 0 1 0
## 26 0 0 0 1 1 0 0
## 27 0 0 0 1 1 0 0
## 28 0 0 1 0 0 1 0
## 29 0 0 1 0 1 0 0
## 30 0 0 1 0 0 1 0
## 31 0 0 1 0 0 1 0
## 32 0 0 0 1 0 1 0
## 33 0 0 1 0 1 0 0
## 34 0 0 1 0 0 0 1
## 35 0 0 1 0 0 1 0
## 36 0 0 1 0 1 0 0
## 37 0 0 1 0 0 1 0
## 38 0 0 1 0 0 0 1
## 39 0 0 1 0 0 0 1
## 40 1 0 1 0 0 1 0
## 41 0 0 1 0 0 0 1
## 42 0 0 0 1 1 0 0
## 43 0 0 1 0 0 1 0
## 44 0 0 0 1 0 1 0
## 45 0 0 1 0 0 1 0
## 46 0 0 1 0 1 0 0
## 47 0 0 1 0 1 0 0
## 48 0 0 0 1 1 0 0
## 49 0 0 1 0 1 0 0
## 50 0 0 0 1 0 1 0
## 51 0 0 1 0 1 0 0
## 52 0 0 1 0 1 0 0
## 53 0 0 0 1 1 0 0
## 54 0 0 0 1 0 0 1
## 55 0 0 1 0 0 1 0
## 56 0 0 0 1 0 1 0
## 57 1 0 1 0 1 0 0
## 58 0 0 1 0 0 1 0
## 59 0 0 1 0 1 0 0
## 60 0 0 1 0 1 0 0
## 61 0 1 1 0 0 0 1
## 62 0 0 1 0 1 0 0
## 63 1 0 1 0 1 0 0
## 64 0 0 1 0 1 0 0
## 65 0 0 1 0 0 1 0
## 66 0 0 1 0 0 1 0
## 67 1 0 0 1 0 1 0
## 68 0 0 1 0 0 1 0
## 69 0 0 1 0 0 0 1
## 70 0 1 0 1 0 0 1
## 71 0 0 1 0 0 1 0
## 72 0 0 1 0 1 0 0
## 73 0 0 1 0 0 1 0
## 74 0 0 1 0 1 0 0
## 75 1 0 0 1 1 0 0
## 76 0 0 1 0 0 1 0
## 77 0 0 0 1 1 0 0
## 78 0 0 0 1 0 1 0
## 79 1 0 1 0 0 0 1
## 80 0 0 1 0 0 1 0
## 81 0 0 1 0 0 0 1
## 82 1 0 1 0 0 1 0
## 83 0 0 1 0 0 1 0
## 84 0 1 0 1 0 1 0
## 85 0 0 1 0 1 0 0
## 86 1 0 1 0 0 0 1
## 87 0 0 1 0 1 0 0
## 88 0 0 0 1 1 0 0
## 89 0 0 1 0 0 0 1
## 90 0 0 0 1 0 1 0
## 91 0 0 1 0 1 0 0
## 92 0 0 0 1 0 1 0
## 93 0 0 1 0 0 1 0
## 94 0 0 0 1 0 1 0
## 95 0 0 1 0 1 0 0
## 96 0 0 1 0 0 0 1
## 97 0 0 0 1 0 1 0
## 98 0 0 1 0 1 0 0
## 99 0 0 0 1 1 0 0
## 100 0 0 1 0 0 1 0
## 101 1 0 1 0 0 0 1
## 102 0 0 1 0 1 0 0
## 103 0 0 1 0 1 0 0
## 104 0 0 1 0 0 1 0
## 105 0 0 1 0 0 0 1
## 106 0 0 1 0 0 0 1
## 107 0 0 0 1 0 0 1
## 108 1 0 0 1 0 0 1
## 109 1 0 0 1 0 1 0
## 110 0 0 1 0 0 1 0
## 111 0 0 1 0 0 0 1
## 112 0 0 0 1 0 0 1
## 113 0 0 1 0 0 1 0
## 114 0 0 0 1 0 0 1
## 115 0 0 1 0 1 0 0
## 116 0 0 1 0 1 0 0
## 117 1 0 1 0 1 0 0
## 118 0 0 0 1 0 1 0
## 119 0 0 1 0 1 0 0
## 120 0 0 1 0 0 1 0
## 121 0 0 0 1 0 0 1
## 122 0 0 1 0 0 0 1
## 123 0 0 0 1 0 1 0
## 124 0 0 1 0 0 1 0
## 125 0 0 0 1 1 0 0
## 126 0 0 0 1 0 1 0
## 127 1 0 0 1 0 0 1
## 128 0 0 1 0 0 1 0
## 129 0 0 1 0 0 0 1
## 130 1 0 0 1 1 0 0
## 131 0 0 0 1 0 0 1
## 132 0 1 0 1 0 1 0
## 133 0 1 0 1 0 0 1
## 134 0 0 1 0 1 0 0
## 135 0 0 1 0 0 1 0
## 136 0 0 0 1 1 0 0
## 137 0 0 1 0 1 0 0
## 138 0 1 1 0 0 0 1
## 139 0 0 1 0 0 1 0
## 140 0 0 0 1 1 0 0
## 141 0 0 0 1 0 0 1
## 142 0 0 1 0 0 1 0
## 143 0 1 0 1 0 1 0
## 144 0 0 0 1 0 1 0
## 145 0 0 0 1 1 0 0
## 146 0 0 1 0 0 1 0
## 147 0 1 0 1 0 1 0
## 148 0 0 1 0 1 0 0
## 149 0 0 1 0 0 1 0
## 150 0 1 1 0 0 1 0
## 151 0 0 1 0 1 0 0
## 152 0 0 0 1 1 0 0
## 153 0 0 0 1 0 0 1
## 154 0 0 0 1 0 1 0
## 155 0 0 1 0 0 0 1
## 156 0 0 1 0 1 0 0
## 157 1 0 0 1 0 1 0
## 158 1 0 1 0 0 1 0
## 159 0 0 1 0 0 1 0
## 160 0 1 1 0 0 0 1
## 161 0 0 1 0 0 1 0
## 162 0 0 1 0 0 0 1
## 163 1 0 1 0 0 1 0
## 164 0 0 1 0 0 1 0
## 165 0 0 1 0 0 1 0
## 166 1 0 1 0 0 0 1
## 167 0 0 1 0 0 1 0
## 168 0 0 1 0 1 0 0
## 169 0 0 1 0 0 0 1
## 170 1 0 1 0 1 0 0
## 171 0 0 0 1 0 0 1
## 172 0 1 1 0 0 0 1
## 173 0 0 1 0 0 1 0
## 174 0 0 1 0 1 0 0
## 175 0 0 0 1 0 1 0
## 176 0 0 1 0 0 1 0
## 177 0 0 1 0 1 0 0
## 178 0 1 0 1 0 1 0
## 179 0 0 0 1 0 1 0
## 180 0 0 0 1 0 1 0
## 181 0 0 1 0 0 0 1
## 182 0 0 1 0 1 0 0
## 183 1 0 1 0 0 0 1
## 184 0 0 1 0 1 0 0
## 185 0 0 0 1 0 0 1
## 186 0 0 1 0 0 1 0
## 187 0 0 0 1 1 0 0
## 188 0 0 0 1 0 1 0
## 189 1 0 0 1 0 1 0
## 190 0 0 0 1 0 1 0
## 191 0 0 1 0 0 1 0
## 192 0 0 1 0 0 1 0
## 193 0 0 1 0 1 0 0
## 194 1 0 1 0 1 0 0
## 195 0 0 1 0 0 0 1
## 196 0 0 1 0 1 0 0
## 197 0 0 1 0 0 1 0
## 198 0 0 0 1 0 1 0
## 199 0 0 1 0 1 0 0
## 200 0 0 1 0 0 1 0
## 201 0 0 1 0 1 0 0
## 202 1 0 1 0 0 1 0
## 203 0 0 1 0 0 1 0
## 204 1 0 1 0 0 0 1
## 205 0 0 1 0 0 1 0
## 206 0 0 1 0 0 0 1
## 207 0 0 0 1 0 1 0
## 208 0 0 1 0 0 1 0
## 209 0 0 1 0 1 0 0
## 210 1 0 1 0 0 0 1
## 211 0 0 1 0 0 0 1
## 212 0 1 0 1 1 0 0
## 213 0 0 1 0 0 1 0
## 214 1 0 1 0 0 1 0
## 215 0 0 0 1 0 1 0
## 216 0 1 1 0 0 1 0
## 217 0 0 1 0 0 1 0
## 218 0 0 1 0 0 0 1
## 219 0 1 1 0 1 0 0
## 220 0 0 1 0 0 0 1
## 221 0 0 1 0 0 1 0
## 222 0 0 1 0 0 1 0
## 223 1 0 1 0 0 1 0
## 224 0 0 1 0 0 1 0
## 225 0 0 0 1 0 1 0
## 226 0 0 0 1 1 0 0
## 227 0 0 1 0 0 1 0
## 228 0 0 0 1 1 0 0
## 229 0 0 1 0 1 0 0
## 230 0 0 1 0 0 1 0
## 231 1 0 0 1 0 0 1
## 232 0 0 1 0 0 1 0
## 233 1 0 0 1 0 1 0
## 234 0 0 1 0 0 1 0
## 235 1 0 1 0 0 1 0
## 236 1 0 1 0 1 0 0
## 237 0 0 1 0 1 0 0
## 238 0 1 1 0 0 1 0
## 239 0 0 1 0 1 0 0
## 240 1 0 1 0 1 0 0
## 241 0 0 1 0 0 1 0
## 242 0 0 1 0 0 1 0
## 243 0 0 1 0 0 1 0
## 244 0 0 1 0 1 0 0
## 245 1 0 0 1 1 0 0
## 246 1 0 0 1 1 0 0
## 247 0 0 0 1 0 1 0
## 248 0 0 1 0 0 0 1
## 249 0 0 0 1 1 0 0
## 250 0 0 1 0 0 0 1
## 251 0 0 1 0 1 0 0
## 252 0 0 1 0 1 0 0
## 253 0 0 1 0 0 1 0
## 254 0 0 1 0 0 1 0
## 255 0 0 1 0 0 1 0
## 256 0 0 0 1 1 0 0
## 257 0 0 1 0 0 1 0
## 258 0 0 1 0 1 0 0
## 259 1 0 1 0 0 0 1
## 260 0 0 1 0 0 1 0
## 261 0 0 1 0 0 0 1
## 262 0 0 1 0 0 1 0
## 263 0 0 1 0 0 1 0
## 264 1 0 1 0 1 0 0
## 265 0 0 1 0 1 0 0
## 266 0 0 1 0 0 1 0
## 267 0 0 1 0 1 0 0
## 268 0 0 1 0 0 1 0
## 269 0 0 1 0 0 1 0
## 270 0 1 1 0 1 0 0
## 271 0 0 1 0 0 0 1
## 272 0 0 1 0 1 0 0
## 273 0 0 1 0 1 0 0
## 274 0 0 1 0 1 0 0
## 275 1 0 1 0 1 0 0
## 276 0 0 1 0 0 1 0
## 277 1 0 1 0 0 1 0
## 278 0 0 1 0 0 0 1
## 279 0 0 1 0 0 0 1
## 280 0 0 1 0 0 1 0
## 281 1 0 1 0 0 1 0
## 282 0 0 0 1 0 1 0
## 283 0 0 1 0 0 0 1
## 284 0 1 0 1 0 1 0
## 285 0 0 1 0 1 0 0
## 286 0 1 1 0 0 1 0
## 287 0 0 1 0 0 0 1
## 288 0 0 1 0 1 0 0
## 289 0 0 1 0 0 1 0
## 290 0 0 1 0 0 1 0
## 291 0 0 1 0 0 1 0
## 292 0 0 1 0 0 1 0
## 293 0 0 1 0 1 0 0
## 294 0 0 1 0 0 1 0
## 295 1 0 1 0 1 0 0
## 296 0 0 1 0 1 0 0
## 297 0 0 1 0 1 0 0
## 298 0 0 1 0 0 1 0
## 299 1 0 1 0 0 1 0
## 300 0 0 1 0 0 0 1
## 301 0 0 1 0 1 0 0
## 302 0 0 1 0 0 1 0
## 303 0 0 1 0 1 0 0
## 304 0 0 1 0 1 0 0
## 305 0 0 1 0 0 0 1
## 306 0 0 1 0 0 1 0
## 307 0 0 1 0 1 0 0
## 308 0 0 1 0 0 1 0
## 309 0 0 0 1 0 1 0
## 310 0 0 1 0 0 0 1
## 311 0 0 1 0 0 1 0
## 312 0 0 1 0 0 0 1
## 313 0 0 1 0 1 0 0
## 314 1 0 1 0 1 0 0
## 315 0 1 0 1 0 1 0
## 316 0 0 1 0 0 1 0
## 317 0 0 1 0 0 1 0
## 318 0 1 1 0 1 0 0
## 319 0 0 1 0 0 0 1
## 320 0 0 1 0 1 0 0
## 321 0 0 0 1 0 1 0
## 322 0 0 1 0 0 1 0
## 323 0 0 1 0 1 0 0
## 324 0 0 1 0 1 0 0
## 325 0 1 1 0 0 1 0
## 326 0 0 1 0 0 0 1
## 327 0 0 1 0 0 0 1
## 328 0 0 1 0 1 0 0
## 329 0 0 1 0 0 1 0
## 330 0 0 0 1 1 0 0
## 331 0 0 1 0 0 1 0
## 332 0 0 1 0 0 1 0
## 333 0 1 0 1 0 0 1
## 334 0 0 1 0 0 0 1
## 335 0 0 1 0 0 1 0
## 336 0 0 1 0 1 0 0
## 337 0 0 1 0 0 1 0
## 338 0 0 1 0 0 0 1
## 339 0 0 1 0 1 0 0
## 340 0 0 1 0 1 0 0
## 341 0 0 1 0 1 0 0
## 342 0 0 1 0 0 1 0
## 343 0 0 1 0 0 1 0
## 344 0 0 1 0 1 0 0
## 345 0 1 1 0 0 1 0
## 346 1 0 1 0 1 0 0
## 347 0 0 1 0 1 0 0
## 348 0 0 0 1 0 1 0
## 349 0 0 1 0 0 1 0
## 350 1 0 1 0 0 0 1
## 351 0 1 1 0 0 0 1
## 352 0 0 1 0 0 1 0
## 353 0 0 1 0 1 0 0
## 354 1 0 1 0 0 1 0
## 355 1 0 1 0 0 1 0
## 356 0 0 1 0 1 0 0
## 357 0 0 0 1 0 0 1
## 358 0 0 1 0 1 0 0
## 359 0 0 1 0 1 0 0
## 360 0 1 0 1 0 0 1
## 361 0 0 0 1 0 1 0
## 362 0 1 0 1 0 0 1
## 363 1 0 1 0 1 0 0
## 364 0 0 1 0 1 0 0
## 365 0 0 1 0 0 1 0
## 366 0 0 1 0 1 0 0
## 367 0 0 1 0 0 0 1
## 368 0 0 1 0 1 0 0
## 369 0 0 1 0 0 0 1
## 370 1 0 0 1 1 0 0
## 371 0 1 1 0 0 1 0
## 372 0 1 1 0 0 1 0
## 373 0 1 1 0 0 0 1
## 374 0 0 0 1 0 1 0
## 375 0 0 1 0 0 0 1
## 376 1 0 1 0 0 1 0
## 377 0 0 0 1 0 0 1
## 378 0 0 1 0 1 0 0
## 379 0 0 0 1 1 0 0
## 380 0 0 0 1 0 1 0
## 381 0 0 1 0 0 1 0
## 382 1 0 1 0 0 0 1
## 383 0 0 1 0 1 0 0
## 384 0 0 1 0 0 0 1
## 385 0 0 0 1 0 0 1
## 386 1 0 0 1 0 1 0
## 387 0 1 1 0 0 0 1
## 388 0 0 1 0 1 0 0
## 389 0 0 1 0 1 0 0
## 390 1 0 1 0 0 1 0
## 391 0 0 1 0 1 0 0
## 392 0 0 1 0 1 0 0
## 393 0 0 0 1 0 1 0
## 394 0 0 1 0 1 0 0
## 395 0 0 0 1 0 1 0
## 396 0 1 1 0 0 1 0
## 397 0 0 1 0 1 0 0
## 398 0 0 1 0 0 1 0
## 399 0 0 1 0 1 0 0
## 400 1 0 1 0 1 0 0
## 401 1 0 1 0 0 1 0
## 402 0 0 1 0 1 0 0
## 403 0 0 1 0 1 0 0
## 404 0 0 0 1 1 0 0
## 405 0 0 1 0 0 1 0
## 406 0 1 1 0 1 0 0
## 407 0 1 0 1 1 0 0
## 408 0 0 1 0 1 0 0
## 409 0 0 0 1 0 1 0
## 410 1 0 0 1 0 1 0
## 411 0 0 1 0 0 0 1
## 412 0 0 0 1 0 1 0
## 413 0 1 1 0 1 0 0
## 414 0 1 1 0 0 1 0
## 415 0 1 1 0 0 1 0
## 416 0 1 0 1 0 0 1
## 417 0 1 1 0 0 1 0
## 418 0 0 0 1 0 1 0
## 419 0 1 1 0 1 0 0
## 420 0 1 0 1 0 1 0
## 421 0 1 1 0 0 1 0
## 422 0 0 1 0 1 0 0
## 423 0 0 0 1 0 0 1
## 424 0 1 1 0 0 0 1
## 425 0 0 1 0 0 0 1
## 426 0 1 1 0 0 1 0
## 427 0 1 1 0 0 0 1
## 428 1 0 0 1 0 1 0
## 429 0 1 0 1 0 1 0
## 430 0 0 0 1 0 1 0
## 431 0 0 0 1 0 1 0
## 432 0 1 0 1 0 0 1
## 433 1 0 1 0 1 0 0
## 434 0 0 1 0 1 0 0
## 435 0 0 1 0 0 0 1
## 436 0 0 0 1 0 0 1
## 437 0 0 0 1 0 1 0
## 438 0 0 1 0 1 0 0
## 439 1 0 1 0 0 1 0
## 440 0 0 1 0 1 0 0
## 441 0 0 0 1 1 0 0
## 442 0 0 1 0 1 0 0
## 443 0 0 0 1 0 1 0
## 444 0 1 1 0 0 0 1
## 445 1 0 1 0 1 0 0
## 446 0 0 1 0 1 0 0
## 447 0 0 0 1 0 0 1
## 448 0 0 1 0 0 1 0
## 449 0 0 1 0 0 0 1
## 450 0 1 1 0 0 0 1
## 451 0 0 1 0 0 1 0
## 452 1 0 0 1 0 0 1
## 453 0 1 0 1 0 0 1
## 454 0 0 1 0 0 1 0
## 455 0 1 1 0 0 1 0
## 456 0 0 1 0 0 1 0
## 457 0 0 1 0 0 1 0
## 458 0 0 1 0 0 0 1
## 459 0 0 0 1 0 1 0
## 460 0 0 1 0 0 0 1
## 461 0 0 0 1 1 0 0
## 462 0 0 1 0 0 1 0
## 463 1 0 1 0 0 1 0
## 464 0 0 1 0 1 0 0
## 465 0 0 1 0 0 1 0
## 466 0 0 1 0 0 1 0
## 467 0 0 1 0 0 1 0
## 468 0 0 1 0 0 1 0
## 469 1 0 1 0 0 0 1
## 470 0 0 0 1 0 1 0
## 471 0 1 0 1 0 0 1
## 472 0 0 1 0 0 1 0
## 473 1 0 1 0 1 0 0
## 474 0 0 1 0 0 0 1
## 475 0 0 1 0 0 1 0
## 476 1 0 1 0 0 1 0
## 477 0 1 1 0 0 0 1
## 478 0 0 0 1 0 1 0
## 479 0 0 1 0 0 1 0
## 480 0 0 0 1 0 1 0
## 481 0 0 1 0 1 0 0
## 482 0 1 1 0 0 0 1
## 483 0 0 1 0 0 1 0
## 484 0 0 1 0 0 1 0
## 485 0 0 1 0 0 1 0
## 486 0 0 1 0 1 0 0
## 487 0 0 0 1 0 0 1
## 488 0 1 1 0 0 0 1
## 489 1 0 1 0 0 0 1
## 490 0 0 1 0 1 0 0
## 491 0 0 1 0 0 1 0
## 492 0 0 1 0 0 1 0
## 493 1 0 1 0 1 0 0
## 494 0 0 1 0 1 0 0
## 495 0 0 1 0 0 0 1
## 496 0 0 1 0 0 1 0
## 497 0 0 1 0 0 1 0
## 498 0 0 1 0 0 1 0
## 499 0 1 1 0 0 1 0
## 500 0 0 1 0 0 0 1
## 501 0 1 1 0 1 0 0
## 502 0 0 1 0 0 0 1
## 503 0 0 1 0 0 1 0
## 504 0 0 0 1 1 0 0
## 505 1 0 1 0 0 1 0
## 506 0 1 1 0 0 1 0
## 507 1 0 1 0 0 1 0
## 508 1 0 1 0 1 0 0
## 509 0 1 1 0 1 0 0
## 510 0 0 1 0 1 0 0
## 511 0 1 1 0 1 0 0
## 512 0 0 1 0 0 1 0
## 513 0 0 0 1 0 1 0
## 514 1 0 1 0 0 1 0
## 515 0 0 1 0 1 0 0
## 516 0 0 0 1 1 0 0
## 517 0 0 1 0 0 0 1
## 518 1 0 1 0 0 1 0
## 519 1 0 1 0 1 0 0
## 520 0 0 0 1 1 0 0
## 521 0 0 1 0 1 0 0
## 522 0 0 1 0 0 0 1
## 523 1 0 1 0 1 0 0
## 524 0 0 1 0 1 0 0
## 525 0 1 1 0 0 1 0
## 526 0 0 1 0 0 1 0
## 527 0 0 0 1 0 0 1
## 528 0 0 1 0 1 0 0
## 529 0 0 1 0 1 0 0
## 530 0 0 1 0 0 1 0
## 531 0 0 1 0 0 1 0
## 532 0 0 0 1 1 0 0
## 533 0 0 0 1 0 1 0
## 534 0 0 0 1 0 1 0
## 535 0 0 1 0 0 1 0
## 536 0 0 0 1 1 0 0
## 537 1 0 0 1 1 0 0
## 538 0 0 1 0 1 0 0
## 539 0 0 1 0 0 1 0
## 540 1 0 1 0 1 0 0
## 541 0 0 1 0 1 0 0
## 542 0 0 0 1 0 1 0
## 543 0 0 0 1 0 0 1
## 544 0 0 1 0 0 1 0
## 545 1 0 0 1 0 0 1
## 546 0 0 0 1 0 1 0
## 547 0 0 1 0 0 1 0
## 548 0 0 1 0 0 1 0
## 549 0 0 1 0 0 1 0
## 550 1 0 1 0 1 0 0
## 551 1 0 0 1 0 1 0
## 552 0 0 1 0 0 0 1
## 553 0 0 0 1 0 1 0
## 554 0 0 1 0 1 0 0
## 555 1 0 0 1 1 0 0
## 556 0 0 1 0 0 0 1
## 557 0 0 0 1 1 0 0
## 558 0 0 1 0 1 0 0
## 559 1 0 1 0 0 1 0
## 560 0 0 1 0 1 0 0
## 561 1 0 1 0 1 0 0
## 562 0 0 1 0 1 0 0
## 563 1 0 1 0 0 1 0
## 564 0 0 0 1 1 0 0
## 565 1 0 1 0 0 1 0
## 566 1 0 1 0 1 0 0
## 567 1 0 1 0 0 1 0
## 568 1 0 1 0 1 0 0
## 569 0 0 1 0 0 1 0
## 570 0 0 1 0 1 0 0
## 571 0 0 1 0 0 1 0
## 572 0 0 0 1 0 0 1
## 573 1 0 1 0 0 1 0
## 574 1 0 1 0 0 1 0
## 575 1 0 1 0 1 0 0
## 576 0 0 1 0 0 1 0
## 577 0 0 1 0 1 0 0
## 578 0 0 1 0 0 1 0
## 579 0 0 1 0 0 1 0
## 580 0 0 1 0 0 1 0
## 581 0 0 1 0 0 1 0
## 582 0 0 0 1 0 1 0
## 583 0 0 0 1 0 1 0
## 584 1 0 0 1 1 0 0
## 585 0 0 0 1 1 0 0
## 586 0 1 0 1 1 0 0
## 587 0 0 1 0 1 0 0
## 588 1 0 1 0 0 1 0
## 589 0 0 1 0 1 0 0
## 590 0 0 1 0 0 1 0
## 591 1 0 1 0 0 1 0
## 592 0 0 1 0 1 0 0
## 593 0 0 1 0 0 1 0
## 594 0 0 1 0 0 1 0
## 595 0 0 1 0 0 1 0
## 596 1 0 1 0 0 1 0
## 597 0 0 1 0 0 1 0
## 598 0 0 1 0 1 0 0
## 599 0 0 1 0 0 1 0
## 600 0 0 1 0 0 1 0
## 601 0 0 0 1 0 1 0
## 602 0 1 1 0 0 1 0
## 603 1 0 0 1 0 1 0
## 604 0 0 1 0 1 0 0
## 605 0 1 1 0 1 0 0
## 606 0 0 1 0 1 0 0
## 607 0 0 1 0 0 1 0
## 608 0 0 1 0 1 0 0
## 609 0 0 1 0 0 1 0
## 610 0 1 1 0 0 0 1
## 611 0 0 1 0 1 0 0
## 612 0 0 1 0 0 1 0
## 613 0 0 0 1 0 1 0
## 614 0 0 1 0 0 0 1
## 615 0 0 1 0 0 1 0
## 616 1 0 1 0 0 0 1
## 617 0 0 0 1 1 0 0
## 618 0 1 1 0 0 0 1
## 619 1 0 1 0 0 0 1
## 620 1 0 1 0 1 0 0
## 621 0 1 0 1 0 0 1
## 622 0 1 1 0 0 0 1
## 623 0 0 0 1 1 0 0
## 624 0 0 1 0 0 1 0
## 625 0 0 1 0 0 1 0
## 626 0 1 0 1 0 1 0
## 627 0 0 0 1 0 1 0
## 628 0 0 1 0 0 0 1
## 629 0 1 0 1 1 0 0
## 630 0 0 1 0 1 0 0
## 631 0 0 1 0 0 0 1
## 632 0 1 0 1 0 1 0
## 633 0 0 0 1 1 0 0
## 634 0 0 0 1 0 0 1
## 635 1 0 0 1 0 1 0
## 636 0 0 0 1 0 1 0
## 637 0 1 1 0 1 0 0
## 638 0 0 1 0 0 0 1
## 639 1 0 1 0 1 0 0
## 640 1 0 1 0 1 0 0
## 641 1 0 1 0 1 0 0
## 642 0 0 1 0 1 0 0
## 643 1 0 1 0 0 1 0
## 644 0 0 0 1 0 1 0
## 645 0 1 0 1 1 0 0
## 646 0 0 1 0 0 1 0
## 647 0 0 1 0 0 1 0
## 648 1 0 1 0 0 1 0
## 649 0 0 1 0 1 0 0
## 650 0 1 1 0 0 0 1
## 651 0 0 0 1 0 0 1
## 652 0 0 1 0 0 0 1
## 653 0 0 0 1 0 1 0
## 654 0 0 0 1 0 1 0
## 655 0 1 1 0 0 1 0
## 656 1 0 1 0 0 0 1
## 657 0 1 1 0 0 1 0
## 658 0 0 1 0 0 0 1
## 659 0 0 1 0 0 1 0
## 660 0 0 1 0 0 1 0
## 661 0 0 1 0 0 0 1
## 662 0 0 0 1 1 0 0
## 663 0 0 0 1 0 0 1
## 664 0 0 1 0 0 1 0
## 665 0 0 1 0 0 0 1
## 666 0 0 1 0 0 1 0
## 667 0 0 1 0 1 0 0
## 668 0 0 0 1 0 1 0
## 669 0 0 1 0 1 0 0
## 670 0 0 1 0 0 1 0
## 671 0 0 1 0 0 0 1
## 672 0 0 0 1 0 1 0
## 673 0 0 0 1 0 0 1
## 674 0 0 1 0 0 1 0
## 675 0 0 0 1 0 0 1
## 676 0 0 1 0 0 1 0
## 677 0 0 1 0 0 1 0
## 678 0 0 1 0 1 0 0
## 679 0 0 0 1 0 0 1
## 680 0 0 1 0 0 1 0
## 681 0 0 1 0 0 1 0
## 682 0 0 1 0 0 1 0
## 683 0 0 1 0 0 1 0
## 684 1 0 0 1 0 1 0
## 685 1 0 1 0 0 1 0
## 686 0 0 1 0 1 0 0
## 687 0 0 0 1 1 0 0
## 688 0 0 1 0 0 1 0
## 689 1 0 1 0 1 0 0
## 690 0 0 0 1 0 1 0
## 691 1 0 0 1 1 0 0
## 692 0 0 1 0 0 1 0
## 693 0 0 1 0 0 1 0
## 694 0 0 1 0 0 1 0
## 695 0 0 1 0 0 0 1
## 696 0 0 1 0 0 1 0
## 697 0 1 1 0 0 0 1
## 698 0 0 1 0 0 1 0
## 699 0 0 1 0 0 0 1
## 700 0 1 1 0 1 0 0
## 701 1 0 0 1 1 0 0
## 702 0 0 1 0 0 1 0
## 703 0 0 0 1 0 1 0
## 704 0 0 0 1 0 1 0
## 705 0 1 1 0 0 0 1
## 706 0 1 0 1 0 0 1
## 707 0 0 1 0 0 1 0
## 708 0 0 1 0 0 1 0
## 709 0 0 1 0 1 0 0
## 710 0 0 1 0 0 1 0
## 711 0 0 1 0 0 1 0
## 712 0 0 1 0 0 0 1
## 713 1 0 1 0 1 0 0
## 714 0 0 0 1 1 0 0
## 715 1 0 1 0 0 1 0
## 716 0 0 1 0 0 1 0
## 717 1 0 1 0 1 0 0
## 718 0 0 1 0 0 1 0
## 719 0 0 1 0 0 1 0
## 720 0 1 0 1 0 1 0
## 721 0 0 0 1 0 1 0
## 722 0 0 1 0 1 0 0
## 723 0 0 0 1 0 1 0
## 724 0 0 1 0 0 1 0
## 725 0 0 1 0 0 1 0
## 726 0 0 0 1 0 0 1
## 727 0 0 0 1 0 1 0
## 728 0 0 1 0 1 0 0
## 729 1 0 1 0 0 1 0
## 730 0 0 0 1 1 0 0
## 731 0 0 0 1 1 0 0
## 732 0 0 1 0 0 1 0
## 733 1 0 1 0 1 0 0
## 734 0 0 1 0 0 1 0
## 735 0 0 1 0 0 1 0
## 736 0 1 1 0 1 0 0
## 737 0 0 1 0 1 0 0
## 738 1 0 1 0 0 1 0
## 739 1 0 0 1 0 1 0
## 740 0 0 1 0 0 1 0
## 741 0 0 0 1 0 1 0
## 742 0 0 1 0 0 0 1
## 743 1 0 1 0 0 0 1
## 744 1 0 1 0 0 0 1
## 745 1 0 0 1 0 1 0
## 746 0 1 1 0 0 0 1
## 747 0 0 1 0 0 1 0
## 748 0 0 1 0 0 0 1
## 749 0 0 0 1 0 1 0
## 750 0 0 1 0 0 1 0
## 751 0 0 0 1 0 0 1
## 752 1 0 0 1 0 1 0
## 753 1 0 1 0 0 0 1
## 754 0 0 1 0 0 0 1
## 755 0 0 1 0 0 1 0
## 756 0 0 1 0 0 1 0
## 757 0 0 1 0 0 0 1
## 758 0 0 1 0 0 1 0
## 759 0 0 0 1 0 1 0
## 760 0 0 1 0 0 1 0
## 761 0 0 1 0 0 1 0
## 762 0 0 1 0 0 1 0
## 763 0 1 1 0 1 0 0
## 764 0 0 1 0 1 0 0
## 765 0 0 1 0 0 1 0
## 766 0 0 0 1 1 0 0
## 767 0 0 1 0 0 1 0
## 768 1 0 1 0 1 0 0
## 769 1 0 1 0 0 1 0
## 770 0 0 0 1 1 0 0
## 771 1 0 0 1 0 1 0
## 772 0 0 1 0 0 0 1
## 773 1 0 0 1 0 0 1
## 774 0 0 0 1 0 0 1
## 775 1 0 1 0 1 0 0
## 776 0 0 0 1 0 1 0
## 777 0 0 0 1 1 0 0
## 778 0 0 0 1 0 1 0
## 779 0 0 1 0 0 1 0
## 780 0 0 0 1 0 0 1
## 781 1 0 1 0 0 0 1
## 782 0 0 1 0 0 1 0
## 783 0 0 1 0 0 1 0
## 784 1 0 0 1 0 1 0
## 785 0 0 0 1 0 0 1
## 786 0 0 1 0 1 0 0
## 787 0 0 1 0 0 1 0
## 788 0 0 1 0 0 1 0
## 789 0 0 1 0 1 0 0
## 790 0 0 1 0 0 0 1
## 791 0 1 1 0 0 1 0
## 792 0 0 1 0 0 1 0
## 793 1 0 1 0 0 1 0
## 794 1 0 0 1 0 0 1
## 795 1 0 0 1 1 0 0
## 796 1 0 1 0 0 0 1
## 797 1 0 0 1 1 0 0
## 798 0 0 1 0 0 1 0
## 799 0 0 1 0 1 0 0
## 800 0 0 1 0 1 0 0
## 801 0 0 0 1 0 1 0
## 802 0 0 1 0 0 0 1
## 803 0 0 1 0 1 0 0
## 804 1 0 1 0 1 0 0
## 805 0 1 0 1 0 1 0
## 806 1 0 1 0 0 1 0
## 807 1 0 1 0 0 0 1
## 808 0 0 1 0 0 0 1
## 809 0 0 1 0 1 0 0
## 810 0 0 1 0 1 0 0
## 811 0 1 0 1 1 0 0
## 812 1 0 1 0 0 1 0
## 813 0 0 1 0 0 0 1
## 814 0 0 0 1 0 0 1
## 815 0 0 0 1 0 1 0
## 816 0 0 0 1 0 1 0
## 817 0 0 1 0 0 0 1
## 818 0 0 0 1 0 1 0
## 819 1 0 1 0 0 1 0
## 820 1 0 1 0 0 1 0
## 821 0 0 1 0 1 0 0
## 822 1 0 0 1 0 1 0
## 823 1 0 1 0 1 0 0
## 824 1 0 1 0 1 0 0
## 825 1 0 1 0 0 1 0
## 826 1 0 1 0 0 1 0
## 827 1 0 1 0 0 1 0
## 828 0 0 1 0 0 1 0
## 829 1 0 1 0 0 0 1
## 830 1 0 1 0 0 1 0
## 831 0 0 1 0 1 0 0
## 832 0 1 1 0 0 1 0
## 833 1 0 1 0 0 0 1
## 834 0 0 1 0 0 1 0
## 835 0 0 1 0 0 0 1
## 836 1 0 0 1 0 0 1
## 837 1 0 1 0 1 0 0
## 838 0 0 1 0 0 0 1
## 839 1 0 1 0 0 1 0
## 840 0 1 1 0 0 1 0
## 841 1 0 0 1 0 1 0
## 842 1 0 1 0 1 0 0
## 843 0 0 0 1 0 0 1
## 844 0 0 1 0 0 1 0
## 845 1 0 1 0 0 1 0
## 846 0 0 0 1 0 1 0
## 847 0 0 1 0 0 1 0
## 848 0 0 1 0 0 1 0
## 849 0 0 0 1 0 1 0
## 850 0 0 0 1 0 0 1
## 851 0 0 0 1 0 1 0
## 852 0 1 0 1 0 1 0
## 853 0 0 1 0 1 0 0
## 854 0 0 1 0 0 0 1
## 855 1 0 1 0 0 0 1
## 856 0 1 1 0 0 1 0
## 857 0 1 0 1 1 0 0
## 858 0 0 0 1 0 1 0
## 859 1 0 1 0 0 1 0
## 860 0 0 1 0 1 0 0
## 861 0 0 0 1 0 0 1
## 862 0 1 1 0 1 0 0
## 863 0 0 0 1 0 1 0
## 864 0 1 1 0 0 0 1
## 865 0 1 1 0 0 0 1
## 866 0 0 1 0 1 0 0
## 867 0 0 1 0 0 1 0
## 868 0 0 1 0 0 0 1
## 869 0 1 0 1 0 1 0
## 870 0 0 1 0 1 0 0
## 871 0 0 0 1 0 1 0
## 872 0 1 1 0 0 1 0
## 873 0 0 1 0 0 1 0
## 874 1 0 1 0 1 0 0
## 875 1 0 1 0 0 0 1
## 876 1 0 0 1 0 0 1
## 877 0 0 1 0 1 0 0
## 878 0 0 1 0 0 0 1
## 879 0 1 1 0 1 0 0
## 880 1 0 0 1 0 1 0
## 881 0 0 1 0 0 1 0
## 882 1 0 0 1 0 0 1
## 883 0 0 1 0 0 1 0
## 884 0 0 0 1 1 0 0
## 885 0 0 1 0 0 1 0
## 886 1 0 1 0 0 1 0
## 887 1 0 1 0 0 1 0
## 888 0 0 0 1 0 0 1
## 889 0 1 1 0 0 1 0
## 890 0 0 1 0 1 0 0
## 891 0 1 1 0 1 0 0
## 892 0 0 1 0 1 0 0
## 893 1 0 1 0 0 1 0
## 894 1 0 1 0 0 0 1
## 895 0 0 0 1 0 1 0
## 896 0 0 1 0 1 0 0
## 897 0 0 1 0 0 1 0
## 898 0 1 1 0 1 0 0
## 899 0 0 1 0 1 0 0
## 900 0 0 1 0 0 1 0
## 901 0 0 1 0 0 1 0
## 902 0 0 1 0 1 0 0
## 903 1 0 1 0 1 0 0
## 904 0 0 1 0 1 0 0
## 905 0 0 0 1 0 0 1
## 906 0 0 1 0 0 0 1
## 907 0 0 1 0 1 0 0
## 908 0 0 0 1 0 0 1
## 909 0 1 0 1 1 0 0
## 910 0 1 1 0 0 1 0
## 911 1 0 1 0 0 1 0
## 912 0 0 1 0 0 1 0
## 913 0 1 1 0 1 0 0
## 914 0 1 1 0 0 1 0
## 915 0 1 0 1 0 1 0
## 916 0 0 1 0 0 0 1
## 917 1 0 1 0 0 1 0
## 918 0 0 1 0 0 0 1
## 919 1 0 0 1 0 1 0
## 920 0 0 0 1 0 1 0
## 921 0 0 0 1 0 1 0
## 922 0 0 1 0 0 1 0
## 923 1 0 1 0 1 0 0
## 924 0 0 1 0 0 1 0
## 925 0 0 0 1 0 1 0
## 926 0 0 0 1 0 0 1
## 927 0 0 1 0 0 1 0
## 928 0 0 0 1 0 1 0
## 929 0 1 0 1 1 0 0
## 930 0 0 1 0 0 0 1
## 931 0 0 1 0 0 1 0
## 932 0 0 0 1 1 0 0
## 933 0 0 0 1 0 1 0
## 934 0 1 0 1 0 0 1
## 935 0 0 1 0 0 1 0
## 936 0 0 1 0 1 0 0
## 937 0 0 0 1 0 0 1
## 938 0 0 1 0 0 1 0
## 939 0 0 1 0 1 0 0
## 940 0 0 1 0 0 0 1
## 941 0 0 1 0 1 0 0
## 942 0 1 0 1 1 0 0
## 943 0 1 1 0 0 0 1
## 944 0 0 0 1 0 0 1
## 945 1 0 0 1 0 0 1
## 946 1 0 1 0 0 1 0
## 947 0 0 0 1 0 1 0
## 948 0 0 1 0 1 0 0
## 949 0 0 0 1 1 0 0
## 950 0 0 1 0 0 0 1
## 951 0 1 1 0 0 0 1
## 952 0 1 0 1 0 0 1
## 953 0 0 1 0 0 0 1
## 954 0 0 1 0 0 1 0
## 955 0 0 1 0 0 1 0
## 956 0 0 0 1 0 0 1
## 957 0 1 1 0 1 0 0
## 958 0 0 1 0 0 0 1
## 959 0 0 1 0 1 0 0
## 960 0 1 0 1 0 1 0
## 961 0 0 0 1 0 0 1
## 962 0 1 1 0 0 1 0
## 963 0 0 0 1 1 0 0
## 964 1 0 1 0 1 0 0
## 965 0 0 1 0 1 0 0
## 966 0 1 1 0 1 0 0
## 967 0 0 0 1 0 1 0
## 968 0 1 1 0 0 1 0
## 969 0 1 1 0 0 1 0
## 970 0 1 0 1 0 0 1
## 971 0 0 1 0 0 0 1
## 972 0 0 0 1 0 1 0
## 973 1 0 1 0 0 1 0
## 974 1 0 1 0 0 0 1
## 975 0 0 1 0 0 0 1
## 976 0 0 0 1 1 0 0
## 977 0 1 1 0 0 0 1
## 978 0 0 0 1 0 1 0
## 979 0 1 0 1 0 0 1
## 980 0 0 1 0 0 1 0
## 981 0 1 0 1 0 1 0
## 982 0 0 1 0 1 0 0
## 983 0 0 0 1 1 0 0
## 984 0 0 1 0 1 0 0
## 985 0 1 0 1 1 0 0
## 986 0 0 0 1 1 0 0
## 987 0 0 1 0 0 1 0
## 988 0 0 0 1 0 1 0
## 989 0 1 1 0 0 0 1
## 990 0 0 1 0 0 0 1
## 991 0 0 0 1 0 0 1
## 992 0 0 0 1 0 0 1
## 993 1 0 1 0 1 0 0
## 994 0 1 1 0 1 0 0
## 995 0 0 1 0 0 1 0
## 996 0 1 1 0 1 0 0
## 997 0 0 0 1 0 1 0
## 998 1 0 0 1 0 0 1
## 999 0 0 1 0 0 1 0
## 1000 1 0 1 0 0 1 0
## 1001 0 0 1 0 1 0 0
## 1002 0 0 1 0 1 0 0
## 1003 0 1 1 0 0 0 1
## 1004 1 0 1 0 0 0 1
## 1005 0 0 0 1 0 0 1
## 1006 0 1 1 0 0 1 0
## 1007 0 0 1 0 0 0 1
## 1008 0 1 0 1 0 1 0
## 1009 0 1 1 0 0 1 0
## 1010 0 1 0 1 0 1 0
## 1011 0 0 1 0 1 0 0
## 1012 0 0 1 0 0 1 0
## 1013 1 0 1 0 1 0 0
## 1014 0 0 0 1 0 0 1
## 1015 0 0 1 0 0 0 1
## 1016 0 0 1 0 0 1 0
## 1017 0 0 1 0 1 0 0
## 1018 0 0 1 0 1 0 0
## 1019 0 0 1 0 0 0 1
## 1020 0 1 1 0 0 0 1
## 1021 0 0 0 1 1 0 0
## 1022 0 1 1 0 0 0 1
## 1023 0 1 1 0 0 1 0
## 1024 1 0 1 0 0 0 1
## 1025 0 1 1 0 0 1 0
## 1026 1 0 1 0 0 1 0
## 1027 0 1 1 0 1 0 0
## 1028 1 0 1 0 0 0 1
## 1029 0 0 0 1 0 0 1
## 1030 0 0 0 1 1 0 0
## 1031 0 0 0 1 0 1 0
## 1032 0 1 1 0 0 0 1
## 1033 0 0 0 1 0 0 1
## 1034 0 0 0 1 1 0 0
## 1035 0 0 0 1 0 0 1
## 1036 0 0 1 0 0 1 0
## 1037 1 0 0 1 1 0 0
## 1038 0 0 1 0 1 0 0
## 1039 0 0 1 0 0 1 0
## 1040 0 0 1 0 1 0 0
## 1041 0 0 1 0 1 0 0
## 1042 1 0 1 0 0 0 1
## 1043 0 0 0 1 0 1 0
## 1044 0 1 1 0 0 0 1
## 1045 0 0 0 1 0 0 1
## 1046 0 0 1 0 0 0 1
## 1047 0 0 1 0 0 0 1
## 1048 0 1 1 0 0 1 0
## 1049 0 1 1 0 1 0 0
## 1050 0 0 0 1 0 0 1
## 1051 0 0 0 1 0 1 0
## 1052 0 0 1 0 1 0 0
## 1053 0 0 0 1 0 1 0
## 1054 0 0 1 0 0 1 0
## 1055 0 0 1 0 0 0 1
## 1056 0 0 0 1 0 1 0
## 1057 0 0 1 0 0 0 1
## 1058 0 1 0 1 0 1 0
## 1059 0 1 0 1 0 1 0
## 1060 0 0 0 1 0 1 0
## 1061 0 1 0 1 0 1 0
## 1062 0 0 1 0 1 0 0
## 1063 0 0 0 1 0 1 0
## 1064 0 0 1 0 1 0 0
## 1065 0 0 1 0 0 0 1
## 1066 0 0 1 0 1 0 0
## 1067 0 0 0 1 0 0 1
## 1068 0 1 0 1 0 0 1
## 1069 0 0 0 1 0 1 0
## 1070 0 0 1 0 0 1 0
## 1071 0 0 0 1 0 0 1
## 1072 0 0 0 1 0 1 0
## 1073 1 0 1 0 1 0 0
## 1074 0 0 1 0 1 0 0
## 1075 0 0 1 0 0 1 0
## 1076 0 0 1 0 1 0 0
## 1077 0 0 0 1 0 1 0
## 1078 1 0 1 0 0 0 1
## 1079 0 1 0 1 0 1 0
## 1080 0 0 1 0 1 0 0
## 1081 0 1 0 1 0 0 1
## 1082 1 0 1 0 0 1 0
## 1083 0 1 1 0 0 1 0
## 1084 0 0 1 0 0 1 0
## 1085 0 0 0 1 0 0 1
## 1086 0 0 1 0 0 0 1
## 1087 0 0 0 1 0 1 0
## 1088 0 1 1 0 0 1 0
## 1089 0 1 0 1 1 0 0
## 1090 0 1 1 0 1 0 0
## 1091 0 0 0 1 0 1 0
## 1092 0 0 1 0 0 1 0
## 1093 1 0 1 0 0 0 1
## 1094 1 0 1 0 1 0 0
## 1095 0 0 1 0 0 0 1
## 1096 0 0 1 0 1 0 0
## 1097 0 0 1 0 1 0 0
## 1098 0 0 0 1 1 0 0
## 1099 0 0 1 0 1 0 0
## 1100 1 0 0 1 0 0 1
## 1101 0 0 1 0 1 0 0
## 1102 0 0 1 0 0 1 0
## 1103 0 1 1 0 0 0 1
## 1104 1 0 1 0 1 0 0
## 1105 0 0 1 0 0 1 0
## 1106 0 1 1 0 1 0 0
## 1107 0 0 1 0 0 1 0
## 1108 0 1 1 0 0 0 1
## 1109 0 1 0 1 0 1 0
## 1110 0 0 0 1 0 0 1
## 1111 0 0 1 0 0 1 0
## 1112 0 0 0 1 0 1 0
## 1113 1 0 1 0 0 1 0
## 1114 0 0 1 0 0 1 0
## 1115 0 0 1 0 0 0 1
## 1116 1 0 1 0 0 1 0
## 1117 1 0 0 1 0 0 1
## 1118 0 0 1 0 1 0 0
## 1119 0 0 1 0 1 0 0
## 1120 0 0 1 0 0 1 0
## 1121 0 0 1 0 0 1 0
## 1122 0 1 0 1 0 1 0
## 1123 1 0 0 1 1 0 0
## 1124 0 0 0 1 0 0 1
## 1125 0 0 1 0 0 1 0
## 1126 0 0 0 1 0 0 1
## 1127 0 0 0 1 0 1 0
## 1128 0 0 1 0 0 1 0
## 1129 1 0 0 1 0 1 0
## 1130 0 0 0 1 0 1 0
## 1131 1 0 1 0 1 0 0
## 1132 0 0 0 1 0 1 0
## 1133 0 0 1 0 0 1 0
## 1134 0 0 0 1 0 1 0
## 1135 0 0 0 1 0 0 1
## 1136 0 0 0 1 0 1 0
## 1137 0 0 1 0 0 0 1
## 1138 0 0 1 0 0 0 1
## 1139 1 0 1 0 0 1 0
## 1140 0 1 1 0 0 1 0
## 1141 1 0 1 0 1 0 0
## 1142 1 0 0 1 1 0 0
## 1143 0 0 1 0 1 0 0
## 1144 0 1 1 0 0 1 0
## 1145 0 0 1 0 0 1 0
## 1146 0 0 0 1 0 0 1
## 1147 0 1 1 0 1 0 0
## 1148 0 1 0 1 0 0 1
## 1149 0 0 0 1 0 1 0
## 1150 0 0 0 1 0 0 1
## 1151 0 0 1 0 1 0 0
## 1152 0 0 1 0 0 1 0
## 1153 0 1 1 0 0 1 0
## 1154 0 0 1 0 0 0 1
## 1155 1 0 1 0 0 1 0
## 1156 0 1 0 1 1 0 0
## 1157 0 0 1 0 1 0 0
## 1158 1 0 0 1 0 1 0
## 1159 0 0 1 0 0 1 0
## 1160 0 1 0 1 1 0 0
## 1161 1 0 0 1 0 1 0
## 1162 0 1 1 0 0 0 1
## 1163 0 1 0 1 0 1 0
## 1164 0 0 1 0 0 1 0
## 1165 0 0 1 0 1 0 0
## 1166 0 0 1 0 1 0 0
## 1167 0 1 1 0 0 1 0
## 1168 0 0 0 1 0 0 1
## 1169 0 0 1 0 0 0 1
## 1170 0 0 0 1 1 0 0
## 1171 1 0 1 0 1 0 0
## 1172 1 0 1 0 1 0 0
## 1173 0 1 1 0 0 1 0
## 1174 1 0 0 1 1 0 0
## 1175 0 0 0 1 0 1 0
## 1176 0 0 0 1 1 0 0
## 1177 1 0 1 0 0 1 0
## 1178 0 0 0 1 0 0 1
## 1179 0 0 1 0 0 1 0
## 1180 0 0 1 0 0 1 0
## 1181 0 1 1 0 1 0 0
## 1182 0 1 1 0 0 1 0
## 1183 1 0 1 0 1 0 0
## 1184 1 0 1 0 1 0 0
## 1185 1 0 1 0 1 0 0
## 1186 1 0 1 0 1 0 0
## 1187 1 0 0 1 0 0 1
## 1188 0 0 1 0 1 0 0
## 1189 0 0 1 0 0 1 0
## 1190 1 0 1 0 0 1 0
## 1191 0 0 1 0 0 0 1
## 1192 1 0 1 0 0 1 0
## 1193 1 0 1 0 1 0 0
## 1194 0 0 0 1 1 0 0
## 1195 1 0 0 1 0 1 0
## 1196 0 1 1 0 0 1 0
## 1197 0 0 1 0 0 1 0
## 1198 0 0 1 0 0 1 0
## 1199 0 1 1 0 0 1 0
## 1200 0 0 0 1 0 1 0
## 1201 0 0 1 0 0 0 1
## 1202 0 0 1 0 0 1 0
## 1203 0 1 0 1 1 0 0
## 1204 0 0 0 1 0 0 1
## 1205 0 0 0 1 0 0 1
## 1206 1 0 1 0 1 0 0
## 1207 0 0 0 1 1 0 0
## 1208 0 0 1 0 0 0 1
## 1209 0 0 0 1 0 0 1
## 1210 0 1 1 0 1 0 0
## 1211 0 0 1 0 0 0 1
## 1212 0 0 0 1 1 0 0
## 1213 0 0 1 0 0 1 0
## 1214 0 0 1 0 0 1 0
## 1215 0 0 1 0 0 0 1
## 1216 1 0 1 0 1 0 0
## 1217 0 0 1 0 0 0 1
## 1218 1 0 1 0 0 0 1
## 1219 0 0 1 0 1 0 0
## 1220 1 0 1 0 0 0 1
## 1221 1 0 0 1 1 0 0
## 1222 1 0 0 1 1 0 0
## 1223 0 0 1 0 1 0 0
## 1224 0 0 1 0 0 0 1
## 1225 1 0 1 0 0 0 1
## 1226 0 0 0 1 0 0 1
## 1227 0 0 0 1 0 0 1
## 1228 0 0 0 1 0 1 0
## 1229 0 1 1 0 0 1 0
## 1230 0 0 1 0 0 1 0
## 1231 0 0 1 0 0 1 0
## 1232 0 0 0 1 0 0 1
## 1233 0 0 1 0 0 1 0
## 1234 0 0 1 0 0 0 1
## 1235 1 0 0 1 0 1 0
## 1236 0 0 1 0 0 1 0
## 1237 1 0 0 1 0 1 0
## 1238 0 0 1 0 0 1 0
## 1239 0 1 1 0 1 0 0
## 1240 1 0 0 1 0 1 0
## 1241 0 1 0 1 0 1 0
## 1242 0 0 1 0 0 0 1
## 1243 0 1 1 0 0 0 1
## 1244 0 1 1 0 0 0 1
## 1245 0 0 1 0 1 0 0
## 1246 0 1 0 1 0 0 1
## 1247 1 0 1 0 0 0 1
## 1248 0 0 0 1 0 0 1
## 1249 0 0 0 1 0 1 0
## 1250 0 0 0 1 0 1 0
## 1251 1 0 0 1 0 0 1
## 1252 0 1 0 1 0 1 0
## 1253 0 0 1 0 0 0 1
## 1254 0 0 1 0 0 1 0
## 1255 0 0 1 0 0 0 1
## 1256 0 0 1 0 0 1 0
## 1257 0 0 1 0 1 0 0
## 1258 0 1 1 0 0 1 0
## 1259 0 1 0 1 0 1 0
## 1260 0 0 1 0 0 1 0
## 1261 0 0 1 0 1 0 0
## 1262 0 1 1 0 0 0 1
## 1263 0 0 1 0 0 0 1
## 1264 1 0 1 0 0 0 1
## 1265 0 0 1 0 0 0 1
## 1266 1 0 0 1 0 0 1
## 1267 0 0 1 0 1 0 0
## 1268 0 0 1 0 0 1 0
## 1269 1 0 1 0 0 1 0
## 1270 1 0 1 0 0 1 0
## 1271 0 1 1 0 1 0 0
## 1272 0 0 1 0 0 1 0
## 1273 0 0 1 0 1 0 0
## 1274 0 0 0 1 0 1 0
## 1275 1 0 0 1 1 0 0
## 1276 0 1 1 0 0 1 0
## 1277 0 0 0 1 0 0 1
## 1278 0 0 1 0 1 0 0
## 1279 0 0 0 1 0 0 1
## 1280 0 0 1 0 1 0 0
## 1281 0 0 1 0 1 0 0
## 1282 1 0 0 1 1 0 0
## 1283 1 0 0 1 0 0 1
## 1284 1 0 0 1 0 0 1
## 1285 0 1 0 1 1 0 0
## 1286 0 0 1 0 0 0 1
## 1287 0 0 0 1 0 1 0
## 1288 0 0 1 0 1 0 0
## 1289 0 0 1 0 0 0 1
## 1290 0 0 1 0 0 1 0
## 1291 0 0 1 0 0 0 1
## 1292 1 0 1 0 0 0 1
## 1293 0 0 1 0 0 1 0
## 1294 0 0 0 1 0 0 1
## 1295 0 0 1 0 1 0 0
## 1296 0 0 0 1 0 1 0
## 1297 0 0 0 1 0 0 1
## 1298 0 0 1 0 0 1 0
## 1299 0 0 0 1 1 0 0
## 1300 0 0 1 0 0 0 1
## 1301 1 0 1 0 0 1 0
## 1302 0 0 1 0 1 0 0
## 1303 1 0 1 0 1 0 0
## 1304 0 0 1 0 0 1 0
## 1305 0 0 0 1 0 1 0
## 1306 0 0 1 0 0 0 1
## 1307 0 0 1 0 0 0 1
## 1308 0 1 0 1 0 1 0
## 1309 0 1 1 0 0 0 1
## 1310 0 1 1 0 0 1 0
## 1311 0 1 1 0 0 0 1
## 1312 0 0 0 1 0 1 0
## 1313 0 0 1 0 0 1 0
## 1314 0 0 1 0 0 1 0
## 1315 0 0 1 0 0 1 0
## 1316 0 0 0 1 0 0 1
## 1317 1 0 1 0 0 1 0
## 1318 0 0 1 0 0 0 1
## 1319 0 0 1 0 1 0 0
## 1320 0 0 1 0 1 0 0
## 1321 0 1 0 1 0 0 1
## 1322 0 0 1 0 1 0 0
## 1323 0 0 0 1 0 0 1
## 1324 0 0 0 1 0 1 0
## 1325 0 0 1 0 0 0 1
## 1326 0 0 0 1 0 1 0
## 1327 0 0 0 1 0 1 0
## 1328 0 1 1 0 0 0 1
## 1329 0 0 1 0 0 0 1
## 1330 0 0 1 0 0 0 1
## 1331 0 0 1 0 0 0 1
## 1332 1 0 1 0 1 0 0
## 1333 1 0 0 1 0 0 1
## 1334 0 1 1 0 0 0 1
## 1335 0 1 1 0 0 0 1
## 1336 0 0 1 0 0 0 1
## 1337 0 0 0 1 0 1 0
## 1338 0 1 1 0 0 0 1
## 1339 1 0 0 1 1 0 0
## 1340 0 1 1 0 0 0 1
## 1341 1 0 1 0 0 1 0
## 1342 0 0 1 0 0 1 0
## 1343 1 0 1 0 0 1 0
## 1344 0 1 0 1 0 0 1
## 1345 0 0 1 0 0 1 0
## 1346 0 0 1 0 1 0 0
## 1347 1 0 1 0 0 0 1
## 1348 0 1 1 0 0 1 0
## 1349 0 0 1 0 0 0 1
## 1350 0 1 1 0 1 0 0
## 1351 0 0 0 1 0 0 1
## 1352 0 1 1 0 0 1 0
## 1353 0 0 0 1 1 0 0
## 1354 0 1 0 1 0 1 0
## 1355 0 1 1 0 1 0 0
## 1356 1 0 1 0 1 0 0
## 1357 0 0 1 0 0 0 1
## 1358 0 0 1 0 0 0 1
## 1359 0 1 1 0 1 0 0
## 1360 0 0 0 1 0 1 0
## 1361 0 1 1 0 0 0 1
## 1362 0 0 1 0 1 0 0
## 1363 0 0 1 0 0 0 1
## 1364 0 0 0 1 0 1 0
## 1365 0 0 1 0 0 1 0
## 1366 0 0 0 1 1 0 0
## 1367 0 0 1 0 1 0 0
## 1368 0 0 0 1 0 0 1
## 1369 1 0 1 0 0 1 0
## 1370 0 0 1 0 0 1 0
## 1371 0 0 1 0 1 0 0
## 1372 0 1 1 0 1 0 0
## 1373 1 0 0 1 0 1 0
## 1374 0 0 0 1 0 1 0
## 1375 1 0 1 0 1 0 0
## 1376 0 0 1 0 0 1 0
## 1377 0 1 0 1 0 1 0
## 1378 0 0 1 0 0 1 0
## 1379 0 1 1 0 0 1 0
## 1380 0 0 1 0 1 0 0
## 1381 1 0 0 1 0 0 1
## 1382 0 1 1 0 0 0 1
## 1383 0 1 0 1 0 0 1
## 1384 0 0 1 0 0 0 1
## 1385 0 0 0 1 1 0 0
## 1386 0 0 1 0 0 1 0
## 1387 1 0 1 0 0 0 1
## 1388 0 0 1 0 1 0 0
## 1389 1 0 1 0 0 1 0
## 1390 0 0 1 0 1 0 0
## 1391 1 0 1 0 0 1 0
## 1392 1 0 0 1 0 1 0
## 1393 0 0 1 0 0 1 0
## 1394 0 0 0 1 0 1 0
## 1395 0 0 1 0 0 1 0
## 1396 0 0 0 1 1 0 0
## 1397 1 0 1 0 0 1 0
## 1398 1 0 1 0 0 1 0
## 1399 0 0 1 0 0 0 1
## 1400 1 0 1 0 1 0 0
## 1401 0 0 1 0 0 0 1
## 1402 0 1 1 0 1 0 0
## 1403 1 0 1 0 1 0 0
## 1404 0 0 1 0 0 1 0
## 1405 0 0 1 0 1 0 0
## 1406 0 1 1 0 0 0 1
## 1407 0 1 1 0 0 1 0
## 1408 1 0 1 0 0 1 0
## 1409 0 0 1 0 1 0 0
## 1410 1 0 0 1 0 1 0
## 1411 0 1 1 0 0 1 0
## 1412 0 0 1 0 0 1 0
## 1413 0 0 1 0 0 1 0
## 1414 0 1 1 0 0 1 0
## 1415 0 1 1 0 1 0 0
## 1416 0 0 0 1 0 0 1
## 1417 0 0 1 0 0 0 1
## 1418 0 0 0 1 0 1 0
## 1419 0 0 0 1 0 1 0
## 1420 0 0 1 0 1 0 0
## 1421 0 1 0 1 0 0 1
## 1422 0 0 1 0 0 1 0
## 1423 0 0 1 0 0 1 0
## 1424 1 0 1 0 0 1 0
## 1425 0 0 1 0 0 1 0
## 1426 0 1 1 0 0 1 0
## 1427 0 0 1 0 0 0 1
## 1428 0 0 1 0 0 0 1
## 1429 0 0 0 1 0 1 0
## 1430 0 0 1 0 0 1 0
## 1431 0 1 0 1 0 0 1
## 1432 0 0 1 0 0 1 0
## 1433 0 0 0 1 0 0 1
## 1434 0 0 0 1 0 1 0
## 1435 0 0 0 1 0 1 0
## 1436 0 1 1 0 0 0 1
## 1437 0 1 0 1 0 1 0
## 1438 0 0 0 1 1 0 0
## 1439 1 0 1 0 0 1 0
## 1440 0 1 1 0 0 1 0
## 1441 0 0 1 0 0 1 0
## 1442 1 0 1 0 0 0 1
## 1443 0 0 0 1 0 1 0res.mca$eig## eigenvalue percentage of variance cumulative percentage of variance
## dim 1 0.29465047 15.599142 15.59914
## dim 2 0.19534952 10.342034 25.94118
## dim 3 0.16938527 8.967455 34.90863
## dim 4 0.13026750 6.896515 41.80515
## dim 5 0.12491918 6.613368 48.41851
## dim 6 0.11517958 6.097743 54.51626
## dim 7 0.11331419 5.998986 60.51524
## dim 8 0.10648238 5.637303 66.15255
## dim 9 0.10435832 5.524852 71.67740
## dim 10 0.10076800 5.334776 77.01217
## dim 11 0.09768371 5.171491 82.18366
## dim 12 0.08344159 4.417496 86.60116
## dim 13 0.07024702 3.718960 90.32012
## dim 14 0.06126647 3.243519 93.56364
## dim 15 0.04966908 2.629540 96.19318
## dim 16 0.04622352 2.447128 98.64031
## dim 17 0.02568309 1.359693 100.00000## Contribucciones
res.mca$var$contrib## Dim 1 Dim 2 Dim 3 Dim 4
## 18-29 9.58674780 6.46994334 5.036202619 1.23633231
## 30-59 3.57429216 3.98716943 0.016420057 5.78527992
## >60 0.49144089 0.68653646 19.716936092 27.17859573
## H 0.31725355 0.25883574 0.191673539 9.33586459
## M 0.14764038 0.13420567 0.103859313 4.44576878
## Otro 0.02667903 0.02306009 0.057482002 0.26153506
## Cali 2.23612486 7.03186159 0.211855660 2.29428246
## Palmira 2.75881039 8.67553202 0.261376100 2.83056211
## Estrato 1-2 6.66735305 5.93244516 2.016847963 1.89924841
## Estrato 3-4 0.27475527 0.14787088 4.343469410 1.51205438
## Estrato 5-6 4.26670667 10.31378962 2.086454554 0.06891745
## Téc/Bach 11.24925738 0.06150919 1.616714927 0.31711590
## Univ 0.00480892 0.86918404 7.999460365 5.31539362
## Mst/Doc 11.66830841 1.64053685 3.500063992 3.98495335
## <1smlv 15.82232964 4.41190793 2.305803248 2.45933321
## 1-3smlv 0.06979527 11.76774244 7.465605893 4.46708195
## >3smlv 13.69924371 2.28218374 1.787803653 0.48079518
## Empleado 4.46238015 3.77100360 3.042330667 1.68570460
## Estudiante 7.45804136 18.63466508 5.600773179 0.32388384
## Independiente 0.43660411 0.10642785 0.053950844 13.63521728
## Jubilado/Desempleado 3.99612815 0.04230874 27.447374265 1.21031132
## No 0.13155068 0.25024441 0.312134132 1.01949601
## Si 0.30786513 0.58564143 0.730480573 2.38590386
## Bajo 0.02190182 4.95206454 1.836164095 2.97378688
## Medio 0.17157142 0.02417543 0.000349034 0.01945029
## Alto 0.15240981 6.93915472 2.258413825 2.87313149
## Dim 5
## 18-29 0.084712679
## 30-59 0.007337573
## >60 0.127685777
## H 22.664838375
## M 10.569466100
## Otro 1.764037589
## Cali 1.334573639
## Palmira 1.646525063
## Estrato 1-2 0.002314975
## Estrato 3-4 2.648995527
## Estrato 5-6 5.226973215
## Téc/Bach 1.861850679
## Univ 3.585380540
## Mst/Doc 0.549876748
## <1smlv 0.014415508
## 1-3smlv 0.272772826
## >3smlv 0.162384428
## Empleado 0.064010757
## Estudiante 0.845887922
## Independiente 0.033584579
## Jubilado/Desempleado 0.381065235
## No 6.785523369
## Si 15.880009550
## Bajo 13.391720855
## Medio 10.041099481
## Alto 0.052957009library(ggrepel)
options(ggrepel.max.overlaps = Inf)
fviz_mca_biplot(res.mca, repel=TRUE,cex=0.6,geom.ind = c("point"),geom.var = c("point", "text"),col.ind ="#C1CDCD", col.var="#8B1A1A")## Warning: Duplicated aesthetics after name standardisation: size
## Duplicated aesthetics after name standardisation: size
cluster
library(FactoMineR)
cluster.votaciones<-HCPC(res.mca,nb.clust=-1,consol=FALSE,graph=FALSE)
library(ggrepel)
options(ggrepel.max.overlaps = Inf)
plot.HCPC(cluster.votaciones,choice='map',draw.tree=FALSE,title = '',axes = c(1,2),ylim=c(-1.7,1.7),ind.names = FALSE)
##P values
summary(cluster.votaciones)## Length Class Mode
## data.clust 10 data.frame list
## desc.var 3 catdes list
## desc.axes 3 catdes list
## desc.ind 2 -none- list
## call 7 -none- list#descripcion de los cluster con los valores test
cluster.votaciones$desc.var #valores test##
## Link between the cluster variable and the categorical variables (chi-square test)
## =================================================================================
## p.value df
## Ingresos 0.000000e+00 4
## Educacion 1.633042e-189 4
## Ocu 1.535558e-175 6
## Edad_g 8.306674e-114 4
## Estrato 3.199314e-76 4
## Municipio 1.598873e-26 2
## IPRG 4.802382e-11 4
## Sexo 9.967112e-06 4
## ViveConEnferCronicas 3.616930e-03 2
##
## Description of each cluster by the categories
## =============================================
## $`1`
## Cla/Mod Mod/Cla Global p.value v.test
## Ingresos=>3smlv 83.582090 86.990291 37.144837 2.763024e-202 30.346871
## Educacion=Mst/Doc 85.491071 74.368932 31.046431 2.156318e-160 26.986024
## Estrato=Estrato 5-6 70.501475 46.407767 23.492723 2.895141e-51 15.061640
## Edad_g=>60 96.739130 17.281553 6.375606 3.086511e-38 12.929039
## Municipio=Cali 48.055207 74.368932 55.232155 2.032146e-28 11.056802
## Edad_g=30-59 43.817787 78.446602 63.894664 2.224735e-18 8.745275
## Ocu=Empleado 44.488712 65.048544 52.182952 2.469413e-13 7.320558
## Sexo=H 44.514768 40.970874 32.848233 1.206921e-06 4.854498
## ViveConEnferCronicas=No 38.180020 74.951456 70.062370 2.385014e-03 3.037561
## Ocu=Independiente 42.264151 21.747573 18.364518 1.436419e-02 2.448024
## ViveConEnferCronicas=Si 29.861111 25.048544 29.937630 2.385014e-03 -3.037561
## Ocu=Jubilado/Desempleado 26.244344 11.262136 15.315315 1.225439e-03 -3.232892
## IPRG=Alto 28.021978 19.805825 25.225225 3.625769e-04 -3.565924
## Sexo=M 31.502591 59.029126 66.874567 2.864759e-06 -4.680285
## Educacion=Univ 19.782214 21.165049 38.184338 3.976028e-24 -10.132200
## Municipio=Palmira 20.433437 25.631068 44.767845 2.032146e-28 -11.056802
## Ocu=Estudiante 4.901961 1.941748 14.137214 7.974452e-29 -11.140418
## Ingresos=1-3smlv 11.089109 10.873786 34.996535 1.827993e-51 -15.092006
## Estrato=Estrato 1-2 7.425743 5.825243 27.997228 6.910301e-52 -15.156049
## Edad_g=18-29 5.128205 4.271845 29.729730 2.840273e-67 -17.329041
## Educacion=Téc/Bach 5.180180 4.466019 30.769231 4.565094e-70 -17.695212
## Ingresos=<1smlv 2.736318 2.135922 27.858628 7.124286e-75 -18.308155
##
## $`2`
## Cla/Mod Mod/Cla Global p.value v.test
## Ingresos=1-3smlv 81.386139 81.386139 34.996535 1.821093e-169 27.748412
## Ocu=Empleado 51.792829 77.227723 52.182952 4.386362e-46 14.251519
## Educacion=Univ 57.713249 62.970297 38.184338 1.182407e-45 14.182108
## Municipio=Palmira 43.808050 56.039604 44.767845 2.860689e-10 6.306146
## Edad_g=30-59 40.672451 74.257426 63.894664 1.167717e-09 6.084615
## Estrato=Estrato 3-4 42.000000 58.217822 48.510049 6.178149e-08 5.413567
## IPRG=Alto 46.703297 33.663366 25.225225 9.257423e-08 5.340728
## Estrato=Estrato 1-2 44.306931 35.445545 27.997228 4.790900e-06 4.573742
## ViveConEnferCronicas=Si 40.509259 34.653465 29.937630 4.368762e-03 2.850230
## Edad_g=18-29 30.303030 25.742574 29.729730 1.465903e-02 -2.440695
## ViveConEnferCronicas=No 32.640950 65.346535 70.062370 4.368762e-03 -2.850230
## IPRG=Bajo 23.556582 20.198020 30.006930 1.272217e-09 -6.070867
## Municipio=Cali 27.854454 43.960396 55.232155 2.860689e-10 -6.306146
## Edad_g=>60 0.000000 0.000000 6.375606 1.183326e-18 -8.816273
## Ocu=Estudiante 3.431373 1.386139 14.137214 2.056435e-31 -11.659334
## Ocu=Jubilado/Desempleado 4.072398 1.782178 15.315315 1.636841e-32 -11.872905
## Estrato=Estrato 5-6 9.439528 6.336634 23.492723 5.364841e-34 -12.155476
## Ingresos=>3smlv 12.500000 13.267327 37.144837 5.082929e-47 -14.401232
## Ingresos=<1smlv 6.716418 5.346535 27.858628 1.396016e-52 -15.260765
## Educacion=Mst/Doc 8.258929 7.326733 31.046431 2.985654e-53 -15.361079
##
## $`3`
## Cla/Mod Mod/Cla Global p.value
## Ingresos=<1smlv 90.547264 86.0520095 27.858628 1.422635e-227
## Ocu=Estudiante 91.666667 44.2080378 14.137214 2.671355e-93
## Edad_g=18-29 64.568765 65.4846336 29.729730 2.374451e-78
## Educacion=Téc/Bach 61.036036 64.0661939 30.769231 4.935667e-67
## Ocu=Jubilado/Desempleado 69.683258 36.4066194 15.315315 2.812254e-42
## Estrato=Estrato 1-2 48.267327 46.0992908 27.997228 6.604890e-22
## Municipio=Palmira 35.758514 54.6099291 44.767845 1.388987e-06
## IPRG=Bajo 37.875289 38.7706856 30.006930 3.981743e-06
## Sexo=M 32.435233 73.9952719 66.874567 1.854597e-04
## IPRG=Alto 25.274725 21.7494090 25.225225 4.911288e-02
## IPRG=Medio 25.851393 39.4799054 44.767845 9.227392e-03
## Ocu=Independiente 20.377358 12.7659574 18.364518 2.958028e-04
## Sexo=H 22.784810 25.5319149 32.848233 1.173631e-04
## Estrato=Estrato 5-6 20.058997 16.0756501 23.492723 1.198242e-05
## Educacion=Univ 22.504537 29.3144208 38.184338 6.588344e-06
## Municipio=Cali 24.090339 45.3900709 55.232155 1.388987e-06
## Estrato=Estrato 3-4 22.857143 37.8250591 48.510049 1.584836e-07
## Edad_g=>60 3.260870 0.7092199 6.375606 5.245939e-11
## Educacion=Mst/Doc 6.250000 6.6193853 31.046431 4.948088e-45
## Ingresos=1-3smlv 7.524752 8.9834515 34.996535 1.725981e-46
## Edad_g=30-59 15.509761 33.8061466 63.894664 5.426837e-52
## Ingresos=>3smlv 3.917910 4.9645390 37.144837 8.326091e-72
## Ocu=Empleado 3.718459 6.6193853 52.182952 2.752197e-124
## v.test
## Ingresos=<1smlv 32.206688
## Ocu=Estudiante 20.489498
## Edad_g=18-29 18.739138
## Educacion=Téc/Bach 17.297229
## Ocu=Jubilado/Desempleado 13.625822
## Estrato=Estrato 1-2 9.619660
## Municipio=Palmira 4.826578
## IPRG=Bajo 4.612333
## Sexo=M 3.738041
## IPRG=Alto -1.967610
## IPRG=Medio -2.603512
## Ocu=Independiente -3.618948
## Sexo=H -3.851570
## Estrato=Estrato 5-6 -4.377907
## Educacion=Univ -4.506572
## Municipio=Cali -4.826578
## Estrato=Estrato 3-4 -5.242421
## Edad_g=>60 -6.563783
## Educacion=Mst/Doc -14.081314
## Ingresos=1-3smlv -14.316501
## Edad_g=30-59 -15.171917
## Ingresos=>3smlv -17.919373
## Ocu=Empleado -23.711374#con todos los ejes del acm
cluster.votaciones$data.clust #conjunto de datos con la clasificacion## Edad_g Sexo Municipio Estrato Educacion Ingresos Ocu
## 1 30-59 H Cali Estrato 3-4 Univ >3smlv Jubilado/Desempleado
## 2 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 3 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 4 18-29 M Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 5 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 6 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 7 30-59 M Cali Estrato 3-4 Univ >3smlv Empleado
## 8 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 9 18-29 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 10 18-29 M Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 11 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 12 18-29 M Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 13 18-29 M Cali Estrato 1-2 Univ 1-3smlv Empleado
## 14 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 15 18-29 M Cali Estrato 5-6 Univ 1-3smlv Empleado
## 16 30-59 M Cali Estrato 3-4 Univ >3smlv Empleado
## 17 18-29 M Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 18 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 19 18-29 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 20 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 21 18-29 M Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 22 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 23 18-29 H Cali Estrato 3-4 Univ 1-3smlv Empleado
## 24 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 25 18-29 M Cali Estrato 1-2 Univ <1smlv Estudiante
## 26 18-29 H Cali Estrato 5-6 Téc/Bach >3smlv Estudiante
## 27 18-29 H Cali Estrato 5-6 Univ 1-3smlv Estudiante
## 28 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 29 18-29 M Cali Estrato 5-6 Univ <1smlv Estudiante
## 30 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 31 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 32 18-29 M Cali Estrato 5-6 Univ 1-3smlv Empleado
## 33 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 34 30-59 H Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 35 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 36 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 37 >60 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 38 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 39 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 40 >60 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 41 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 42 18-29 H Cali Estrato 3-4 Univ 1-3smlv Empleado
## 43 30-59 H Cali Estrato 5-6 Univ >3smlv Empleado
## 44 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 45 18-29 M Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 46 30-59 H Cali Estrato 3-4 Univ >3smlv Empleado
## 47 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 48 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 49 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 50 18-29 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 51 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 52 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 53 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 54 30-59 M Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 55 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 56 18-29 M Cali Estrato 5-6 Univ 1-3smlv Estudiante
## 57 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Independiente
## 58 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 59 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 60 18-29 M Cali Estrato 5-6 Univ >3smlv Empleado
## 61 >60 H Cali Estrato 3-4 Univ >3smlv Jubilado/Desempleado
## 62 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 63 30-59 M Cali Estrato 5-6 Mst/Doc 1-3smlv Independiente
## 64 30-59 M Cali Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 65 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 66 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 67 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 68 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 69 18-29 M Cali Estrato 3-4 Univ >3smlv Empleado
## 70 >60 M Cali Estrato 5-6 Mst/Doc >3smlv Jubilado/Desempleado
## 71 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 72 18-29 M Cali Estrato 5-6 Univ <1smlv Estudiante
## 73 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 74 18-29 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 75 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 76 18-29 M Cali Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 77 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 78 >60 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 79 30-59 M Cali Estrato 3-4 Téc/Bach <1smlv Independiente
## 80 18-29 H Cali Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 81 30-59 M Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 82 30-59 H Cali Estrato 5-6 Univ >3smlv Independiente
## 83 30-59 H Cali Estrato 3-4 Univ >3smlv Empleado
## 84 >60 M Cali Estrato 5-6 Mst/Doc >3smlv Jubilado/Desempleado
## 85 30-59 H Cali Estrato 3-4 Univ 1-3smlv Empleado
## 86 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 87 18-29 H Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 88 18-29 M Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 89 18-29 M Cali Estrato 3-4 Téc/Bach 1-3smlv Estudiante
## 90 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 91 18-29 M Cali Estrato 1-2 Univ 1-3smlv Empleado
## 92 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 93 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 94 >60 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 95 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 96 30-59 H Cali Estrato 3-4 Univ >3smlv Empleado
## 97 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 98 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 99 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 100 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 101 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 102 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 103 18-29 H Cali Estrato 3-4 Univ 1-3smlv Empleado
## 104 30-59 M Cali Estrato 3-4 Univ >3smlv Empleado
## 105 30-59 M Cali Estrato 3-4 Univ >3smlv Empleado
## 106 30-59 M Cali Estrato 5-6 Univ 1-3smlv Empleado
## 107 30-59 H Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 108 >60 M Cali Estrato 3-4 Univ 1-3smlv Independiente
## 109 18-29 H Cali Estrato 3-4 Mst/Doc >3smlv Independiente
## 110 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 111 30-59 H Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 112 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 113 30-59 H Cali Estrato 3-4 Univ >3smlv Empleado
## 114 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 115 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 116 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 117 18-29 M Cali Estrato 1-2 Univ >3smlv Independiente
## 118 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 119 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 120 30-59 M Cali Estrato 3-4 Univ >3smlv Empleado
## 121 18-29 M Cali Estrato 3-4 Univ 1-3smlv Estudiante
## 122 30-59 M Cali Estrato 1-2 Univ 1-3smlv Empleado
## 123 30-59 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 124 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 125 30-59 M Cali Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 126 30-59 M Cali Estrato 5-6 Téc/Bach 1-3smlv Empleado
## 127 30-59 M Cali Estrato 3-4 Univ >3smlv Independiente
## 128 18-29 M Cali Estrato 3-4 Univ <1smlv Estudiante
## 129 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 130 30-59 M Cali Estrato 3-4 Téc/Bach 1-3smlv Independiente
## 131 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 132 >60 M Cali Estrato 3-4 Mst/Doc >3smlv Jubilado/Desempleado
## 133 30-59 H Cali Estrato 5-6 Mst/Doc <1smlv Jubilado/Desempleado
## 134 30-59 H Cali Estrato 5-6 Univ >3smlv Empleado
## 135 30-59 M Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 136 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 137 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 138 >60 H Cali Estrato 5-6 Univ >3smlv Jubilado/Desempleado
## 139 18-29 H Cali Estrato 3-4 Univ >3smlv Empleado
## 140 18-29 H Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 141 30-59 M Cali Estrato 1-2 Univ >3smlv Empleado
## 142 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 143 >60 H Cali Estrato 3-4 Univ >3smlv Jubilado/Desempleado
## 144 30-59 M Cali Estrato 3-4 Univ >3smlv Empleado
## 145 30-59 M Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 146 30-59 H Cali Estrato 3-4 Univ >3smlv Empleado
## 147 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Jubilado/Desempleado
## 148 18-29 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 149 30-59 M Cali Estrato 1-2 Univ >3smlv Empleado
## 150 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 151 18-29 H Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 152 30-59 M Cali Estrato 5-6 Univ <1smlv Estudiante
## 153 18-29 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 154 30-59 H Cali Estrato 5-6 Mst/Doc 1-3smlv Estudiante
## 155 30-59 H Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 156 18-29 H Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 157 30-59 M Cali Estrato 3-4 Univ >3smlv Independiente
## 158 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 159 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 160 >60 H Cali Estrato 5-6 Mst/Doc >3smlv Jubilado/Desempleado
## 161 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 162 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 163 30-59 M Cali Estrato 5-6 Univ 1-3smlv Independiente
## 164 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 165 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Estudiante
## 166 30-59 M Cali Estrato 3-4 Univ 1-3smlv Independiente
## 167 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 168 30-59 M Cali Estrato 3-4 Univ >3smlv Empleado
## 169 18-29 M Cali Estrato 1-2 Univ 1-3smlv Empleado
## 170 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Independiente
## 171 18-29 H Cali Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 172 >60 M Cali Estrato 5-6 Mst/Doc >3smlv Jubilado/Desempleado
## 173 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 174 30-59 H Cali Estrato 3-4 Univ >3smlv Empleado
## 175 18-29 M Cali Estrato 3-4 Univ >3smlv Empleado
## 176 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 177 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 178 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 179 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 180 >60 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 181 18-29 H Cali Estrato 1-2 Téc/Bach <1smlv Estudiante
## 182 30-59 M Cali Estrato 1-2 Univ >3smlv Empleado
## 183 18-29 M Cali Estrato 1-2 Univ 1-3smlv Independiente
## 184 >60 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 185 18-29 M Cali Estrato 1-2 Mst/Doc >3smlv Empleado
## 186 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 187 30-59 M Cali Estrato 3-4 Univ >3smlv Empleado
## 188 30-59 M Cali Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 189 30-59 M Cali Estrato 5-6 Univ 1-3smlv Independiente
## 190 18-29 M Cali Estrato 5-6 Univ <1smlv Estudiante
## 191 18-29 M Cali Estrato 5-6 Univ >3smlv Empleado
## 192 30-59 M Cali Estrato 3-4 Univ 1-3smlv Estudiante
## 193 30-59 H Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 194 30-59 M Cali Estrato 3-4 Mst/Doc <1smlv Independiente
## 195 >60 H Cali Estrato 5-6 Univ >3smlv Empleado
## 196 18-29 M Cali Estrato 5-6 Univ <1smlv Estudiante
## 197 30-59 M Cali Estrato 1-2 Univ >3smlv Empleado
## 198 18-29 H Cali Estrato 1-2 Téc/Bach <1smlv Estudiante
## 199 18-29 H Cali Estrato 1-2 Téc/Bach <1smlv Estudiante
## 200 30-59 H Cali Estrato 3-4 Univ 1-3smlv Empleado
## 201 18-29 H Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 202 >60 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 203 30-59 M Cali Estrato 5-6 Univ >3smlv Estudiante
## 204 30-59 M Cali Estrato 3-4 Téc/Bach 1-3smlv Independiente
## 205 18-29 M Cali Estrato 1-2 Univ 1-3smlv Empleado
## 206 30-59 M Cali Estrato 3-4 Univ >3smlv Empleado
## 207 18-29 M Cali Estrato 5-6 Univ <1smlv Estudiante
## 208 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 209 18-29 H Cali Estrato 1-2 Téc/Bach <1smlv Estudiante
## 210 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 211 30-59 H Cali Estrato 5-6 Mst/Doc 1-3smlv Empleado
## 212 >60 M Cali Estrato 3-4 Mst/Doc >3smlv Jubilado/Desempleado
## 213 18-29 H Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 214 30-59 M Cali Estrato 3-4 Univ >3smlv Independiente
## 215 30-59 M Cali Estrato 1-2 Univ >3smlv Empleado
## 216 >60 M Cali Estrato 3-4 Mst/Doc >3smlv Jubilado/Desempleado
## 217 30-59 M Cali Estrato 3-4 Univ >3smlv Empleado
## 218 30-59 M Cali Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 219 18-29 M Cali Estrato 3-4 Univ <1smlv Jubilado/Desempleado
## 220 18-29 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 221 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 222 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 223 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Independiente
## 224 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 225 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 226 18-29 H Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 227 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 228 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 229 30-59 H Cali Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 230 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 231 30-59 M Cali Estrato 3-4 Univ >3smlv Independiente
## 232 >60 H Cali Estrato 3-4 Téc/Bach >3smlv Empleado
## 233 >60 H Cali Estrato 3-4 Univ 1-3smlv Independiente
## 234 30-59 M Cali Estrato 5-6 Téc/Bach 1-3smlv Empleado
## 235 30-59 H Cali Estrato 5-6 Univ >3smlv Independiente
## 236 30-59 M Cali Estrato 3-4 Univ <1smlv Independiente
## 237 18-29 H Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 238 >60 M Cali Estrato 5-6 Univ 1-3smlv Jubilado/Desempleado
## 239 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 240 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Independiente
## 241 >60 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 242 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 243 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 244 18-29 H Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 245 30-59 H Cali Estrato 5-6 Univ 1-3smlv Independiente
## 246 30-59 M Cali Estrato 5-6 Mst/Doc 1-3smlv Independiente
## 247 18-29 M Cali Estrato 3-4 Univ <1smlv Estudiante
## 248 30-59 H Cali Estrato 3-4 Univ >3smlv Empleado
## 249 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 250 30-59 H Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 251 30-59 H Cali Estrato 3-4 Univ >3smlv Empleado
## 252 18-29 H Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 253 30-59 M Cali Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 254 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 255 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 256 30-59 H Cali Estrato 5-6 Univ >3smlv Empleado
## 257 18-29 H Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 258 30-59 H Cali Estrato 3-4 Univ >3smlv Empleado
## 259 30-59 H Cali Estrato 3-4 Téc/Bach 1-3smlv Independiente
## 260 30-59 M Cali Estrato 1-2 Mst/Doc >3smlv Empleado
## 261 30-59 H Cali Estrato 3-4 Univ 1-3smlv Empleado
## 262 30-59 M Cali Estrato 1-2 Mst/Doc 1-3smlv Empleado
## 263 18-29 M Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 264 30-59 M Cali Estrato 5-6 Univ >3smlv Independiente
## 265 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 266 18-29 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 267 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 268 18-29 H Cali Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 269 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 270 30-59 M Cali Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 271 30-59 H Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 272 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 273 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 274 30-59 H Cali Estrato 3-4 Univ 1-3smlv Empleado
## 275 30-59 M Cali Estrato 3-4 Mst/Doc 1-3smlv Independiente
## 276 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 277 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 278 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 279 18-29 M Cali Estrato 5-6 Univ 1-3smlv Empleado
## 280 30-59 M Cali Estrato 1-2 Mst/Doc >3smlv Empleado
## 281 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 282 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 283 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 284 >60 H Cali Estrato 5-6 Mst/Doc >3smlv Jubilado/Desempleado
## 285 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 286 >60 H Cali Estrato 3-4 Mst/Doc >3smlv Jubilado/Desempleado
## 287 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 288 30-59 H Cali Estrato 3-4 Mst/Doc 1-3smlv Estudiante
## 289 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 290 18-29 M Cali Estrato 5-6 Univ 1-3smlv Estudiante
## 291 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 292 30-59 M Cali Estrato 1-2 Mst/Doc 1-3smlv Estudiante
## 293 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 294 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 295 30-59 M Cali Estrato 3-4 Univ 1-3smlv Independiente
## 296 18-29 M Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 297 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 298 18-29 M Cali Estrato 3-4 Univ >3smlv Estudiante
## 299 30-59 M Cali Estrato 1-2 Univ <1smlv Independiente
## 300 30-59 H Cali Estrato 3-4 Téc/Bach <1smlv Empleado
## 301 18-29 M Cali Estrato 1-2 Univ <1smlv Estudiante
## 302 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 303 30-59 H Cali Estrato 1-2 Mst/Doc >3smlv Empleado
## 304 30-59 M Cali Estrato 5-6 Univ 1-3smlv Empleado
## 305 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 306 18-29 M Cali Estrato 5-6 Mst/Doc 1-3smlv Estudiante
## 307 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 308 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 309 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 310 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 311 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 312 30-59 H Cali Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 313 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 314 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 315 >60 M Cali Estrato 5-6 Mst/Doc >3smlv Jubilado/Desempleado
## 316 30-59 M Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 317 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 318 >60 M Cali Estrato 3-4 Mst/Doc >3smlv Jubilado/Desempleado
## 319 30-59 M Cali Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 320 18-29 M Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 321 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 322 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 323 30-59 H Cali Estrato 1-2 Mst/Doc >3smlv Empleado
## 324 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 325 18-29 M Cali Estrato 5-6 Univ <1smlv Jubilado/Desempleado
## 326 30-59 M Cali Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 327 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 328 18-29 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 329 >60 H Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 330 30-59 M Cali Estrato 1-2 Univ >3smlv Empleado
## 331 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 332 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 333 >60 M Cali Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 334 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 335 30-59 M Cali Estrato 3-4 Univ >3smlv Empleado
## 336 18-29 Otro Cali Estrato 5-6 Univ >3smlv Estudiante
## 337 18-29 M Cali Estrato 3-4 Univ 1-3smlv Estudiante
## 338 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 339 30-59 M Cali Estrato 1-2 Mst/Doc 1-3smlv Empleado
## 340 30-59 M Cali Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 341 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 342 18-29 M Cali Estrato 3-4 Univ 1-3smlv Estudiante
## 343 30-59 M Cali Estrato 5-6 Mst/Doc 1-3smlv Estudiante
## 344 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 345 >60 H Cali Estrato 5-6 Mst/Doc >3smlv Jubilado/Desempleado
## 346 18-29 M Cali Estrato 1-2 Univ 1-3smlv Independiente
## 347 30-59 H Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 348 30-59 H Cali Estrato 5-6 Univ >3smlv Empleado
## 349 18-29 M Cali Estrato 5-6 Univ 1-3smlv Estudiante
## 350 30-59 M Cali Estrato 5-6 Téc/Bach <1smlv Independiente
## 351 30-59 M Cali Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 352 18-29 M Cali Estrato 3-4 Univ <1smlv Estudiante
## 353 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 354 18-29 M Cali Estrato 3-4 Univ >3smlv Independiente
## 355 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 356 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 357 30-59 M Cali Estrato 5-6 Univ 1-3smlv Empleado
## 358 18-29 H Cali Estrato 3-4 Univ >3smlv Empleado
## 359 18-29 M Cali Estrato 3-4 Univ 1-3smlv Estudiante
## 360 30-59 M Cali Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 361 30-59 M Cali Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 362 >60 M Cali Estrato 5-6 Mst/Doc >3smlv Jubilado/Desempleado
## 363 30-59 M Cali Estrato 3-4 Téc/Bach 1-3smlv Independiente
## 364 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 365 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 366 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 367 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 368 30-59 H Cali Estrato 3-4 Univ >3smlv Empleado
## 369 30-59 H Cali Estrato 3-4 Univ >3smlv Empleado
## 370 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Independiente
## 371 30-59 M Cali Estrato 3-4 Univ 1-3smlv Jubilado/Desempleado
## 372 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Jubilado/Desempleado
## 373 >60 M Cali Estrato 5-6 Mst/Doc >3smlv Jubilado/Desempleado
## 374 30-59 H Cali Estrato 5-6 Univ >3smlv Empleado
## 375 >60 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 376 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Independiente
## 377 30-59 M Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 378 18-29 H Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 379 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 380 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 381 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 382 >60 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 383 18-29 M Cali Estrato 5-6 Univ 1-3smlv Estudiante
## 384 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 385 30-59 M Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 386 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 387 >60 H Cali Estrato 3-4 Mst/Doc >3smlv Jubilado/Desempleado
## 388 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 389 >60 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 390 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 391 18-29 M Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 392 18-29 M Cali Estrato 3-4 Univ 1-3smlv Estudiante
## 393 18-29 M Cali Estrato 1-2 Téc/Bach <1smlv Estudiante
## 394 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 395 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 396 >60 H Cali Estrato 3-4 Téc/Bach 1-3smlv Jubilado/Desempleado
## 397 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 398 30-59 H Cali Estrato 5-6 Univ 1-3smlv Estudiante
## 399 18-29 M Cali Estrato 5-6 Univ 1-3smlv Empleado
## 400 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Independiente
## 401 18-29 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 402 30-59 M Cali Estrato 1-2 Mst/Doc >3smlv Empleado
## 403 18-29 M Cali Estrato 3-4 Univ <1smlv Estudiante
## 404 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 405 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 406 30-59 M Cali Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 407 18-29 H Cali Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 408 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 409 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 410 30-59 M Cali Estrato 3-4 Univ 1-3smlv Independiente
## 411 18-29 H Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 412 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 413 18-29 M Cali Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 414 18-29 M Cali Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 415 30-59 M Cali Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 416 30-59 M Cali Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 417 18-29 M Cali Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 418 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 419 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 420 >60 M Cali Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 421 30-59 M Cali Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 422 18-29 M Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 423 18-29 M Cali Estrato 1-2 Téc/Bach <1smlv Empleado
## 424 30-59 M Cali Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 425 18-29 M Cali Estrato 1-2 Téc/Bach <1smlv Empleado
## 426 30-59 M Cali Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 427 30-59 M Cali Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 428 30-59 M Cali Estrato 1-2 Téc/Bach <1smlv Independiente
## 429 30-59 M Cali Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 430 18-29 M Cali Estrato 1-2 Téc/Bach <1smlv Estudiante
## 431 18-29 H Cali Estrato 1-2 Téc/Bach <1smlv Empleado
## 432 30-59 M Cali Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 433 30-59 H Cali Estrato 3-4 Téc/Bach 1-3smlv Independiente
## 434 18-29 H Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 435 >60 H Cali Estrato 3-4 Univ 1-3smlv Empleado
## 436 30-59 M Cali Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 437 30-59 M Cali Estrato 1-2 Téc/Bach <1smlv Empleado
## 438 18-29 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 439 30-59 H Cali Estrato 3-4 Mst/Doc 1-3smlv Independiente
## 440 18-29 M Cali Estrato 3-4 Univ <1smlv Estudiante
## 441 18-29 M Cali Estrato 5-6 Univ <1smlv Estudiante
## 442 30-59 H Cali Estrato 5-6 Mst/Doc 1-3smlv Estudiante
## 443 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 444 >60 M Cali Estrato 1-2 Téc/Bach 1-3smlv Jubilado/Desempleado
## 445 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 446 30-59 M Cali Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 447 >60 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 448 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 449 30-59 M Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 450 30-59 H Cali Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 451 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 452 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Independiente
## 453 >60 M Cali Estrato 5-6 Téc/Bach 1-3smlv Jubilado/Desempleado
## 454 30-59 H Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 455 >60 M Cali Estrato 5-6 Univ 1-3smlv Jubilado/Desempleado
## 456 30-59 M Cali Estrato 1-2 Mst/Doc 1-3smlv Empleado
## 457 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 458 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 459 18-29 M Cali Estrato 3-4 Univ <1smlv Estudiante
## 460 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 461 >60 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 462 30-59 M Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 463 18-29 M Cali Estrato 1-2 Univ <1smlv Independiente
## 464 18-29 M Cali Estrato 3-4 Univ <1smlv Estudiante
## 465 18-29 M Cali Estrato 3-4 Univ <1smlv Estudiante
## 466 18-29 H Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 467 18-29 M Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 468 18-29 M Cali Estrato 5-6 Univ >3smlv Empleado
## 469 30-59 H Cali Estrato 5-6 Mst/Doc 1-3smlv Independiente
## 470 18-29 H Cali Estrato 3-4 Univ 1-3smlv Empleado
## 471 30-59 H Cali Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 472 18-29 H Cali Estrato 3-4 Univ <1smlv Estudiante
## 473 30-59 M Cali Estrato 3-4 Univ >3smlv Independiente
## 474 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 475 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 476 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Independiente
## 477 30-59 H Cali Estrato 5-6 Univ 1-3smlv Jubilado/Desempleado
## 478 18-29 M Cali Estrato 3-4 Univ >3smlv Empleado
## 479 18-29 M Cali Estrato 5-6 Univ 1-3smlv Estudiante
## 480 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 481 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 482 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Jubilado/Desempleado
## 483 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 484 18-29 H Cali Estrato 3-4 Univ 1-3smlv Empleado
## 485 >60 M Cali Estrato 3-4 Univ >3smlv Empleado
## 486 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 487 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 488 >60 H Cali Estrato 5-6 Univ >3smlv Jubilado/Desempleado
## 489 >60 H Cali Estrato 5-6 Univ >3smlv Independiente
## 490 30-59 M Cali Estrato 3-4 Univ >3smlv Empleado
## 491 18-29 H Cali Estrato 5-6 Téc/Bach 1-3smlv Estudiante
## 492 30-59 H Cali Estrato 3-4 Univ 1-3smlv Empleado
## 493 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 494 18-29 H Cali Estrato 3-4 Univ >3smlv Empleado
## 495 18-29 H Cali Estrato 3-4 Univ >3smlv Empleado
## 496 30-59 H Cali Estrato 3-4 Univ >3smlv Empleado
## 497 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 498 30-59 H Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 499 30-59 M Cali Estrato 5-6 Mst/Doc <1smlv Jubilado/Desempleado
## 500 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 501 30-59 M Cali Estrato 3-4 Univ >3smlv Jubilado/Desempleado
## 502 30-59 H Cali Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 503 30-59 M Cali Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 504 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 505 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 506 >60 M Cali Estrato 3-4 Téc/Bach 1-3smlv Jubilado/Desempleado
## 507 30-59 H Cali Estrato 3-4 Mst/Doc 1-3smlv Independiente
## 508 30-59 M Cali Estrato 3-4 Univ 1-3smlv Independiente
## 509 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Jubilado/Desempleado
## 510 18-29 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 511 30-59 M Cali Estrato 3-4 Mst/Doc <1smlv Jubilado/Desempleado
## 512 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 513 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 514 30-59 M Cali Estrato 3-4 Téc/Bach <1smlv Independiente
## 515 18-29 H Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 516 18-29 M Cali Estrato 3-4 Téc/Bach 1-3smlv Estudiante
## 517 18-29 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 518 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Independiente
## 519 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 520 18-29 H Cali Estrato 3-4 Univ 1-3smlv Empleado
## 521 18-29 H Cali Estrato 3-4 Univ 1-3smlv Empleado
## 522 30-59 M Cali Estrato 1-2 Mst/Doc >3smlv Empleado
## 523 30-59 M Cali Estrato 5-6 Téc/Bach >3smlv Independiente
## 524 18-29 H Cali Estrato 5-6 Univ 1-3smlv Empleado
## 525 18-29 M Cali Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 526 18-29 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 527 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 528 18-29 H Cali Estrato 3-4 Téc/Bach 1-3smlv Estudiante
## 529 18-29 M Cali Estrato 3-4 Univ <1smlv Estudiante
## 530 >60 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 531 18-29 M Cali Estrato 3-4 Univ <1smlv Estudiante
## 532 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 533 18-29 H Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 534 18-29 H Cali Estrato 3-4 Univ <1smlv Estudiante
## 535 18-29 M Cali Estrato 3-4 Téc/Bach 1-3smlv Estudiante
## 536 18-29 H Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 537 18-29 M Cali Estrato 1-2 Univ 1-3smlv Independiente
## 538 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 539 18-29 H Cali Estrato 1-2 Téc/Bach <1smlv Estudiante
## 540 18-29 M Cali Estrato 1-2 Univ 1-3smlv Independiente
## 541 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 542 18-29 M Cali Estrato 3-4 Univ <1smlv Estudiante
## 543 >60 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 544 18-29 H Cali Estrato 3-4 Univ <1smlv Estudiante
## 545 30-59 M Cali Estrato 3-4 Univ 1-3smlv Independiente
## 546 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 547 18-29 H Cali Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 548 30-59 M Cali Estrato 3-4 Univ >3smlv Empleado
## 549 18-29 M Cali Estrato 1-2 Univ <1smlv Estudiante
## 550 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 551 30-59 H Cali Estrato 3-4 Mst/Doc 1-3smlv Independiente
## 552 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 553 18-29 H Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 554 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 555 30-59 M Cali Estrato 3-4 Univ 1-3smlv Independiente
## 556 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 557 18-29 H Cali Estrato 3-4 Univ >3smlv Empleado
## 558 18-29 M Cali Estrato 1-2 Téc/Bach <1smlv Estudiante
## 559 30-59 H Cali Estrato 3-4 Univ >3smlv Independiente
## 560 30-59 M Cali Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 561 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 562 30-59 M Cali Estrato 5-6 Univ 1-3smlv Empleado
## 563 30-59 M Cali Estrato 5-6 Mst/Doc <1smlv Independiente
## 564 18-29 M Cali Estrato 3-4 Univ <1smlv Estudiante
## 565 30-59 M Cali Estrato 5-6 Univ >3smlv Independiente
## 566 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 567 30-59 M Cali Estrato 3-4 Univ 1-3smlv Independiente
## 568 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 569 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 570 18-29 M Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 571 18-29 H Cali Estrato 5-6 Univ >3smlv Empleado
## 572 18-29 H Cali Estrato 5-6 Univ <1smlv Estudiante
## 573 30-59 M Cali Estrato 1-2 Mst/Doc >3smlv Independiente
## 574 30-59 H Cali Estrato 3-4 Univ >3smlv Independiente
## 575 30-59 M Cali Estrato 1-2 Téc/Bach <1smlv Independiente
## 576 18-29 M Cali Estrato 5-6 Univ >3smlv Empleado
## 577 >60 H Cali Estrato 5-6 Univ >3smlv Empleado
## 578 18-29 M Cali Estrato 1-2 Mst/Doc 1-3smlv Empleado
## 579 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 580 18-29 M Cali Estrato 5-6 Mst/Doc >3smlv Estudiante
## 581 30-59 H Cali Estrato 3-4 Univ 1-3smlv Empleado
## 582 18-29 M Cali Estrato 1-2 Univ 1-3smlv Empleado
## 583 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 584 30-59 M Cali Estrato 3-4 Téc/Bach 1-3smlv Independiente
## 585 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 586 18-29 M Cali Estrato 5-6 Univ <1smlv Jubilado/Desempleado
## 587 18-29 M Cali Estrato 3-4 Univ 1-3smlv Estudiante
## 588 30-59 M Cali Estrato 3-4 Univ >3smlv Independiente
## 589 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 590 18-29 M Cali Estrato 1-2 Univ 1-3smlv Empleado
## 591 30-59 H Cali Estrato 3-4 Téc/Bach 1-3smlv Independiente
## 592 18-29 M Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 593 18-29 H Cali Estrato 3-4 Univ 1-3smlv Empleado
## 594 18-29 M Cali Estrato 3-4 Univ <1smlv Estudiante
## 595 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 596 >60 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 597 30-59 M Cali Estrato 5-6 Mst/Doc 1-3smlv Estudiante
## 598 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Estudiante
## 599 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 600 30-59 H Cali Estrato 1-2 Mst/Doc 1-3smlv Empleado
## 601 >60 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 602 30-59 M Cali Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 603 18-29 M Cali Estrato 5-6 Univ 1-3smlv Independiente
## 604 18-29 M Cali Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 605 30-59 M Cali Estrato 3-4 Univ 1-3smlv Jubilado/Desempleado
## 606 18-29 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 607 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 608 18-29 H Cali Estrato 3-4 Univ >3smlv Empleado
## 609 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 610 >60 H Cali Estrato 5-6 Mst/Doc >3smlv Jubilado/Desempleado
## 611 18-29 M Cali Estrato 1-2 Univ <1smlv Estudiante
## 612 30-59 H Cali Estrato 5-6 Univ >3smlv Empleado
## 613 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 614 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 615 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 616 >60 H Cali Estrato 5-6 Univ >3smlv Independiente
## 617 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 618 >60 M Cali Estrato 5-6 Mst/Doc >3smlv Jubilado/Desempleado
## 619 18-29 M Cali Estrato 3-4 Téc/Bach >3smlv Independiente
## 620 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Independiente
## 621 >60 M Cali Estrato 3-4 Univ 1-3smlv Jubilado/Desempleado
## 622 >60 M Cali Estrato 5-6 Téc/Bach 1-3smlv Jubilado/Desempleado
## 623 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 624 30-59 H Cali Estrato 5-6 Univ 1-3smlv Estudiante
## 625 18-29 H Cali Estrato 5-6 Mst/Doc >3smlv Estudiante
## 626 >60 M Cali Estrato 3-4 Univ 1-3smlv Jubilado/Desempleado
## 627 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Empleado
## 628 18-29 M Cali Estrato 3-4 Univ >3smlv Empleado
## 629 >60 H Cali Estrato 5-6 Mst/Doc >3smlv Jubilado/Desempleado
## 630 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 631 18-29 H Cali Estrato 3-4 Univ <1smlv Estudiante
## 632 30-59 M Cali Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 633 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 634 18-29 M Cali Estrato 3-4 Univ >3smlv Empleado
## 635 30-59 M Cali Estrato 5-6 Univ >3smlv Independiente
## 636 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 637 30-59 M Cali Estrato 5-6 Univ <1smlv Jubilado/Desempleado
## 638 30-59 H Cali Estrato 3-4 Univ 1-3smlv Empleado
## 639 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 640 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Independiente
## 641 30-59 M Cali Estrato 5-6 Univ >3smlv Independiente
## 642 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 643 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 644 30-59 H Cali Estrato 5-6 Univ >3smlv Empleado
## 645 >60 M Cali Estrato 5-6 Mst/Doc >3smlv Jubilado/Desempleado
## 646 30-59 H Cali Estrato 3-4 Univ >3smlv Empleado
## 647 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 648 30-59 M Cali Estrato 5-6 Téc/Bach 1-3smlv Independiente
## 649 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 650 30-59 M Cali Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 651 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 652 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 653 18-29 H Cali Estrato 3-4 Univ <1smlv Estudiante
## 654 30-59 H Cali Estrato 5-6 Univ 1-3smlv Empleado
## 655 30-59 Otro Cali Estrato 1-2 Univ <1smlv Jubilado/Desempleado
## 656 30-59 M Cali Estrato 3-4 Mst/Doc 1-3smlv Independiente
## 657 30-59 H Cali Estrato 5-6 Univ 1-3smlv Jubilado/Desempleado
## 658 30-59 M Cali Estrato 1-2 Mst/Doc >3smlv Empleado
## 659 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 660 18-29 M Cali Estrato 1-2 Téc/Bach 1-3smlv Estudiante
## 661 30-59 M Cali Estrato 5-6 Univ 1-3smlv Empleado
## 662 30-59 M Cali Estrato 3-4 Univ >3smlv Empleado
## 663 30-59 M Cali Estrato 1-2 Univ 1-3smlv Empleado
## 664 18-29 H Cali Estrato 3-4 Univ 1-3smlv Empleado
## 665 30-59 M Cali Estrato 5-6 Univ 1-3smlv Empleado
## 666 18-29 M Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 667 18-29 H Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 668 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 669 30-59 H Cali Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 670 30-59 H Cali Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 671 30-59 H Cali Estrato 3-4 Univ 1-3smlv Empleado
## 672 30-59 H Cali Estrato 3-4 Univ 1-3smlv Empleado
## 673 30-59 M Cali Estrato 1-2 Mst/Doc 1-3smlv Empleado
## 674 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 675 18-29 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 676 30-59 M Cali Estrato 1-2 Univ 1-3smlv Empleado
## 677 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 678 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 679 30-59 H Cali Estrato 1-2 Univ 1-3smlv Empleado
## 680 30-59 M Cali Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 681 18-29 H Cali Estrato 3-4 Univ <1smlv Estudiante
## 682 30-59 M Cali Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 683 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 684 >60 H Cali Estrato 3-4 Téc/Bach 1-3smlv Independiente
## 685 30-59 H Cali Estrato 3-4 Univ >3smlv Independiente
## 686 30-59 M Cali Estrato 1-2 Mst/Doc 1-3smlv Empleado
## 687 18-29 H Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 688 18-29 M Cali Estrato 1-2 Univ <1smlv Estudiante
## 689 30-59 M Cali Estrato 5-6 Univ <1smlv Independiente
## 690 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 691 30-59 M Cali Estrato 1-2 Mst/Doc >3smlv Independiente
## 692 30-59 M Cali Estrato 1-2 Univ <1smlv Estudiante
## 693 18-29 M Cali Estrato 5-6 Univ <1smlv Estudiante
## 694 18-29 H Cali Estrato 3-4 Univ 1-3smlv Empleado
## 695 30-59 M Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 696 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 697 >60 H Cali Estrato 1-2 Téc/Bach 1-3smlv Jubilado/Desempleado
## 698 30-59 H Cali Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 699 30-59 M Cali Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 700 30-59 M Cali Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 701 30-59 M Cali Estrato 1-2 Téc/Bach <1smlv Independiente
## 702 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 703 30-59 M Cali Estrato 3-4 Univ >3smlv Empleado
## 704 30-59 M Cali Estrato 5-6 Téc/Bach 1-3smlv Empleado
## 705 >60 M Cali Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 706 >60 H Cali Estrato 3-4 Téc/Bach 1-3smlv Jubilado/Desempleado
## 707 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 708 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 709 30-59 H Cali Estrato 3-4 Univ >3smlv Empleado
## 710 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 711 30-59 M Cali Estrato 3-4 Univ 1-3smlv Estudiante
## 712 30-59 H Cali Estrato 1-2 Univ 1-3smlv Empleado
## 713 30-59 M Cali Estrato 1-2 Univ <1smlv Independiente
## 714 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 715 30-59 H Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 716 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 717 30-59 M Cali Estrato 5-6 Univ >3smlv Independiente
## 718 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 719 30-59 H Cali Estrato 5-6 Mst/Doc 1-3smlv Empleado
## 720 >60 M Cali Estrato 5-6 Univ <1smlv Jubilado/Desempleado
## 721 18-29 M Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 722 18-29 H Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 723 30-59 M Cali Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 724 18-29 H Cali Estrato 3-4 Univ 1-3smlv Estudiante
## 725 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 726 30-59 M Cali Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 727 18-29 H Cali Estrato 5-6 Téc/Bach <1smlv Estudiante
## 728 30-59 M Cali Estrato 3-4 Univ 1-3smlv Empleado
## 729 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 730 18-29 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 731 18-29 M Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 732 30-59 M Cali Estrato 1-2 Téc/Bach <1smlv Empleado
## 733 30-59 M Cali Estrato 1-2 Téc/Bach <1smlv Independiente
## 734 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 735 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 736 18-29 H Cali Estrato 3-4 Univ <1smlv Jubilado/Desempleado
## 737 18-29 M Cali Estrato 1-2 Univ 1-3smlv Empleado
## 738 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Independiente
## 739 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Independiente
## 740 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 741 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 742 30-59 H Cali Estrato 5-6 Univ >3smlv Empleado
## 743 30-59 M Cali Estrato 3-4 Univ 1-3smlv Independiente
## 744 30-59 M Cali Estrato 5-6 Univ >3smlv Independiente
## 745 30-59 M Cali Estrato 3-4 Univ 1-3smlv Independiente
## 746 30-59 H Cali Estrato 5-6 Univ <1smlv Jubilado/Desempleado
## 747 >60 M Cali Estrato 1-2 Téc/Bach <1smlv Empleado
## 748 30-59 M Cali Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 749 30-59 H Cali Estrato 3-4 Univ >3smlv Empleado
## 750 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 751 18-29 H Cali Estrato 1-2 Univ <1smlv Estudiante
## 752 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 753 30-59 M Cali Estrato 1-2 Téc/Bach <1smlv Independiente
## 754 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 755 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 756 18-29 M Cali Estrato 3-4 Mst/Doc 1-3smlv Estudiante
## 757 30-59 M Cali Estrato 1-2 Univ <1smlv Estudiante
## 758 30-59 H Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 759 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 760 18-29 H Cali Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 761 30-59 M Cali Estrato 1-2 Univ 1-3smlv Empleado
## 762 30-59 H Cali Estrato 3-4 Univ >3smlv Empleado
## 763 18-29 H Cali Estrato 3-4 Univ <1smlv Jubilado/Desempleado
## 764 18-29 H Cali Estrato 1-2 Univ <1smlv Estudiante
## 765 >60 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 766 30-59 M Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 767 30-59 M Cali Estrato 1-2 Univ 1-3smlv Empleado
## 768 30-59 H Cali Estrato 1-2 Univ >3smlv Independiente
## 769 30-59 H Cali Estrato 1-2 Téc/Bach 1-3smlv Independiente
## 770 30-59 M Cali Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 771 18-29 M Cali Estrato 1-2 Univ 1-3smlv Independiente
## 772 >60 H Cali Estrato 5-6 Univ >3smlv Empleado
## 773 30-59 M Cali Estrato 5-6 Univ 1-3smlv Independiente
## 774 30-59 M Cali Estrato 3-4 Téc/Bach <1smlv Empleado
## 775 >60 H Cali Estrato 3-4 Univ >3smlv Independiente
## 776 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 777 18-29 H Cali Estrato 3-4 Téc/Bach <1smlv Estudiante
## 778 30-59 H Cali Estrato 3-4 Mst/Doc >3smlv Empleado
## 779 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 780 30-59 M Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 781 18-29 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 782 30-59 M Cali Estrato 5-6 Univ <1smlv Estudiante
## 783 30-59 M Cali Estrato 5-6 Univ >3smlv Empleado
## 784 30-59 H Cali Estrato 3-4 Univ 1-3smlv Independiente
## 785 30-59 M Cali Estrato 1-2 Univ >3smlv Empleado
## 786 30-59 M Cali Estrato 3-4 Univ >3smlv Empleado
## 787 30-59 H Cali Estrato 3-4 Univ >3smlv Empleado
## 788 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Empleado
## 789 18-29 M Cali Estrato 1-2 Téc/Bach <1smlv Estudiante
## 790 30-59 M Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 791 >60 M Cali Estrato 5-6 Mst/Doc >3smlv Jubilado/Desempleado
## 792 18-29 M Cali Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 793 30-59 H Cali Estrato 1-2 Téc/Bach 1-3smlv Independiente
## 794 30-59 M Cali Estrato 3-4 Univ >3smlv Independiente
## 795 18-29 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 796 30-59 M Cali Estrato 3-4 Téc/Bach 1-3smlv Independiente
## 797 30-59 M Cali Estrato 5-6 Mst/Doc >3smlv Independiente
## 798 30-59 M Palmira Estrato 3-4 Univ >3smlv Empleado
## 799 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 800 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 801 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 802 30-59 M Palmira Estrato 1-2 Mst/Doc >3smlv Empleado
## 803 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 804 18-29 M Palmira Estrato 3-4 Univ 1-3smlv Independiente
## 805 >60 H Palmira Estrato 5-6 Univ >3smlv Jubilado/Desempleado
## 806 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Independiente
## 807 18-29 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Independiente
## 808 18-29 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 809 30-59 M Palmira Estrato 5-6 Mst/Doc >3smlv Empleado
## 810 30-59 H Palmira Estrato 1-2 Univ >3smlv Empleado
## 811 30-59 M Palmira Estrato 1-2 Univ <1smlv Jubilado/Desempleado
## 812 30-59 H Palmira Estrato 3-4 Univ 1-3smlv Independiente
## 813 18-29 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 814 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 815 18-29 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 816 18-29 M Palmira Estrato 5-6 Mst/Doc >3smlv Empleado
## 817 30-59 M Palmira Estrato 5-6 Mst/Doc >3smlv Empleado
## 818 30-59 H Palmira Estrato 3-4 Univ >3smlv Empleado
## 819 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Independiente
## 820 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Independiente
## 821 18-29 H Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 822 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Independiente
## 823 18-29 H Palmira Estrato 5-6 Univ >3smlv Independiente
## 824 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Independiente
## 825 30-59 M Palmira Estrato 5-6 Mst/Doc >3smlv Independiente
## 826 18-29 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Independiente
## 827 30-59 H Palmira Estrato 3-4 Univ 1-3smlv Independiente
## 828 18-29 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 829 >60 H Palmira Estrato 5-6 Téc/Bach <1smlv Independiente
## 830 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Independiente
## 831 18-29 M Palmira Estrato 5-6 Téc/Bach <1smlv Estudiante
## 832 30-59 M Palmira Estrato 1-2 Mst/Doc <1smlv Jubilado/Desempleado
## 833 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Independiente
## 834 18-29 H Palmira Estrato 5-6 Univ >3smlv Empleado
## 835 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 836 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Independiente
## 837 30-59 H Palmira Estrato 5-6 Mst/Doc >3smlv Independiente
## 838 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 839 30-59 M Palmira Estrato 5-6 Univ 1-3smlv Independiente
## 840 30-59 M Palmira Estrato 3-4 Univ <1smlv Jubilado/Desempleado
## 841 18-29 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Independiente
## 842 30-59 M Palmira Estrato 1-2 Univ >3smlv Independiente
## 843 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Empleado
## 844 30-59 M Palmira Estrato 5-6 Mst/Doc >3smlv Empleado
## 845 30-59 H Palmira Estrato 3-4 Mst/Doc 1-3smlv Independiente
## 846 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 847 18-29 H Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 848 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Estudiante
## 849 18-29 H Palmira Estrato 5-6 Univ 1-3smlv Empleado
## 850 30-59 M Palmira Estrato 1-2 Mst/Doc 1-3smlv Empleado
## 851 30-59 H Palmira Estrato 3-4 Univ >3smlv Empleado
## 852 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 853 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 854 30-59 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 855 18-29 H Palmira Estrato 3-4 Univ 1-3smlv Independiente
## 856 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 857 18-29 M Palmira Estrato 1-2 Univ <1smlv Jubilado/Desempleado
## 858 30-59 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 859 30-59 M Palmira Estrato 3-4 Mst/Doc <1smlv Independiente
## 860 18-29 H Palmira Estrato 5-6 Téc/Bach <1smlv Estudiante
## 861 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 862 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Jubilado/Desempleado
## 863 30-59 M Palmira Estrato 5-6 Mst/Doc >3smlv Empleado
## 864 30-59 M Palmira Estrato 3-4 Mst/Doc <1smlv Jubilado/Desempleado
## 865 30-59 M Palmira Estrato 3-4 Univ <1smlv Jubilado/Desempleado
## 866 18-29 H Palmira Estrato 3-4 Téc/Bach <1smlv Estudiante
## 867 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Estudiante
## 868 30-59 M Palmira Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 869 30-59 M Palmira Estrato 3-4 Mst/Doc <1smlv Jubilado/Desempleado
## 870 18-29 H Palmira Estrato 3-4 Téc/Bach 1-3smlv Estudiante
## 871 30-59 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 872 >60 H Palmira Estrato 3-4 Mst/Doc <1smlv Jubilado/Desempleado
## 873 30-59 H Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 874 30-59 H Palmira Estrato 1-2 Mst/Doc >3smlv Independiente
## 875 18-29 M Palmira Estrato 3-4 Univ >3smlv Independiente
## 876 >60 H Palmira Estrato 3-4 Univ 1-3smlv Independiente
## 877 18-29 M Palmira Estrato 5-6 Univ 1-3smlv Empleado
## 878 18-29 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 879 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 880 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Independiente
## 881 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 882 30-59 H Palmira Estrato 5-6 Téc/Bach <1smlv Independiente
## 883 30-59 H Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 884 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 885 30-59 M Palmira Estrato 1-2 Mst/Doc 1-3smlv Empleado
## 886 30-59 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Independiente
## 887 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Independiente
## 888 30-59 H Palmira Estrato 3-4 Univ >3smlv Empleado
## 889 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 890 30-59 H Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 891 >60 H Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 892 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 893 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Independiente
## 894 18-29 M Palmira Estrato 5-6 Mst/Doc >3smlv Independiente
## 895 30-59 H Palmira Estrato 1-2 Univ >3smlv Empleado
## 896 30-59 H Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 897 18-29 M Palmira Estrato 5-6 Univ <1smlv Estudiante
## 898 18-29 H Palmira Estrato 1-2 Mst/Doc <1smlv Jubilado/Desempleado
## 899 18-29 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 900 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 901 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 902 30-59 M Palmira Estrato 5-6 Mst/Doc >3smlv Empleado
## 903 18-29 M Palmira Estrato 5-6 Mst/Doc >3smlv Independiente
## 904 18-29 H Palmira Estrato 5-6 Univ <1smlv Estudiante
## 905 30-59 M Palmira Estrato 5-6 Univ >3smlv Empleado
## 906 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 907 30-59 H Palmira Estrato 3-4 Univ <1smlv Empleado
## 908 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 909 30-59 M Palmira Estrato 1-2 Univ >3smlv Jubilado/Desempleado
## 910 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 911 30-59 H Palmira Estrato 3-4 Univ >3smlv Independiente
## 912 18-29 H Palmira Estrato 3-4 Téc/Bach <1smlv Estudiante
## 913 30-59 H Palmira Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 914 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 915 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Jubilado/Desempleado
## 916 30-59 H Palmira Estrato 5-6 Mst/Doc >3smlv Empleado
## 917 30-59 M Palmira Estrato 1-2 Univ <1smlv Independiente
## 918 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 919 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Independiente
## 920 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Estudiante
## 921 30-59 M Palmira Estrato 3-4 Univ >3smlv Empleado
## 922 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 923 18-29 H Palmira Estrato 3-4 Univ 1-3smlv Independiente
## 924 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 925 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Empleado
## 926 18-29 H Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 927 18-29 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 928 30-59 H Palmira Estrato 1-2 Univ 1-3smlv Estudiante
## 929 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 930 30-59 H Palmira Estrato 5-6 Mst/Doc >3smlv Empleado
## 931 18-29 H Palmira Estrato 3-4 Téc/Bach <1smlv Estudiante
## 932 18-29 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 933 18-29 H Palmira Estrato 1-2 Univ 1-3smlv Estudiante
## 934 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 935 18-29 H Palmira Estrato 3-4 Univ <1smlv Estudiante
## 936 18-29 H Palmira Estrato 3-4 Téc/Bach <1smlv Estudiante
## 937 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 938 18-29 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 939 30-59 H Palmira Estrato 3-4 Téc/Bach >3smlv Empleado
## 940 18-29 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 941 18-29 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 942 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 943 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 944 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 945 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Independiente
## 946 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Independiente
## 947 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 948 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 949 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 950 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 951 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 952 18-29 M Palmira Estrato 3-4 Univ 1-3smlv Jubilado/Desempleado
## 953 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 954 18-29 H Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 955 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 956 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 957 18-29 H Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 958 30-59 M Palmira Estrato 5-6 Mst/Doc >3smlv Empleado
## 959 18-29 H Palmira Estrato 1-2 Téc/Bach <1smlv Estudiante
## 960 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 961 18-29 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 962 30-59 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Jubilado/Desempleado
## 963 18-29 H Palmira Estrato 3-4 Téc/Bach <1smlv Estudiante
## 964 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Independiente
## 965 30-59 M Palmira Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 966 30-59 M Palmira Estrato 3-4 Mst/Doc <1smlv Jubilado/Desempleado
## 967 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 968 30-59 H Palmira Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 969 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 970 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 971 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 972 30-59 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 973 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Independiente
## 974 18-29 M Palmira Estrato 3-4 Univ 1-3smlv Independiente
## 975 30-59 H Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 976 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Estudiante
## 977 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 978 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 979 >60 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Jubilado/Desempleado
## 980 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 981 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 982 30-59 M Palmira Estrato 1-2 Téc/Bach >3smlv Empleado
## 983 30-59 M Palmira Estrato 1-2 Mst/Doc 1-3smlv Empleado
## 984 30-59 H Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 985 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 986 18-29 H Palmira Estrato 3-4 Téc/Bach <1smlv Estudiante
## 987 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 988 18-29 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 989 >60 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 990 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 991 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 992 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Estudiante
## 993 30-59 M Palmira Estrato 5-6 Univ 1-3smlv Independiente
## 994 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 995 30-59 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 996 30-59 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Jubilado/Desempleado
## 997 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 998 30-59 M Palmira Estrato 5-6 Univ 1-3smlv Independiente
## 999 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1000 30-59 H Palmira Estrato 3-4 Mst/Doc 1-3smlv Independiente
## 1001 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1002 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1003 30-59 M Palmira Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 1004 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Independiente
## 1005 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1006 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1007 18-29 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1008 30-59 M Palmira Estrato 3-4 Mst/Doc <1smlv Jubilado/Desempleado
## 1009 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1010 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1011 30-59 M Palmira Estrato 3-4 Univ >3smlv Empleado
## 1012 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1013 30-59 M Palmira Estrato 5-6 Univ <1smlv Independiente
## 1014 18-29 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1015 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1016 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1017 18-29 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1018 18-29 H Palmira Estrato 1-2 Téc/Bach <1smlv Estudiante
## 1019 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Estudiante
## 1020 30-59 M Palmira Estrato 1-2 Téc/Bach >3smlv Jubilado/Desempleado
## 1021 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1022 30-59 M Palmira Estrato 1-2 Univ <1smlv Jubilado/Desempleado
## 1023 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1024 18-29 H Palmira Estrato 1-2 Mst/Doc >3smlv Independiente
## 1025 18-29 H Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1026 30-59 M Palmira Estrato 3-4 Univ <1smlv Independiente
## 1027 30-59 M Palmira Estrato 3-4 Univ <1smlv Jubilado/Desempleado
## 1028 30-59 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Independiente
## 1029 30-59 M Palmira Estrato 1-2 Univ >3smlv Empleado
## 1030 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1031 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1032 18-29 M Palmira Estrato 1-2 Univ <1smlv Jubilado/Desempleado
## 1033 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1034 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Empleado
## 1035 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1036 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1037 18-29 H Palmira Estrato 5-6 Univ 1-3smlv Independiente
## 1038 18-29 H Palmira Estrato 3-4 Téc/Bach <1smlv Estudiante
## 1039 18-29 M Palmira Estrato 1-2 Univ <1smlv Estudiante
## 1040 18-29 M Palmira Estrato 3-4 Univ <1smlv Estudiante
## 1041 30-59 H Palmira Estrato 3-4 Univ >3smlv Empleado
## 1042 18-29 M Palmira Estrato 3-4 Univ 1-3smlv Independiente
## 1043 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1044 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1045 18-29 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1046 18-29 H Palmira Estrato 1-2 Téc/Bach <1smlv Estudiante
## 1047 30-59 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1048 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1049 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 1050 18-29 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1051 18-29 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1052 30-59 H Palmira Estrato 3-4 Univ >3smlv Empleado
## 1053 30-59 H Palmira Estrato 5-6 Mst/Doc >3smlv Empleado
## 1054 30-59 H Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1055 18-29 H Palmira Estrato 1-2 Univ <1smlv Estudiante
## 1056 18-29 M Palmira Estrato 3-4 Univ <1smlv Estudiante
## 1057 30-59 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1058 >60 H Palmira Estrato 1-2 Téc/Bach >3smlv Jubilado/Desempleado
## 1059 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1060 18-29 H Palmira Estrato 3-4 Téc/Bach <1smlv Estudiante
## 1061 18-29 M Palmira Estrato 3-4 Univ <1smlv Jubilado/Desempleado
## 1062 30-59 M Palmira Estrato 3-4 Univ >3smlv Empleado
## 1063 30-59 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1064 18-29 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1065 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1066 18-29 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1067 30-59 H Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1068 30-59 M Palmira Estrato 1-2 Univ <1smlv Jubilado/Desempleado
## 1069 30-59 M Palmira Estrato 3-4 Téc/Bach >3smlv Empleado
## 1070 30-59 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1071 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Estudiante
## 1072 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Estudiante
## 1073 30-59 H Palmira Estrato 3-4 Téc/Bach <1smlv Independiente
## 1074 18-29 H Palmira Estrato 1-2 Univ <1smlv Empleado
## 1075 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1076 18-29 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1077 18-29 M Palmira Estrato 3-4 Univ <1smlv Estudiante
## 1078 30-59 H Palmira Estrato 3-4 Téc/Bach 1-3smlv Independiente
## 1079 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1080 30-59 M Palmira Estrato 5-6 Mst/Doc >3smlv Empleado
## 1081 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1082 30-59 H Palmira Estrato 3-4 Téc/Bach 1-3smlv Independiente
## 1083 18-29 M Palmira Estrato 1-2 Univ <1smlv Jubilado/Desempleado
## 1084 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1085 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1086 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1087 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1088 18-29 H Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1089 18-29 H Palmira Estrato 3-4 Univ <1smlv Jubilado/Desempleado
## 1090 18-29 H Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1091 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Estudiante
## 1092 30-59 H Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1093 30-59 M Palmira Estrato 1-2 Mst/Doc 1-3smlv Independiente
## 1094 30-59 M Palmira Estrato 5-6 Mst/Doc >3smlv Independiente
## 1095 18-29 H Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1096 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Estudiante
## 1097 18-29 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1098 18-29 H Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1099 18-29 M Palmira Estrato 3-4 Univ <1smlv Estudiante
## 1100 30-59 H Palmira Estrato 1-2 Univ 1-3smlv Independiente
## 1101 18-29 H Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1102 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1103 30-59 M Palmira Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 1104 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Independiente
## 1105 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1106 >60 H Palmira Estrato 3-4 Univ <1smlv Jubilado/Desempleado
## 1107 >60 H Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1108 30-59 M Palmira Estrato 3-4 Mst/Doc <1smlv Jubilado/Desempleado
## 1109 30-59 M Palmira Estrato 3-4 Univ <1smlv Jubilado/Desempleado
## 1110 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1111 30-59 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1112 30-59 M Palmira Estrato 1-2 Univ >3smlv Empleado
## 1113 30-59 H Palmira Estrato 3-4 Univ <1smlv Independiente
## 1114 30-59 H Palmira Estrato 5-6 Mst/Doc >3smlv Empleado
## 1115 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Empleado
## 1116 30-59 M Palmira Estrato 1-2 Univ <1smlv Independiente
## 1117 30-59 M Palmira Estrato 3-4 Mst/Doc 1-3smlv Independiente
## 1118 30-59 H Palmira Estrato 3-4 Univ >3smlv Empleado
## 1119 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1120 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1121 18-29 H Palmira Estrato 1-2 Téc/Bach <1smlv Empleado
## 1122 30-59 H Palmira Estrato 1-2 Univ 1-3smlv Jubilado/Desempleado
## 1123 30-59 M Palmira Estrato 3-4 Mst/Doc 1-3smlv Independiente
## 1124 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1125 30-59 M Palmira Estrato 1-2 Univ >3smlv Empleado
## 1126 30-59 M Palmira Estrato 5-6 Mst/Doc >3smlv Estudiante
## 1127 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1128 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1129 30-59 M Palmira Estrato 3-4 Univ <1smlv Independiente
## 1130 >60 H Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1131 18-29 H Palmira Estrato 3-4 Univ 1-3smlv Independiente
## 1132 30-59 M Palmira Estrato 1-2 Mst/Doc >3smlv Empleado
## 1133 30-59 M Palmira Estrato 3-4 Univ >3smlv Empleado
## 1134 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1135 30-59 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1136 18-29 H Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1137 30-59 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1138 30-59 H Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1139 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Independiente
## 1140 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1141 30-59 H Palmira Estrato 3-4 Mst/Doc 1-3smlv Independiente
## 1142 30-59 M Palmira Estrato 5-6 Univ >3smlv Independiente
## 1143 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1144 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1145 18-29 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1146 30-59 M Palmira Estrato 1-2 Mst/Doc >3smlv Empleado
## 1147 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1148 30-59 M Palmira Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 1149 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1150 30-59 M Palmira Estrato 5-6 Mst/Doc >3smlv Empleado
## 1151 30-59 M Palmira Estrato 3-4 Univ >3smlv Empleado
## 1152 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1153 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1154 30-59 M Palmira Estrato 3-4 Univ >3smlv Empleado
## 1155 30-59 M Palmira Estrato 1-2 Mst/Doc 1-3smlv Independiente
## 1156 18-29 H Palmira Estrato 1-2 Univ <1smlv Jubilado/Desempleado
## 1157 18-29 H Palmira Estrato 5-6 Mst/Doc >3smlv Empleado
## 1158 18-29 M Palmira Estrato 1-2 Univ <1smlv Independiente
## 1159 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1160 >60 H Palmira Estrato 5-6 Univ 1-3smlv Jubilado/Desempleado
## 1161 30-59 M Palmira Estrato 5-6 Univ 1-3smlv Independiente
## 1162 >60 H Palmira Estrato 1-2 Mst/Doc >3smlv Jubilado/Desempleado
## 1163 >60 H Palmira Estrato 3-4 Téc/Bach 1-3smlv Jubilado/Desempleado
## 1164 30-59 H Palmira Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 1165 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1166 30-59 H Palmira Estrato 5-6 Mst/Doc >3smlv Empleado
## 1167 >60 H Palmira Estrato 1-2 Univ 1-3smlv Jubilado/Desempleado
## 1168 30-59 M Palmira Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 1169 30-59 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1170 30-59 H Palmira Estrato 1-2 Mst/Doc >3smlv Empleado
## 1171 30-59 H Palmira Estrato 3-4 Univ <1smlv Independiente
## 1172 >60 M Palmira Estrato 1-2 Univ <1smlv Independiente
## 1173 18-29 M Palmira Estrato 1-2 Univ <1smlv Jubilado/Desempleado
## 1174 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Independiente
## 1175 30-59 M Palmira Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 1176 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Estudiante
## 1177 30-59 H Palmira Estrato 1-2 Téc/Bach >3smlv Independiente
## 1178 30-59 H Palmira Estrato 5-6 Mst/Doc >3smlv Empleado
## 1179 30-59 M Palmira Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 1180 30-59 M Palmira Estrato 3-4 Univ >3smlv Empleado
## 1181 30-59 M Palmira Estrato 1-2 Mst/Doc <1smlv Jubilado/Desempleado
## 1182 30-59 M Palmira Estrato 5-6 Univ <1smlv Jubilado/Desempleado
## 1183 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Independiente
## 1184 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Independiente
## 1185 >60 H Palmira Estrato 3-4 Mst/Doc >3smlv Independiente
## 1186 30-59 H Palmira Estrato 1-2 Mst/Doc <1smlv Independiente
## 1187 18-29 M Palmira Estrato 3-4 Univ <1smlv Independiente
## 1188 30-59 M Palmira Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 1189 30-59 M Palmira Estrato 5-6 Mst/Doc 1-3smlv Empleado
## 1190 30-59 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Independiente
## 1191 30-59 M Palmira Estrato 5-6 Téc/Bach 1-3smlv Empleado
## 1192 30-59 M Palmira Estrato 3-4 Univ <1smlv Independiente
## 1193 30-59 M Palmira Estrato 3-4 Téc/Bach <1smlv Independiente
## 1194 30-59 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1195 30-59 M Palmira Estrato 3-4 Téc/Bach <1smlv Independiente
## 1196 >60 H Palmira Estrato 3-4 Mst/Doc >3smlv Jubilado/Desempleado
## 1197 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1198 18-29 H Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1199 30-59 H Palmira Estrato 1-2 Univ 1-3smlv Jubilado/Desempleado
## 1200 30-59 H Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1201 30-59 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1202 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1203 18-29 H Palmira Estrato 3-4 Univ <1smlv Jubilado/Desempleado
## 1204 30-59 M Palmira Estrato 3-4 Univ >3smlv Empleado
## 1205 18-29 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1206 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Independiente
## 1207 30-59 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1208 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1209 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1210 18-29 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Jubilado/Desempleado
## 1211 30-59 H Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1212 18-29 M Palmira Estrato 1-2 Univ <1smlv Estudiante
## 1213 30-59 M Palmira Estrato 3-4 Téc/Bach <1smlv Empleado
## 1214 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1215 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1216 18-29 H Palmira Estrato 5-6 Univ >3smlv Independiente
## 1217 >60 H Palmira Estrato 3-4 Téc/Bach >3smlv Empleado
## 1218 30-59 M Palmira Estrato 1-2 Univ <1smlv Independiente
## 1219 18-29 M Palmira Estrato 1-2 Univ <1smlv Estudiante
## 1220 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Independiente
## 1221 30-59 H Palmira Estrato 5-6 Mst/Doc >3smlv Independiente
## 1222 30-59 M Palmira Estrato 3-4 Mst/Doc 1-3smlv Independiente
## 1223 18-29 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1224 18-29 M Palmira Estrato 3-4 Univ >3smlv Empleado
## 1225 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Independiente
## 1226 18-29 H Palmira Estrato 1-2 Téc/Bach <1smlv Empleado
## 1227 30-59 H Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1228 18-29 M Palmira Estrato 3-4 Univ <1smlv Estudiante
## 1229 30-59 M Palmira Estrato 1-2 Univ <1smlv Jubilado/Desempleado
## 1230 30-59 H Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1231 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1232 30-59 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1233 30-59 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1234 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1235 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Independiente
## 1236 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1237 >60 H Palmira Estrato 1-2 Téc/Bach <1smlv Independiente
## 1238 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Estudiante
## 1239 18-29 M Palmira Estrato 5-6 Téc/Bach <1smlv Jubilado/Desempleado
## 1240 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Independiente
## 1241 18-29 H Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1242 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1243 30-59 H Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1244 30-59 M Palmira Estrato 5-6 Univ <1smlv Jubilado/Desempleado
## 1245 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1246 30-59 M Palmira Estrato 1-2 Univ <1smlv Jubilado/Desempleado
## 1247 30-59 M Palmira Estrato 3-4 Mst/Doc 1-3smlv Independiente
## 1248 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1249 30-59 M Palmira Estrato 5-6 Mst/Doc >3smlv Empleado
## 1250 30-59 M Palmira Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 1251 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Independiente
## 1252 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 1253 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Empleado
## 1254 30-59 H Palmira Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 1255 18-29 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1256 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1257 18-29 M Palmira Estrato 1-2 Univ <1smlv Estudiante
## 1258 30-59 M Palmira Estrato 5-6 Mst/Doc 1-3smlv Jubilado/Desempleado
## 1259 30-59 H Palmira Estrato 1-2 Univ <1smlv Jubilado/Desempleado
## 1260 18-29 H Palmira Estrato 1-2 Téc/Bach <1smlv Estudiante
## 1261 30-59 H Palmira Estrato 1-2 Univ >3smlv Empleado
## 1262 30-59 M Palmira Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 1263 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1264 30-59 H Palmira Estrato 5-6 Mst/Doc >3smlv Independiente
## 1265 30-59 H Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1266 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Independiente
## 1267 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1268 30-59 H Palmira Estrato 1-2 Mst/Doc >3smlv Empleado
## 1269 18-29 M Palmira Estrato 3-4 Univ 1-3smlv Independiente
## 1270 30-59 H Palmira Estrato 3-4 Mst/Doc 1-3smlv Independiente
## 1271 30-59 M Palmira Estrato 3-4 Mst/Doc <1smlv Jubilado/Desempleado
## 1272 30-59 H Palmira Estrato 3-4 Téc/Bach >3smlv Empleado
## 1273 30-59 M Palmira Estrato 1-2 Univ >3smlv Empleado
## 1274 30-59 H Palmira Estrato 1-2 Univ <1smlv Empleado
## 1275 18-29 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Independiente
## 1276 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 1277 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1278 30-59 H Palmira Estrato 1-2 Téc/Bach <1smlv Empleado
## 1279 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Estudiante
## 1280 30-59 M Palmira Estrato 3-4 Mst/Doc 1-3smlv Empleado
## 1281 18-29 M Palmira Estrato 3-4 Mst/Doc <1smlv Estudiante
## 1282 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Independiente
## 1283 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Independiente
## 1284 18-29 H Palmira Estrato 1-2 Téc/Bach <1smlv Independiente
## 1285 >60 M Palmira Estrato 1-2 Téc/Bach >3smlv Jubilado/Desempleado
## 1286 30-59 H Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1287 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Estudiante
## 1288 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1289 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1290 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1291 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1292 30-59 M Palmira Estrato 3-4 Univ <1smlv Independiente
## 1293 18-29 M Palmira Estrato 3-4 Univ <1smlv Estudiante
## 1294 30-59 H Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1295 18-29 H Palmira Estrato 3-4 Univ >3smlv Empleado
## 1296 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1297 30-59 H Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1298 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1299 18-29 M Palmira Estrato 1-2 Mst/Doc >3smlv Empleado
## 1300 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1301 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Independiente
## 1302 18-29 M Palmira Estrato 3-4 Univ <1smlv Empleado
## 1303 18-29 M Palmira Estrato 3-4 Univ <1smlv Independiente
## 1304 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1305 30-59 H Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1306 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1307 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Estudiante
## 1308 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 1309 30-59 M Palmira Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 1310 18-29 M Palmira Estrato 1-2 Univ <1smlv Jubilado/Desempleado
## 1311 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 1312 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1313 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1314 30-59 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1315 30-59 H Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1316 18-29 M Palmira Estrato 1-2 Univ >3smlv Empleado
## 1317 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Independiente
## 1318 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1319 18-29 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1320 30-59 H Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1321 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1322 30-59 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1323 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Empleado
## 1324 30-59 H Palmira Estrato 1-2 Téc/Bach <1smlv Empleado
## 1325 18-29 M Palmira Estrato 3-4 Mst/Doc <1smlv Empleado
## 1326 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1327 18-29 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1328 30-59 M Palmira Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 1329 30-59 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1330 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Empleado
## 1331 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1332 30-59 M Palmira Estrato 5-6 Mst/Doc 1-3smlv Independiente
## 1333 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Independiente
## 1334 18-29 M Palmira Estrato 1-2 Univ <1smlv Jubilado/Desempleado
## 1335 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1336 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1337 18-29 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1338 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1339 30-59 H Palmira Estrato 1-2 Téc/Bach <1smlv Independiente
## 1340 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1341 18-29 H Palmira Estrato 3-4 Univ 1-3smlv Independiente
## 1342 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1343 18-29 M Palmira Estrato 5-6 Univ 1-3smlv Independiente
## 1344 18-29 H Palmira Estrato 1-2 Univ <1smlv Jubilado/Desempleado
## 1345 30-59 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1346 30-59 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1347 30-59 M Palmira Estrato 3-4 Mst/Doc <1smlv Independiente
## 1348 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1349 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Empleado
## 1350 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1351 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Estudiante
## 1352 30-59 M Palmira Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 1353 18-29 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1354 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Jubilado/Desempleado
## 1355 30-59 M Palmira Estrato 3-4 Univ <1smlv Jubilado/Desempleado
## 1356 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Independiente
## 1357 30-59 H Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1358 30-59 H Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1359 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Jubilado/Desempleado
## 1360 30-59 H Palmira Estrato 3-4 Univ >3smlv Empleado
## 1361 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1362 18-29 H Palmira Estrato 3-4 Univ <1smlv Estudiante
## 1363 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1364 30-59 M Palmira Estrato 5-6 Mst/Doc >3smlv Empleado
## 1365 30-59 H Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1366 18-29 M Palmira Estrato 3-4 Univ <1smlv Empleado
## 1367 18-29 M Palmira Estrato 1-2 Univ <1smlv Estudiante
## 1368 30-59 M Palmira Estrato 1-2 Univ >3smlv Empleado
## 1369 30-59 M Palmira Estrato 5-6 Mst/Doc >3smlv Independiente
## 1370 30-59 M Palmira Estrato 1-2 Mst/Doc 1-3smlv Empleado
## 1371 30-59 H Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1372 30-59 M Palmira Estrato 3-4 Mst/Doc <1smlv Jubilado/Desempleado
## 1373 30-59 M Palmira Estrato 1-2 Mst/Doc 1-3smlv Independiente
## 1374 30-59 H Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1375 30-59 M Palmira Estrato 1-2 Univ <1smlv Independiente
## 1376 30-59 M Palmira Estrato 3-4 Univ >3smlv Empleado
## 1377 30-59 H Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1378 18-29 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1379 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1380 30-59 H Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1381 30-59 M Palmira Estrato 3-4 Mst/Doc <1smlv Independiente
## 1382 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1383 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1384 18-29 M Palmira Estrato 3-4 Mst/Doc >3smlv Estudiante
## 1385 30-59 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1386 30-59 M Palmira Estrato 1-2 Univ >3smlv Empleado
## 1387 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Independiente
## 1388 30-59 H Palmira Estrato 1-2 Univ >3smlv Empleado
## 1389 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Independiente
## 1390 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Estudiante
## 1391 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Independiente
## 1392 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Independiente
## 1393 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1394 30-59 H Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1395 18-29 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1396 18-29 M Palmira Estrato 3-4 Univ <1smlv Estudiante
## 1397 30-59 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Independiente
## 1398 30-59 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Independiente
## 1399 30-59 H Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1400 30-59 H Palmira Estrato 3-4 Mst/Doc >3smlv Independiente
## 1401 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1402 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1403 18-29 H Palmira Estrato 3-4 Téc/Bach <1smlv Independiente
## 1404 18-29 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1405 18-29 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1406 30-59 M Palmira Estrato 3-4 Téc/Bach <1smlv Jubilado/Desempleado
## 1407 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1408 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Independiente
## 1409 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Estudiante
## 1410 30-59 Otro Palmira Estrato 3-4 Univ 1-3smlv Independiente
## 1411 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1412 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Estudiante
## 1413 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Estudiante
## 1414 18-29 H Palmira Estrato 1-2 Univ <1smlv Jubilado/Desempleado
## 1415 30-59 M Palmira Estrato 3-4 Mst/Doc <1smlv Jubilado/Desempleado
## 1416 30-59 M Palmira Estrato 3-4 Téc/Bach <1smlv Empleado
## 1417 30-59 H Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1418 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Empleado
## 1419 18-29 M Palmira Estrato 3-4 Téc/Bach <1smlv Estudiante
## 1420 18-29 H Palmira Estrato 1-2 Univ >3smlv Empleado
## 1421 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Jubilado/Desempleado
## 1422 30-59 H Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1423 30-59 H Palmira Estrato 3-4 Univ >3smlv Empleado
## 1424 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Independiente
## 1425 30-59 M Palmira Estrato 1-2 Téc/Bach <1smlv Empleado
## 1426 30-59 H Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1427 30-59 H Palmira Estrato 1-2 Univ >3smlv Empleado
## 1428 18-29 M Palmira Estrato 1-2 Univ 1-3smlv Estudiante
## 1429 18-29 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1430 30-59 H Palmira Estrato 3-4 Univ >3smlv Empleado
## 1431 18-29 M Palmira Estrato 1-2 Téc/Bach <1smlv Jubilado/Desempleado
## 1432 30-59 Otro Palmira Estrato 3-4 Téc/Bach <1smlv Empleado
## 1433 30-59 H Palmira Estrato 3-4 Téc/Bach 1-3smlv Empleado
## 1434 18-29 M Palmira Estrato 1-2 Univ 1-3smlv Empleado
## 1435 30-59 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Empleado
## 1436 18-29 M Palmira Estrato 3-4 Téc/Bach 1-3smlv Jubilado/Desempleado
## 1437 18-29 M Palmira Estrato 1-2 Téc/Bach 1-3smlv Jubilado/Desempleado
## 1438 30-59 M Palmira Estrato 3-4 Univ 1-3smlv Empleado
## 1439 30-59 M Palmira Estrato 1-2 Univ 1-3smlv Independiente
## 1440 30-59 M Palmira Estrato 1-2 Univ <1smlv Jubilado/Desempleado
## 1441 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## 1442 18-29 M Palmira Estrato 1-2 Univ <1smlv Independiente
## 1443 30-59 M Palmira Estrato 3-4 Mst/Doc >3smlv Empleado
## ViveConEnferCronicas IPRG clust
## 1 No Bajo 1
## 2 No Bajo 1
## 3 No Medio 3
## 4 Si Medio 3
## 5 No Medio 1
## 6 No Alto 1
## 7 No Bajo 1
## 8 No Medio 1
## 9 No Medio 2
## 10 No Bajo 3
## 11 No Medio 1
## 12 No Bajo 3
## 13 Si Medio 2
## 14 No Medio 1
## 15 Si Medio 2
## 16 No Medio 2
## 17 No Bajo 3
## 18 No Medio 1
## 19 No Alto 2
## 20 No Medio 1
## 21 Si Alto 3
## 22 No Bajo 1
## 23 No Bajo 2
## 24 No Bajo 1
## 25 No Medio 3
## 26 Si Bajo 3
## 27 Si Bajo 3
## 28 No Medio 1
## 29 No Bajo 3
## 30 No Medio 1
## 31 No Medio 1
## 32 Si Medio 2
## 33 No Bajo 1
## 34 No Alto 2
## 35 No Medio 1
## 36 No Bajo 3
## 37 No Medio 1
## 38 No Alto 1
## 39 No Alto 2
## 40 No Medio 1
## 41 No Alto 2
## 42 Si Bajo 2
## 43 No Medio 1
## 44 Si Medio 1
## 45 No Medio 3
## 46 No Bajo 1
## 47 No Bajo 1
## 48 Si Bajo 1
## 49 No Bajo 1
## 50 Si Medio 2
## 51 No Bajo 2
## 52 No Bajo 1
## 53 Si Bajo 1
## 54 Si Alto 2
## 55 No Medio 1
## 56 Si Medio 3
## 57 No Bajo 1
## 58 No Medio 1
## 59 No Bajo 3
## 60 No Bajo 1
## 61 No Alto 1
## 62 No Bajo 1
## 63 No Bajo 1
## 64 No Bajo 1
## 65 No Medio 1
## 66 No Medio 1
## 67 Si Medio 1
## 68 No Medio 1
## 69 No Alto 2
## 70 Si Alto 1
## 71 No Medio 1
## 72 No Bajo 3
## 73 No Medio 1
## 74 No Bajo 1
## 75 Si Bajo 1
## 76 No Medio 2
## 77 Si Bajo 1
## 78 Si Medio 1
## 79 No Alto 3
## 80 No Medio 2
## 81 No Alto 2
## 82 No Medio 1
## 83 No Medio 1
## 84 Si Medio 1
## 85 No Bajo 2
## 86 No Alto 1
## 87 No Bajo 3
## 88 Si Bajo 3
## 89 No Alto 3
## 90 Si Medio 1
## 91 No Bajo 2
## 92 Si Medio 1
## 93 No Medio 1
## 94 Si Medio 1
## 95 No Bajo 1
## 96 No Alto 1
## 97 Si Medio 2
## 98 No Bajo 1
## 99 Si Bajo 1
## 100 No Medio 1
## 101 No Alto 1
## 102 No Bajo 1
## 103 No Bajo 2
## 104 No Medio 2
## 105 No Alto 2
## 106 No Alto 2
## 107 Si Alto 2
## 108 Si Alto 1
## 109 Si Medio 1
## 110 No Medio 2
## 111 No Alto 2
## 112 Si Alto 1
## 113 No Medio 1
## 114 Si Alto 2
## 115 No Bajo 1
## 116 No Bajo 1
## 117 No Bajo 3
## 118 Si Medio 1
## 119 No Bajo 1
## 120 No Medio 2
## 121 Si Alto 3
## 122 No Alto 2
## 123 Si Medio 3
## 124 No Medio 1
## 125 Si Bajo 2
## 126 Si Medio 2
## 127 Si Alto 2
## 128 No Medio 3
## 129 No Alto 1
## 130 Si Bajo 2
## 131 Si Alto 1
## 132 Si Medio 1
## 133 Si Alto 3
## 134 No Bajo 1
## 135 No Medio 2
## 136 Si Bajo 3
## 137 No Bajo 1
## 138 No Alto 1
## 139 No Medio 2
## 140 Si Bajo 3
## 141 Si Alto 2
## 142 No Medio 1
## 143 Si Medio 1
## 144 Si Medio 2
## 145 Si Bajo 2
## 146 No Medio 1
## 147 Si Medio 1
## 148 No Bajo 1
## 149 No Medio 2
## 150 No Medio 3
## 151 No Bajo 3
## 152 Si Bajo 3
## 153 Si Alto 2
## 154 Si Medio 1
## 155 No Alto 2
## 156 No Bajo 3
## 157 Si Medio 2
## 158 No Medio 1
## 159 No Medio 2
## 160 No Alto 1
## 161 No Medio 3
## 162 No Alto 1
## 163 No Medio 2
## 164 No Medio 1
## 165 No Medio 1
## 166 No Alto 2
## 167 No Medio 1
## 168 No Bajo 1
## 169 No Alto 2
## 170 No Bajo 1
## 171 Si Alto 2
## 172 No Alto 1
## 173 No Medio 1
## 174 No Bajo 1
## 175 Si Medio 2
## 176 No Medio 1
## 177 No Bajo 1
## 178 Si Medio 3
## 179 Si Medio 1
## 180 Si Medio 1
## 181 No Alto 3
## 182 No Bajo 1
## 183 No Alto 2
## 184 No Bajo 1
## 185 Si Alto 2
## 186 No Medio 1
## 187 Si Bajo 2
## 188 Si Medio 2
## 189 Si Medio 2
## 190 Si Medio 3
## 191 No Medio 1
## 192 No Medio 2
## 193 No Bajo 2
## 194 No Bajo 3
## 195 No Alto 1
## 196 No Bajo 3
## 197 No Medio 2
## 198 Si Medio 3
## 199 No Bajo 3
## 200 No Medio 2
## 201 No Bajo 3
## 202 No Medio 1
## 203 No Medio 1
## 204 No Alto 2
## 205 No Medio 2
## 206 No Alto 2
## 207 Si Medio 3
## 208 No Medio 1
## 209 No Bajo 3
## 210 No Alto 1
## 211 No Alto 1
## 212 Si Bajo 1
## 213 No Medio 3
## 214 No Medio 1
## 215 Si Medio 2
## 216 No Medio 1
## 217 No Medio 2
## 218 No Alto 2
## 219 No Bajo 3
## 220 No Alto 2
## 221 No Medio 1
## 222 No Medio 1
## 223 No Medio 1
## 224 No Medio 1
## 225 Si Medio 1
## 226 Si Bajo 3
## 227 No Medio 1
## 228 Si Bajo 1
## 229 No Bajo 2
## 230 No Medio 2
## 231 Si Alto 2
## 232 No Medio 1
## 233 Si Medio 1
## 234 No Medio 2
## 235 No Medio 1
## 236 No Bajo 3
## 237 No Bajo 3
## 238 No Medio 1
## 239 No Bajo 1
## 240 No Bajo 1
## 241 No Medio 1
## 242 No Medio 3
## 243 No Medio 1
## 244 No Bajo 3
## 245 Si Bajo 2
## 246 Si Bajo 1
## 247 Si Medio 3
## 248 No Alto 1
## 249 Si Bajo 1
## 250 No Alto 2
## 251 No Bajo 1
## 252 No Bajo 3
## 253 No Medio 2
## 254 No Medio 1
## 255 No Medio 2
## 256 Si Bajo 1
## 257 No Medio 3
## 258 No Bajo 1
## 259 No Alto 2
## 260 No Medio 1
## 261 No Alto 2
## 262 No Medio 2
## 263 No Medio 3
## 264 No Bajo 1
## 265 No Bajo 1
## 266 No Medio 1
## 267 No Bajo 1
## 268 No Medio 2
## 269 No Medio 1
## 270 No Bajo 3
## 271 No Alto 2
## 272 No Bajo 1
## 273 No Bajo 1
## 274 No Bajo 2
## 275 No Bajo 1
## 276 No Medio 1
## 277 No Medio 1
## 278 No Alto 1
## 279 No Alto 2
## 280 No Medio 1
## 281 No Medio 1
## 282 Si Medio 1
## 283 No Alto 1
## 284 Si Medio 1
## 285 No Bajo 1
## 286 No Medio 1
## 287 No Alto 1
## 288 No Bajo 2
## 289 No Medio 1
## 290 No Medio 3
## 291 No Medio 1
## 292 No Medio 2
## 293 No Bajo 1
## 294 No Medio 1
## 295 No Bajo 2
## 296 No Bajo 3
## 297 No Bajo 1
## 298 No Medio 3
## 299 No Medio 2
## 300 No Alto 2
## 301 No Bajo 3
## 302 No Medio 1
## 303 No Bajo 1
## 304 No Bajo 2
## 305 No Alto 1
## 306 No Medio 3
## 307 No Bajo 1
## 308 No Medio 3
## 309 Si Medio 1
## 310 No Alto 2
## 311 No Medio 2
## 312 No Alto 1
## 313 No Bajo 1
## 314 No Bajo 1
## 315 Si Medio 1
## 316 No Medio 2
## 317 No Medio 1
## 318 No Bajo 1
## 319 No Alto 2
## 320 No Bajo 3
## 321 Si Medio 1
## 322 No Medio 1
## 323 No Bajo 1
## 324 No Bajo 1
## 325 No Medio 3
## 326 No Alto 2
## 327 No Alto 1
## 328 No Bajo 2
## 329 No Medio 1
## 330 Si Bajo 2
## 331 No Medio 2
## 332 No Medio 1
## 333 Si Alto 1
## 334 No Alto 1
## 335 No Medio 2
## 336 No Bajo 3
## 337 No Medio 3
## 338 No Alto 1
## 339 No Bajo 1
## 340 No Bajo 2
## 341 No Bajo 1
## 342 No Medio 3
## 343 No Medio 1
## 344 No Bajo 1
## 345 No Medio 1
## 346 No Bajo 2
## 347 No Bajo 2
## 348 Si Medio 1
## 349 No Medio 3
## 350 No Alto 3
## 351 No Alto 3
## 352 No Medio 3
## 353 No Bajo 1
## 354 No Medio 2
## 355 No Medio 1
## 356 No Bajo 1
## 357 Si Alto 2
## 358 No Bajo 2
## 359 No Bajo 3
## 360 Si Alto 3
## 361 Si Medio 2
## 362 Si Alto 1
## 363 No Bajo 1
## 364 No Bajo 1
## 365 No Medio 1
## 366 No Bajo 1
## 367 No Alto 1
## 368 No Bajo 1
## 369 No Alto 1
## 370 Si Bajo 1
## 371 No Medio 2
## 372 No Medio 1
## 373 No Alto 1
## 374 Si Medio 1
## 375 No Alto 1
## 376 No Medio 1
## 377 Si Alto 2
## 378 No Bajo 3
## 379 Si Bajo 1
## 380 Si Medio 1
## 381 No Medio 1
## 382 No Alto 1
## 383 No Bajo 3
## 384 No Alto 1
## 385 Si Alto 2
## 386 Si Medio 1
## 387 No Alto 1
## 388 No Bajo 1
## 389 No Bajo 1
## 390 No Medio 1
## 391 No Bajo 3
## 392 No Bajo 3
## 393 Si Medio 3
## 394 No Bajo 3
## 395 Si Medio 1
## 396 No Medio 1
## 397 No Bajo 1
## 398 No Medio 2
## 399 No Bajo 2
## 400 No Bajo 1
## 401 No Medio 1
## 402 No Bajo 1
## 403 No Bajo 3
## 404 Si Bajo 1
## 405 No Medio 1
## 406 No Bajo 3
## 407 Si Bajo 3
## 408 No Bajo 3
## 409 Si Medio 3
## 410 Si Medio 2
## 411 No Alto 2
## 412 Si Medio 3
## 413 No Bajo 3
## 414 No Medio 3
## 415 No Medio 3
## 416 Si Alto 3
## 417 No Medio 3
## 418 Si Medio 1
## 419 No Bajo 3
## 420 Si Medio 1
## 421 No Medio 3
## 422 No Bajo 2
## 423 Si Alto 3
## 424 No Alto 3
## 425 No Alto 3
## 426 No Medio 3
## 427 No Alto 3
## 428 Si Medio 2
## 429 Si Medio 3
## 430 Si Medio 3
## 431 Si Medio 3
## 432 Si Alto 3
## 433 No Bajo 2
## 434 No Bajo 2
## 435 No Alto 1
## 436 Si Alto 2
## 437 Si Medio 3
## 438 No Bajo 2
## 439 No Medio 1
## 440 No Bajo 3
## 441 Si Bajo 3
## 442 No Bajo 1
## 443 Si Medio 1
## 444 No Alto 1
## 445 No Bajo 1
## 446 No Bajo 1
## 447 Si Alto 1
## 448 No Medio 1
## 449 No Alto 2
## 450 No Alto 3
## 451 No Medio 1
## 452 Si Alto 1
## 453 Si Alto 1
## 454 No Medio 3
## 455 No Medio 1
## 456 No Medio 2
## 457 No Medio 1
## 458 No Alto 1
## 459 Si Medio 3
## 460 No Alto 1
## 461 Si Bajo 1
## 462 No Medio 2
## 463 No Medio 3
## 464 No Bajo 3
## 465 No Medio 3
## 466 No Medio 3
## 467 No Medio 3
## 468 No Medio 1
## 469 No Alto 1
## 470 Si Medio 2
## 471 Si Alto 3
## 472 No Medio 3
## 473 No Bajo 1
## 474 No Alto 1
## 475 No Medio 1
## 476 No Medio 1
## 477 No Alto 3
## 478 Si Medio 2
## 479 No Medio 3
## 480 Si Medio 1
## 481 No Bajo 1
## 482 No Alto 1
## 483 No Medio 1
## 484 No Medio 2
## 485 No Medio 1
## 486 No Bajo 1
## 487 Si Alto 1
## 488 No Alto 1
## 489 No Alto 1
## 490 No Bajo 1
## 491 No Medio 3
## 492 No Medio 2
## 493 No Bajo 1
## 494 No Bajo 2
## 495 No Alto 2
## 496 No Medio 1
## 497 No Medio 2
## 498 No Medio 2
## 499 No Medio 1
## 500 No Alto 2
## 501 No Bajo 3
## 502 No Alto 2
## 503 No Medio 2
## 504 Si Bajo 1
## 505 No Medio 1
## 506 No Medio 1
## 507 No Medio 1
## 508 No Bajo 2
## 509 No Bajo 1
## 510 No Bajo 1
## 511 No Bajo 3
## 512 No Medio 1
## 513 Si Medio 3
## 514 No Medio 3
## 515 No Bajo 3
## 516 Si Bajo 3
## 517 No Alto 2
## 518 No Medio 1
## 519 No Bajo 1
## 520 Si Bajo 2
## 521 No Bajo 2
## 522 No Alto 1
## 523 No Bajo 3
## 524 No Bajo 2
## 525 No Medio 3
## 526 No Medio 1
## 527 Si Alto 1
## 528 No Bajo 3
## 529 No Bajo 3
## 530 No Medio 1
## 531 No Medio 3
## 532 Si Bajo 3
## 533 Si Medio 3
## 534 Si Medio 3
## 535 No Medio 3
## 536 Si Bajo 3
## 537 Si Bajo 2
## 538 No Bajo 1
## 539 No Medio 3
## 540 No Bajo 2
## 541 No Bajo 1
## 542 Si Medio 3
## 543 Si Alto 1
## 544 No Medio 3
## 545 Si Alto 2
## 546 Si Medio 1
## 547 No Medio 2
## 548 No Medio 2
## 549 No Medio 3
## 550 No Bajo 1
## 551 Si Medio 2
## 552 No Alto 2
## 553 Si Medio 2
## 554 No Bajo 1
## 555 Si Bajo 2
## 556 No Alto 1
## 557 Si Bajo 2
## 558 No Bajo 3
## 559 No Medio 1
## 560 No Bajo 2
## 561 No Bajo 1
## 562 No Bajo 2
## 563 No Medio 1
## 564 Si Bajo 3
## 565 No Medio 1
## 566 No Bajo 1
## 567 No Medio 2
## 568 No Bajo 1
## 569 No Medio 3
## 570 No Bajo 3
## 571 No Medio 1
## 572 Si Alto 3
## 573 No Medio 1
## 574 No Medio 1
## 575 No Bajo 3
## 576 No Medio 1
## 577 No Bajo 1
## 578 No Medio 2
## 579 No Medio 2
## 580 No Medio 1
## 581 No Medio 2
## 582 Si Medio 2
## 583 Si Medio 1
## 584 Si Bajo 2
## 585 Si Bajo 2
## 586 Si Bajo 3
## 587 No Bajo 3
## 588 No Medio 1
## 589 No Bajo 3
## 590 No Medio 2
## 591 No Medio 2
## 592 No Bajo 3
## 593 No Medio 2
## 594 No Medio 3
## 595 No Medio 2
## 596 No Medio 1
## 597 No Medio 1
## 598 No Bajo 1
## 599 No Medio 1
## 600 No Medio 2
## 601 Si Medio 1
## 602 No Medio 3
## 603 Si Medio 2
## 604 No Bajo 2
## 605 No Bajo 2
## 606 No Bajo 2
## 607 No Medio 1
## 608 No Bajo 2
## 609 No Medio 1
## 610 No Alto 1
## 611 No Bajo 3
## 612 No Medio 1
## 613 Si Medio 3
## 614 No Alto 3
## 615 No Medio 1
## 616 No Alto 1
## 617 Si Bajo 1
## 618 No Alto 1
## 619 No Alto 3
## 620 No Bajo 1
## 621 Si Alto 1
## 622 No Alto 1
## 623 Si Bajo 3
## 624 No Medio 2
## 625 No Medio 1
## 626 Si Medio 1
## 627 Si Medio 3
## 628 No Alto 2
## 629 Si Bajo 1
## 630 No Bajo 1
## 631 No Alto 3
## 632 Si Medio 3
## 633 Si Bajo 1
## 634 Si Alto 2
## 635 Si Medio 1
## 636 Si Medio 1
## 637 No Bajo 3
## 638 No Alto 2
## 639 No Bajo 1
## 640 No Bajo 1
## 641 No Bajo 1
## 642 No Bajo 3
## 643 No Medio 1
## 644 Si Medio 1
## 645 Si Bajo 1
## 646 No Medio 1
## 647 No Medio 1
## 648 No Medio 1
## 649 No Bajo 1
## 650 No Alto 3
## 651 Si Alto 1
## 652 No Alto 1
## 653 Si Medio 3
## 654 Si Medio 2
## 655 No Medio 3
## 656 No Alto 2
## 657 No Medio 3
## 658 No Alto 1
## 659 No Medio 1
## 660 No Medio 3
## 661 No Alto 2
## 662 Si Bajo 2
## 663 Si Alto 2
## 664 No Medio 2
## 665 No Alto 2
## 666 No Medio 3
## 667 No Bajo 3
## 668 Si Medio 2
## 669 No Bajo 1
## 670 No Medio 2
## 671 No Alto 2
## 672 Si Medio 2
## 673 Si Alto 2
## 674 No Medio 2
## 675 Si Alto 2
## 676 No Medio 2
## 677 No Medio 2
## 678 No Bajo 2
## 679 Si Alto 2
## 680 No Medio 2
## 681 No Medio 3
## 682 No Medio 2
## 683 No Medio 2
## 684 Si Medio 1
## 685 No Medio 1
## 686 No Bajo 1
## 687 Si Bajo 3
## 688 No Medio 3
## 689 No Bajo 3
## 690 Si Medio 1
## 691 Si Bajo 1
## 692 No Medio 3
## 693 No Medio 3
## 694 No Medio 2
## 695 No Alto 2
## 696 No Medio 1
## 697 No Alto 1
## 698 No Medio 2
## 699 No Alto 2
## 700 No Bajo 3
## 701 Si Bajo 3
## 702 No Medio 1
## 703 Si Medio 2
## 704 Si Medio 2
## 705 No Alto 1
## 706 Si Alto 1
## 707 No Medio 1
## 708 No Medio 1
## 709 No Bajo 1
## 710 No Medio 1
## 711 No Medio 2
## 712 No Alto 2
## 713 No Bajo 3
## 714 Si Bajo 1
## 715 No Medio 1
## 716 No Medio 1
## 717 No Bajo 1
## 718 No Medio 1
## 719 No Medio 1
## 720 Si Medio 1
## 721 Si Medio 3
## 722 No Bajo 3
## 723 Si Medio 2
## 724 No Medio 3
## 725 No Medio 1
## 726 Si Alto 2
## 727 Si Medio 3
## 728 No Bajo 2
## 729 No Medio 1
## 730 Si Bajo 1
## 731 Si Bajo 3
## 732 No Medio 3
## 733 No Bajo 3
## 734 No Medio 1
## 735 No Medio 1
## 736 No Bajo 3
## 737 No Bajo 2
## 738 No Medio 1
## 739 Si Medio 1
## 740 No Medio 1
## 741 Si Medio 1
## 742 No Alto 1
## 743 No Alto 2
## 744 No Alto 1
## 745 Si Medio 2
## 746 No Alto 3
## 747 No Medio 3
## 748 No Alto 2
## 749 Si Medio 2
## 750 No Medio 1
## 751 Si Alto 3
## 752 Si Medio 1
## 753 No Alto 3
## 754 No Alto 1
## 755 No Medio 1
## 756 No Medio 3
## 757 No Alto 3
## 758 No Medio 2
## 759 Si Medio 1
## 760 No Medio 2
## 761 No Medio 2
## 762 No Medio 1
## 763 No Bajo 3
## 764 No Bajo 3
## 765 No Medio 1
## 766 Si Bajo 1
## 767 No Medio 2
## 768 No Bajo 3
## 769 No Medio 2
## 770 Si Bajo 2
## 771 Si Medio 2
## 772 No Alto 1
## 773 Si Alto 2
## 774 Si Alto 2
## 775 No Bajo 1
## 776 Si Medio 1
## 777 Si Bajo 3
## 778 Si Medio 1
## 779 No Medio 1
## 780 Si Alto 2
## 781 No Alto 1
## 782 No Medio 3
## 783 No Medio 1
## 784 Si Medio 2
## 785 Si Alto 2
## 786 No Bajo 1
## 787 No Medio 1
## 788 No Medio 1
## 789 No Bajo 3
## 790 No Alto 2
## 791 No Medio 1
## 792 No Medio 2
## 793 No Medio 2
## 794 Si Alto 2
## 795 Si Bajo 1
## 796 No Alto 2
## 797 Si Bajo 1
## 798 No Medio 2
## 799 No Bajo 1
## 800 No Bajo 1
## 801 Si Medio 2
## 802 No Alto 1
## 803 No Bajo 1
## 804 No Bajo 2
## 805 Si Medio 1
## 806 No Medio 1
## 807 No Alto 2
## 808 No Alto 2
## 809 No Bajo 1
## 810 No Bajo 2
## 811 Si Bajo 3
## 812 No Medio 2
## 813 No Alto 2
## 814 Si Alto 1
## 815 Si Medio 2
## 816 Si Medio 1
## 817 No Alto 1
## 818 Si Medio 2
## 819 No Medio 1
## 820 No Medio 3
## 821 No Bajo 2
## 822 Si Medio 2
## 823 No Bajo 3
## 824 No Bajo 1
## 825 No Medio 1
## 826 No Medio 2
## 827 No Medio 2
## 828 No Medio 1
## 829 No Alto 1
## 830 No Medio 2
## 831 No Bajo 3
## 832 No Medio 3
## 833 No Alto 1
## 834 No Medio 2
## 835 No Alto 1
## 836 Si Alto 3
## 837 No Bajo 1
## 838 No Alto 1
## 839 No Medio 2
## 840 No Medio 3
## 841 Si Medio 2
## 842 No Bajo 3
## 843 Si Alto 3
## 844 No Medio 1
## 845 No Medio 1
## 846 Si Medio 1
## 847 No Medio 2
## 848 No Medio 3
## 849 Si Medio 2
## 850 Si Alto 2
## 851 Si Medio 2
## 852 Si Medio 3
## 853 No Bajo 1
## 854 No Alto 2
## 855 No Alto 2
## 856 No Medio 3
## 857 Si Bajo 3
## 858 Si Medio 2
## 859 No Medio 3
## 860 No Bajo 3
## 861 Si Alto 1
## 862 No Bajo 2
## 863 Si Medio 1
## 864 No Alto 3
## 865 No Alto 2
## 866 No Bajo 3
## 867 No Medio 3
## 868 No Alto 2
## 869 Si Medio 3
## 870 No Bajo 3
## 871 Si Medio 2
## 872 No Medio 1
## 873 No Medio 2
## 874 No Bajo 1
## 875 No Alto 2
## 876 Si Alto 1
## 877 No Bajo 2
## 878 No Alto 2
## 879 No Bajo 3
## 880 Si Medio 1
## 881 No Medio 1
## 882 Si Alto 3
## 883 No Medio 2
## 884 Si Bajo 2
## 885 No Medio 2
## 886 No Medio 2
## 887 No Medio 1
## 888 Si Alto 2
## 889 No Medio 3
## 890 No Bajo 2
## 891 No Bajo 1
## 892 No Bajo 1
## 893 No Medio 2
## 894 No Alto 1
## 895 Si Medio 2
## 896 No Bajo 2
## 897 No Medio 3
## 898 No Bajo 3
## 899 No Bajo 2
## 900 No Medio 2
## 901 No Medio 2
## 902 No Bajo 1
## 903 No Bajo 3
## 904 No Bajo 3
## 905 Si Alto 2
## 906 No Alto 1
## 907 No Bajo 2
## 908 Si Alto 2
## 909 Si Bajo 3
## 910 No Medio 3
## 911 No Medio 1
## 912 No Medio 3
## 913 No Bajo 3
## 914 No Medio 3
## 915 Si Medio 2
## 916 No Alto 1
## 917 No Medio 2
## 918 No Alto 1
## 919 Si Medio 3
## 920 Si Medio 3
## 921 Si Medio 2
## 922 No Medio 1
## 923 No Bajo 2
## 924 No Medio 2
## 925 Si Medio 3
## 926 Si Alto 2
## 927 No Medio 1
## 928 Si Medio 2
## 929 Si Bajo 3
## 930 No Alto 1
## 931 No Medio 3
## 932 Si Bajo 2
## 933 Si Medio 3
## 934 Si Alto 3
## 935 No Medio 3
## 936 No Bajo 3
## 937 Si Alto 1
## 938 No Medio 2
## 939 No Bajo 2
## 940 No Alto 2
## 941 No Bajo 2
## 942 Si Bajo 3
## 943 No Alto 3
## 944 Si Alto 1
## 945 Si Alto 3
## 946 No Medio 3
## 947 Si Medio 1
## 948 No Bajo 2
## 949 Si Bajo 1
## 950 No Alto 2
## 951 No Alto 3
## 952 Si Alto 2
## 953 No Alto 2
## 954 No Medio 2
## 955 No Medio 1
## 956 Si Alto 2
## 957 No Bajo 3
## 958 No Alto 1
## 959 No Bajo 3
## 960 Si Medio 3
## 961 Si Alto 2
## 962 No Medio 2
## 963 Si Bajo 3
## 964 No Bajo 3
## 965 No Bajo 1
## 966 No Bajo 3
## 967 Si Medio 2
## 968 No Medio 3
## 969 No Medio 3
## 970 Si Alto 3
## 971 No Alto 2
## 972 Si Medio 2
## 973 No Medio 3
## 974 No Alto 2
## 975 No Alto 2
## 976 Si Bajo 3
## 977 No Alto 3
## 978 Si Medio 2
## 979 Si Alto 1
## 980 No Medio 1
## 981 Si Medio 3
## 982 No Bajo 3
## 983 Si Bajo 2
## 984 No Bajo 2
## 985 Si Bajo 3
## 986 Si Bajo 3
## 987 No Medio 2
## 988 Si Medio 2
## 989 No Alto 1
## 990 No Alto 2
## 991 Si Alto 2
## 992 Si Alto 3
## 993 No Bajo 1
## 994 No Bajo 3
## 995 No Medio 2
## 996 No Bajo 3
## 997 Si Medio 2
## 998 Si Alto 2
## 999 No Medio 1
## 1000 No Medio 1
## 1001 No Bajo 2
## 1002 No Bajo 2
## 1003 No Alto 3
## 1004 No Alto 3
## 1005 Si Alto 2
## 1006 No Medio 3
## 1007 No Alto 2
## 1008 Si Medio 3
## 1009 No Medio 3
## 1010 Si Medio 3
## 1011 No Bajo 1
## 1012 No Medio 2
## 1013 No Bajo 3
## 1014 Si Alto 2
## 1015 No Alto 2
## 1016 No Medio 2
## 1017 No Bajo 3
## 1018 No Bajo 3
## 1019 No Alto 3
## 1020 No Alto 3
## 1021 Si Bajo 1
## 1022 No Alto 3
## 1023 No Medio 3
## 1024 No Alto 3
## 1025 No Medio 3
## 1026 No Medio 2
## 1027 No Bajo 3
## 1028 No Alto 2
## 1029 Si Alto 2
## 1030 Si Bajo 2
## 1031 Si Medio 2
## 1032 No Alto 3
## 1033 Si Alto 2
## 1034 Si Bajo 3
## 1035 Si Alto 2
## 1036 No Medio 1
## 1037 Si Bajo 2
## 1038 No Bajo 3
## 1039 No Medio 3
## 1040 No Bajo 3
## 1041 No Bajo 1
## 1042 No Alto 2
## 1043 Si Medio 1
## 1044 No Alto 3
## 1045 Si Alto 2
## 1046 No Alto 3
## 1047 No Alto 2
## 1048 No Medio 3
## 1049 No Bajo 3
## 1050 Si Alto 2
## 1051 Si Medio 2
## 1052 No Bajo 1
## 1053 Si Medio 1
## 1054 No Medio 2
## 1055 No Alto 3
## 1056 Si Medio 3
## 1057 No Alto 2
## 1058 Si Medio 1
## 1059 Si Medio 3
## 1060 Si Medio 3
## 1061 Si Medio 3
## 1062 No Bajo 1
## 1063 Si Medio 2
## 1064 No Bajo 2
## 1065 No Alto 2
## 1066 No Bajo 3
## 1067 Si Alto 2
## 1068 Si Alto 3
## 1069 Si Medio 2
## 1070 No Medio 2
## 1071 Si Alto 3
## 1072 Si Medio 3
## 1073 No Bajo 3
## 1074 No Bajo 3
## 1075 No Medio 1
## 1076 No Bajo 2
## 1077 Si Medio 3
## 1078 No Alto 2
## 1079 Si Medio 3
## 1080 No Bajo 1
## 1081 Si Alto 3
## 1082 No Medio 2
## 1083 No Medio 3
## 1084 No Medio 2
## 1085 Si Alto 2
## 1086 No Alto 2
## 1087 Si Medio 1
## 1088 No Medio 3
## 1089 Si Bajo 3
## 1090 No Bajo 3
## 1091 Si Medio 3
## 1092 No Medio 2
## 1093 No Alto 2
## 1094 No Bajo 1
## 1095 No Alto 2
## 1096 No Bajo 3
## 1097 No Bajo 3
## 1098 Si Bajo 2
## 1099 No Bajo 3
## 1100 Si Alto 2
## 1101 No Bajo 2
## 1102 No Medio 2
## 1103 No Alto 3
## 1104 No Bajo 1
## 1105 No Medio 2
## 1106 No Bajo 1
## 1107 No Medio 1
## 1108 No Alto 3
## 1109 Si Medio 3
## 1110 Si Alto 2
## 1111 No Medio 2
## 1112 Si Medio 2
## 1113 No Medio 2
## 1114 No Medio 1
## 1115 No Alto 3
## 1116 No Medio 2
## 1117 Si Alto 2
## 1118 No Bajo 1
## 1119 No Bajo 1
## 1120 No Medio 2
## 1121 No Medio 3
## 1122 Si Medio 2
## 1123 Si Bajo 1
## 1124 Si Alto 2
## 1125 No Medio 2
## 1126 Si Alto 1
## 1127 Si Medio 1
## 1128 No Medio 1
## 1129 Si Medio 2
## 1130 Si Medio 1
## 1131 No Bajo 2
## 1132 Si Medio 1
## 1133 No Medio 2
## 1134 Si Medio 2
## 1135 Si Alto 2
## 1136 Si Medio 2
## 1137 No Alto 2
## 1138 No Alto 2
## 1139 No Medio 2
## 1140 No Medio 3
## 1141 No Bajo 2
## 1142 Si Bajo 1
## 1143 No Bajo 2
## 1144 No Medio 3
## 1145 No Medio 2
## 1146 Si Alto 1
## 1147 No Bajo 3
## 1148 Si Alto 3
## 1149 Si Medio 1
## 1150 Si Alto 1
## 1151 No Bajo 1
## 1152 No Medio 2
## 1153 No Medio 3
## 1154 No Alto 2
## 1155 No Medio 2
## 1156 Si Bajo 3
## 1157 No Bajo 1
## 1158 Si Medio 3
## 1159 No Medio 2
## 1160 Si Bajo 1
## 1161 Si Medio 2
## 1162 No Alto 1
## 1163 Si Medio 1
## 1164 No Medio 1
## 1165 No Bajo 2
## 1166 No Bajo 1
## 1167 No Medio 1
## 1168 Si Alto 2
## 1169 No Alto 2
## 1170 Si Bajo 1
## 1171 No Bajo 2
## 1172 No Bajo 3
## 1173 No Medio 3
## 1174 Si Bajo 2
## 1175 Si Medio 2
## 1176 Si Bajo 3
## 1177 No Medio 3
## 1178 Si Alto 1
## 1179 No Medio 2
## 1180 No Medio 2
## 1181 No Bajo 3
## 1182 No Medio 3
## 1183 No Bajo 1
## 1184 No Bajo 1
## 1185 No Bajo 1
## 1186 No Bajo 3
## 1187 Si Alto 2
## 1188 No Bajo 1
## 1189 No Medio 1
## 1190 No Medio 2
## 1191 No Alto 2
## 1192 No Medio 2
## 1193 No Bajo 3
## 1194 Si Bajo 2
## 1195 Si Medio 2
## 1196 No Medio 1
## 1197 No Medio 2
## 1198 No Medio 2
## 1199 No Medio 2
## 1200 Si Medio 2
## 1201 No Alto 2
## 1202 No Medio 2
## 1203 Si Bajo 3
## 1204 Si Alto 2
## 1205 Si Alto 2
## 1206 No Bajo 3
## 1207 Si Bajo 2
## 1208 No Alto 1
## 1209 Si Alto 1
## 1210 No Bajo 3
## 1211 No Alto 2
## 1212 Si Bajo 3
## 1213 No Medio 2
## 1214 No Medio 2
## 1215 No Alto 2
## 1216 No Bajo 3
## 1217 No Alto 1
## 1218 No Alto 2
## 1219 No Bajo 3
## 1220 No Alto 3
## 1221 Si Bajo 1
## 1222 Si Bajo 1
## 1223 No Bajo 2
## 1224 No Alto 2
## 1225 No Alto 3
## 1226 Si Alto 3
## 1227 Si Alto 2
## 1228 Si Medio 3
## 1229 No Medio 3
## 1230 No Medio 2
## 1231 No Medio 1
## 1232 Si Alto 2
## 1233 No Medio 2
## 1234 No Alto 2
## 1235 Si Medio 2
## 1236 No Medio 1
## 1237 Si Medio 3
## 1238 No Medio 3
## 1239 No Bajo 3
## 1240 Si Medio 2
## 1241 Si Medio 3
## 1242 No Alto 2
## 1243 No Alto 3
## 1244 No Alto 3
## 1245 No Bajo 1
## 1246 Si Alto 3
## 1247 No Alto 2
## 1248 Si Alto 1
## 1249 Si Medio 1
## 1250 Si Medio 2
## 1251 Si Alto 2
## 1252 Si Medio 3
## 1253 No Alto 3
## 1254 No Medio 1
## 1255 No Alto 2
## 1256 No Medio 2
## 1257 No Bajo 3
## 1258 No Medio 3
## 1259 Si Medio 3
## 1260 No Medio 3
## 1261 No Bajo 2
## 1262 No Alto 3
## 1263 No Alto 2
## 1264 No Alto 1
## 1265 No Alto 2
## 1266 Si Alto 3
## 1267 No Bajo 2
## 1268 No Medio 1
## 1269 No Medio 2
## 1270 No Medio 1
## 1271 No Bajo 3
## 1272 No Medio 2
## 1273 No Bajo 1
## 1274 Si Medio 2
## 1275 Si Bajo 3
## 1276 No Medio 3
## 1277 Si Alto 1
## 1278 No Bajo 3
## 1279 Si Alto 3
## 1280 No Bajo 1
## 1281 No Bajo 3
## 1282 Si Bajo 1
## 1283 Si Alto 2
## 1284 Si Alto 3
## 1285 Si Bajo 1
## 1286 No Alto 2
## 1287 Si Medio 3
## 1288 No Bajo 2
## 1289 No Alto 2
## 1290 No Medio 2
## 1291 No Alto 2
## 1292 No Alto 2
## 1293 No Medio 3
## 1294 Si Alto 2
## 1295 No Bajo 2
## 1296 Si Medio 2
## 1297 Si Alto 2
## 1298 No Medio 2
## 1299 Si Bajo 3
## 1300 No Alto 1
## 1301 No Medio 3
## 1302 No Bajo 2
## 1303 No Bajo 2
## 1304 No Medio 2
## 1305 Si Medio 2
## 1306 No Alto 2
## 1307 No Alto 3
## 1308 Si Medio 3
## 1309 No Alto 3
## 1310 No Medio 3
## 1311 No Alto 3
## 1312 Si Medio 2
## 1313 No Medio 2
## 1314 No Medio 2
## 1315 No Medio 2
## 1316 Si Alto 2
## 1317 No Medio 3
## 1318 No Alto 1
## 1319 No Bajo 3
## 1320 No Bajo 2
## 1321 Si Alto 3
## 1322 No Bajo 2
## 1323 Si Alto 2
## 1324 Si Medio 2
## 1325 No Alto 3
## 1326 Si Medio 2
## 1327 Si Medio 2
## 1328 No Alto 3
## 1329 No Alto 2
## 1330 No Alto 3
## 1331 No Alto 1
## 1332 No Bajo 1
## 1333 Si Alto 2
## 1334 No Alto 3
## 1335 No Alto 3
## 1336 No Alto 2
## 1337 Si Medio 2
## 1338 No Alto 3
## 1339 Si Bajo 3
## 1340 No Alto 3
## 1341 No Medio 2
## 1342 No Medio 2
## 1343 No Medio 2
## 1344 Si Alto 3
## 1345 No Medio 2
## 1346 No Bajo 2
## 1347 No Alto 3
## 1348 No Medio 3
## 1349 No Alto 3
## 1350 No Bajo 3
## 1351 Si Alto 3
## 1352 No Medio 3
## 1353 Si Bajo 2
## 1354 Si Medio 3
## 1355 No Bajo 3
## 1356 No Bajo 2
## 1357 No Alto 2
## 1358 No Alto 2
## 1359 No Bajo 3
## 1360 Si Medio 2
## 1361 No Alto 3
## 1362 No Bajo 3
## 1363 No Alto 2
## 1364 Si Medio 1
## 1365 No Medio 2
## 1366 Si Bajo 2
## 1367 No Bajo 3
## 1368 Si Alto 2
## 1369 No Medio 1
## 1370 No Medio 2
## 1371 No Bajo 2
## 1372 No Bajo 3
## 1373 Si Medio 2
## 1374 Si Medio 2
## 1375 No Bajo 3
## 1376 No Medio 2
## 1377 Si Medio 3
## 1378 No Medio 2
## 1379 No Medio 3
## 1380 No Bajo 2
## 1381 Si Alto 2
## 1382 No Alto 3
## 1383 Si Alto 3
## 1384 No Alto 3
## 1385 Si Bajo 2
## 1386 No Medio 2
## 1387 No Alto 3
## 1388 No Bajo 2
## 1389 No Medio 2
## 1390 No Bajo 3
## 1391 No Medio 3
## 1392 Si Medio 1
## 1393 No Medio 2
## 1394 Si Medio 2
## 1395 No Medio 2
## 1396 Si Bajo 3
## 1397 No Medio 2
## 1398 No Medio 2
## 1399 No Alto 2
## 1400 No Bajo 1
## 1401 No Alto 2
## 1402 No Bajo 3
## 1403 No Bajo 3
## 1404 No Medio 2
## 1405 No Bajo 3
## 1406 No Alto 3
## 1407 No Medio 3
## 1408 No Medio 1
## 1409 No Bajo 3
## 1410 Si Medio 2
## 1411 No Medio 3
## 1412 No Medio 3
## 1413 No Medio 3
## 1414 No Medio 3
## 1415 No Bajo 3
## 1416 Si Alto 2
## 1417 No Alto 2
## 1418 Si Medio 3
## 1419 Si Medio 3
## 1420 No Bajo 2
## 1421 Si Alto 3
## 1422 No Medio 2
## 1423 No Medio 1
## 1424 No Medio 2
## 1425 No Medio 3
## 1426 No Medio 3
## 1427 No Alto 2
## 1428 No Alto 3
## 1429 Si Medio 2
## 1430 No Medio 1
## 1431 Si Alto 3
## 1432 No Medio 2
## 1433 Si Alto 2
## 1434 Si Medio 2
## 1435 Si Medio 2
## 1436 No Alto 3
## 1437 Si Medio 3
## 1438 Si Bajo 2
## 1439 No Medio 2
## 1440 No Medio 3
## 1441 No Medio 1
## 1442 No Alto 3
## 1443 Si Medio 1#library(Factoshiny)
#HCPCshiny(res.mca)ECM
#Nfinal<-c()
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
Severidad=CaliyPalmira[,c(19:34)]
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Coord1_severidad <-res.mfa_severidad$global.pca$var$coord[,c(1)];Coord1_severidad
lp_severidad<-res.mfa_severidad$eig[c(1)];lp_severidad #VALOR PROPIO
Vp_severidad<-Coord1_severidad/sqrt(lp_severidad);Vp_severidad #VECTOR PROPIO
Pesos_severidad<-(Vp_severidad/sum(Vp_severidad));Pesos_severidad # PESOS RELATIVOS DE LAS VARIABLES
data.frame(round(Pesos_severidad,3))
Ind_severidad<-as.matrix(Severidad)%*%Pesos_severidad
Imin_severidad<-min(Ind_severidad);Imin_severidad
Imax_severidad<-max(Ind_severidad);Imax_severidad
Ind_2_severidad<-round(((Ind_severidad-Imin_severidad)/(Imax_severidad-Imin_severidad))*100,2) #con este índice se hace el cluster
NfinalMedia[i]=median(sample(Ind_2_severidad,replace = TRUE))
}; NfinalMedia## [1] 53.92 54.31 54.06 53.27 53.60 53.47 53.31 53.48 53.32 53.70 53.67 53.62
## [13] 53.40 53.49 54.18 53.91 53.53 54.06 53.92 53.92 52.40 54.61 53.40 53.40
## [25] 53.11 53.40 54.06 52.69 53.73 54.47 54.17 52.65 54.18 53.46 53.53 54.50
## [37] 54.06 53.27 53.08 53.18 53.49 53.46 51.83 52.65 52.76 52.47 53.91 53.40
## [49] 52.58 53.40 53.40 53.49 53.73 52.57 53.31 53.49 54.55 53.52 53.39 53.40
## [61] 52.95 52.83 53.67 53.48 53.31 53.18 53.18 52.03 55.04 54.32 53.32 53.53
## [73] 52.59 52.94 54.06 53.76 54.06 53.62 53.70 54.17 53.40 53.32 52.92 54.56
## [85] 53.48 53.49 53.62 53.46 52.26 53.11 53.31 53.67 53.53 52.67 52.71 54.17
## [97] 53.73 53.60 53.92 52.59 54.17 53.18 54.56 53.11 53.46 54.32 53.32 53.76
## [109] 54.18 53.40 53.82 54.29 53.31 53.48 54.32 52.65 52.67 52.67 52.94 52.63
## [121] 53.40 52.76 52.46 53.53 53.49 54.18 53.70 53.70 53.06 53.20 52.76 53.91
## [133] 53.47 52.94 54.44 53.40 54.55 54.17 53.27 53.62 53.08 54.66 53.70 53.48
## [145] 53.40 52.76 54.47 53.49 53.52 53.08 53.47 53.73 53.60 53.46 52.59 54.31
## [157] 54.06 53.46 53.31 53.92 52.83 53.70 53.62 53.53 53.47 53.92 53.31 52.94
## [169] 53.91 52.83 54.45 54.06 53.70 53.73 53.49 53.40 53.47 53.32 52.59 53.18
## [181] 53.60 53.47 53.47 53.49 53.40 52.03 54.17 53.53 53.49 52.10 52.67 54.18
## [193] 54.41 53.31 53.60 54.06 53.49 54.31 54.61 53.21 53.31 53.52 53.46 54.06
## [205] 53.46 52.67 54.06 51.58 54.80 52.61 52.94 53.27 53.73 54.18 53.48 53.08
## [217] 52.83 53.18 53.18 53.27 53.49 55.49 54.14 52.94 53.70 53.48 53.67 53.70
## [229] 53.32 53.46 53.08 53.76 52.92 53.76 53.40 53.49 52.83 54.80 53.67 52.26
## [241] 52.71 54.39 53.11 53.52 52.46 54.39 53.40 53.40 52.69 53.76 54.50 53.49
## [253] 54.45 53.48 53.08 53.08 52.14 54.17 53.06 53.48 53.62 53.49 53.18 52.59
## [265] 53.46 53.49 54.48 53.49 53.53 53.16 52.83 53.82 52.40 53.21 54.15 52.92
## [277] 52.76 54.29 53.40 54.47 54.48 53.40 53.40 53.49 52.58 53.16 54.66 53.82
## [289] 53.21 54.17 53.32 53.31 53.62 53.40 53.49 53.70 52.63 54.17 53.31 52.63
## [301] 53.60 52.65 54.50 54.18 54.14 53.48 53.32 52.67 54.14 53.21 53.27 53.92
## [313] 53.40 53.49 53.39 54.44 53.82 52.71 53.08 53.18 52.95 53.47 53.49 53.18
## [325] 54.14 53.91 53.21 54.48 53.47 53.54 52.95 54.15 53.46 53.49 53.53 54.45
## [337] 52.69 53.32 53.16 53.70 54.15 53.21 52.03 53.20 53.08 53.21 54.45 53.16
## [349] 52.59 54.18 52.63 53.47 54.17 54.95 52.76 54.18 53.21 54.41 53.40 53.46
## [361] 54.06 53.39 53.06 52.57 53.49 53.60 53.70 53.62 54.48 53.60 53.46 53.27
## [373] 53.82 53.92 52.92 54.45 53.16 52.06 53.40 53.40 53.67 53.52 53.49 53.18
## [385] 53.46 52.40 53.46 53.08 52.65 53.49 53.46 53.47 53.08 53.49 53.49 53.49
## [397] 53.73 53.49 53.08 53.48 53.18 54.32 53.49 53.47 54.31 52.76 53.49 53.18
## [409] 54.61 53.21 53.73 53.62 53.40 54.55 53.82 53.62 53.62 54.95 52.18 53.31
## [421] 53.21 53.70 53.40 53.40 53.08 52.71 53.39 54.15 53.70 52.94 52.95 53.49
## [433] 53.40 54.32 53.54 53.32 53.62 52.69 53.49 53.48 52.58 52.83 52.35 54.17
## [445] 53.11 54.55 53.52 53.46 52.40 53.82 53.82 53.11 53.82 53.67 53.31 54.31
## [457] 53.49 53.46 52.83 53.70 52.37 53.27 53.62 53.49 52.61 52.95 52.71 52.94
## [469] 52.46 52.95 53.47 53.48 53.49 53.52 53.76 53.82 53.49 54.14 53.40 54.06
## [481] 53.49 52.94 53.11 53.47 52.47 53.82 52.61 53.70 53.70 54.15 52.95 52.83
## [493] 53.18 53.47 53.31 53.46 54.61 53.40 52.76 54.71 53.20 52.26 53.40 53.54
## [505] 53.49 53.60 53.27 53.40 53.20 54.69 53.52 53.40 53.49 53.70 52.94 53.27
## [517] 54.14 52.65 54.18 53.40 52.76 54.29 53.16 52.76 53.40 53.47 54.18 53.70
## [529] 53.49 53.40 54.31 54.32 53.91 52.09 53.08 53.47 52.46 53.32 53.16 53.60
## [541] 52.67 54.95 53.40 53.39 52.94 53.08 52.40 53.82 52.92 53.49 53.73 51.47
## [553] 53.27 53.70 53.40 53.16 53.67 54.55 53.27 53.47 53.27 54.45 53.49 53.67
## [565] 53.31 53.82 53.82 52.92 53.49 53.49 52.71 53.16 53.20 53.21 53.47 52.69
## [577] 53.67 54.39 53.70 53.49 53.54 53.49 53.46 53.18 52.95 54.06 53.32 52.83
## [589] 53.40 54.17 52.71 53.20 53.40 53.40 54.44 54.17 52.57 51.82 52.92 52.95
## [601] 53.53 54.15 53.40 53.53 53.76 53.47 53.82 52.71 52.46 54.47 53.91 52.26
## [613] 53.52 53.46 53.52 53.11 53.48 53.39 52.02 53.70 52.47 53.52 53.49 53.47
## [625] 53.70 54.18 53.40 53.54 53.76 53.39 53.47 54.17 52.67 54.71 54.15 53.46
## [637] 52.59 53.60 53.16 53.52 54.06 53.54 54.95 53.06 53.49 53.16 54.39 52.76
## [649] 53.52 54.18 52.94 53.53 53.91 55.04 53.40 54.39 54.47 54.17 53.60 53.11
## [661] 54.14 53.53 53.60 52.92 52.71 53.73 54.31 54.69 53.39 52.95 52.63 53.70
## [673] 53.47 52.10 53.53 51.47 53.06 52.92 52.10 54.17 53.73 53.46 53.27 53.16
## [685] 53.40 53.11 53.18 53.48 52.71 54.17 53.32 53.46 53.16 53.82 54.17 53.11
## [697] 54.17 53.82 54.44 53.92 53.54 53.91 52.83 53.40 53.82 52.63 54.55 53.47
## [709] 53.82 53.49 52.67 53.49 53.06 53.92 53.06 54.61 54.45 53.31 52.95 53.82
## [721] 52.67 53.11 53.40 54.18 53.52 54.48 54.14 53.48 52.71 52.35 53.20 53.49
## [733] 52.03 53.48 54.17 53.21 53.47 53.06 53.60 53.31 53.08 53.49 52.95 54.29
## [745] 53.48 53.18 52.18 53.39 53.67 53.47 53.62 54.50 53.27 53.46 53.11 55.04
## [757] 53.49 53.32 53.40 53.82 53.53 52.71 53.31 52.76 53.08 53.53 53.46 52.95
## [769] 53.47 53.67 53.08 54.61 53.16 52.35 55.16 53.46 52.67 52.95 53.73 53.67
## [781] 53.53 52.57 53.46 53.11 52.59 54.32 53.27 53.16 54.44 53.92 54.87 52.26
## [793] 52.65 53.46 54.91 53.70 53.40 54.45 53.46 52.46 53.46 53.06 53.48 53.16
## [805] 52.69 53.32 53.46 54.50 53.16 53.06 52.95 52.40 54.17 53.08 54.17 53.16
## [817] 54.44 53.47 53.48 53.32 53.91 54.44 53.82 53.67 52.40 53.16 52.65 53.31
## [829] 52.63 54.47 52.37 53.49 53.67 53.67 52.14 54.15 53.20 53.91 53.49 53.60
## [841] 51.93 54.06 54.50 52.95 53.21 54.31 54.17 52.69 53.73 53.70 53.47 53.49
## [853] 53.60 53.18 53.40 53.76 53.47 53.32 53.21 53.52 54.18 53.20 53.40 54.47
## [865] 53.53 53.20 53.60 54.31 52.83 53.47 53.32 53.49 53.31 53.54 54.61 54.39
## [877] 53.11 53.76 53.21 53.52 54.15 54.14 53.31 53.16 53.48 53.32 54.41 54.06
## [889] 53.92 53.21 52.69 53.48 52.94 53.32 54.18 54.18 53.47 53.32 52.83 53.62
## [901] 53.49 53.31 53.62 53.82 53.76 53.32 53.46 53.40 53.92 53.49 53.46 53.48
## [913] 53.47 52.95 54.14 52.63 53.47 53.53 54.18 52.40 53.18 53.52 53.67 53.08
## [925] 53.92 53.39 53.92 54.18 53.49 54.45 53.39 53.31 54.47 53.52 54.15 53.49
## [937] 53.46 53.76 52.63 52.02 53.16 53.62 54.15 53.16 53.70 52.59 54.76 54.76
## [949] 53.73 53.54 53.21 53.70 52.94 53.49 54.17 53.39 53.20 53.73 53.40 54.32
## [961] 54.56 53.76 54.18 52.94 52.92 55.04 53.70 53.62 53.31 53.73 52.14 53.70
## [973] 53.60 53.20 53.48 54.50 53.53 53.20 53.91 53.27 53.82 53.70 53.48 53.27
## [985] 53.82 54.69 53.92 53.76 53.73 53.31 54.17 52.46 53.21 52.67 53.08 53.91
## [997] 53.27 53.31 54.32 53.31hist(NfinalMedia)
I_mediana=round(median(NfinalMedia,3));I_mediana## [1] 53#EE=I_referencia-I_mediana;EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-median(C8$Ind_2_severidad))^2+(median(NfinalMedia)-median(C8$Ind_2_severidad))^2,2);EE## [1] 0.04Índices por separado
Probabilidad de contagio
#-----------------------------------probabilidad de contagio-----------------------------------
Proba<-CaliyPalmira.FMA$separate.analyses$`Probabilidad de contagio`$ind$coord[,1]
Imin_Proba<-min(Proba);Imin_Proba## [1] -5.561753Imax_Proba<-max(Proba);Imax_Proba## [1] 3.500155Ind_2_Proba3<-round(((Proba-Imin_Proba)/(Imax_Proba-Imin_Proba))*100,2) #con este índice se hace el cluster
min(Ind_2_Proba3)## [1] 0max(Ind_2_Proba3)## [1] 100C8<-cbind(Ind_2_Proba3,CaliyPalmira)
summary(C8$Ind_2_Proba3);sd(C8$Ind_2_Proba3)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 48.23 60.96 61.38 74.47 100.00## [1] 19.62697K-means
set.seed(1234)
kmeans3<- kmeans(C8$Ind_2_Proba3, 3, iter.max = 1000, nstart = 10)
C8$cluster <- kmeans3$cluster
summary(C8)## Ind_2_Proba3 x11 x12 x21
## Min. : 0.00 Min. :0.000 Min. :0.000 Min. :1.000
## 1st Qu.: 48.23 1st Qu.:3.000 1st Qu.:6.000 1st Qu.:5.000
## Median : 60.96 Median :4.000 Median :7.000 Median :5.000
## Mean : 61.38 Mean :3.735 Mean :6.367 Mean :5.375
## 3rd Qu.: 74.47 3rd Qu.:5.000 3rd Qu.:7.000 3rd Qu.:6.000
## Max. :100.00 Max. :5.000 Max. :8.000 Max. :7.000
## x22 x23 x24 x25 x31
## Min. :1.000 Min. :0.000 Min. :1.000 Min. :0.000 No : 21
## 1st Qu.:3.000 1st Qu.:7.000 1st Qu.:5.000 1st Qu.:6.000 No sabe: 20
## Median :4.000 Median :8.000 Median :6.000 Median :7.000 Si :1402
## Mean :3.633 Mean :7.319 Mean :5.796 Mean :6.517
## 3rd Qu.:4.000 3rd Qu.:8.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :4.000 Max. :8.000 Max. :7.000 Max. :7.000
## x32 x33 x41 x42 x43
## No :702 0: 7 Min. :1.000 Min. :1.000 Min. :1.00
## No sabe:124 1:1303 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.00
## Si :617 2: 133 Median :4.000 Median :4.000 Median :4.00
## Mean :3.671 Mean :3.773 Mean :3.96
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00
## Max. :7.000 Max. :7.000 Max. :7.00
## x44 x51 x52 x61 x62
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :0.000 Min. :1.00
## 1st Qu.:2.00 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:2.00
## Median :3.00 Median :5.000 Median :5.000 Median :2.000 Median :4.00
## Mean :3.45 Mean :4.459 Mean :5.252 Mean :2.256 Mean :3.43
## 3rd Qu.:5.00 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:3.000 3rd Qu.:5.00
## Max. :7.00 Max. :7.000 Max. :7.000 Max. :6.000 Max. :7.00
## x71 x72 x73 x74
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:5.000
## Median :4.000 Median :5.000 Median :4.000 Median :6.000
## Mean :4.283 Mean :4.578 Mean :4.112 Mean :5.415
## 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x75 x76 x77 x81
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.500 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :3.000 Median :5.000 Median :5.000
## Mean :3.717 Mean :3.286 Mean :4.398 Mean :4.995
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :7.000
## x82 x83 x84 x91
## Min. :1.000 Min. :1.000 Min. :1.0 Min. : 0.0000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.0 1st Qu.: 0.0000
## Median :4.000 Median :4.000 Median :4.0 Median : 0.0000
## Mean :4.084 Mean :4.236 Mean :3.9 Mean : 0.6189
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.0 3rd Qu.: 1.0000
## Max. :7.000 Max. :7.000 Max. :7.0 Max. :13.0000
## x92 x93 x94 x95
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.500 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :5.000
## Mean :3.625 Mean :3.685 Mean :3.743 Mean :4.604
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x101 x102 x103 x104
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :4.000 Median :5.000
## Mean :4.426 Mean :4.639 Mean :4.131 Mean :4.516
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## x105 x106 id Municipio
## Min. : 0.000 Min. : 0.000 Min. : 1.0 Length:1443
## 1st Qu.: 4.000 1st Qu.: 2.000 1st Qu.: 368.5 Class :character
## Median : 9.000 Median : 6.000 Median : 736.0 Mode :character
## Mean : 8.069 Mean : 5.881 Mean : 734.0
## 3rd Qu.:12.000 3rd Qu.:10.000 3rd Qu.:1099.5
## Max. :14.000 Max. :12.000 Max. :1460.0
## cluster
## Min. :1.000
## 1st Qu.:1.000
## Median :2.000
## Mean :2.169
## 3rd Qu.:3.000
## Max. :3.000kmeans3$centers## [,1]
## 1 85.61910
## 2 37.86926
## 3 61.10051tapply(Ind_2_Proba3,kmeans3$cluster,summary)## $`1`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 73.55 77.48 83.87 85.62 93.50 100.00
##
## $`2`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 32.78 40.56 37.87 45.62 49.42
##
## $`3`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 49.57 55.37 61.00 61.10 67.09 73.29BASES CON IPRG K-MEANS
library(car)
C8$cluster<-factor(C8$cluster,levels=c("2","3","1"),labels = c("1.Bajo","2.Medio","3.Alto"))
summary(C8)## Ind_2_Proba3 x11 x12 x21
## Min. : 0.00 Min. :0.000 Min. :0.000 Min. :1.000
## 1st Qu.: 48.23 1st Qu.:3.000 1st Qu.:6.000 1st Qu.:5.000
## Median : 60.96 Median :4.000 Median :7.000 Median :5.000
## Mean : 61.38 Mean :3.735 Mean :6.367 Mean :5.375
## 3rd Qu.: 74.47 3rd Qu.:5.000 3rd Qu.:7.000 3rd Qu.:6.000
## Max. :100.00 Max. :5.000 Max. :8.000 Max. :7.000
## x22 x23 x24 x25 x31
## Min. :1.000 Min. :0.000 Min. :1.000 Min. :0.000 No : 21
## 1st Qu.:3.000 1st Qu.:7.000 1st Qu.:5.000 1st Qu.:6.000 No sabe: 20
## Median :4.000 Median :8.000 Median :6.000 Median :7.000 Si :1402
## Mean :3.633 Mean :7.319 Mean :5.796 Mean :6.517
## 3rd Qu.:4.000 3rd Qu.:8.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :4.000 Max. :8.000 Max. :7.000 Max. :7.000
## x32 x33 x41 x42 x43
## No :702 0: 7 Min. :1.000 Min. :1.000 Min. :1.00
## No sabe:124 1:1303 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.00
## Si :617 2: 133 Median :4.000 Median :4.000 Median :4.00
## Mean :3.671 Mean :3.773 Mean :3.96
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00
## Max. :7.000 Max. :7.000 Max. :7.00
## x44 x51 x52 x61 x62
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :0.000 Min. :1.00
## 1st Qu.:2.00 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:2.00
## Median :3.00 Median :5.000 Median :5.000 Median :2.000 Median :4.00
## Mean :3.45 Mean :4.459 Mean :5.252 Mean :2.256 Mean :3.43
## 3rd Qu.:5.00 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:3.000 3rd Qu.:5.00
## Max. :7.00 Max. :7.000 Max. :7.000 Max. :6.000 Max. :7.00
## x71 x72 x73 x74
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:5.000
## Median :4.000 Median :5.000 Median :4.000 Median :6.000
## Mean :4.283 Mean :4.578 Mean :4.112 Mean :5.415
## 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x75 x76 x77 x81
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.500 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :3.000 Median :5.000 Median :5.000
## Mean :3.717 Mean :3.286 Mean :4.398 Mean :4.995
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :7.000
## x82 x83 x84 x91
## Min. :1.000 Min. :1.000 Min. :1.0 Min. : 0.0000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.0 1st Qu.: 0.0000
## Median :4.000 Median :4.000 Median :4.0 Median : 0.0000
## Mean :4.084 Mean :4.236 Mean :3.9 Mean : 0.6189
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.0 3rd Qu.: 1.0000
## Max. :7.000 Max. :7.000 Max. :7.0 Max. :13.0000
## x92 x93 x94 x95
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.500 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :5.000
## Mean :3.625 Mean :3.685 Mean :3.743 Mean :4.604
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x101 x102 x103 x104
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :4.000 Median :5.000
## Mean :4.426 Mean :4.639 Mean :4.131 Mean :4.516
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## x105 x106 id Municipio
## Min. : 0.000 Min. : 0.000 Min. : 1.0 Length:1443
## 1st Qu.: 4.000 1st Qu.: 2.000 1st Qu.: 368.5 Class :character
## Median : 9.000 Median : 6.000 Median : 736.0 Mode :character
## Mean : 8.069 Mean : 5.881 Mean : 734.0
## 3rd Qu.:12.000 3rd Qu.:10.000 3rd Qu.:1099.5
## Max. :14.000 Max. :12.000 Max. :1460.0
## cluster
## 1.Bajo :403
## 2.Medio:642
## 3.Alto :398
##
##
## table(C8$cluster)##
## 1.Bajo 2.Medio 3.Alto
## 403 642 398ECM
#Nfinal<-c()
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
Severidad=CaliyPalmira[,c(19:34)]
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Proba<-CaliyPalmira.FMA$separate.analyses$`Probabilidad de contagio`$ind$coord[,1]
Imin_Proba<-min(Proba)
Imax_Proba<-max(Proba)
Ind_2_Proba3<-round(((Proba-Imin_Proba)/(Imax_Proba-Imin_Proba))*100,2)
#aquí están las replicaciones
NfinalMedia[i]=median(sample(Ind_2_Proba3,replace = TRUE))
}; NfinalMedia## [1] 61.32 60.84 60.71 60.51 61.00 61.67 60.84 60.84 60.96 61.21 60.84 61.12
## [13] 61.47 61.27 61.71 60.81 61.12 60.24 61.00 59.90 61.15 61.32 60.72 60.84
## [25] 60.84 61.27 60.88 60.84 60.84 60.96 61.45 60.84 61.47 60.96 61.12 61.35
## [37] 61.27 61.23 60.81 60.96 61.00 60.81 60.84 61.00 61.27 61.32 60.71 61.27
## [49] 61.27 60.84 60.84 61.27 60.84 60.51 61.27 60.84 60.84 61.38 61.08 60.84
## [61] 60.52 61.08 61.12 61.67 61.12 61.59 61.27 60.72 61.71 61.43 60.56 61.21
## [73] 60.96 60.84 61.27 61.27 60.72 60.71 61.51 61.38 61.23 60.84 61.23 61.71
## [85] 61.23 61.31 60.84 60.19 60.71 60.64 61.12 61.00 61.43 61.00 60.84 60.84
## [97] 61.47 60.84 61.27 61.27 61.12 60.84 61.75 61.38 61.00 61.75 60.24 61.47
## [109] 60.88 60.08 61.23 61.28 61.21 61.27 60.84 61.00 60.70 60.19 60.84 60.84
## [121] 60.96 61.00 60.96 60.84 61.32 61.75 60.88 61.00 61.12 60.84 60.04 61.47
## [133] 60.84 60.53 61.38 60.84 61.00 60.84 60.84 61.47 61.79 61.32 60.84 61.00
## [145] 60.84 60.84 61.28 61.32 61.43 60.52 60.71 60.84 61.23 61.00 61.00 61.35
## [157] 60.19 60.84 61.28 61.27 60.84 61.27 61.00 61.28 61.00 60.84 61.27 60.84
## [169] 61.28 61.00 61.08 60.84 60.84 61.27 61.08 61.12 60.84 60.84 60.84 61.27
## [181] 60.96 61.47 61.00 61.28 60.88 60.70 60.84 59.78 61.32 61.12 60.84 60.96
## [193] 61.38 60.44 61.51 60.88 61.35 61.00 61.43 61.00 61.28 60.84 60.96 60.96
## [205] 61.35 60.84 60.84 60.81 61.00 61.00 60.84 60.84 60.71 60.84 60.64 60.84
## [217] 60.84 60.84 60.84 60.84 60.84 61.31 61.28 61.00 60.84 60.84 61.38 60.72
## [229] 61.27 60.84 60.96 60.81 60.88 60.84 61.15 60.84 60.84 61.00 61.12 60.19
## [241] 60.84 61.86 60.96 61.27 60.84 61.47 61.21 60.96 61.47 61.71 61.28 61.28
## [253] 61.28 60.84 60.41 60.23 60.84 61.31 59.78 61.00 61.27 61.12 60.84 60.72
## [265] 60.84 61.45 61.51 60.84 61.27 61.71 61.00 60.72 60.52 61.00 59.78 61.60
## [277] 60.84 60.84 61.38 61.12 61.23 60.84 60.84 60.84 60.71 61.27 60.24 60.84
## [289] 60.84 60.84 60.84 60.84 60.52 60.24 60.84 60.84 61.00 61.23 61.27 60.84
## [301] 61.28 60.84 60.84 60.96 60.81 60.72 60.84 60.41 61.51 60.23 61.38 61.12
## [313] 60.84 60.84 60.70 61.60 60.84 60.84 61.08 61.12 60.96 61.21 60.84 61.38
## [325] 61.00 61.43 60.84 60.84 61.35 60.84 61.35 60.84 60.84 61.28 61.59 61.27
## [337] 60.09 60.09 60.84 60.71 61.08 60.84 60.84 61.00 61.08 61.28 61.27 61.51
## [349] 60.71 61.31 60.71 61.00 61.35 61.28 61.43 61.43 61.00 61.32 61.28 61.27
## [361] 61.00 61.00 60.72 61.12 61.27 61.28 61.60 61.15 61.00 60.09 60.84 60.84
## [373] 61.00 60.70 60.84 60.84 61.08 60.88 60.71 61.12 60.64 60.84 60.84 61.08
## [385] 60.84 60.84 60.64 61.43 60.84 61.47 60.84 60.96 60.52 60.84 60.84 60.88
## [397] 61.12 61.12 60.84 59.76 61.12 61.47 61.27 60.84 60.84 60.84 61.00 61.00
## [409] 61.15 61.38 61.59 61.15 60.70 61.43 61.71 61.21 61.59 60.84 60.71 60.84
## [421] 61.00 60.72 60.84 61.00 61.08 61.00 60.72 61.00 60.84 61.47 61.21 60.84
## [433] 60.84 61.31 61.00 60.84 60.84 61.08 60.84 60.84 60.84 59.77 60.84 61.00
## [445] 60.56 61.43 61.27 60.84 60.70 61.27 60.71 60.96 61.12 60.84 61.32 61.47
## [457] 60.96 60.84 60.84 61.00 60.71 60.84 61.38 60.84 60.41 60.72 60.84 59.33
## [469] 60.44 61.28 61.27 61.55 60.84 61.12 60.84 61.00 61.12 60.84 61.55 61.12
## [481] 61.15 61.23 60.72 60.84 60.84 61.08 61.00 61.15 60.88 60.84 61.00 61.00
## [493] 61.15 61.12 60.84 61.00 60.72 61.27 60.88 61.38 61.23 60.53 60.84 61.55
## [505] 61.08 60.84 60.96 60.72 60.96 61.38 60.84 61.28 60.84 61.27 60.96 60.72
## [517] 61.12 60.53 61.28 60.81 60.84 61.59 60.84 60.84 60.84 60.44 61.28 61.59
## [529] 61.27 60.84 60.84 61.08 61.08 60.70 59.61 61.27 61.51 61.12 60.84 61.00
## [541] 60.41 61.12 60.88 60.84 60.84 60.51 61.12 61.27 60.72 61.71 60.84 60.84
## [553] 60.84 61.71 61.43 60.84 60.41 62.65 60.84 60.24 60.64 60.84 61.00 60.84
## [565] 61.00 61.28 60.96 60.84 61.08 61.35 61.00 60.84 60.84 60.64 61.43 60.72
## [577] 61.71 61.15 60.84 60.96 60.84 61.12 61.27 60.70 60.84 61.21 60.88 61.28
## [589] 60.84 61.27 61.35 60.84 61.28 61.23 60.96 60.84 60.84 60.09 61.00 61.00
## [601] 60.84 60.84 60.84 60.84 60.84 60.84 60.56 61.79 60.84 61.75 60.84 61.27
## [613] 60.08 60.56 61.43 60.84 60.84 61.12 60.84 61.67 61.00 61.32 60.96 60.84
## [625] 60.84 61.28 61.31 61.00 60.84 60.84 61.38 60.84 60.84 60.52 60.96 61.27
## [637] 60.84 60.84 60.88 60.96 61.47 61.08 61.51 60.72 60.84 60.84 60.84 60.84
## [649] 60.96 61.28 60.84 61.21 61.43 61.90 60.84 60.09 60.84 61.23 61.12 59.30
## [661] 60.96 60.84 60.84 60.09 60.84 60.84 61.08 61.67 60.71 61.27 60.72 60.84
## [673] 61.00 60.84 61.27 60.71 60.84 61.27 60.88 61.31 61.27 61.35 60.84 60.84
## [685] 60.84 61.21 61.59 60.84 60.96 61.00 60.84 61.12 60.84 61.32 61.38 61.28
## [697] 60.84 61.00 61.27 61.27 60.72 61.00 60.84 60.84 61.67 60.64 61.27 60.84
## [709] 60.84 60.84 61.35 61.43 61.32 61.47 60.84 61.27 60.84 60.96 60.84 60.84
## [721] 59.90 61.12 60.84 61.75 60.84 61.00 61.90 61.00 61.35 60.44 60.09 60.84
## [733] 60.96 61.23 60.72 60.84 60.84 60.96 60.84 60.96 60.84 61.21 61.35 60.84
## [745] 61.23 60.84 60.84 60.84 60.84 60.96 61.00 60.71 60.84 61.27 61.43 61.55
## [757] 60.70 60.53 60.84 60.56 60.88 61.21 60.51 60.71 60.84 60.84 60.84 61.43
## [769] 60.52 61.31 61.47 60.84 61.31 60.37 61.59 60.71 60.71 60.84 61.59 61.00
## [781] 61.27 60.84 60.84 61.12 60.84 61.27 60.84 60.19 61.12 61.00 61.79 60.84
## [793] 59.91 60.71 61.47 61.27 61.38 61.27 60.84 60.84 60.23 60.52 60.71 60.84
## [805] 61.27 59.49 60.09 61.71 61.31 59.33 61.23 60.84 60.70 60.84 61.32 61.00
## [817] 61.27 61.27 61.15 61.23 61.71 60.96 61.43 61.31 60.84 61.27 60.84 60.72
## [829] 60.71 61.55 60.71 60.09 61.51 61.59 60.84 61.43 59.90 61.79 60.84 61.12
## [841] 60.84 61.00 60.84 61.31 60.71 60.84 60.84 60.51 60.84 61.00 61.27 61.47
## [853] 61.28 60.51 61.31 61.51 60.84 60.88 60.84 61.00 61.59 61.12 60.84 60.96
## [865] 61.00 59.61 61.15 61.00 60.70 60.84 60.84 60.72 60.41 61.47 61.71 61.51
## [877] 60.84 61.27 61.27 61.71 61.08 60.88 60.96 61.00 61.27 60.84 60.88 61.08
## [889] 60.84 60.84 60.37 60.23 61.71 60.84 61.51 60.72 60.84 61.23 60.84 61.12
## [901] 61.00 61.47 61.27 61.31 60.96 61.38 61.28 61.45 61.45 60.84 61.47 61.32
## [913] 60.56 60.64 61.27 60.84 60.84 60.88 61.71 60.84 61.23 61.67 61.00 61.28
## [925] 61.27 61.00 60.84 60.84 60.84 61.59 60.84 60.84 61.67 60.84 61.51 61.21
## [937] 61.28 60.84 61.08 60.88 60.84 60.52 61.71 61.00 60.72 61.43 61.35 60.88
## [949] 60.81 61.12 60.84 61.15 61.27 61.12 60.84 60.84 60.84 61.21 60.70 61.15
## [961] 61.28 60.84 61.35 61.15 60.84 61.51 60.72 61.23 61.27 61.15 60.09 60.84
## [973] 60.84 61.08 61.31 61.15 60.96 60.84 61.79 61.23 61.23 60.84 60.84 60.84
## [985] 60.84 61.21 61.31 61.28 61.15 60.84 61.35 60.53 60.84 61.35 60.84 60.84
## [997] 60.96 60.84 60.84 60.96hist(NfinalMedia)
I_mediana=round(mean(NfinalMedia,3));I_mediana## [1] 61#EE=I_referencia-I_mediana;EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-median(C8$Ind_2_Proba3))^2+(median(NfinalMedia)-median(C8$Ind_2_Proba3))^2,2);EE## [1] 1.3Severidad
#-----------------------------------severidad-----------------------------------
Sev<-CaliyPalmira.FMA$separate.analyses$Severidad$ind$coord[,1]
Imin_Sev<-min(Sev);Imin_Sev## [1] -4.142719Imax_Sev<-max(Sev);Imax_Sev## [1] 3.366186Ind_2_Sev3<-round(((Sev-Imin_Sev)/(Imax_Sev-Imin_Sev))*100,2) #con este índice se hace el cluster
min(Ind_2_Sev3)## [1] 0max(Ind_2_Sev3)## [1] 100sd(Ind_2_Sev3)## [1] 22.00786#rbind(summary(Ind_2_Sev))
#print(xtable(rbind(summary(Ind_2_Sev))), include.rownames = FALSE)
C8<-cbind(Ind_2_Sev3,CaliyPalmira)
summary(C8$Ind_2_Sev3)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 41.66 54.28 55.17 70.83 100.00K-means
set.seed(1234)
kmeans3<- kmeans(C8$Ind_2_Sev3, 3, iter.max = 1000, nstart = 10)
C8$cluster <- kmeans3$cluster
summary(C8)## Ind_2_Sev3 x11 x12 x21
## Min. : 0.00 Min. :0.000 Min. :0.000 Min. :1.000
## 1st Qu.: 41.66 1st Qu.:3.000 1st Qu.:6.000 1st Qu.:5.000
## Median : 54.28 Median :4.000 Median :7.000 Median :5.000
## Mean : 55.17 Mean :3.735 Mean :6.367 Mean :5.375
## 3rd Qu.: 70.83 3rd Qu.:5.000 3rd Qu.:7.000 3rd Qu.:6.000
## Max. :100.00 Max. :5.000 Max. :8.000 Max. :7.000
## x22 x23 x24 x25 x31
## Min. :1.000 Min. :0.000 Min. :1.000 Min. :0.000 No : 21
## 1st Qu.:3.000 1st Qu.:7.000 1st Qu.:5.000 1st Qu.:6.000 No sabe: 20
## Median :4.000 Median :8.000 Median :6.000 Median :7.000 Si :1402
## Mean :3.633 Mean :7.319 Mean :5.796 Mean :6.517
## 3rd Qu.:4.000 3rd Qu.:8.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :4.000 Max. :8.000 Max. :7.000 Max. :7.000
## x32 x33 x41 x42 x43
## No :702 0: 7 Min. :1.000 Min. :1.000 Min. :1.00
## No sabe:124 1:1303 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.00
## Si :617 2: 133 Median :4.000 Median :4.000 Median :4.00
## Mean :3.671 Mean :3.773 Mean :3.96
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00
## Max. :7.000 Max. :7.000 Max. :7.00
## x44 x51 x52 x61 x62
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :0.000 Min. :1.00
## 1st Qu.:2.00 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:2.00
## Median :3.00 Median :5.000 Median :5.000 Median :2.000 Median :4.00
## Mean :3.45 Mean :4.459 Mean :5.252 Mean :2.256 Mean :3.43
## 3rd Qu.:5.00 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:3.000 3rd Qu.:5.00
## Max. :7.00 Max. :7.000 Max. :7.000 Max. :6.000 Max. :7.00
## x71 x72 x73 x74
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:5.000
## Median :4.000 Median :5.000 Median :4.000 Median :6.000
## Mean :4.283 Mean :4.578 Mean :4.112 Mean :5.415
## 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x75 x76 x77 x81
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.500 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :3.000 Median :5.000 Median :5.000
## Mean :3.717 Mean :3.286 Mean :4.398 Mean :4.995
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :7.000
## x82 x83 x84 x91
## Min. :1.000 Min. :1.000 Min. :1.0 Min. : 0.0000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.0 1st Qu.: 0.0000
## Median :4.000 Median :4.000 Median :4.0 Median : 0.0000
## Mean :4.084 Mean :4.236 Mean :3.9 Mean : 0.6189
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.0 3rd Qu.: 1.0000
## Max. :7.000 Max. :7.000 Max. :7.0 Max. :13.0000
## x92 x93 x94 x95
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.500 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :5.000
## Mean :3.625 Mean :3.685 Mean :3.743 Mean :4.604
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x101 x102 x103 x104
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :4.000 Median :5.000
## Mean :4.426 Mean :4.639 Mean :4.131 Mean :4.516
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## x105 x106 id Municipio
## Min. : 0.000 Min. : 0.000 Min. : 1.0 Length:1443
## 1st Qu.: 4.000 1st Qu.: 2.000 1st Qu.: 368.5 Class :character
## Median : 9.000 Median : 6.000 Median : 736.0 Mode :character
## Mean : 8.069 Mean : 5.881 Mean : 734.0
## 3rd Qu.:12.000 3rd Qu.:10.000 3rd Qu.:1099.5
## Max. :14.000 Max. :12.000 Max. :1460.0
## cluster
## Min. :1.000
## 1st Qu.:1.000
## Median :2.000
## Mean :2.237
## 3rd Qu.:3.000
## Max. :3.000kmeans3$centers## [,1]
## 1 83.32348
## 2 26.63181
## 3 54.53598tapply(Ind_2_Sev3,kmeans3$cluster,summary)## $`1`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 70.50 75.11 83.33 83.32 91.68 100.00
##
## $`2`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 20.95 29.21 26.63 33.48 38.11
##
## $`3`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 41.53 49.91 54.28 54.54 62.54 67.15BASES CON IPRG K-MEANS
library(car)
C8$cluster<-factor(C8$cluster,levels=c("2","3","1"),labels = c("1.Bajo","2.Medio","3.Alto"))
summary(C8)## Ind_2_Sev3 x11 x12 x21
## Min. : 0.00 Min. :0.000 Min. :0.000 Min. :1.000
## 1st Qu.: 41.66 1st Qu.:3.000 1st Qu.:6.000 1st Qu.:5.000
## Median : 54.28 Median :4.000 Median :7.000 Median :5.000
## Mean : 55.17 Mean :3.735 Mean :6.367 Mean :5.375
## 3rd Qu.: 70.83 3rd Qu.:5.000 3rd Qu.:7.000 3rd Qu.:6.000
## Max. :100.00 Max. :5.000 Max. :8.000 Max. :7.000
## x22 x23 x24 x25 x31
## Min. :1.000 Min. :0.000 Min. :1.000 Min. :0.000 No : 21
## 1st Qu.:3.000 1st Qu.:7.000 1st Qu.:5.000 1st Qu.:6.000 No sabe: 20
## Median :4.000 Median :8.000 Median :6.000 Median :7.000 Si :1402
## Mean :3.633 Mean :7.319 Mean :5.796 Mean :6.517
## 3rd Qu.:4.000 3rd Qu.:8.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :4.000 Max. :8.000 Max. :7.000 Max. :7.000
## x32 x33 x41 x42 x43
## No :702 0: 7 Min. :1.000 Min. :1.000 Min. :1.00
## No sabe:124 1:1303 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.00
## Si :617 2: 133 Median :4.000 Median :4.000 Median :4.00
## Mean :3.671 Mean :3.773 Mean :3.96
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00
## Max. :7.000 Max. :7.000 Max. :7.00
## x44 x51 x52 x61 x62
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :0.000 Min. :1.00
## 1st Qu.:2.00 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:2.00
## Median :3.00 Median :5.000 Median :5.000 Median :2.000 Median :4.00
## Mean :3.45 Mean :4.459 Mean :5.252 Mean :2.256 Mean :3.43
## 3rd Qu.:5.00 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:3.000 3rd Qu.:5.00
## Max. :7.00 Max. :7.000 Max. :7.000 Max. :6.000 Max. :7.00
## x71 x72 x73 x74
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:5.000
## Median :4.000 Median :5.000 Median :4.000 Median :6.000
## Mean :4.283 Mean :4.578 Mean :4.112 Mean :5.415
## 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x75 x76 x77 x81
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.500 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :3.000 Median :5.000 Median :5.000
## Mean :3.717 Mean :3.286 Mean :4.398 Mean :4.995
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :7.000
## x82 x83 x84 x91
## Min. :1.000 Min. :1.000 Min. :1.0 Min. : 0.0000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.0 1st Qu.: 0.0000
## Median :4.000 Median :4.000 Median :4.0 Median : 0.0000
## Mean :4.084 Mean :4.236 Mean :3.9 Mean : 0.6189
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.0 3rd Qu.: 1.0000
## Max. :7.000 Max. :7.000 Max. :7.0 Max. :13.0000
## x92 x93 x94 x95
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.500 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :5.000
## Mean :3.625 Mean :3.685 Mean :3.743 Mean :4.604
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x101 x102 x103 x104
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :4.000 Median :5.000
## Mean :4.426 Mean :4.639 Mean :4.131 Mean :4.516
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## x105 x106 id Municipio
## Min. : 0.000 Min. : 0.000 Min. : 1.0 Length:1443
## 1st Qu.: 4.000 1st Qu.: 2.000 1st Qu.: 368.5 Class :character
## Median : 9.000 Median : 6.000 Median : 736.0 Mode :character
## Mean : 8.069 Mean : 5.881 Mean : 734.0
## 3rd Qu.:12.000 3rd Qu.:10.000 3rd Qu.:1099.5
## Max. :14.000 Max. :12.000 Max. :1460.0
## cluster
## 1.Bajo :353
## 2.Medio:716
## 3.Alto :374
##
##
## table(C8$cluster)##
## 1.Bajo 2.Medio 3.Alto
## 353 716 374ECM
#Nfinal<-c()
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
Severidad=CaliyPalmira[,c(19:34)]
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Sev<-CaliyPalmira.FMA$separate.analyses$Severidad$ind$coord[,1]
Imin_Sev<-min(Sev)
Imax_Sev<-max(Sev)
Ind_2_Sev3<-round(((Sev-Imin_Sev)/(Imax_Sev-Imin_Sev))*100,2) #con este índice se hace el cluster
#aquí están las replicaciones
NfinalMedia[i]=median(sample(Ind_2_Sev3,replace = TRUE))
}; NfinalMedia## [1] 54.28 54.59 54.42 54.28 54.32 54.28 54.43 54.28 54.47 54.18 54.28 54.28
## [13] 54.28 54.28 54.28 54.32 54.34 54.43 54.58 54.28 54.14 54.32 54.28 54.28
## [25] 54.16 54.22 54.28 54.16 54.28 54.34 54.28 54.28 54.28 54.28 54.34 54.50
## [37] 54.28 54.25 54.28 54.28 54.14 54.28 53.99 54.28 54.12 54.28 54.34 54.34
## [49] 54.22 54.34 54.20 54.47 54.28 54.16 54.28 54.28 54.28 54.28 54.28 54.28
## [61] 54.28 54.18 54.31 54.28 54.28 54.18 54.28 54.06 54.57 54.28 54.34 54.28
## [73] 54.19 54.14 54.28 54.31 54.32 54.28 54.55 54.34 54.28 54.16 54.28 54.28
## [85] 54.31 54.16 54.28 54.28 54.28 54.28 54.20 54.31 54.28 54.28 54.28 54.28
## [97] 54.32 54.28 54.28 54.28 54.28 54.28 54.28 54.28 54.28 54.64 54.28 54.28
## [109] 54.32 54.28 54.28 54.43 54.28 54.28 54.28 54.28 54.14 54.28 54.28 54.14
## [121] 54.28 54.41 54.16 54.40 54.28 54.31 54.40 54.28 54.28 54.28 54.16 54.43
## [133] 54.22 54.16 54.47 54.28 54.43 54.42 54.28 54.28 54.28 54.57 54.16 54.59
## [145] 54.28 54.28 54.34 54.28 54.19 54.28 54.28 54.28 54.28 54.18 54.28 54.58
## [157] 54.28 54.32 54.28 54.28 54.28 54.28 54.34 54.28 54.22 54.34 54.22 54.12
## [169] 54.28 54.22 54.28 54.34 54.32 54.28 54.28 54.34 54.16 54.31 54.28 54.14
## [181] 54.28 54.44 54.28 54.28 54.28 54.28 54.28 54.16 54.28 54.28 54.20 54.28
## [193] 54.34 54.28 54.34 54.34 54.40 54.32 54.28 54.28 54.18 54.43 54.28 54.28
## [205] 54.28 54.28 54.28 54.22 54.28 54.16 54.28 54.28 54.28 54.57 54.28 54.28
## [217] 54.22 54.28 54.31 54.16 54.30 54.28 54.55 54.19 54.28 54.16 54.28 54.32
## [229] 54.28 54.28 54.31 54.47 54.28 54.28 54.28 54.28 54.28 54.64 54.30 50.51
## [241] 54.36 54.40 54.38 54.28 54.06 54.32 54.28 54.28 54.16 54.28 54.63 54.12
## [253] 54.31 54.47 54.28 54.28 54.28 54.28 54.31 54.28 54.28 54.28 54.28 54.16
## [265] 54.28 54.28 54.34 54.28 54.28 54.28 54.28 54.32 54.28 54.28 54.28 54.28
## [277] 54.16 54.36 54.43 54.28 54.28 54.28 54.28 54.28 54.28 54.18 54.41 54.12
## [289] 54.28 54.43 54.32 54.28 54.30 54.28 54.28 54.28 54.25 54.28 54.34 54.14
## [301] 54.28 54.28 54.31 54.28 54.28 54.20 54.22 54.16 54.28 54.28 54.28 54.28
## [313] 54.28 54.38 54.28 54.40 54.28 54.25 54.28 54.28 54.28 54.19 54.28 54.28
## [325] 54.25 54.28 54.28 54.28 54.57 54.14 54.28 54.28 54.28 54.28 54.32 54.32
## [337] 54.16 54.32 54.25 54.28 54.28 54.28 54.16 54.22 54.28 54.28 54.32 54.28
## [349] 54.28 54.28 54.28 54.28 54.63 54.41 54.28 54.32 54.38 54.28 54.28 54.28
## [361] 54.28 54.28 54.16 54.28 54.28 54.28 54.42 54.25 54.42 54.28 54.28 54.28
## [373] 54.28 54.31 54.28 54.28 54.22 54.28 54.28 54.22 54.28 54.28 54.28 54.28
## [385] 54.28 54.28 54.28 54.28 54.22 54.32 54.28 54.28 54.16 54.28 54.50 54.28
## [397] 54.31 54.28 54.28 54.28 54.28 54.28 54.18 54.28 54.28 54.28 54.22 54.18
## [409] 54.57 54.43 54.28 54.28 54.28 54.47 54.38 54.28 54.47 54.28 54.19 54.28
## [421] 54.28 54.34 54.28 54.28 54.28 54.22 54.28 54.58 54.28 54.28 54.28 54.19
## [433] 54.28 54.22 54.28 54.28 54.28 54.28 54.28 54.18 54.28 54.28 54.16 54.28
## [445] 54.28 54.32 54.34 54.28 54.28 54.42 54.28 54.28 54.28 54.28 54.28 54.28
## [457] 54.42 54.22 54.28 54.34 54.28 54.28 54.31 54.28 54.14 54.28 54.28 54.25
## [469] 54.14 54.28 54.28 54.28 54.28 54.28 54.28 54.28 54.42 54.28 54.31 54.28
## [481] 54.31 54.22 54.25 54.28 54.25 54.22 54.28 54.28 54.28 54.32 54.28 54.28
## [493] 54.25 54.34 54.28 54.28 54.55 54.28 54.28 54.41 54.28 54.20 54.25 54.28
## [505] 54.28 54.28 54.14 54.28 54.30 54.34 54.28 54.20 54.31 54.28 54.28 54.25
## [517] 54.32 54.14 54.28 54.28 54.28 54.28 54.28 54.28 54.40 54.28 54.30 54.28
## [529] 54.28 54.28 54.28 54.47 54.28 54.28 54.25 54.58 54.28 54.28 54.30 54.28
## [541] 54.19 54.59 54.28 54.22 54.28 54.28 54.28 54.28 54.19 54.28 54.12 54.16
## [553] 54.16 54.32 54.34 54.25 54.28 54.43 54.22 54.28 54.25 54.32 54.28 54.32
## [565] 54.28 54.28 54.30 54.16 54.28 54.28 54.19 54.28 54.16 54.25 54.28 54.16
## [577] 54.34 54.28 54.47 54.28 54.32 54.28 54.28 54.28 54.28 54.19 54.57 54.28
## [589] 54.28 54.32 54.28 54.14 54.28 54.34 54.34 54.28 54.28 50.33 54.28 54.28
## [601] 54.28 54.28 54.16 54.28 54.32 54.16 54.28 54.28 54.08 54.28 54.28 54.28
## [613] 54.22 54.28 54.40 54.28 54.28 54.28 54.25 54.31 54.28 54.22 54.28 54.28
## [625] 54.28 54.43 54.28 54.38 54.34 54.36 54.28 54.59 54.28 54.47 54.42 54.28
## [637] 54.28 54.32 54.28 54.31 54.36 54.28 54.32 54.19 54.28 54.12 54.34 54.28
## [649] 54.25 54.28 54.28 54.28 54.28 54.28 54.12 54.34 54.34 54.59 54.47 54.25
## [661] 54.53 54.28 54.32 54.28 54.28 54.28 54.47 54.32 54.31 54.12 54.16 54.28
## [673] 54.28 54.28 54.28 54.10 54.22 54.28 54.16 54.28 54.50 54.34 54.28 54.28
## [685] 54.28 54.28 54.30 54.28 54.28 54.32 54.28 54.28 54.28 54.28 54.28 54.28
## [697] 54.50 54.28 54.28 54.38 54.38 54.28 54.16 54.28 54.28 50.47 54.34 54.28
## [709] 54.28 54.28 54.34 54.28 54.32 54.28 54.19 54.31 54.31 54.14 54.31 54.28
## [721] 54.28 54.28 54.19 54.34 54.28 54.43 54.28 54.28 54.34 50.56 54.28 54.28
## [733] 54.28 54.16 54.28 54.28 54.25 54.16 54.28 54.28 54.28 54.28 54.16 54.28
## [745] 54.40 54.22 54.22 54.28 54.30 54.28 54.32 54.32 54.28 54.19 54.25 54.34
## [757] 54.36 54.22 54.28 54.28 54.28 54.06 54.14 54.28 54.40 54.14 54.28 54.19
## [769] 54.31 54.28 54.16 54.43 54.28 54.22 54.64 54.28 54.28 54.28 54.25 54.42
## [781] 54.30 54.12 54.28 54.28 54.28 54.43 54.06 54.28 54.44 54.28 54.58 50.51
## [793] 54.32 54.28 54.44 54.28 54.25 54.28 54.28 54.28 54.22 54.14 54.22 54.25
## [805] 54.18 54.28 54.28 54.28 54.28 54.28 54.28 54.19 54.28 54.28 54.28 54.14
## [817] 54.36 54.58 54.22 54.20 54.28 54.28 54.28 54.28 54.14 54.28 54.28 54.06
## [829] 54.28 54.28 54.19 54.28 54.22 54.32 54.06 54.28 54.28 54.28 54.22 54.28
## [841] 54.12 54.28 54.28 54.25 54.28 54.32 54.32 54.10 54.28 54.69 54.28 54.22
## [853] 54.28 54.32 54.28 54.28 54.28 54.28 54.28 54.31 54.28 54.28 54.25 54.34
## [865] 54.28 54.28 54.44 54.28 54.16 54.28 54.28 54.40 54.28 54.28 54.28 54.47
## [877] 54.32 54.36 54.28 54.28 54.42 54.31 54.28 54.47 54.42 54.10 54.38 54.34
## [889] 54.28 54.28 54.16 54.28 54.28 54.42 54.30 54.28 54.42 54.16 54.28 54.28
## [901] 54.28 54.32 54.28 54.28 54.28 54.28 54.28 54.28 54.38 54.28 54.34 54.28
## [913] 54.28 54.28 54.42 54.28 54.30 54.31 54.28 54.28 54.28 54.28 54.25 54.28
## [925] 54.31 54.16 54.47 54.43 54.63 54.34 54.38 54.28 54.58 54.16 54.28 54.28
## [937] 54.28 54.50 54.25 54.14 54.28 54.36 54.44 54.16 54.28 54.28 58.30 54.61
## [949] 54.28 54.28 54.16 54.28 54.34 54.28 54.34 54.34 54.28 54.31 54.28 54.40
## [961] 54.28 54.32 54.28 54.28 54.16 54.43 54.43 54.19 54.30 54.32 54.22 54.31
## [973] 54.28 54.28 54.28 54.28 54.34 54.28 54.28 54.14 54.28 54.28 54.28 54.28
## [985] 54.28 54.42 58.26 54.22 54.28 54.28 54.32 54.28 54.16 54.28 54.28 54.30
## [997] 54.19 54.28 54.28 54.28hist(NfinalMedia)
I_mediana=round(mean(NfinalMedia,3));I_mediana## [1] 54#EE=I_referencia-I_mediana;EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
EE<-(1/(1000-1))*sum(NfinalMedia-median(C8$Ind_2_Sev3))^2+(median(NfinalMedia)-median(C8$Ind_2_Sev3))^2;EE## [1] 0.003171572Susceptibilidad
#-----------------------------------susceptibilidad-----------------------------------
SU<-CaliyPalmira.FMA$separate.analyses$Susceptibilidad$ind$coord[,1]
Imin_SU<-min(SU);Imin_SU## [1] -3.522199Imax_SU<-max(SU);Imax_SU## [1] 6.746319Ind_2_SU3<-round(((SU-Imin_SU)/(Imax_SU-Imin_SU))*100,2) #con este índice se hace el cluster
min(Ind_2_SU3)## [1] 0max(Ind_2_SU3)## [1] 100sd(Ind_2_SU3)## [1] 17.49717#rbind(summary(Ind_2_SU))
#print(xtable(rbind(summary(Ind_2_SU))), include.rownames = FALSE)
#-----------------------------------k-mean--------------------------------####
C8<-cbind(Ind_2_SU3,CaliyPalmira)
summary(C8$Ind_2_SU3)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 21.91 34.02 34.30 45.38 100.00K-means
set.seed(1234)
kmeans3<- kmeans(C8$Ind_2_SU3, 3, iter.max = 1000, nstart = 10)
C8$cluster <- kmeans3$cluster
summary(C8)## Ind_2_SU3 x11 x12 x21
## Min. : 0.00 Min. :0.000 Min. :0.000 Min. :1.000
## 1st Qu.: 21.91 1st Qu.:3.000 1st Qu.:6.000 1st Qu.:5.000
## Median : 34.02 Median :4.000 Median :7.000 Median :5.000
## Mean : 34.30 Mean :3.735 Mean :6.367 Mean :5.375
## 3rd Qu.: 45.38 3rd Qu.:5.000 3rd Qu.:7.000 3rd Qu.:6.000
## Max. :100.00 Max. :5.000 Max. :8.000 Max. :7.000
## x22 x23 x24 x25 x31
## Min. :1.000 Min. :0.000 Min. :1.000 Min. :0.000 No : 21
## 1st Qu.:3.000 1st Qu.:7.000 1st Qu.:5.000 1st Qu.:6.000 No sabe: 20
## Median :4.000 Median :8.000 Median :6.000 Median :7.000 Si :1402
## Mean :3.633 Mean :7.319 Mean :5.796 Mean :6.517
## 3rd Qu.:4.000 3rd Qu.:8.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :4.000 Max. :8.000 Max. :7.000 Max. :7.000
## x32 x33 x41 x42 x43
## No :702 0: 7 Min. :1.000 Min. :1.000 Min. :1.00
## No sabe:124 1:1303 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.00
## Si :617 2: 133 Median :4.000 Median :4.000 Median :4.00
## Mean :3.671 Mean :3.773 Mean :3.96
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00
## Max. :7.000 Max. :7.000 Max. :7.00
## x44 x51 x52 x61 x62
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :0.000 Min. :1.00
## 1st Qu.:2.00 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:2.00
## Median :3.00 Median :5.000 Median :5.000 Median :2.000 Median :4.00
## Mean :3.45 Mean :4.459 Mean :5.252 Mean :2.256 Mean :3.43
## 3rd Qu.:5.00 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:3.000 3rd Qu.:5.00
## Max. :7.00 Max. :7.000 Max. :7.000 Max. :6.000 Max. :7.00
## x71 x72 x73 x74
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:5.000
## Median :4.000 Median :5.000 Median :4.000 Median :6.000
## Mean :4.283 Mean :4.578 Mean :4.112 Mean :5.415
## 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x75 x76 x77 x81
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.500 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :3.000 Median :5.000 Median :5.000
## Mean :3.717 Mean :3.286 Mean :4.398 Mean :4.995
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :7.000
## x82 x83 x84 x91
## Min. :1.000 Min. :1.000 Min. :1.0 Min. : 0.0000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.0 1st Qu.: 0.0000
## Median :4.000 Median :4.000 Median :4.0 Median : 0.0000
## Mean :4.084 Mean :4.236 Mean :3.9 Mean : 0.6189
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.0 3rd Qu.: 1.0000
## Max. :7.000 Max. :7.000 Max. :7.0 Max. :13.0000
## x92 x93 x94 x95
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.500 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :5.000
## Mean :3.625 Mean :3.685 Mean :3.743 Mean :4.604
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x101 x102 x103 x104
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :4.000 Median :5.000
## Mean :4.426 Mean :4.639 Mean :4.131 Mean :4.516
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## x105 x106 id Municipio
## Min. : 0.000 Min. : 0.000 Min. : 1.0 Length:1443
## 1st Qu.: 4.000 1st Qu.: 2.000 1st Qu.: 368.5 Class :character
## Median : 9.000 Median : 6.000 Median : 736.0 Mode :character
## Mean : 8.069 Mean : 5.881 Mean : 734.0
## 3rd Qu.:12.000 3rd Qu.:10.000 3rd Qu.:1099.5
## Max. :14.000 Max. :12.000 Max. :1460.0
## cluster
## Min. :1.000
## 1st Qu.:2.000
## Median :2.000
## Mean :2.222
## 3rd Qu.:3.000
## Max. :3.000kmeans3$centers## [,1]
## 1 61.37931
## 2 38.83473
## 3 17.44862tapply(Ind_2_SU3,kmeans3$cluster,summary)## $`1`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 50.13 54.34 59.67 61.38 67.79 100.00
##
## $`2`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 28.20 34.02 38.41 38.83 44.33 49.89
##
## $`3`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 12.39 18.83 17.45 23.64 28.13BASES CON IPRG K-MEANS
library(car)
C8$cluster<-factor(C8$cluster,levels=c("2","3","1"),labels = c("1.Bajo","2.Medio","3.Alto"))
summary(C8)## Ind_2_SU3 x11 x12 x21
## Min. : 0.00 Min. :0.000 Min. :0.000 Min. :1.000
## 1st Qu.: 21.91 1st Qu.:3.000 1st Qu.:6.000 1st Qu.:5.000
## Median : 34.02 Median :4.000 Median :7.000 Median :5.000
## Mean : 34.30 Mean :3.735 Mean :6.367 Mean :5.375
## 3rd Qu.: 45.38 3rd Qu.:5.000 3rd Qu.:7.000 3rd Qu.:6.000
## Max. :100.00 Max. :5.000 Max. :8.000 Max. :7.000
## x22 x23 x24 x25 x31
## Min. :1.000 Min. :0.000 Min. :1.000 Min. :0.000 No : 21
## 1st Qu.:3.000 1st Qu.:7.000 1st Qu.:5.000 1st Qu.:6.000 No sabe: 20
## Median :4.000 Median :8.000 Median :6.000 Median :7.000 Si :1402
## Mean :3.633 Mean :7.319 Mean :5.796 Mean :6.517
## 3rd Qu.:4.000 3rd Qu.:8.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :4.000 Max. :8.000 Max. :7.000 Max. :7.000
## x32 x33 x41 x42 x43
## No :702 0: 7 Min. :1.000 Min. :1.000 Min. :1.00
## No sabe:124 1:1303 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.00
## Si :617 2: 133 Median :4.000 Median :4.000 Median :4.00
## Mean :3.671 Mean :3.773 Mean :3.96
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00
## Max. :7.000 Max. :7.000 Max. :7.00
## x44 x51 x52 x61 x62
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :0.000 Min. :1.00
## 1st Qu.:2.00 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:2.00
## Median :3.00 Median :5.000 Median :5.000 Median :2.000 Median :4.00
## Mean :3.45 Mean :4.459 Mean :5.252 Mean :2.256 Mean :3.43
## 3rd Qu.:5.00 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:3.000 3rd Qu.:5.00
## Max. :7.00 Max. :7.000 Max. :7.000 Max. :6.000 Max. :7.00
## x71 x72 x73 x74
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:5.000
## Median :4.000 Median :5.000 Median :4.000 Median :6.000
## Mean :4.283 Mean :4.578 Mean :4.112 Mean :5.415
## 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x75 x76 x77 x81
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.500 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :3.000 Median :5.000 Median :5.000
## Mean :3.717 Mean :3.286 Mean :4.398 Mean :4.995
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :7.000
## x82 x83 x84 x91
## Min. :1.000 Min. :1.000 Min. :1.0 Min. : 0.0000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.0 1st Qu.: 0.0000
## Median :4.000 Median :4.000 Median :4.0 Median : 0.0000
## Mean :4.084 Mean :4.236 Mean :3.9 Mean : 0.6189
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.0 3rd Qu.: 1.0000
## Max. :7.000 Max. :7.000 Max. :7.0 Max. :13.0000
## x92 x93 x94 x95
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.500 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :5.000
## Mean :3.625 Mean :3.685 Mean :3.743 Mean :4.604
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x101 x102 x103 x104
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :4.000 Median :5.000
## Mean :4.426 Mean :4.639 Mean :4.131 Mean :4.516
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## x105 x106 id Municipio
## Min. : 0.000 Min. : 0.000 Min. : 1.0 Length:1443
## 1st Qu.: 4.000 1st Qu.: 2.000 1st Qu.: 368.5 Class :character
## Median : 9.000 Median : 6.000 Median : 736.0 Mode :character
## Mean : 8.069 Mean : 5.881 Mean : 734.0
## 3rd Qu.:12.000 3rd Qu.:10.000 3rd Qu.:1099.5
## Max. :14.000 Max. :12.000 Max. :1460.0
## cluster
## 1.Bajo :603
## 2.Medio:580
## 3.Alto :260
##
##
## table(C8$cluster)##
## 1.Bajo 2.Medio 3.Alto
## 603 580 260ECM
#Nfinal<-c()
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
Severidad=CaliyPalmira[,c(19:34)]
res.mfa_severidad=CaliyPalmira.FMA
#Datos
SU<-CaliyPalmira.FMA$separate.analyses$Susceptibilidad$ind$coord[,1]
Imin_SU<-min(SU)
Imax_SU<-max(SU)
Ind_2_SU3<-round(((SU-Imin_SU)/(Imax_SU-Imin_SU))*100,2) #con este índice se hace el cluster
#aquí están las replicaciones
NfinalMedia[i]=median(sample(Ind_2_SU3,replace = TRUE))
}; NfinalMedia## [1] 34.02 34.04 34.02 34.02 34.02 34.02 33.27 34.02 34.02 34.02 34.02 34.02
## [13] 34.02 34.02 34.02 34.02 34.02 34.02 34.02 34.02 34.02 34.02 34.02 33.28
## [25] 33.73 33.88 34.02 33.27 34.02 34.41 34.02 33.28 34.02 34.02 34.02 34.02
## [37] 34.02 33.27 34.02 33.43 34.02 34.02 31.83 34.02 33.28 31.45 34.02 33.42
## [49] 32.99 34.02 32.99 34.02 33.73 32.21 34.02 34.02 34.02 34.02 34.02 33.73
## [61] 34.02 33.43 34.02 34.02 34.02 32.35 34.02 32.21 34.02 34.02 34.02 34.02
## [73] 33.12 34.02 34.02 34.02 34.02 33.12 34.02 34.04 33.11 34.02 33.73 34.02
## [85] 33.12 34.02 33.88 34.02 33.27 34.02 33.27 34.02 34.02 33.12 33.12 34.02
## [97] 34.02 34.02 33.88 34.02 34.02 34.02 34.41 34.02 34.02 34.02 33.11 34.02
## [109] 34.02 34.02 34.02 34.02 33.43 34.02 34.02 34.02 32.51 33.27 34.02 32.60
## [121] 34.02 33.42 31.75 34.02 34.02 33.27 34.02 34.02 33.27 34.02 34.02 34.02
## [133] 34.02 33.73 33.43 34.02 34.17 33.88 34.02 34.02 32.99 34.02 34.02 34.02
## [145] 33.73 34.02 34.02 34.02 34.02 34.02 34.02 34.02 33.73 33.28 33.28 34.02
## [157] 34.02 34.02 34.02 34.02 34.02 34.02 34.02 33.27 34.02 34.02 33.27 34.02
## [169] 34.02 34.02 34.02 34.02 34.02 34.02 34.02 34.02 33.88 34.02 34.02 33.27
## [181] 34.02 33.73 33.27 34.02 34.02 33.12 34.02 34.02 34.02 33.88 33.12 34.02
## [193] 34.02 34.02 34.02 34.05 34.02 34.04 34.02 34.02 33.27 34.02 34.02 34.02
## [205] 34.02 32.48 34.02 31.75 34.54 33.73 33.43 33.88 34.02 34.02 34.02 34.02
## [217] 34.02 34.02 33.88 32.36 34.02 34.02 34.02 34.02 34.02 34.02 34.02 34.02
## [229] 34.02 33.27 34.02 34.02 33.27 34.02 32.60 34.02 34.02 34.02 34.02 33.11
## [241] 34.02 34.02 33.28 34.02 32.51 34.02 34.02 33.27 33.28 34.02 34.02 34.02
## [253] 34.02 33.88 33.28 34.02 32.48 34.02 34.02 33.88 34.02 34.02 34.02 33.88
## [265] 34.02 33.28 34.02 34.02 34.02 33.88 34.02 34.02 33.28 32.99 34.02 33.89
## [277] 32.35 34.02 34.02 34.02 34.02 33.89 34.02 34.02 33.12 33.88 34.02 34.02
## [289] 34.02 34.02 34.02 33.27 34.02 34.02 34.02 34.02 33.27 34.02 33.27 33.11
## [301] 34.02 34.02 34.02 34.02 34.02 33.73 34.02 33.28 33.27 34.02 32.99 34.02
## [313] 34.02 34.02 34.02 34.04 34.02 34.02 34.02 33.27 33.12 33.42 34.02 33.57
## [325] 34.02 34.02 33.89 34.02 33.73 34.02 32.36 33.89 34.02 33.27 34.02 34.02
## [337] 32.01 34.02 34.02 34.02 34.02 33.27 32.82 34.02 34.02 34.02 34.02 33.43
## [349] 33.27 34.02 34.02 34.02 34.02 34.56 34.02 32.21 34.02 34.02 34.02 33.42
## [361] 34.02 33.73 33.88 34.02 34.02 33.12 33.12 34.02 34.02 34.02 34.02 34.02
## [373] 34.02 34.02 34.02 34.02 33.88 32.51 34.02 34.02 34.02 34.02 34.02 32.35
## [385] 34.02 33.27 33.12 33.27 32.53 33.12 33.88 33.27 33.57 33.88 34.02 33.73
## [397] 33.89 34.02 33.88 34.02 34.02 34.02 34.02 34.02 34.02 34.02 34.02 33.27
## [409] 34.02 33.12 34.02 34.02 34.02 34.02 34.02 34.02 34.02 34.02 34.02 34.02
## [421] 34.02 34.02 34.02 33.89 33.42 34.02 34.02 34.02 34.02 33.27 33.28 33.73
## [433] 34.02 34.02 34.02 34.02 34.02 34.02 34.02 34.02 33.43 33.57 33.42 34.02
## [445] 33.88 34.04 33.88 33.27 34.02 34.02 34.02 34.02 34.02 33.73 33.88 34.02
## [457] 34.02 34.02 33.42 34.02 33.27 34.02 34.02 32.99 33.12 34.02 33.27 34.02
## [469] 31.45 33.88 33.27 33.89 33.28 34.02 34.02 34.02 33.73 34.02 34.02 34.02
## [481] 34.02 34.02 33.11 34.02 33.43 34.02 34.02 34.02 33.73 34.02 34.02 34.02
## [493] 33.88 34.02 34.02 34.02 34.04 33.27 33.73 34.02 34.02 33.43 33.88 34.02
## [505] 34.02 34.02 33.88 33.43 34.02 34.02 34.02 32.35 34.02 34.02 34.02 34.02
## [517] 34.02 34.02 34.02 34.02 34.02 34.02 34.02 32.21 34.02 33.28 34.02 34.02
## [529] 34.02 33.73 34.02 34.02 33.88 33.73 34.02 34.02 32.36 34.02 34.02 33.42
## [541] 33.88 34.02 34.02 34.02 34.02 33.28 33.88 34.02 33.88 34.02 33.27 33.11
## [553] 33.73 34.02 34.02 34.02 34.02 34.16 33.88 33.27 34.02 34.02 33.27 34.02
## [565] 33.73 34.02 34.02 32.99 34.02 34.02 33.27 34.02 33.27 34.02 33.27 32.51
## [577] 34.02 34.02 34.02 33.43 34.02 33.27 34.02 33.12 33.43 32.53 33.27 32.82
## [589] 34.02 34.02 33.28 32.99 34.02 34.02 34.02 34.02 33.89 32.99 33.89 33.89
## [601] 34.02 34.02 34.02 34.02 34.02 34.02 34.02 34.02 33.12 34.02 34.02 32.60
## [613] 34.02 34.02 34.02 33.43 34.02 33.11 32.99 34.02 31.83 34.02 34.02 34.02
## [625] 34.02 34.41 33.12 33.27 34.02 34.02 34.02 34.02 32.51 34.02 34.02 34.02
## [637] 33.89 33.43 34.02 34.02 34.02 33.12 34.02 33.12 34.02 33.73 34.02 34.02
## [649] 34.02 34.02 33.27 34.02 34.02 34.02 34.02 34.02 34.02 34.02 34.02 33.27
## [661] 34.02 33.88 34.02 34.02 33.28 34.02 34.02 34.02 34.02 33.73 33.12 33.89
## [673] 34.02 32.82 34.02 31.83 33.12 33.88 32.12 34.04 34.02 34.02 34.02 32.89
## [685] 34.02 33.28 34.02 33.88 34.02 34.02 34.02 34.02 34.02 34.02 34.02 33.88
## [697] 34.02 34.02 34.02 34.02 34.18 33.89 32.89 34.02 34.02 33.57 34.02 33.27
## [709] 34.02 33.89 33.43 34.02 34.02 34.02 32.99 34.02 34.02 33.88 34.02 34.02
## [721] 34.02 33.88 33.42 34.02 34.02 34.02 34.04 34.02 34.02 32.36 34.02 34.02
## [733] 33.12 34.02 34.02 34.02 34.02 33.27 34.02 33.88 32.35 34.02 32.36 34.02
## [745] 34.02 31.45 33.28 34.02 34.02 33.88 34.02 34.02 34.02 34.02 34.02 34.41
## [757] 34.02 34.02 34.02 34.02 33.27 33.11 34.02 33.27 34.02 32.82 34.02 33.42
## [769] 34.02 34.02 34.02 34.02 34.02 33.27 34.02 34.02 33.28 34.02 33.57 34.02
## [781] 34.02 33.27 33.27 33.27 34.02 34.02 33.73 34.02 34.02 34.02 34.02 33.27
## [793] 34.02 34.02 34.02 34.02 34.02 34.02 34.02 33.27 33.88 34.02 34.02 34.02
## [805] 34.02 33.12 33.28 34.02 34.02 32.99 33.88 32.60 34.02 34.02 34.04 33.89
## [817] 34.02 34.02 33.28 34.02 34.02 34.02 34.02 34.02 33.27 34.02 34.02 33.12
## [829] 34.02 34.02 33.27 33.73 34.02 34.02 33.73 34.02 34.02 34.02 34.02 34.02
## [841] 33.27 34.02 34.02 34.02 33.27 34.02 34.02 33.42 34.02 33.73 34.02 33.28
## [853] 34.02 33.73 34.02 34.02 34.02 33.88 33.27 34.02 34.02 33.89 33.88 34.02
## [865] 34.04 32.48 34.02 33.73 33.42 33.89 34.02 34.02 33.43 34.02 34.02 34.02
## [877] 32.82 34.04 33.27 34.02 34.02 34.02 33.27 34.02 34.02 34.02 34.02 34.02
## [889] 34.02 34.02 33.73 33.73 33.27 33.89 34.02 34.02 33.28 33.27 34.02 33.57
## [901] 34.02 34.02 34.02 33.88 34.02 33.27 34.02 33.88 34.02 33.28 33.73 34.02
## [913] 34.02 32.99 34.02 32.34 34.02 34.02 34.02 33.27 34.02 33.27 34.02 33.12
## [925] 34.02 33.27 34.02 34.02 33.88 34.02 34.02 34.02 34.02 34.02 34.02 34.02
## [937] 34.02 34.02 33.57 32.82 33.42 34.02 34.02 34.02 34.02 32.35 34.02 34.02
## [949] 34.02 34.02 33.43 34.02 34.02 34.02 34.02 34.04 33.73 34.02 34.02 34.04
## [961] 34.02 34.02 34.02 33.27 34.02 34.02 34.02 34.02 33.73 34.02 32.36 34.02
## [973] 34.02 34.02 34.02 34.02 34.02 34.02 34.02 33.73 34.02 33.88 34.02 34.02
## [985] 34.02 34.02 34.02 34.02 34.02 33.73 34.02 32.51 34.02 32.36 33.43 34.02
## [997] 34.02 34.02 34.02 33.12hist(NfinalMedia)
I_mediana=round(mean(NfinalMedia,3));I_mediana## [1] 34#EE=I_referencia-I_mediana;EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
EE<-(1/(1000-1))*sum(NfinalMedia-median(C8$Ind_2_SU3))^2+(median(NfinalMedia)-median(C8$Ind_2_SU3))^2;EE## [1] 56.715CALI
#---------------------Índice de percepción por ciudad---------------------------####
library(dplyr)
CaliyPalmira$Municipio<-as.factor(CaliyPalmira$Municipio)
Cali<-filter(CaliyPalmira, CaliyPalmira$Municipio=="Cali")
summary(Cali)## x11 x12 x21 x22
## Min. :0.000 Min. :2.000 Min. :1.000 Min. :2.000
## 1st Qu.:3.000 1st Qu.:6.000 1st Qu.:5.000 1st Qu.:3.000
## Median :4.000 Median :7.000 Median :5.000 Median :4.000
## Mean :3.565 Mean :6.294 Mean :5.386 Mean :3.656
## 3rd Qu.:4.000 3rd Qu.:7.000 3rd Qu.:6.000 3rd Qu.:4.000
## Max. :5.000 Max. :8.000 Max. :7.000 Max. :4.000
## x23 x24 x25 x31 x32
## Min. :0.000 Min. :1.000 Min. :3.000 No : 5 No :386
## 1st Qu.:7.000 1st Qu.:5.000 1st Qu.:6.000 No sabe: 9 No sabe: 61
## Median :8.000 Median :6.000 Median :7.000 Si :783 Si :350
## Mean :7.329 Mean :5.864 Mean :6.504
## 3rd Qu.:8.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :8.000 Max. :7.000 Max. :7.000
## x33 x41 x42 x43 x44
## 0: 4 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1:720 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:2.000
## 2: 73 Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.839 Mean :3.704 Mean :3.991 Mean :3.512
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x51 x52 x61 x62
## Min. :1.000 Min. :1.000 Min. :0.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:5.000 1st Qu.:1.000 1st Qu.:2.000
## Median :5.000 Median :6.000 Median :2.000 Median :4.000
## Mean :4.731 Mean :5.528 Mean :2.221 Mean :3.478
## 3rd Qu.:6.000 3rd Qu.:7.000 3rd Qu.:3.000 3rd Qu.:5.000
## Max. :7.000 Max. :7.000 Max. :6.000 Max. :7.000
## x71 x72 x73 x74
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:5.000
## Median :4.000 Median :5.000 Median :4.000 Median :6.000
## Mean :4.289 Mean :4.575 Mean :4.088 Mean :5.428
## 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x75 x76 x77 x81
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000
## Median :3.000 Median :3.000 Median :5.000 Median :5.000
## Mean :3.118 Mean :3.287 Mean :4.438 Mean :4.881
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :7.000
## x82 x83 x84 x91
## Min. :1.000 Min. :1.000 Min. :1.000 Min. : 0.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.: 0.000
## Median :4.000 Median :4.000 Median :4.000 Median : 0.000
## Mean :3.964 Mean :4.092 Mean :3.757 Mean : 0.601
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.: 1.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :13.000
## x92 x93 x94 x95
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.000
## Median :3.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.449 Mean :3.601 Mean :3.606 Mean :4.464
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x101 x102 x103 x104
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.00 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :4.00 Median :5.000
## Mean :4.494 Mean :4.675 Mean :4.12 Mean :4.588
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.00 Max. :5.000
## x105 x106 id Municipio
## Min. : 0.000 Min. : 0.000 Min. : 1.0 Cali :797
## 1st Qu.: 4.000 1st Qu.: 2.000 1st Qu.:202.0 Palmira: 0
## Median : 8.000 Median : 6.000 Median :407.0
## Mean : 7.853 Mean : 5.762 Mean :406.9
## 3rd Qu.:12.000 3rd Qu.:10.000 3rd Qu.:611.0
## Max. :14.000 Max. :12.000 Max. :814.0## AFM CON TODAS LAS VARIABLES
##Análisis factorial múltiple
CaliyPalmira.FMA<-MFA(Cali[,c(19:34)],
group=c(#2,
#5,
#3,
#4,
#2,
#2, #3
7,
4,
5
#6
),
type=c(#'s',
#'s',
#'n',
#'s', #n
#'s',
#'s', #n
's',
's',
's'#,
#'s'
),
name.group=c(#"Voluntariedad",
#"Conocimiento",
#"Incertidumbre",
#"Confianza gubernamental",
#"Confianza sector salud",
#"Confianza medios",
"Probabilidad de contagio",
"Severidad",
"Susceptibilidad"), #,
#"Cumplimiento"),
#num.group.sup=c(3),
graph=FALSE)
CaliyPalmira.FMA$eig## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 1.88558352 35.330673 35.33067
## comp 2 0.80127659 15.013730 50.34440
## comp 3 0.46792772 8.767684 59.11209
## comp 4 0.33608339 6.297283 65.40937
## comp 5 0.31549687 5.911548 71.32092
## comp 6 0.26084766 4.887571 76.20849
## comp 7 0.23464033 4.396517 80.60501
## comp 8 0.19947998 3.737709 84.34272
## comp 9 0.16751433 3.138760 87.48148
## comp 10 0.14802976 2.773672 90.25515
## comp 11 0.12433215 2.329644 92.58479
## comp 12 0.10948924 2.051529 94.63632
## comp 13 0.08829007 1.654314 96.29063
## comp 14 0.07611270 1.426144 97.71678
## comp 15 0.06563418 1.229805 98.94658
## comp 16 0.05622042 1.053417 100.00000CaliyPalmira.FMA$group$contrib## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## Probabilidad de contagio 19.82260 79.665204 13.19394 91.207231 85.477448
## Severidad 39.72483 11.178689 50.39997 5.018541 7.488267
## Susceptibilidad 40.45257 9.156107 36.40609 3.774228 7.034285CaliyPalmira.FMA$group$correlation[,1:3]## Dim.1 Dim.2 Dim.3
## Probabilidad de contagio 0.6159570 0.8031239 0.4393622
## Severidad 0.8709080 0.3042487 0.6027138
## Susceptibilidad 0.8754346 0.2824650 0.4531235Coordenadas<-round(CaliyPalmira.FMA$quanti.var$coord[,c(1,2,3)],3);Coordenadas## Dim.1 Dim.2 Dim.3
## x71 0.519 0.714 -0.077
## x72 0.523 0.638 -0.060
## x73 0.490 0.679 -0.060
## x74 0.395 0.470 0.265
## x75 0.032 0.091 -0.035
## x76 0.349 0.585 0.061
## x77 0.282 0.034 0.316
## x81 0.592 -0.246 0.602
## x82 0.796 -0.288 -0.003
## x83 0.646 -0.272 0.525
## x84 0.793 -0.159 0.031
## x91 0.329 -0.209 -0.505
## x92 0.790 -0.173 -0.356
## x93 0.777 -0.200 -0.244
## x94 0.821 -0.191 -0.284
## x95 0.649 -0.289 -0.143Contribu<-round(CaliyPalmira.FMA$quanti.var$contrib[,c(1,2,3)],3);Contribu## Dim.1 Dim.2 Dim.3
## x71 4.676 20.813 0.418
## x72 4.744 16.620 0.248
## x73 4.174 18.858 0.254
## x74 2.715 9.028 4.929
## x75 0.018 0.336 0.088
## x76 2.113 13.964 0.262
## x77 1.382 0.047 6.994
## x81 6.868 2.781 28.610
## x82 12.399 3.808 0.001
## x83 8.165 3.418 21.713
## x84 12.293 1.171 0.077
## x91 1.804 1.707 17.124
## x92 10.378 1.178 8.505
## x93 10.053 1.559 4.005
## x94 11.210 1.433 5.395
## x95 7.008 3.278 1.377Tabla<-cbind(Coordenadas,Contribu);Tabla## Dim.1 Dim.2 Dim.3 Dim.1 Dim.2 Dim.3
## x71 0.519 0.714 -0.077 4.676 20.813 0.418
## x72 0.523 0.638 -0.060 4.744 16.620 0.248
## x73 0.490 0.679 -0.060 4.174 18.858 0.254
## x74 0.395 0.470 0.265 2.715 9.028 4.929
## x75 0.032 0.091 -0.035 0.018 0.336 0.088
## x76 0.349 0.585 0.061 2.113 13.964 0.262
## x77 0.282 0.034 0.316 1.382 0.047 6.994
## x81 0.592 -0.246 0.602 6.868 2.781 28.610
## x82 0.796 -0.288 -0.003 12.399 3.808 0.001
## x83 0.646 -0.272 0.525 8.165 3.418 21.713
## x84 0.793 -0.159 0.031 12.293 1.171 0.077
## x91 0.329 -0.209 -0.505 1.804 1.707 17.124
## x92 0.790 -0.173 -0.356 10.378 1.178 8.505
## x93 0.777 -0.200 -0.244 10.053 1.559 4.005
## x94 0.821 -0.191 -0.284 11.210 1.433 5.395
## x95 0.649 -0.289 -0.143 7.008 3.278 1.377plot.MFA(CaliyPalmira.FMA, choix="group",title="Representación de grupos")
#plot.MFA(CaliyPalmira.FMA, choix="ind",lab.par=FALSE)
library(ggrepel)
options(ggrepel.max.overlaps = Inf)
#dim 1-2
plot.MFA(CaliyPalmira.FMA, choix="var",habillage='group',title="Círculo de correlación", repel = TRUE)
IPRG
#--------------------------ÍNDICE DE PERCEPCIÓN GLOBAL-----------------------------------------------#####
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Severidad=Cali[,c(19:34)]
Coord1_severidad <-res.mfa_severidad$global.pca$var$coord[,1];Coord1_severidad## x71 x72 x73 x74 x75 x76 x77
## 0.51897714 0.52273671 0.49032346 0.39540629 0.03201124 0.34883299 0.28217312
## x81 x82 x83 x84 x91 x92 x93
## 0.59244118 0.79605796 0.64597488 0.79262979 0.32918363 0.78957486 0.77708632
## x94 x95
## 0.82059472 0.64882058lp_severidad<-res.mfa_severidad$eig[1];lp_severidad #VALOR PROPIO## [1] 1.885584Vp_severidad<-Coord1_severidad/sqrt(lp_severidad);Vp_severidad #VECTOR PROPIO## x71 x72 x73 x74 x75 x76 x77 x81
## 0.3779422 0.3806800 0.3570753 0.2879524 0.0233120 0.2540356 0.2054910 0.4314419
## x82 x83 x84 x91 x92 x93 x94 x95
## 0.5797247 0.4704275 0.5772282 0.2397261 0.5750034 0.5659087 0.5975935 0.4724999Pesos_severidad<-(Vp_severidad/sum(Vp_severidad));Pesos_severidad # PESOS RELATIVOS DE LAS VARIABLES## x71 x72 x73 x74 x75 x76
## 0.059090002 0.059518062 0.055827535 0.045020400 0.003644754 0.039717630
## x77 x81 x82 x83 x84 x91
## 0.032127832 0.067454514 0.090638033 0.073549784 0.090247705 0.037480382
## x92 x93 x94 x95
## 0.089899876 0.088477947 0.093431752 0.073873792sum(Pesos_severidad)## [1] 1data.frame(round(Pesos_severidad,3))## round.Pesos_severidad..3.
## x71 0.059
## x72 0.060
## x73 0.056
## x74 0.045
## x75 0.004
## x76 0.040
## x77 0.032
## x81 0.067
## x82 0.091
## x83 0.074
## x84 0.090
## x91 0.037
## x92 0.090
## x93 0.088
## x94 0.093
## x95 0.074res.mfa_severidad$eig## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 1.88558352 35.330673 35.33067
## comp 2 0.80127659 15.013730 50.34440
## comp 3 0.46792772 8.767684 59.11209
## comp 4 0.33608339 6.297283 65.40937
## comp 5 0.31549687 5.911548 71.32092
## comp 6 0.26084766 4.887571 76.20849
## comp 7 0.23464033 4.396517 80.60501
## comp 8 0.19947998 3.737709 84.34272
## comp 9 0.16751433 3.138760 87.48148
## comp 10 0.14802976 2.773672 90.25515
## comp 11 0.12433215 2.329644 92.58479
## comp 12 0.10948924 2.051529 94.63632
## comp 13 0.08829007 1.654314 96.29063
## comp 14 0.07611270 1.426144 97.71678
## comp 15 0.06563418 1.229805 98.94658
## comp 16 0.05622042 1.053417 100.00000Ind_severidad<-as.matrix(Severidad)%*%Pesos_severidad
Imin_severidad<-min(Ind_severidad);Imin_severidad## [1] 0.9625196Imax_severidad<-max(Ind_severidad);Imax_severidad## [1] 6.661618Ind_2_severidad1<-round(((Ind_severidad-Imin_severidad)/(Imax_severidad-Imin_severidad))*100,2) #con este índice se hace el cluster
min(Ind_2_severidad1)## [1] 0max(Ind_2_severidad1)## [1] 100C8<-cbind(Ind_2_severidad1,Cali)
summary(C8$Ind_2_severidad1);sd(C8$Ind_2_severidad1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 39.76 50.89 52.05 63.37 100.00## [1] 17.58605ECM
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
Severidad=Cali[,c(19:34)]
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Coord1_severidad <-res.mfa_severidad$global.pca$var$coord[,c(1)];Coord1_severidad
lp_severidad<-res.mfa_severidad$eig[c(1)];lp_severidad #VALOR PROPIO
Vp_severidad<-Coord1_severidad/sqrt(lp_severidad);Vp_severidad #VECTOR PROPIO
Pesos_severidad<-(Vp_severidad/sum(Vp_severidad));Pesos_severidad # PESOS RELATIVOS DE LAS VARIABLES
data.frame(round(Pesos_severidad,3))
Ind_severidad<-as.matrix(Severidad)%*%Pesos_severidad
Imin_severidad<-min(Ind_severidad);Imin_severidad
Imax_severidad<-max(Ind_severidad);Imax_severidad
Ind_2_severidad<-round(((Ind_severidad-Imin_severidad)/(Imax_severidad-Imin_severidad))*100,2) #con este índice se hace el cluster
NfinalMedia[i]=median(sample(Ind_2_severidad,replace = TRUE))
}#; NfinalMedia
hist(NfinalMedia)
I_mediana=round(median(NfinalMedia,3));I_mediana## [1] 51#EE=I_referencia-I_mediana;EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-median(C8$Ind_2_severidad1))^2+(median(NfinalMedia)-median(C8$Ind_2_severidad1))^2,2);EE## [1] 0.01Índices por separado
Probabilidad de contagio
#-----------------------------------probabilidad de contagio-----------------------------------
Proba<-CaliyPalmira.FMA$separate.analyses$`Probabilidad de contagio`$ind$coord[,1]
Imin_Proba<-min(Proba);Imin_Proba## [1] -5.543756Imax_Proba<-max(Proba);Imax_Proba## [1] 3.539925Ind_2_Proba<-round(((Proba-Imin_Proba)/(Imax_Proba-Imin_Proba))*100,2) #con este índice se hace el cluster
min(Ind_2_Proba)## [1] 0max(Ind_2_Proba)## [1] 100C8<-cbind(Ind_2_Proba,Cali)
summary(C8$Ind_2_Proba);sd(C8$Ind_2_Proba)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 49.02 60.59 61.03 73.73 100.00## [1] 19.25214ECM
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
Severidad=Cali[,c(19:34)]
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Proba<-CaliyPalmira.FMA$separate.analyses$`Probabilidad de contagio`$ind$coord[,1]
Imin_Proba<-min(Proba)
Imax_Proba<-max(Proba)
Ind_2_Proba<-round(((Proba-Imin_Proba)/(Imax_Proba-Imin_Proba))*100,2)
#repeticiones
NfinalMedia[i]=median(sample(Ind_2_Proba,replace = TRUE))
}#; NfinalMedia
hist(NfinalMedia)
I_mediana=round(median(NfinalMedia,3));I_mediana## [1] 61#EE=I_referencia-I_mediana;EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-median(C8$Ind_2_Proba))^2+(median(NfinalMedia)-median(C8$Ind_2_Proba))^2,2);EE## [1] 0.72Severidad
#-----------------------------------severidad-----------------------------------
Sev<-CaliyPalmira.FMA$separate.analyses$Severidad$ind$coord[,1]
Imin_Sev<-min(Sev);Imin_Sev## [1] -3.969254Imax_Sev<-max(Sev);Imax_Sev## [1] 3.521764Ind_2_Sev<-round(((Sev-Imin_Sev)/(Imax_Sev-Imin_Sev))*100,2) #con este índice se hace el cluster
min(Ind_2_Sev)## [1] 0max(Ind_2_Sev)## [1] 100sd(Ind_2_Sev)## [1] 21.99115#rbind(summary(Ind_2_Sev))
#print(xtable(rbind(summary(Ind_2_Sev))), include.rownames = FALSE)
C8<-cbind(Ind_2_Sev,Cali)
summary(C8$Ind_2_Sev)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 37.60 50.26 52.99 66.90 100.00ECM
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
Severidad=Cali[,c(19:34)]
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Sev<-CaliyPalmira.FMA$separate.analyses$Severidad$ind$coord[,1]
Imin_Sev<-min(Sev)
Imax_Sev<-max(Sev)
Ind_2_Sev<-round(((Sev-Imin_Sev)/(Imax_Sev-Imin_Sev))*100,2) #con este índice se hace el cluster
#repeticiones
NfinalMedia[i]=median(sample(Ind_2_Sev,replace = TRUE))
}#; NfinalMedia
hist(NfinalMedia)
I_mediana=round(median(NfinalMedia,3));I_mediana## [1] 50#EE=I_referencia-I_mediana;EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-median(C8$Ind_2_Sev))^2+(median(NfinalMedia)-median(C8$Ind_2_Sev))^2,2);EE## [1] 794.21Susceptibilidad
#-----------------------------------susceptibilidad-----------------------------------
SU<-CaliyPalmira.FMA$separate.analyses$Susceptibilidad$ind$coord[,1]
Imin_SU<-min(SU);Imin_SU## [1] -3.393132Imax_SU<-max(SU);Imax_SU## [1] 6.640584Ind_2_SU<-round(((SU-Imin_SU)/(Imax_SU-Imin_SU))*100,2) #con este índice se hace el cluster
min(Ind_2_SU)## [1] 0max(Ind_2_SU)## [1] 100sd(Ind_2_SU)## [1] 17.79983#rbind(summary(Ind_2_SU))
#print(xtable(rbind(summary(Ind_2_SU))), include.rownames = FALSE)
#-----------------------------------k-mean--------------------------------####
C8<-cbind(Ind_2_SU,Cali)
summary(C8$Ind_2_SU)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 21.30 32.61 33.82 45.43 100.00ECM
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
Severidad=Cali[,c(19:34)]
res.mfa_severidad=CaliyPalmira.FMA
#Datos
SU<-CaliyPalmira.FMA$separate.analyses$Susceptibilidad$ind$coord[,1]
Imin_SU<-min(SU)
Imax_SU<-max(SU)
Ind_2_SU<-round(((SU-Imin_SU)/(Imax_SU-Imin_SU))*100,2) #con este índice se hace el cluster
#repeticiones
NfinalMedia[i]=median(sample(Ind_2_SU,replace = TRUE))
}#; NfinalMedia
hist(NfinalMedia)
I_mediana=round(median(NfinalMedia,3));I_mediana## [1] 33#EE=I_referencia-I_mediana;EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-median(C8$Ind_2_SU))^2+(median(NfinalMedia)-median(C8$Ind_2_SU))^2,2);EE## [1] 32.39PALMIRA
library(dplyr)
CaliyPalmira$Municipio<-as.factor(CaliyPalmira$Municipio)
Palmira<-filter(CaliyPalmira, CaliyPalmira$Municipio=="Palmira")AFM CON TODAS LAS VARIABLES
##Análisis factorial múltiple
CaliyPalmira.FMA<-MFA(Palmira[,c(19:34)],
group=c(#2,
#5,
#3,
#4,
#2,
#2, #3
7,
4,
5
#6
),
type=c(#'s',
#'s',
#'n',
#'s', #n
#'s',
#'s', #n
's',
's',
's'#,
#'s'
),
name.group=c(#"Voluntariedad",
#"Conocimiento",
#"Incertidumbre",
#"Confianza gubernamental",
#"Confianza sector salud",
#"Confianza medios",
"Probabilidad de contagio",
"Severidad",
"Susceptibilidad"), #,
#"Cumplimiento"),
#num.group.sup=c(3),
graph=FALSE)
CaliyPalmira.FMA$eig## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 2.04287583 40.5263204 40.52632
## comp 2 0.70280965 13.9422517 54.46857
## comp 3 0.48852501 9.6912993 64.15987
## comp 4 0.32139975 6.3758888 70.53576
## comp 5 0.22235609 4.4110729 74.94683
## comp 6 0.21048293 4.1755345 79.12237
## comp 7 0.18525508 3.6750675 82.79744
## comp 8 0.16106274 3.1951430 85.99258
## comp 9 0.13244381 2.6274041 88.61998
## comp 10 0.11596779 2.3005548 90.92054
## comp 11 0.10876158 2.1575989 93.07814
## comp 12 0.09656076 1.9155604 94.99370
## comp 13 0.07852095 1.5576890 96.55139
## comp 14 0.06938844 1.3765194 97.92790
## comp 15 0.05687026 1.1281852 99.05609
## comp 16 0.04758121 0.9439101 100.00000CaliyPalmira.FMA$group$contrib## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## Probabilidad de contagio 25.03689 75.462789 32.35813 60.09339 20.54200
## Severidad 37.54348 15.833327 31.50396 25.29624 26.00522
## Susceptibilidad 37.41964 8.703884 36.13791 14.61037 53.45278CaliyPalmira.FMA$group$correlation[,1:3]## Dim.1 Dim.2 Dim.3
## Probabilidad de contagio 0.7225955 0.7538494 0.5811008
## Severidad 0.8805261 0.3520768 0.5469880
## Susceptibilidad 0.8769671 0.2667586 0.4634630Coordenadas<-round(CaliyPalmira.FMA$quanti.var$coord[,c(1,2,3)],3);Coordenadas## Dim.1 Dim.2 Dim.3
## x71 0.545 0.685 -0.043
## x72 0.531 0.650 -0.050
## x73 0.567 0.627 -0.084
## x74 0.519 0.420 0.237
## x75 0.409 0.167 0.495
## x76 0.451 0.559 0.070
## x77 0.457 -0.113 0.472
## x81 0.626 -0.296 0.499
## x82 0.788 -0.295 -0.069
## x83 0.632 -0.347 0.402
## x84 0.825 -0.094 -0.067
## x91 0.359 -0.188 -0.503
## x92 0.793 -0.149 -0.389
## x93 0.808 -0.156 -0.264
## x94 0.811 -0.148 -0.313
## x95 0.656 -0.310 -0.069Contribu<-round(CaliyPalmira.FMA$quanti.var$contrib[,c(1,2,3)],3);Contribu## Dim.1 Dim.2 Dim.3
## x71 4.257 19.529 0.111
## x72 4.027 17.544 0.152
## x73 4.604 16.356 0.423
## x74 3.848 7.338 3.364
## x75 2.398 1.154 14.671
## x76 2.911 13.014 0.289
## x77 2.991 0.528 13.348
## x81 7.034 4.569 18.684
## x82 11.140 4.536 0.353
## x83 7.163 6.265 12.135
## x84 12.206 0.463 0.332
## x91 1.933 1.545 15.850
## x92 9.422 0.966 9.470
## x93 9.784 1.060 4.377
## x94 9.836 0.958 6.140
## x95 6.444 4.175 0.301Tabla<-cbind(Coordenadas,Contribu);Tabla## Dim.1 Dim.2 Dim.3 Dim.1 Dim.2 Dim.3
## x71 0.545 0.685 -0.043 4.257 19.529 0.111
## x72 0.531 0.650 -0.050 4.027 17.544 0.152
## x73 0.567 0.627 -0.084 4.604 16.356 0.423
## x74 0.519 0.420 0.237 3.848 7.338 3.364
## x75 0.409 0.167 0.495 2.398 1.154 14.671
## x76 0.451 0.559 0.070 2.911 13.014 0.289
## x77 0.457 -0.113 0.472 2.991 0.528 13.348
## x81 0.626 -0.296 0.499 7.034 4.569 18.684
## x82 0.788 -0.295 -0.069 11.140 4.536 0.353
## x83 0.632 -0.347 0.402 7.163 6.265 12.135
## x84 0.825 -0.094 -0.067 12.206 0.463 0.332
## x91 0.359 -0.188 -0.503 1.933 1.545 15.850
## x92 0.793 -0.149 -0.389 9.422 0.966 9.470
## x93 0.808 -0.156 -0.264 9.784 1.060 4.377
## x94 0.811 -0.148 -0.313 9.836 0.958 6.140
## x95 0.656 -0.310 -0.069 6.444 4.175 0.301plot.MFA(CaliyPalmira.FMA, choix="group",title="Representación de grupos")
#plot.MFA(CaliyPalmira.FMA, choix="ind",lab.par=FALSE)
library(ggrepel)
options(ggrepel.max.overlaps = Inf)
#dim 1-2
plot.MFA(CaliyPalmira.FMA, choix="var",habillage='group',title="Círculo de correlación", repel = TRUE)
IPRG
#--------------------------ÍNDICE DE PERCEPCIÓN GLOBAL-----------------------------------------------#####
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Severidad=Palmira[,c(19:34)]
Coord1_severidad <-res.mfa_severidad$global.pca$var$coord[,1];Coord1_severidad## x71 x72 x73 x74 x75 x76 x77 x81
## 0.5454594 0.5305513 0.5672769 0.5186316 0.4093872 0.4510565 0.4572500 0.6262629
## x82 x83 x84 x91 x92 x93 x94 x95
## 0.7881412 0.6319853 0.8249893 0.3593309 0.7932819 0.8083693 0.8105135 0.6560303lp_severidad<-res.mfa_severidad$eig[1];lp_severidad #VALOR PROPIO## [1] 2.042876Vp_severidad<-Coord1_severidad/sqrt(lp_severidad);Vp_severidad #VECTOR PROPIO## x71 x72 x73 x74 x75 x76 x77 x81
## 0.3816290 0.3711987 0.3968936 0.3628591 0.2864265 0.3155803 0.3199136 0.4381630
## x82 x83 x84 x91 x92 x93 x94 x95
## 0.5514207 0.4421667 0.5772013 0.2514048 0.5550173 0.5655732 0.5670734 0.4589897Pesos_severidad<-(Vp_severidad/sum(Vp_severidad));Pesos_severidad # PESOS RELATIVOS DE LAS VARIABLES## x71 x72 x73 x74 x75 x76 x77
## 0.05578140 0.05425683 0.05801257 0.05303785 0.04186598 0.04612728 0.04676066
## x81 x82 x83 x84 x91 x92 x93
## 0.06404477 0.08059925 0.06462997 0.08436752 0.03674697 0.08112496 0.08266788
## x94 x95
## 0.08288716 0.06708894sum(Pesos_severidad)## [1] 1data.frame(round(Pesos_severidad,3))## round.Pesos_severidad..3.
## x71 0.056
## x72 0.054
## x73 0.058
## x74 0.053
## x75 0.042
## x76 0.046
## x77 0.047
## x81 0.064
## x82 0.081
## x83 0.065
## x84 0.084
## x91 0.037
## x92 0.081
## x93 0.083
## x94 0.083
## x95 0.067res.mfa_severidad$eig## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 2.04287583 40.5263204 40.52632
## comp 2 0.70280965 13.9422517 54.46857
## comp 3 0.48852501 9.6912993 64.15987
## comp 4 0.32139975 6.3758888 70.53576
## comp 5 0.22235609 4.4110729 74.94683
## comp 6 0.21048293 4.1755345 79.12237
## comp 7 0.18525508 3.6750675 82.79744
## comp 8 0.16106274 3.1951430 85.99258
## comp 9 0.13244381 2.6274041 88.61998
## comp 10 0.11596779 2.3005548 90.92054
## comp 11 0.10876158 2.1575989 93.07814
## comp 12 0.09656076 1.9155604 94.99370
## comp 13 0.07852095 1.5576890 96.55139
## comp 14 0.06938844 1.3765194 97.92790
## comp 15 0.05687026 1.1281852 99.05609
## comp 16 0.04758121 0.9439101 100.00000Ind_severidad<-as.matrix(Severidad)%*%Pesos_severidad
Imin_severidad<-min(Ind_severidad);Imin_severidad## [1] 0.963253Imax_severidad<-max(Ind_severidad);Imax_severidad## [1] 6.546757Ind_2_severidad2<-round(((Ind_severidad-Imin_severidad)/(Imax_severidad-Imin_severidad))*100,2) #con este índice se hace el cluster
min(Ind_2_severidad2)## [1] 0max(Ind_2_severidad2)## [1] 100C8<-cbind(Ind_2_severidad2,Palmira)
summary(C8$Ind_2_severidad2);sd(C8$Ind_2_severidad2)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 44.15 57.09 57.03 69.84 100.00## [1] 18.0409ECM
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
Severidad=Palmira[,c(19:34)]
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Coord1_severidad <-res.mfa_severidad$global.pca$var$coord[,c(1)];Coord1_severidad
lp_severidad<-res.mfa_severidad$eig[c(1)];lp_severidad #VALOR PROPIO
Vp_severidad<-Coord1_severidad/sqrt(lp_severidad);Vp_severidad #VECTOR PROPIO
Pesos_severidad<-(Vp_severidad/sum(Vp_severidad));Pesos_severidad # PESOS RELATIVOS DE LAS VARIABLES
data.frame(round(Pesos_severidad,3))
Ind_severidad<-as.matrix(Severidad)%*%Pesos_severidad
Imin_severidad<-min(Ind_severidad);Imin_severidad
Imax_severidad<-max(Ind_severidad);Imax_severidad
Ind_2_severidad<-round(((Ind_severidad-Imin_severidad)/(Imax_severidad-Imin_severidad))*100,2) #con este índice se hace el cluster
NfinalMedia[i]=median(sample(Ind_2_severidad,replace = TRUE))
}#; NfinalMedia
hist(NfinalMedia)
I_mediana=round(median(NfinalMedia,3));I_mediana## [1] 57#EE=I_referencia-I_mediana;EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-median(C8$Ind_2_severidad2))^2+(median(NfinalMedia)-median(C8$Ind_2_severidad2))^2,2);EE## [1] 3.35Índices por separado
Probabilidad de contagio
#-----------------------------------probabilidad de contagio-----------------------------------
Proba<-CaliyPalmira.FMA$separate.analyses$`Probabilidad de contagio`$ind$coord[,1]
Imin_Proba<-min(Proba);Imin_Proba## [1] -6.214496Imax_Proba<-max(Proba);Imax_Proba## [1] 3.395533Ind_2_Proba2<-round(((Proba-Imin_Proba)/(Imax_Proba-Imin_Proba))*100,2) #con este índice se hace el cluster
min(Ind_2_Proba2)## [1] 0max(Ind_2_Proba2)## [1] 100C8<-cbind(Ind_2_Proba2,Palmira)
summary(C8$Ind_2_Proba2);sd(C8$Ind_2_Proba2)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 51.83 64.84 64.67 77.22 100.00## [1] 19.26239ECM
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
Severidad=Palmira[,c(19:34)]
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Proba<-CaliyPalmira.FMA$separate.analyses$`Probabilidad de contagio`$ind$coord[,1]
Imin_Proba<-min(Proba)
Imax_Proba<-max(Proba)
Ind_2_Proba2<-round(((Proba-Imin_Proba)/(Imax_Proba-Imin_Proba))*100,2) #con este índice se hace el cluster
#repeticiones
NfinalMedia[i]=median(sample(Ind_2_Proba2,replace = TRUE))
}#; NfinalMedia
hist(NfinalMedia)
I_mediana=round(median(NfinalMedia,3));I_mediana## [1] 65#EE=I_referencia-I_mediana;EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-median(C8$Ind_2_Proba2))^2+(median(NfinalMedia)-median(C8$Ind_2_Proba2))^2,2);EE## [1] 3.33Severidad
#-----------------------------------severidad-----------------------------------
Sev<-CaliyPalmira.FMA$separate.analyses$Severidad$ind$coord[,1]
Imin_Sev<-min(Sev);Imin_Sev## [1] -4.397929Imax_Sev<-max(Sev);Imax_Sev## [1] 3.203421Ind_2_Sev2<-round(((Sev-Imin_Sev)/(Imax_Sev-Imin_Sev))*100,2) #con este índice se hace el cluster
min(Ind_2_Sev2)## [1] 0max(Ind_2_Sev2)## [1] 100sd(Ind_2_Sev2)## [1] 21.75114#rbind(summary(Ind_2_Sev))
#print(xtable(rbind(summary(Ind_2_Sev))), include.rownames = FALSE)
C8<-cbind(Ind_2_Sev2,Palmira)
summary(C8$Ind_2_Sev2)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 45.23 58.40 57.86 71.08 100.00ECM
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
Severidad=Palmira[,c(19:34)]
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Sev<-CaliyPalmira.FMA$separate.analyses$Severidad$ind$coord[,1]
Imin_Sev<-min(Sev)
Imax_Sev<-max(Sev)
Ind_2_Sev2<-round(((Sev-Imin_Sev)/(Imax_Sev-Imin_Sev))*100,2) #con este índice se hace el cluster
#repeticiones
NfinalMedia[i]=median(sample(Ind_2_Sev2,replace = TRUE))
}#; NfinalMedia
hist(NfinalMedia)
I_mediana=round(median(NfinalMedia,3));I_mediana## [1] 58#EE=I_referencia-I_mediana;EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-median(C8$Ind_2_Sev2))^2+(median(NfinalMedia)-median(C8$Ind_2_Sev2))^2,2);EE## [1] 198.53Susceptibilidad
#-----------------------------------susceptibilidad-----------------------------------
SU<-CaliyPalmira.FMA$separate.analyses$Susceptibilidad$ind$coord[,1]
Imin_SU<-min(SU);Imin_SU## [1] -3.712545Imax_SU<-max(SU);Imax_SU## [1] 4.942211Ind_2_SU2<-round(((SU-Imin_SU)/(Imax_SU-Imin_SU))*100,2) #con este índice se hace el cluster
min(Ind_2_SU2)## [1] 0max(Ind_2_SU2)## [1] 100sd(Ind_2_SU2)## [1] 20.9077#rbind(summary(Ind_2_SU))
#print(xtable(rbind(summary(Ind_2_SU))), include.rownames = FALSE)
#-----------------------------------k-mean--------------------------------####
C8<-cbind(Ind_2_SU2,Palmira)
summary(C8$Ind_2_SU2)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 26.64 41.91 42.90 57.20 100.00ECM
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
Severidad=Palmira[,c(19:34)]
res.mfa_severidad=CaliyPalmira.FMA
#Datos
SU<-CaliyPalmira.FMA$separate.analyses$Susceptibilidad$ind$coord[,1]
Imin_SU<-min(SU)
Imax_SU<-max(SU)
Ind_2_SU2<-round(((SU-Imin_SU)/(Imax_SU-Imin_SU))*100,2) #con este índice se hace el cluster
#repeticiones
NfinalMedia[i]=median(sample(Ind_2_SU2,replace = TRUE))
}#; NfinalMedia
hist(NfinalMedia)
I_mediana=round(median(NfinalMedia,3));I_mediana## [1] 42#EE=I_referencia-I_mediana;EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-median(C8$Ind_2_SU2))^2+(median(NfinalMedia)-median(C8$Ind_2_SU2))^2,2);EE## [1] 21.79visualizacion
pdf("AFM_plots.pdf")
#png("mi_plot.png")
#windows(height=10,width=20) # Abre una nueva ventana grafica con las dimensiones predeterminadas
par(cex.main=0.7,cex.lab=0.7,mfrow=c(3,4),oma = c(1,1,3,1))
#par(mfrow=c(4,5), mar = c(5, 3.7, 2, 0.8),oma = c(1,1,3,1))
#cali
hist(Ind_2_severidad1,col="azure2",xlab="IPRG",main = "Distribución de IPRG",freq = FALSE,ylim = c(0,0.035))
hist(Ind_2_Proba,col="azure2",xlab="IPPC",main = "Distribución de IPPC",freq = FALSE,ylim = c(0,0.035))
hist(Ind_2_Sev,col="azure2",xlab="IPSE",main = "Distribución de IPSE",freq = FALSE,ylim = c(0,0.035))
hist(Ind_2_SU,col="azure2",xlab="IPSU",main = "Distribución de IPSU",freq = FALSE,ylim = c(0,0.035))
#palmira
hist(Ind_2_severidad2,col="azure2",xlab="IPRG",main = "Distribución de IPRG",freq = FALSE,ylim = c(0,0.035))
hist(Ind_2_Proba2,col="azure2",xlab="IPPC",main = "Distribución de IPPC",freq = FALSE,ylim = c(0,0.035))
hist(Ind_2_Sev2,col="azure2",xlab="IPSE",main = "Distribución de IPSE",freq = FALSE,ylim = c(0,0.035))
hist(Ind_2_SU2,col="azure2",xlab="IPSU",main = "Distribución de IPSU",freq = FALSE,ylim = c(0,0.035))
#mtext("Índices Heuristicos", side = 3, line = -1, outer = TRUE)
mtext("Índices AFM de Cali", side = 3, line = 0, outer = TRUE,cex=0.7,font=0.6)
mtext("Índices AFM de Palmira", side = 3, line = -16.8, outer = TRUE,cex=0.7,font=0.6)
mtext("Índices AFM de Cali y Palmira", side = 3, line = -33, outer = TRUE,cex=0.7,font=0.6)
#Cali y palmira
hist(Ind_2_severidad3,col="azure2",xlab="IPRG",main = "Distribución de IPRG",freq = FALSE,ylim = c(0,0.035))
hist(Ind_2_Proba3,col="azure2",xlab="IPPC",main = "Distribución de IPPC",freq = FALSE,ylim = c(0,0.035))
hist(Ind_2_Sev3,col="azure2",xlab="IPSE",main = "Distribución de IPSE",freq = FALSE,ylim = c(0,0.035))
hist(Ind_2_SU3,col="azure2",xlab="IPSU",main = "Distribución de IPSU",freq = FALSE,ylim = c(0,0.035))
dev.off()## png
## 2