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/ve3WBa
library(FactoMineR)
library(haven)
library(ade4)
##
## Attaching package: 'ade4'
## The following object is masked from 'package:FactoMineR':
##
## reconst
library(xtable)
library(readr)
library(data.table)
#library(ff)
#library(ffbase)
library(bigmemory)
library(foreach)
library(doParallel)
## Loading required package: iterators
## Loading required package: parallel
library(biglm)
## Loading required package: DBI
library(Factoshiny)
## Loading required package: shiny
## Loading required package: FactoInvestigate
library(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: carData
summary(CaliyPalmira$x11) #de 0 a 5
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 1.000 1.265 2.000 5.000
CaliyPalmira$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 8.000
CaliyPalmira$x12 <- recode(CaliyPalmira$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 42 151 269 938
AFM 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$eig
## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 1.95371969 37.354849 37.35485
## comp 2 0.76278418 14.584328 51.93918
## comp 3 0.46061251 8.806847 60.74602
## comp 4 0.31307706 5.985990 66.73201
## comp 5 0.27980231 5.349781 72.08179
## comp 6 0.24472973 4.679198 76.76099
## comp 7 0.21919631 4.191003 80.95200
## comp 8 0.19355882 3.700818 84.65281
## comp 9 0.16004627 3.060062 87.71288
## comp 10 0.14217163 2.718302 90.43118
## comp 11 0.11679648 2.233132 92.66431
## comp 12 0.10867422 2.077836 94.74215
## comp 13 0.08312730 1.589382 96.33153
## comp 14 0.07144414 1.366002 97.69753
## comp 15 0.06704119 1.281818 98.97935
## comp 16 0.05338169 1.020651 100.00000
CaliyPalmira.FMA$group$contrib
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## Probabilidad de contagio 22.07821 77.726865 15.65889 91.047532 84.042533
## Severidad 38.70329 13.287416 45.28145 6.939909 9.329046
## Susceptibilidad 39.21850 8.985719 39.05966 2.012559 6.628421
CaliyPalmira.FMA$group$correlation[,1:3]
## Dim.1 Dim.2 Dim.3
## Probabilidad de contagio 0.6636843 0.7801080 0.4438594
## Severidad 0.8746823 0.3278311 0.5848514
## Susceptibilidad 0.8775728 0.2757276 0.4690437
Coordenadas<-round(CaliyPalmira.FMA$quanti.var$coord[,c(1,2,3)],3);Coordenadas
## Dim.1 Dim.2 Dim.3
## x71 0.534 0.705 -0.037
## x72 0.529 0.644 -0.040
## x73 0.531 0.659 -0.043
## x74 0.451 0.444 0.230
## x75 0.178 0.067 0.180
## x76 0.396 0.570 0.063
## x77 0.354 -0.037 0.365
## x81 0.608 -0.274 0.570
## x82 0.792 -0.295 -0.026
## x83 0.641 -0.310 0.494
## x84 0.810 -0.135 0.003
## x91 0.342 -0.197 -0.528
## x92 0.795 -0.165 -0.369
## x93 0.793 -0.182 -0.252
## x94 0.818 -0.177 -0.297
## x95 0.652 -0.300 -0.117
Contribu<-round(CaliyPalmira.FMA$quanti.var$contrib[,c(1,2,3)],3);Contribu
## Dim.1 Dim.2 Dim.3
## x71 4.620 20.610 0.094
## x72 4.527 17.215 0.109
## x73 4.562 18.004 0.128
## x74 3.287 8.178 3.647
## x75 0.516 0.187 2.237
## x76 2.533 13.476 0.271
## x77 2.034 0.057 9.172
## x81 6.924 3.594 25.829
## x82 11.764 4.187 0.055
## x83 7.713 4.627 19.396
## x84 12.301 0.880 0.001
## x91 1.851 1.583 18.745
## x92 10.029 1.113 9.175
## x93 9.969 1.351 4.290
## x94 10.615 1.276 5.929
## x95 6.755 3.664 0.920
Tabla<-cbind(Coordenadas,Contribu);Tabla
## Dim.1 Dim.2 Dim.3 Dim.1 Dim.2 Dim.3
## x71 0.534 0.705 -0.037 4.620 20.610 0.094
## x72 0.529 0.644 -0.040 4.527 17.215 0.109
## x73 0.531 0.659 -0.043 4.562 18.004 0.128
## x74 0.451 0.444 0.230 3.287 8.178 3.647
## x75 0.178 0.067 0.180 0.516 0.187 2.237
## x76 0.396 0.570 0.063 2.533 13.476 0.271
## x77 0.354 -0.037 0.365 2.034 0.057 9.172
## x81 0.608 -0.274 0.570 6.924 3.594 25.829
## x82 0.792 -0.295 -0.026 11.764 4.187 0.055
## x83 0.641 -0.310 0.494 7.713 4.627 19.396
## x84 0.810 -0.135 0.003 12.301 0.880 0.001
## x91 0.342 -0.197 -0.528 1.851 1.583 18.745
## x92 0.795 -0.165 -0.369 10.029 1.113 9.175
## x93 0.793 -0.182 -0.252 9.969 1.351 4.290
## x94 0.818 -0.177 -0.297 10.615 1.276 5.929
## x95 0.652 -0.300 -0.117 6.755 3.664 0.920
plot.MFA(CaliyPalmira.FMA, choix="group",title="Representación de grupos")
#plot.MFA(CaliyPalmira.FMA, choix="ind",lab.par=FALSE)
library(ggrepel)
options(ggrepel.max.overlaps = Inf)
#dim 1-2
plot.MFA(CaliyPalmira.FMA, choix="var",habillage='group',title="Círculo de correlación", repel = TRUE)
IPRG
#--------------------------ÍNDICE DE PERCEPCIÓN GLOBAL-----------------------------------------------#####
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Severidad=CaliyPalmira[,c(19:34)]
Coord1_severidad <-res.mfa_severidad$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 0.6524851
lp_severidad<-res.mfa_severidad$eig[1];lp_severidad #VALOR PROPIO
## [1] 1.95372
Vp_severidad<-Coord1_severidad/sqrt(lp_severidad);Vp_severidad #VECTOR PROPIO
## x71 x72 x73 x74 x75 x76 x77 x81
## 0.3821594 0.3782699 0.3797571 0.3223229 0.1276550 0.2829815 0.2535734 0.4347071
## x82 x83 x84 x91 x92 x93 x94 x95
## 0.5666176 0.4587934 0.5794061 0.2443638 0.5687687 0.5670673 0.5851496 0.4668093
Pesos_severidad<-(Vp_severidad/sum(Vp_severidad));Pesos_severidad # PESOS RELATIVOS DE LAS VARIABLES
## x71 x72 x73 x74 x75 x76 x77
## 0.05791696 0.05732750 0.05755288 0.04884863 0.01934636 0.04288637 0.03842951
## x81 x82 x83 x84 x91 x92 x93
## 0.06588067 0.08587194 0.06953098 0.08781007 0.03703379 0.08619794 0.08594010
## x94 x95
## 0.08868050 0.07074581
sum(Pesos_severidad)
## [1] 1
data.frame(round(Pesos_severidad,3))
## round.Pesos_severidad..3.
## x71 0.058
## x72 0.057
## x73 0.058
## x74 0.049
## x75 0.019
## x76 0.043
## x77 0.038
## x81 0.066
## x82 0.086
## x83 0.070
## x84 0.088
## x91 0.037
## x92 0.086
## x93 0.086
## x94 0.089
## x95 0.071
res.mfa_severidad$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 100.00000
Ind_severidad<-as.matrix(Severidad)%*%Pesos_severidad
Imin_severidad<-min(Ind_severidad);Imin_severidad
## [1] 0.9629662
Imax_severidad<-max(Ind_severidad);Imax_severidad
## [1] 6.613507
Ind_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] 0
max(Ind_2_severidad3)
## [1] 100
C8<-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] 17.83391
K-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.:182.5 Class :character
## Median : 9.000 Median : 6.000 Median :366.0 Mode :character
## Mean : 8.069 Mean : 5.881 Mean :370.4
## 3rd Qu.:12.000 3rd Qu.:10.000 3rd Qu.:549.5
## Max. :14.000 Max. :12.000 Max. :814.0
## cluster
## Min. :1.000
## 1st Qu.:1.000
## Median :2.000
## Mean :1.805
## 3rd Qu.:3.000
## Max. :3.000
kmeans$centers
## [,1]
## 1 54.69625
## 2 33.72363
## 3 77.28025
tapply(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
##
## $`3`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 66.05 70.30 75.33 77.28 82.27 100.00
BASES CON IPRG K-MEANS
#descriptivas final ####
#recodificar la voluntariedad
library(car)
#summary(C8$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.:182.5 Class :character
## Median : 9.000 Median : 6.000 Median :366.0 Mode :character
## Mean : 8.069 Mean : 5.881 Mean :370.4
## 3rd Qu.:12.000 3rd Qu.:10.000 3rd Qu.:549.5
## Max. :14.000 Max. :12.000 Max. :814.0
## cluster
## 1.Bajo :433
## 2.Medio:646
## 3.Alto :364
##
##
##
table(C8$cluster)
##
## 1.Bajo 2.Medio 3.Alto
## 433 646 364
DESCRIPTIVAS
###------------------------------descriptivas-------------------------------####
library(readxl)
exploratorio <- read_excel("C:/LAURA LUCIA/U/9/Tesis/MARZO/exploratorio/exploratorio.xlsx")
C9<-cbind(C8$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 0
summary(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$Grupo);TablasFinal
## Bajo Medio Alto %Bajo %Medio %Alto
## >60 15 42 35 16.30435 45.65217 38.043478
## 18-29 171 175 83 39.86014 40.79254 19.347319
## 30-59 247 429 246 26.78959 46.52928 26.681128
## Hombre 163 204 107 34.38819 43.03797 22.573840
## Mujer 269 439 257 27.87565 45.49223 26.632124
## 0 394 593 322 30.09931 45.30176 24.598930
## 1 39 53 42 29.10448 39.55224 31.343284
## Rural 25 52 35 22.32143 46.42857 31.250000
## Urbana 408 594 329 30.65364 44.62810 24.718257
## 1 101 176 127 25.00000 43.56436 31.435644
## 2 219 309 172 31.28571 44.14286 24.571429
## 3 113 161 65 33.33333 47.49263 19.174041
## Esp/Maestría/Doctorado 144 218 86 32.14286 48.66071 19.196429
## Técnico/Bachillerato o menos 124 180 140 27.92793 40.54054 31.531532
## Universitario 165 248 138 29.94555 45.00907 25.045372
## 0 174 253 109 32.46269 47.20149 20.335821
## 1 141 166 95 35.07463 41.29353 23.631841
## 2 118 227 160 23.36634 44.95050 31.683168
## 1 54 93 73 24.54545 42.27273 33.181818
## 2 89 113 63 33.58491 42.64151 23.773585
## 3 89 95 20 43.62745 46.56863 9.803922
## 4 201 345 208 26.65782 45.75597 27.586207
## p-valor
## >60 0.000
## 18-29 0.000
## 30-59 0.000
## Hombre 0.030
## Mujer 0.030
## 0 0.209
## 1 0.209
## Rural 0.122
## Urbana 0.122
## 1 0.002
## 2 0.002
## 3 0.002
## Esp/Maestría/Doctorado 0.001
## Técnico/Bachillerato o menos 0.001
## Universitario 0.001
## 0 0.000
## 1 0.000
## 2 0.000
## 1 0.000
## 2 0.000
## 3 0.000
## 4 0.000
setwd("C:/LAURA LUCIA/U/9/Tesis/MARZO")
write.csv2(TablasFinal,"Tablas2.csv")
#LATEX
#TablasF2 <- read.csv2("C:/LAURA LUCIA/U/9/Tesis/MARZO/TablasF2.csv")
#View(TablasF2)
#print(xtable(TablasF2), include.rownames = FALSE)
#summary(C8$Ind_2_severidad)
#round(sd(C8$Ind_2_severidad),2)
ECM
#Nfinal<-c()
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
Severidad=CaliyPalmira[,c(19:34)]
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Coord1_severidad <-res.mfa_severidad$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] 53.18 54.44 54.39 53.11 53.70 53.49 53.49 53.18 53.54 53.27 53.73 53.53
## [13] 53.47 53.52 54.18 54.06 53.48 53.73 54.87 53.48 53.08 54.39 53.52 53.31
## [25] 52.59 53.40 54.18 52.63 53.52 54.32 54.71 52.76 53.60 53.92 53.46 54.29
## [37] 54.18 53.40 53.20 53.31 53.32 53.49 51.93 52.59 53.08 52.46 53.49 53.47
## [49] 52.61 53.53 53.06 53.53 53.47 52.35 53.48 53.49 53.92 54.06 53.46 53.08
## [61] 53.31 52.67 54.06 53.48 53.31 52.71 53.46 52.02 54.66 53.82 53.47 53.53
## [73] 52.63 52.63 54.18 53.60 54.39 53.20 53.76 54.45 53.27 53.16 53.32 54.29
## [85] 53.52 53.52 53.39 53.49 52.40 53.27 53.08 54.32 53.46 52.95 52.47 54.17
## [97] 53.53 53.62 53.70 52.95 53.53 53.46 54.56 53.47 53.18 54.61 53.32 53.70
## [109] 54.47 53.27 53.91 54.50 53.27 53.46 53.70 53.49 52.59 52.35 53.31 52.69
## [121] 53.27 52.76 52.37 53.82 53.39 54.15 54.18 53.53 53.39 53.06 52.83 54.29
## [133] 53.32 52.92 54.32 53.11 54.95 54.17 53.18 53.53 53.49 54.32 53.06 53.76
## [145] 53.40 52.83 54.06 53.76 54.14 53.06 53.27 53.70 53.82 53.11 53.46 54.14
## [157] 53.62 53.49 53.49 53.52 53.31 54.15 53.62 53.18 53.39 54.56 53.08 53.27
## [169] 53.91 52.69 54.18 54.39 53.62 53.92 53.48 53.52 53.11 53.20 53.18 52.83
## [181] 53.70 53.62 53.46 53.40 53.21 52.57 53.53 53.40 53.92 52.63 52.35 54.17
## [193] 54.17 53.76 53.47 54.17 53.49 54.06 54.48 53.32 53.06 53.67 53.47 53.92
## [205] 53.27 53.08 53.82 51.80 54.80 52.58 52.76 53.39 53.21 54.66 53.40 53.54
## [217] 52.06 53.16 53.47 53.16 53.52 54.47 54.50 53.18 54.39 53.32 53.62 53.70
## [229] 53.39 53.31 53.47 53.76 52.83 53.91 53.11 53.52 53.11 54.17 53.54 52.40
## [241] 52.69 54.41 53.18 53.82 51.93 54.17 53.67 53.18 52.71 54.14 54.55 52.71
## [253] 55.02 53.70 52.71 53.21 52.09 54.29 53.08 53.67 53.54 53.49 53.31 52.71
## [265] 53.18 53.49 54.31 53.73 53.46 53.70 52.83 53.73 52.92 52.69 53.76 53.40
## [277] 52.46 54.50 53.27 54.18 54.80 53.40 53.39 53.70 52.63 53.16 54.97 53.47
## [289] 53.21 54.31 53.48 53.32 53.60 52.95 53.73 53.91 52.63 54.29 53.47 52.57
## [301] 53.31 53.39 54.32 53.70 54.14 53.49 53.54 52.58 54.32 53.11 53.11 54.14
## [313] 53.49 53.70 52.76 54.29 53.49 53.46 53.40 53.06 52.92 53.39 53.53 53.46
## [325] 53.49 53.92 53.46 53.70 53.73 53.48 53.11 53.46 53.67 53.20 53.70 54.18
## [337] 52.67 53.76 53.06 53.76 53.52 53.32 52.14 53.39 53.06 53.53 53.92 53.31
## [349] 52.61 53.67 53.46 53.46 54.06 54.48 53.08 53.82 53.40 53.91 53.70 53.46
## [361] 53.82 53.40 52.59 53.27 53.70 53.40 53.62 53.49 54.32 53.60 53.60 53.31
## [373] 53.82 53.52 53.16 54.29 53.32 52.58 52.67 53.54 53.54 53.48 53.49 53.20
## [385] 53.54 52.26 53.32 53.48 52.26 53.67 53.31 53.49 52.67 53.46 54.18 53.40
## [397] 53.62 53.54 53.21 53.20 53.18 54.32 53.53 53.31 53.76 53.40 53.49 52.95
## [409] 54.61 53.32 53.91 53.49 53.52 53.76 54.44 53.62 54.17 54.18 52.69 52.95
## [421] 53.32 53.62 53.39 53.46 53.08 53.21 53.31 54.18 53.40 53.11 53.06 53.20
## [433] 53.70 53.76 53.62 53.53 53.47 53.27 53.70 53.11 52.76 52.71 52.65 53.70
## [445] 52.83 54.39 53.92 53.21 52.59 54.14 53.54 53.47 53.73 53.67 53.11 54.14
## [457] 53.49 53.47 52.69 53.67 52.63 53.16 53.49 53.47 52.69 53.31 52.59 52.92
## [469] 51.80 53.11 53.08 54.32 53.08 54.06 53.53 53.91 53.47 53.49 53.62 53.39
## [481] 53.54 53.53 52.13 53.49 52.63 53.49 52.76 53.92 53.52 53.52 53.08 53.49
## [493] 53.11 53.54 53.16 53.49 54.55 53.40 52.76 54.80 53.06 53.11 53.20 53.48
## [505] 53.49 54.06 53.40 52.83 53.49 54.29 54.45 52.65 53.52 53.31 53.47 53.21
## [517] 53.49 52.71 53.82 53.46 53.47 53.70 53.39 52.59 53.76 53.16 54.66 53.47
## [529] 53.92 53.21 53.82 54.50 53.49 52.65 53.40 53.54 52.58 53.39 52.76 53.49
## [541] 52.69 54.95 53.47 53.20 53.40 52.26 53.40 53.16 52.69 54.06 53.46 52.92
## [553] 52.67 54.17 53.46 52.95 53.73 54.69 52.58 53.48 53.32 53.82 53.53 53.73
## [565] 52.63 54.41 54.15 52.71 53.40 53.70 52.63 53.31 52.63 53.49 53.47 52.63
## [577] 53.53 54.29 54.44 53.48 53.46 53.52 53.52 52.65 53.27 53.73 53.52 52.69
## [589] 53.39 54.29 53.16 52.18 53.49 53.49 54.41 53.70 53.21 51.46 52.58 53.18
## [601] 53.53 54.17 53.53 53.62 53.48 53.27 53.73 53.40 52.14 54.39 54.15 52.67
## [613] 52.94 53.39 53.60 53.49 53.32 53.08 52.37 53.46 52.58 53.70 53.52 53.39
## [625] 53.46 54.50 53.54 53.49 53.70 53.39 53.46 53.91 53.06 54.61 53.82 53.53
## [637] 52.59 53.73 53.18 53.67 53.91 53.52 54.69 52.63 53.73 53.27 53.70 53.31
## [649] 53.52 53.53 53.54 53.54 54.18 54.87 53.49 53.76 54.45 54.55 54.15 52.58
## [661] 54.17 53.48 53.70 53.06 52.94 53.49 54.32 54.61 54.06 52.13 52.76 53.49
## [673] 53.54 52.57 53.49 51.47 52.37 52.95 52.40 53.62 53.62 54.32 53.18 52.65
## [685] 53.48 53.11 53.47 53.27 53.08 53.76 54.18 53.11 52.69 54.15 53.91 53.06
## [697] 54.47 53.54 53.62 54.39 54.18 53.76 52.83 52.71 54.80 52.02 54.45 53.47
## [709] 54.32 52.61 53.21 53.46 53.21 53.91 53.18 54.17 54.56 53.27 53.31 53.40
## [721] 53.11 53.39 53.11 54.41 53.54 54.06 54.06 53.54 53.32 51.75 52.95 53.49
## [733] 52.02 53.67 53.76 53.39 53.60 52.61 53.60 53.53 52.67 53.70 52.76 54.18
## [745] 54.15 52.46 52.67 53.18 53.62 53.21 54.41 54.47 53.32 52.95 53.53 55.04
## [757] 53.53 53.32 53.54 53.49 53.70 52.95 53.11 53.16 53.32 53.18 53.52 53.46
## [769] 53.40 53.52 53.40 54.39 53.49 52.63 54.31 53.62 52.59 53.08 53.46 54.14
## [781] 53.67 52.46 53.40 53.06 52.40 54.41 53.40 53.20 54.55 53.53 55.18 51.98
## [793] 53.08 53.46 54.61 53.67 53.49 54.17 53.82 52.69 52.69 53.49 53.20 53.16
## [805] 53.32 53.21 53.40 53.70 53.49 52.58 53.11 52.63 53.70 53.46 54.48 52.71
## [817] 54.15 53.53 53.48 53.47 53.67 54.15 54.14 53.92 52.14 53.39 53.40 52.14
## [829] 53.18 53.49 53.18 53.47 53.82 53.91 51.82 54.31 53.27 53.91 53.48 53.67
## [841] 52.47 53.49 54.15 53.16 53.20 54.31 54.18 52.83 53.76 53.73 53.46 53.47
## [853] 53.92 53.20 53.46 53.54 53.48 53.48 52.67 53.54 54.18 53.27 52.95 54.29
## [865] 54.17 52.69 54.17 53.73 52.71 53.47 53.49 53.48 53.40 53.70 54.18 54.29
## [877] 53.11 54.17 53.49 53.52 53.70 53.70 53.40 53.46 53.67 52.76 54.18 54.06
## [889] 53.47 53.47 53.11 53.48 53.18 53.70 53.70 53.67 53.62 53.20 53.31 53.62
## [901] 53.39 53.46 53.76 53.47 54.18 53.46 53.39 53.40 54.18 53.32 53.39 54.15
## [913] 53.48 52.94 53.76 52.63 53.39 53.49 54.32 52.92 53.40 53.46 53.27 53.49
## [925] 54.15 53.21 54.14 54.18 53.32 54.48 53.46 53.32 54.39 53.70 54.29 53.39
## [937] 53.48 53.76 52.63 52.02 53.18 53.62 53.73 53.16 53.62 52.61 54.76 54.44
## [949] 53.70 54.14 52.95 53.73 53.11 53.40 54.41 53.46 52.76 53.67 53.47 54.39
## [961] 54.31 53.70 54.18 53.46 52.67 54.91 54.17 53.49 53.39 53.70 52.47 53.49
## [973] 53.92 53.06 53.46 54.80 53.76 53.20 53.91 53.08 53.49 54.18 53.46 53.39
## [985] 53.70 54.29 54.41 54.15 53.49 53.27 53.91 52.63 53.31 52.92 52.95 54.14
## [997] 53.27 53.39 54.15 52.95
hist(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$Ind_2_severidad))^2,2);EE
## [1] 0.03
Índices por separado
Probabilidad de contagio
#-----------------------------------probabilidad de contagio-----------------------------------
Proba<-CaliyPalmira.FMA$separate.analyses$`Probabilidad de contagio`$ind$coord[,1]
Imin_Proba<-min(Proba);Imin_Proba
## [1] -5.561753
Imax_Proba<-max(Proba);Imax_Proba
## [1] 3.500155
Ind_2_Proba3<-round(((Proba-Imin_Proba)/(Imax_Proba-Imin_Proba))*100,2) #con este índice se hace el cluster
min(Ind_2_Proba3)
## [1] 0
max(Ind_2_Proba3)
## [1] 100
C8<-cbind(Ind_2_Proba3,CaliyPalmira)
summary(C8$Ind_2_Proba3);sd(C8$Ind_2_Proba3)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 48.23 60.96 61.38 74.47 100.00
## [1] 19.62697
K-means
set.seed(1234)
kmeans3<- kmeans(C8$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.:182.5 Class :character
## Median : 9.000 Median : 6.000 Median :366.0 Mode :character
## Mean : 8.069 Mean : 5.881 Mean :370.4
## 3rd Qu.:12.000 3rd Qu.:10.000 3rd Qu.:549.5
## Max. :14.000 Max. :12.000 Max. :814.0
## cluster
## Min. :1.000
## 1st Qu.:1.000
## Median :2.000
## Mean :2.169
## 3rd Qu.:3.000
## Max. :3.000
kmeans3$centers
## [,1]
## 1 85.61910
## 2 37.86926
## 3 61.10051
tapply(Ind_2_Proba3,kmeans3$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
##
## $`3`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 49.57 55.37 61.00 61.10 67.09 73.29
BASES CON IPRG K-MEANS
library(car)
C8$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.:182.5 Class :character
## Median : 9.000 Median : 6.000 Median :366.0 Mode :character
## Mean : 8.069 Mean : 5.881 Mean :370.4
## 3rd Qu.:12.000 3rd Qu.:10.000 3rd Qu.:549.5
## Max. :14.000 Max. :12.000 Max. :814.0
## cluster
## 1.Bajo :403
## 2.Medio:642
## 3.Alto :398
##
##
##
table(C8$cluster)
##
## 1.Bajo 2.Medio 3.Alto
## 403 642 398
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
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 60.96
hist(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] 1.3
Severidad
#-----------------------------------severidad-----------------------------------
Sev<-CaliyPalmira.FMA$separate.analyses$Severidad$ind$coord[,1]
Imin_Sev<-min(Sev);Imin_Sev
## [1] -4.142719
Imax_Sev<-max(Sev);Imax_Sev
## [1] 3.366186
Ind_2_Sev3<-round(((Sev-Imin_Sev)/(Imax_Sev-Imin_Sev))*100,2) #con este índice se hace el cluster
min(Ind_2_Sev3)
## [1] 0
max(Ind_2_Sev3)
## [1] 100
sd(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$Ind_2_Sev3)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 41.66 54.28 55.17 70.83 100.00
K-means
set.seed(1234)
kmeans3<- kmeans(C8$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.:182.5 Class :character
## Median : 9.000 Median : 6.000 Median :366.0 Mode :character
## Mean : 8.069 Mean : 5.881 Mean :370.4
## 3rd Qu.:12.000 3rd Qu.:10.000 3rd Qu.:549.5
## Max. :14.000 Max. :12.000 Max. :814.0
## cluster
## Min. :1.000
## 1st Qu.:1.000
## Median :2.000
## Mean :2.237
## 3rd Qu.:3.000
## Max. :3.000
kmeans3$centers
## [,1]
## 1 83.32348
## 2 26.63181
## 3 54.53598
tapply(Ind_2_Sev3,kmeans3$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
##
## $`3`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 41.53 49.91 54.28 54.54 62.54 67.15
BASES CON IPRG K-MEANS
library(car)
C8$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.:182.5 Class :character
## Median : 9.000 Median : 6.000 Median :366.0 Mode :character
## Mean : 8.069 Mean : 5.881 Mean :370.4
## 3rd Qu.:12.000 3rd Qu.:10.000 3rd Qu.:549.5
## Max. :14.000 Max. :12.000 Max. :814.0
## cluster
## 1.Bajo :353
## 2.Medio:716
## 3.Alto :374
##
##
##
table(C8$cluster)
##
## 1.Bajo 2.Medio 3.Alto
## 353 716 374
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
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 54.28
hist(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] 0.003171572
Susceptibilidad
#-----------------------------------susceptibilidad-----------------------------------
SU<-CaliyPalmira.FMA$separate.analyses$Susceptibilidad$ind$coord[,1]
Imin_SU<-min(SU);Imin_SU
## [1] -3.522199
Imax_SU<-max(SU);Imax_SU
## [1] 6.746319
Ind_2_SU3<-round(((SU-Imin_SU)/(Imax_SU-Imin_SU))*100,2) #con este índice se hace el cluster
min(Ind_2_SU3)
## [1] 0
max(Ind_2_SU3)
## [1] 100
sd(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$Ind_2_SU3)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 21.91 34.02 34.30 45.38 100.00
K-means
set.seed(1234)
kmeans3<- kmeans(C8$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.:182.5 Class :character
## Median : 9.000 Median : 6.000 Median :366.0 Mode :character
## Mean : 8.069 Mean : 5.881 Mean :370.4
## 3rd Qu.:12.000 3rd Qu.:10.000 3rd Qu.:549.5
## Max. :14.000 Max. :12.000 Max. :814.0
## cluster
## Min. :1.000
## 1st Qu.:2.000
## Median :2.000
## Mean :2.222
## 3rd Qu.:3.000
## Max. :3.000
kmeans3$centers
## [,1]
## 1 61.37931
## 2 38.83473
## 3 17.44862
tapply(Ind_2_SU3,kmeans3$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
##
## $`3`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 12.39 18.83 17.45 23.64 28.13
BASES CON IPRG K-MEANS
library(car)
C8$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.:182.5 Class :character
## Median : 9.000 Median : 6.000 Median :366.0 Mode :character
## Mean : 8.069 Mean : 5.881 Mean :370.4
## 3rd Qu.:12.000 3rd Qu.:10.000 3rd Qu.:549.5
## Max. :14.000 Max. :12.000 Max. :814.0
## cluster
## 1.Bajo :603
## 2.Medio:580
## 3.Alto :260
##
##
##
table(C8$cluster)
##
## 1.Bajo 2.Medio 3.Alto
## 603 580 260
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
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 33.12
hist(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] 56.715
CALI
#---------------------Índice de percepción por ciudad---------------------------####
library(dplyr)
##
## Attaching package: 'dplyr'
## 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
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 100.00000
CaliyPalmira.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 7.034285
CaliyPalmira.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 0.4531235
Coordenadas<-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 -0.143
Contribu<-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 1.377
Tabla<-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 1.377
plot.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$coord[,1];Coord1_severidad
## x71 x72 x73 x74 x75 x76 x77
## 0.51897714 0.52273671 0.49032346 0.39540629 0.03201124 0.34883299 0.28217312
## x81 x82 x83 x84 x91 x92 x93
## 0.59244118 0.79605796 0.64597488 0.79262979 0.32918363 0.78957486 0.77708632
## x94 x95
## 0.82059472 0.64882058
lp_severidad<-res.mfa_severidad$eig[1];lp_severidad #VALOR PROPIO
## [1] 1.885584
Vp_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 0.4724999
Pesos_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 0.073873792
sum(Pesos_severidad)
## [1] 1
data.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 0.074
res.mfa_severidad$eig
## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 1.88558352 35.330673 35.33067
## comp 2 0.80127659 15.013730 50.34440
## comp 3 0.46792772 8.767684 59.11209
## comp 4 0.33608339 6.297283 65.40937
## comp 5 0.31549687 5.911548 71.32092
## comp 6 0.26084766 4.887571 76.20849
## comp 7 0.23464033 4.396517 80.60501
## comp 8 0.19947998 3.737709 84.34272
## comp 9 0.16751433 3.138760 87.48148
## comp 10 0.14802976 2.773672 90.25515
## comp 11 0.12433215 2.329644 92.58479
## comp 12 0.10948924 2.051529 94.63632
## comp 13 0.08829007 1.654314 96.29063
## comp 14 0.07611270 1.426144 97.71678
## comp 15 0.06563418 1.229805 98.94658
## comp 16 0.05622042 1.053417 100.00000
Ind_severidad<-as.matrix(Severidad)%*%Pesos_severidad
Imin_severidad<-min(Ind_severidad);Imin_severidad
## [1] 0.9625196
Imax_severidad<-max(Ind_severidad);Imax_severidad
## [1] 6.661618
Ind_2_severidad1<-round(((Ind_severidad-Imin_severidad)/(Imax_severidad-Imin_severidad))*100,2) #con este índice se hace el cluster
min(Ind_2_severidad1)
## [1] 0
max(Ind_2_severidad1)
## [1] 100
C8<-cbind(Ind_2_severidad1,Cali)
summary(C8$Ind_2_severidad1);sd(C8$Ind_2_severidad1)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 39.76 50.89 52.05 63.37 100.00
## [1] 17.58605
ECM
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
Severidad=Cali[,c(19:34)]
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Coord1_severidad <-res.mfa_severidad$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$Ind_2_severidad1))^2,2);EE
## [1] 0.01
Índices por separado
Probabilidad de contagio
#-----------------------------------probabilidad de contagio-----------------------------------
Proba<-CaliyPalmira.FMA$separate.analyses$`Probabilidad de contagio`$ind$coord[,1]
Imin_Proba<-min(Proba);Imin_Proba
## [1] -5.543756
Imax_Proba<-max(Proba);Imax_Proba
## [1] 3.539925
Ind_2_Proba<-round(((Proba-Imin_Proba)/(Imax_Proba-Imin_Proba))*100,2) #con este índice se hace el cluster
min(Ind_2_Proba)
## [1] 0
max(Ind_2_Proba)
## [1] 100
C8<-cbind(Ind_2_Proba,Cali)
summary(C8$Ind_2_Proba);sd(C8$Ind_2_Proba)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 49.02 60.59 61.03 73.73 100.00
## [1] 19.25214
ECM
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
Severidad=Cali[,c(19:34)]
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Proba<-CaliyPalmira.FMA$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$Ind_2_Proba))^2,2);EE
## [1] 0.72
Severidad
#-----------------------------------severidad-----------------------------------
Sev<-CaliyPalmira.FMA$separate.analyses$Severidad$ind$coord[,1]
Imin_Sev<-min(Sev);Imin_Sev
## [1] -3.969254
Imax_Sev<-max(Sev);Imax_Sev
## [1] 3.521764
Ind_2_Sev<-round(((Sev-Imin_Sev)/(Imax_Sev-Imin_Sev))*100,2) #con este índice se hace el cluster
min(Ind_2_Sev)
## [1] 0
max(Ind_2_Sev)
## [1] 100
sd(Ind_2_Sev)
## [1] 21.99115
#rbind(summary(Ind_2_Sev))
#print(xtable(rbind(summary(Ind_2_Sev))), include.rownames = FALSE)
C8<-cbind(Ind_2_Sev,Cali)
summary(C8$Ind_2_Sev)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 37.60 50.26 52.99 66.90 100.00
ECM
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] 794.21
Susceptibilidad
#-----------------------------------susceptibilidad-----------------------------------
SU<-CaliyPalmira.FMA$separate.analyses$Susceptibilidad$ind$coord[,1]
Imin_SU<-min(SU);Imin_SU
## [1] -3.393132
Imax_SU<-max(SU);Imax_SU
## [1] 6.640584
Ind_2_SU<-round(((SU-Imin_SU)/(Imax_SU-Imin_SU))*100,2) #con este índice se hace el cluster
min(Ind_2_SU)
## [1] 0
max(Ind_2_SU)
## [1] 100
sd(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$Ind_2_SU)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 21.30 32.61 33.82 45.43 100.00
ECM
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
Severidad=Cali[,c(19:34)]
res.mfa_severidad=CaliyPalmira.FMA
#Datos
SU<-CaliyPalmira.FMA$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$Ind_2_SU))^2,2);EE
## [1] 32.39
PALMIRA
library(dplyr)
CaliyPalmira$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$eig
## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 2.04287583 40.5263204 40.52632
## comp 2 0.70280965 13.9422517 54.46857
## comp 3 0.48852501 9.6912993 64.15987
## comp 4 0.32139975 6.3758888 70.53576
## comp 5 0.22235609 4.4110729 74.94683
## comp 6 0.21048293 4.1755345 79.12237
## comp 7 0.18525508 3.6750675 82.79744
## comp 8 0.16106274 3.1951430 85.99258
## comp 9 0.13244381 2.6274041 88.61998
## comp 10 0.11596779 2.3005548 90.92054
## comp 11 0.10876158 2.1575989 93.07814
## comp 12 0.09656076 1.9155604 94.99370
## comp 13 0.07852095 1.5576890 96.55139
## comp 14 0.06938844 1.3765194 97.92790
## comp 15 0.05687026 1.1281852 99.05609
## comp 16 0.04758121 0.9439101 100.00000
CaliyPalmira.FMA$group$contrib
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## Probabilidad de contagio 25.03689 75.462789 32.35813 60.09339 20.54200
## Severidad 37.54348 15.833327 31.50396 25.29624 26.00522
## Susceptibilidad 37.41964 8.703884 36.13791 14.61037 53.45278
CaliyPalmira.FMA$group$correlation[,1:3]
## Dim.1 Dim.2 Dim.3
## Probabilidad de contagio 0.7225955 0.7538494 0.5811008
## Severidad 0.8805261 0.3520768 0.5469880
## Susceptibilidad 0.8769671 0.2667586 0.4634630
Coordenadas<-round(CaliyPalmira.FMA$quanti.var$coord[,c(1,2,3)],3);Coordenadas
## Dim.1 Dim.2 Dim.3
## x71 0.545 0.685 -0.043
## x72 0.531 0.650 -0.050
## x73 0.567 0.627 -0.084
## x74 0.519 0.420 0.237
## x75 0.409 0.167 0.495
## x76 0.451 0.559 0.070
## x77 0.457 -0.113 0.472
## x81 0.626 -0.296 0.499
## x82 0.788 -0.295 -0.069
## x83 0.632 -0.347 0.402
## x84 0.825 -0.094 -0.067
## x91 0.359 -0.188 -0.503
## x92 0.793 -0.149 -0.389
## x93 0.808 -0.156 -0.264
## x94 0.811 -0.148 -0.313
## x95 0.656 -0.310 -0.069
Contribu<-round(CaliyPalmira.FMA$quanti.var$contrib[,c(1,2,3)],3);Contribu
## Dim.1 Dim.2 Dim.3
## x71 4.257 19.529 0.111
## x72 4.027 17.544 0.152
## x73 4.604 16.356 0.423
## x74 3.848 7.338 3.364
## x75 2.398 1.154 14.671
## x76 2.911 13.014 0.289
## x77 2.991 0.528 13.348
## x81 7.034 4.569 18.684
## x82 11.140 4.536 0.353
## x83 7.163 6.265 12.135
## x84 12.206 0.463 0.332
## x91 1.933 1.545 15.850
## x92 9.422 0.966 9.470
## x93 9.784 1.060 4.377
## x94 9.836 0.958 6.140
## x95 6.444 4.175 0.301
Tabla<-cbind(Coordenadas,Contribu);Tabla
## Dim.1 Dim.2 Dim.3 Dim.1 Dim.2 Dim.3
## x71 0.545 0.685 -0.043 4.257 19.529 0.111
## x72 0.531 0.650 -0.050 4.027 17.544 0.152
## x73 0.567 0.627 -0.084 4.604 16.356 0.423
## x74 0.519 0.420 0.237 3.848 7.338 3.364
## x75 0.409 0.167 0.495 2.398 1.154 14.671
## x76 0.451 0.559 0.070 2.911 13.014 0.289
## x77 0.457 -0.113 0.472 2.991 0.528 13.348
## x81 0.626 -0.296 0.499 7.034 4.569 18.684
## x82 0.788 -0.295 -0.069 11.140 4.536 0.353
## x83 0.632 -0.347 0.402 7.163 6.265 12.135
## x84 0.825 -0.094 -0.067 12.206 0.463 0.332
## x91 0.359 -0.188 -0.503 1.933 1.545 15.850
## x92 0.793 -0.149 -0.389 9.422 0.966 9.470
## x93 0.808 -0.156 -0.264 9.784 1.060 4.377
## x94 0.811 -0.148 -0.313 9.836 0.958 6.140
## x95 0.656 -0.310 -0.069 6.444 4.175 0.301
plot.MFA(CaliyPalmira.FMA, choix="group",title="Representación de grupos")
#plot.MFA(CaliyPalmira.FMA, choix="ind",lab.par=FALSE)
library(ggrepel)
options(ggrepel.max.overlaps = Inf)
#dim 1-2
plot.MFA(CaliyPalmira.FMA, choix="var",habillage='group',title="Círculo de correlación", repel = TRUE)
IPRG
#--------------------------ÍNDICE DE PERCEPCIÓN GLOBAL-----------------------------------------------#####
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Severidad=Palmira[,c(19:34)]
Coord1_severidad <-res.mfa_severidad$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 0.6560303
lp_severidad<-res.mfa_severidad$eig[1];lp_severidad #VALOR PROPIO
## [1] 2.042876
Vp_severidad<-Coord1_severidad/sqrt(lp_severidad);Vp_severidad #VECTOR PROPIO
## x71 x72 x73 x74 x75 x76 x77 x81
## 0.3816290 0.3711987 0.3968936 0.3628591 0.2864265 0.3155803 0.3199136 0.4381630
## x82 x83 x84 x91 x92 x93 x94 x95
## 0.5514207 0.4421667 0.5772013 0.2514048 0.5550173 0.5655732 0.5670734 0.4589897
Pesos_severidad<-(Vp_severidad/sum(Vp_severidad));Pesos_severidad # PESOS RELATIVOS DE LAS VARIABLES
## x71 x72 x73 x74 x75 x76 x77
## 0.05578140 0.05425683 0.05801257 0.05303785 0.04186598 0.04612728 0.04676066
## x81 x82 x83 x84 x91 x92 x93
## 0.06404477 0.08059925 0.06462997 0.08436752 0.03674697 0.08112496 0.08266788
## x94 x95
## 0.08288716 0.06708894
sum(Pesos_severidad)
## [1] 1
data.frame(round(Pesos_severidad,3))
## round.Pesos_severidad..3.
## x71 0.056
## x72 0.054
## x73 0.058
## x74 0.053
## x75 0.042
## x76 0.046
## x77 0.047
## x81 0.064
## x82 0.081
## x83 0.065
## x84 0.084
## x91 0.037
## x92 0.081
## x93 0.083
## x94 0.083
## x95 0.067
res.mfa_severidad$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 100.00000
Ind_severidad<-as.matrix(Severidad)%*%Pesos_severidad
Imin_severidad<-min(Ind_severidad);Imin_severidad
## [1] 0.963253
Imax_severidad<-max(Ind_severidad);Imax_severidad
## [1] 6.546757
Ind_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] 0
max(Ind_2_severidad2)
## [1] 100
C8<-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] 18.0409
ECM
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] 3.35
Índices 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] -6.214496
Imax_Proba<-max(Proba);Imax_Proba
## [1] 3.395533
Ind_2_Proba2<-round(((Proba-Imin_Proba)/(Imax_Proba-Imin_Proba))*100,2) #con este índice se hace el cluster
min(Ind_2_Proba2)
## [1] 0
max(Ind_2_Proba2)
## [1] 100
C8<-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] 19.26239
ECM
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] 3.33
Severidad
#-----------------------------------severidad-----------------------------------
Sev<-CaliyPalmira.FMA$separate.analyses$Severidad$ind$coord[,1]
Imin_Sev<-min(Sev);Imin_Sev
## [1] -4.397929
Imax_Sev<-max(Sev);Imax_Sev
## [1] 3.203421
Ind_2_Sev2<-round(((Sev-Imin_Sev)/(Imax_Sev-Imin_Sev))*100,2) #con este índice se hace el cluster
min(Ind_2_Sev2)
## [1] 0
max(Ind_2_Sev2)
## [1] 100
sd(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$Ind_2_Sev2)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 45.23 58.40 57.86 71.08 100.00
ECM
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
Severidad=Palmira[,c(19:34)]
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Sev<-CaliyPalmira.FMA$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$Ind_2_Sev2))^2,2);EE
## [1] 198.53
Susceptibilidad
#-----------------------------------susceptibilidad-----------------------------------
SU<-CaliyPalmira.FMA$separate.analyses$Susceptibilidad$ind$coord[,1]
Imin_SU<-min(SU);Imin_SU
## [1] -3.712545
Imax_SU<-max(SU);Imax_SU
## [1] 4.942211
Ind_2_SU2<-round(((SU-Imin_SU)/(Imax_SU-Imin_SU))*100,2) #con este índice se hace el cluster
min(Ind_2_SU2)
## [1] 0
max(Ind_2_SU2)
## [1] 100
sd(Ind_2_SU2)
## [1] 20.9077
#rbind(summary(Ind_2_SU))
#print(xtable(rbind(summary(Ind_2_SU))), include.rownames = FALSE)
#-----------------------------------k-mean--------------------------------####
C8<-cbind(Ind_2_SU2,Palmira)
summary(C8$Ind_2_SU2)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 26.64 41.91 42.90 57.20 100.00
ECM
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] 21.79
visualizacion
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