Todas las variables
#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_severidad<-round(((Ind_severidad-Imin_severidad)/(Imax_severidad-Imin_severidad))*100,2) #con este índice se hace el cluster
min(Ind_2_severidad)
## [1] 0
max(Ind_2_severidad)
## [1] 100
dim(Ind_2_severidad)
## [1] 1443 1
View(Ind_2_severidad)
C8<-cbind(Ind_2_severidad,CaliyPalmira)
summary(C8)
## Ind_2_severidad 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
K-means
set.seed(1234)
kmeans <- kmeans(C8$Ind_2_severidad, 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_severidad 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
sum(kmeans$cluster==1)/1443#bajo
## [1] 0.4476784
sum(kmeans$cluster==2)/1443#alto
## [1] 0.3000693
sum(kmeans$cluster==3)/1443#medio
## [1] 0.2522523
tapply(Ind_2_severidad,kmeans$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
##
## $`3`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 66.05 70.30 75.33 77.28 82.27 100.00
Sin x75
AFM sin x75 [23]
##Análisis factorial múltiple
CaliyPalmira.FMA<-MFA(CaliyPalmira[,c(19:22,24:34)],
group=c(#2,
#5,
#3,
#4,
#2,
#2, #3
6, #7
4,
5 #6
),
type=c(#'s',
#'s',
#'n',
#'s', #n
#'s',
#'s', #n
's',
's',
's'#,
#'s'
),
name.group=c(#"Voluntariedad",
#"Conocimiento",
#"Incertidumbre",
#"Confianza gubernamental",
#"Confianza sector salud",
#"Confianza medios",
"Probabilidad de contagio",
"Severidad",
"Susceptibilidad"), #,
#"Cumplimiento"),
#num.group.sup=c(3),
graph=FALSE)
CaliyPalmira.FMA$eig
## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 1.94865806 39.532688 39.53269
## comp 2 0.76674789 15.555118 55.08781
## comp 3 0.45760806 9.283556 64.37136
## comp 4 0.28914893 5.866003 70.23737
## comp 5 0.24548788 4.980246 75.21761
## comp 6 0.22110364 4.485559 79.70317
## comp 7 0.19526277 3.961322 83.66449
## comp 8 0.16029229 3.251871 86.91636
## comp 9 0.14254013 2.891731 89.80809
## comp 10 0.11731053 2.379895 92.18799
## comp 11 0.10916646 2.214675 94.40266
## comp 12 0.08324968 1.688898 96.09156
## comp 13 0.07152339 1.451005 97.54257
## comp 14 0.06767919 1.373017 98.91558
## comp 15 0.05345343 1.084417 100.00000
CaliyPalmira.FMA$group$contrib
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## Probabilidad de contagio 21.85591 77.770448 13.53163 79.883870 26.68765
## Severidad 38.76336 13.335142 46.75953 14.329403 18.06880
## Susceptibilidad 39.38073 8.894409 39.70884 5.786728 55.24355
CaliyPalmira.FMA$group$correlation[,1:3]
## Dim.1 Dim.2 Dim.3
## Probabilidad de contagio 0.6593827 0.7815111 0.4180963
## Severidad 0.8742936 0.3292176 0.5899898
## Susceptibilidad 0.8782315 0.2750706 0.4726707
Coordenadas<-round(CaliyPalmira.FMA$quanti.var$coord[,c(1,2,3)],3);Coordenadas
## Dim.1 Dim.2 Dim.3
## x71 0.536 0.706 -0.026
## x72 0.530 0.645 -0.032
## x73 0.532 0.659 -0.034
## x74 0.451 0.443 0.240
## x76 0.395 0.568 0.061
## x77 0.353 -0.041 0.361
## x81 0.607 -0.275 0.577
## x82 0.792 -0.295 -0.022
## x83 0.640 -0.312 0.500
## x84 0.810 -0.136 0.005
## x91 0.342 -0.196 -0.534
## x92 0.796 -0.165 -0.369
## x93 0.794 -0.182 -0.249
## x94 0.818 -0.177 -0.299
## x95 0.653 -0.300 -0.116
Contribu<-round(CaliyPalmira.FMA$quanti.var$contrib[,c(1,2,3)],3);Contribu
## Dim.1 Dim.2 Dim.3
## x71 4.702 20.725 0.046
## x72 4.597 17.284 0.069
## x73 4.635 18.085 0.081
## x74 3.329 8.172 4.004
## x76 2.555 13.435 0.262
## x77 2.037 0.069 9.069
## x81 6.920 3.627 26.693
## x82 11.806 4.164 0.039
## x83 7.708 4.659 20.026
## x84 12.329 0.885 0.002
## x91 1.862 1.552 19.331
## x92 10.077 1.096 9.212
## x93 10.019 1.333 4.212
## x94 10.644 1.271 6.042
## x95 6.778 3.642 0.911
Tabla<-cbind(Coordenadas,Contribu);Tabla
## Dim.1 Dim.2 Dim.3 Dim.1 Dim.2 Dim.3
## x71 0.536 0.706 -0.026 4.702 20.725 0.046
## x72 0.530 0.645 -0.032 4.597 17.284 0.069
## x73 0.532 0.659 -0.034 4.635 18.085 0.081
## x74 0.451 0.443 0.240 3.329 8.172 4.004
## x76 0.395 0.568 0.061 2.555 13.435 0.262
## x77 0.353 -0.041 0.361 2.037 0.069 9.069
## x81 0.607 -0.275 0.577 6.920 3.627 26.693
## x82 0.792 -0.295 -0.022 11.806 4.164 0.039
## x83 0.640 -0.312 0.500 7.708 4.659 20.026
## x84 0.810 -0.136 0.005 12.329 0.885 0.002
## x91 0.342 -0.196 -0.534 1.862 1.552 19.331
## x92 0.796 -0.165 -0.369 10.077 1.096 9.212
## x93 0.794 -0.182 -0.249 10.019 1.333 4.212
## x94 0.818 -0.177 -0.299 10.644 1.271 6.042
## x95 0.653 -0.300 -0.116 6.778 3.642 0.911
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(19:22,24:34)]
Coord1_severidad <-res.mfa_severidad$global.pca$var$coord[,1];Coord1_severidad
## x71 x72 x73 x74 x76 x77 x81 x82
## 0.5360222 0.5299913 0.5321828 0.4510423 0.3951212 0.3528036 0.6066522 0.7923790
## x83 x84 x91 x92 x93 x94 x95
## 0.6402254 0.8097176 0.3421249 0.7958928 0.7935924 0.8179807 0.6527216
lp_severidad<-res.mfa_severidad$eig[1];lp_severidad #VALOR PROPIO
## [1] 1.948658
Vp_severidad<-Coord1_severidad/sqrt(lp_severidad);Vp_severidad #VECTOR PROPIO
## x71 x72 x73 x74 x76 x77 x81 x82
## 0.3839856 0.3796653 0.3812352 0.3231093 0.2830496 0.2527349 0.4345822 0.5676298
## x83 x84 x91 x92 x93 x94 x95
## 0.4586328 0.5800504 0.2450851 0.5701469 0.5684989 0.5859698 0.4675846
Pesos_severidad<-(Vp_severidad/sum(Vp_severidad));Pesos_severidad # PESOS RELATIVOS DE LAS VARIABLES
## x71 x72 x73 x74 x76 x77 x81
## 0.05923912 0.05857261 0.05881480 0.04984747 0.04366728 0.03899051 0.06704488
## x82 x83 x84 x91 x92 x93 x94
## 0.08757069 0.07075526 0.08948688 0.03781033 0.08795902 0.08770478 0.09040009
## x95
## 0.07213629
sum(Pesos_severidad)
## [1] 1
data.frame(round(Pesos_severidad,3))
## round.Pesos_severidad..3.
## x71 0.059
## x72 0.059
## x73 0.059
## x74 0.050
## x76 0.044
## x77 0.039
## x81 0.067
## x82 0.088
## x83 0.071
## x84 0.089
## x91 0.038
## x92 0.088
## x93 0.088
## x94 0.090
## x95 0.072
res.mfa_severidad$eig
## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 1.94865806 39.532688 39.53269
## comp 2 0.76674789 15.555118 55.08781
## comp 3 0.45760806 9.283556 64.37136
## comp 4 0.28914893 5.866003 70.23737
## comp 5 0.24548788 4.980246 75.21761
## comp 6 0.22110364 4.485559 79.70317
## comp 7 0.19526277 3.961322 83.66449
## comp 8 0.16029229 3.251871 86.91636
## comp 9 0.14254013 2.891731 89.80809
## comp 10 0.11731053 2.379895 92.18799
## comp 11 0.10916646 2.214675 94.40266
## comp 12 0.08324968 1.688898 96.09156
## comp 13 0.07152339 1.451005 97.54257
## comp 14 0.06767919 1.373017 98.91558
## comp 15 0.05345343 1.084417 100.00000
Ind_severidad<-as.matrix(Severidad)%*%Pesos_severidad
Imin_severidad<-min(Ind_severidad);Imin_severidad
## [1] 0.9621897
Imax_severidad<-max(Ind_severidad);Imax_severidad
## [1] 6.645633
Ind_2_severidad<-round(((Ind_severidad-Imin_severidad)/(Imax_severidad-Imin_severidad))*100,2) #con este índice se hace el cluster
min(Ind_2_severidad)
## [1] 0
max(Ind_2_severidad)
## [1] 100
dim(Ind_2_severidad)
## [1] 1443 1
View(Ind_2_severidad)
C8<-cbind(Ind_2_severidad,CaliyPalmira)
summary(C8)
## Ind_2_severidad x11 x12 x21
## Min. : 0.00 Min. :0.000 Min. :0.000 Min. :1.000
## 1st Qu.: 41.16 1st Qu.:3.000 1st Qu.:6.000 1st Qu.:5.000
## Median : 53.17 Median :4.000 Median :7.000 Median :5.000
## Mean : 53.90 Mean :3.735 Mean :6.367 Mean :5.375
## 3rd Qu.: 65.97 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
K-means
set.seed(1234)
kmeans <- kmeans(C8$Ind_2_severidad, 3, iter.max = 1000, nstart = 10);kmeans
## K-means clustering with 3 clusters of sizes 642, 451, 350
##
## Cluster means:
## [,1]
## 1 55.04913
## 2 33.77845
## 3 77.72700
##
## 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 2 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 1 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 3 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 2 1 3 1 3 1 2 3 2 1 3 1 2 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 2 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 1 2 3 1 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 2 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 1 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 2 2 1 2 2 1 2 1 1 2 2 1 1 2
## [741] 1 1 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 2 1 2 3 2 1 3 3 2 2 3 2 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 1 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 1 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 2 2 3 3 2 1 3 3 2 3 1
## [1037] 2 2 1 2 2 3 1 3 3 3 1 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 2 2 3 1 1 1 3 3 1 1 2 2 1 1 3 2 3 2 2 2 2 3 2 2 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 2 1 1 1 2 1 1 1 3 1 3 3 1 1 2 2 2 1 1 3 2
## [1148] 1 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 2 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 1 2 1 2 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 1 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 1 2 2 2 3 3 2 3 1 2 3 1 3 3 1 3 2
## [1296] 1 3 1 2 1 1 2 2 2 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 1 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 1 1
##
## Within cluster sum of squares by cluster:
## [1] 24012.53 34717.25 25942.18
## (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_severidad x11 x12 x21
## Min. : 0.00 Min. :0.000 Min. :0.000 Min. :1.000
## 1st Qu.: 41.16 1st Qu.:3.000 1st Qu.:6.000 1st Qu.:5.000
## Median : 53.17 Median :4.000 Median :7.000 Median :5.000
## Mean : 53.90 Mean :3.735 Mean :6.367 Mean :5.375
## 3rd Qu.: 65.97 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 :1.798
## 3rd Qu.:2.000
## Max. :3.000
kmeans$centers
## [,1]
## 1 55.04913
## 2 33.77845
## 3 77.72700
sum(kmeans$cluster==1)/1443#bajo
## [1] 0.4449064
sum(kmeans$cluster==2)/1443#alto
## [1] 0.3125433
sum(kmeans$cluster==3)/1443#medio
## [1] 0.2425502
tapply(Ind_2_severidad,kmeans$cluster,summary)
## $`1`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 44.43 49.67 54.74 55.05 60.31 66.35
##
## $`2`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 29.34 36.06 33.78 40.28 44.39
##
## $`3`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 66.45 70.73 75.56 77.73 82.70 100.00
Sin x75 y x77
AFM sin x75 [23] y x77 [25]
##Análisis factorial múltiple
CaliyPalmira.FMA<-MFA(CaliyPalmira[,c(19:22,24,26:34)],
group=c(#2,
#5,
#3,
#4,
#2,
#2, #3
5, #7
4,
5 #6
),
type=c(#'s',
#'s',
#'n',
#'s', #n
#'s',
#'s', #n
's',
's',
's'#,
#'s'
),
name.group=c(#"Voluntariedad",
#"Conocimiento",
#"Incertidumbre",
#"Confianza gubernamental",
#"Confianza sector salud",
#"Confianza medios",
"Probabilidad de contagio",
"Severidad",
"Susceptibilidad"), #,
#"Cumplimiento"),
#num.group.sup=c(3),
graph=FALSE)
CaliyPalmira.FMA$eig
## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 1.92075448 41.445703 41.44570
## comp 2 0.77532670 16.729864 58.17557
## comp 3 0.44107863 9.517517 67.69308
## comp 4 0.24670485 5.323354 73.01644
## comp 5 0.22646400 4.886600 77.90304
## comp 6 0.19852715 4.283784 82.18682
## comp 7 0.16336268 3.525011 85.71183
## comp 8 0.15646009 3.376068 89.08790
## comp 9 0.11867588 2.560767 91.64867
## comp 10 0.10988535 2.371087 94.01976
## comp 11 0.08344743 1.800614 95.82037
## comp 12 0.07156271 1.544168 97.36454
## comp 13 0.06860665 1.480382 98.84492
## comp 14 0.05353089 1.155080 100.00000
CaliyPalmira.FMA$group$contrib
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## Probabilidad de contagio 20.86834 76.906147 4.648738 27.07576 79.18873
## Severidad 39.01107 13.630131 55.701557 17.58221 3.16972
## Susceptibilidad 40.12059 9.463722 39.649705 55.34203 17.64155
CaliyPalmira.FMA$group$correlation[,1:3]
## Dim.1 Dim.2 Dim.3
## Probabilidad de contagio 0.6338206 0.7730918 0.2634208
## Severidad 0.8712772 0.3338554 0.6133721
## Susceptibilidad 0.8799128 0.2838135 0.4633125
Coordenadas<-round(CaliyPalmira.FMA$quanti.var$coord[,c(1,2,3)],3);Coordenadas
## Dim.1 Dim.2 Dim.3
## x71 0.544 0.700 0.014
## x72 0.537 0.640 0.027
## x73 0.542 0.653 0.011
## x74 0.452 0.441 -0.247
## x76 0.395 0.567 -0.033
## x81 0.597 -0.276 -0.602
## x82 0.790 -0.303 0.004
## x83 0.636 -0.315 -0.553
## x84 0.812 -0.145 -0.045
## x91 0.347 -0.203 0.531
## x92 0.800 -0.175 0.357
## x93 0.795 -0.191 0.238
## x94 0.822 -0.188 0.278
## x95 0.646 -0.305 0.143
Contribu<-round(CaliyPalmira.FMA$quanti.var$contrib[,c(1,2,3)],3);Contribu
## Dim.1 Dim.2 Dim.3
## x71 4.989 20.487 0.015
## x72 4.852 17.079 0.054
## x73 4.948 17.817 0.009
## x74 3.446 8.120 4.491
## x76 2.633 13.403 0.080
## x81 6.799 3.606 30.140
## x82 11.921 4.329 0.002
## x83 7.708 4.701 25.390
## x84 12.583 0.994 0.170
## x91 1.949 1.645 19.825
## x92 10.322 1.228 8.978
## x93 10.209 1.460 3.996
## x94 10.902 1.413 5.418
## x95 6.739 3.719 1.433
Tabla<-cbind(Coordenadas,Contribu);Tabla
## Dim.1 Dim.2 Dim.3 Dim.1 Dim.2 Dim.3
## x71 0.544 0.700 0.014 4.989 20.487 0.015
## x72 0.537 0.640 0.027 4.852 17.079 0.054
## x73 0.542 0.653 0.011 4.948 17.817 0.009
## x74 0.452 0.441 -0.247 3.446 8.120 4.491
## x76 0.395 0.567 -0.033 2.633 13.403 0.080
## x81 0.597 -0.276 -0.602 6.799 3.606 30.140
## x82 0.790 -0.303 0.004 11.921 4.329 0.002
## x83 0.636 -0.315 -0.553 7.708 4.701 25.390
## x84 0.812 -0.145 -0.045 12.583 0.994 0.170
## x91 0.347 -0.203 0.531 1.949 1.645 19.825
## x92 0.800 -0.175 0.357 10.322 1.228 8.978
## x93 0.795 -0.191 0.238 10.209 1.460 3.996
## x94 0.822 -0.188 0.278 10.902 1.413 5.418
## x95 0.646 -0.305 0.143 6.739 3.719 1.433
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(19:22,24,26:34)]
Coord1_severidad <-res.mfa_severidad$global.pca$var$coord[,1];Coord1_severidad
## x71 x72 x73 x74 x76 x81 x82 x83
## 0.5440844 0.5365410 0.5418145 0.4521674 0.3952785 0.5969804 0.7904771 0.6356579
## x84 x91 x92 x93 x94 x95
## 0.8121504 0.3474920 0.7997088 0.7953142 0.8218740 0.6461650
lp_severidad<-res.mfa_severidad$eig[1];lp_severidad #VALOR PROPIO
## [1] 1.920754
Vp_severidad<-Coord1_severidad/sqrt(lp_severidad);Vp_severidad #VECTOR PROPIO
## x71 x72 x73 x74 x76 x81 x82 x83
## 0.3925819 0.3871390 0.3909441 0.3262596 0.2852117 0.4307489 0.5703657 0.4586564
## x84 x91 x92 x93 x94 x95
## 0.5860039 0.2507315 0.5770267 0.5738559 0.5930200 0.4662378
Pesos_severidad<-(Vp_severidad/sum(Vp_severidad));Pesos_severidad # PESOS RELATIVOS DE LAS VARIABLES
## x71 x72 x73 x74 x76 x81 x82
## 0.06242574 0.06156025 0.06216531 0.05187961 0.04535244 0.06849479 0.09069571
## x83 x84 x91 x92 x93 x94 x95
## 0.07293246 0.09318240 0.03986963 0.09175491 0.09125070 0.09429805 0.07413800
sum(Pesos_severidad)
## [1] 1
data.frame(round(Pesos_severidad,3))
## round.Pesos_severidad..3.
## x71 0.062
## x72 0.062
## x73 0.062
## x74 0.052
## x76 0.045
## x81 0.068
## x82 0.091
## x83 0.073
## x84 0.093
## x91 0.040
## x92 0.092
## x93 0.091
## x94 0.094
## x95 0.074
res.mfa_severidad$eig
## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 1.92075448 41.445703 41.44570
## comp 2 0.77532670 16.729864 58.17557
## comp 3 0.44107863 9.517517 67.69308
## comp 4 0.24670485 5.323354 73.01644
## comp 5 0.22646400 4.886600 77.90304
## comp 6 0.19852715 4.283784 82.18682
## comp 7 0.16336268 3.525011 85.71183
## comp 8 0.15646009 3.376068 89.08790
## comp 9 0.11867588 2.560767 91.64867
## comp 10 0.10988535 2.371087 94.01976
## comp 11 0.08344743 1.800614 95.82037
## comp 12 0.07156271 1.544168 97.36454
## comp 13 0.06860665 1.480382 98.84492
## comp 14 0.05353089 1.155080 100.00000
Ind_severidad<-as.matrix(Severidad)%*%Pesos_severidad
Imin_severidad<-min(Ind_severidad);Imin_severidad
## [1] 0.9601304
Imax_severidad<-max(Ind_severidad);Imax_severidad
## [1] 6.709947
Ind_2_severidad<-round(((Ind_severidad-Imin_severidad)/(Imax_severidad-Imin_severidad))*100,2) #con este índice se hace el cluster
min(Ind_2_severidad)
## [1] 0
max(Ind_2_severidad)
## [1] 100
dim(Ind_2_severidad)
## [1] 1443 1
View(Ind_2_severidad)
C8<-cbind(Ind_2_severidad,CaliyPalmira)
summary(C8)
## Ind_2_severidad x11 x12 x21
## Min. : 0.00 Min. :0.000 Min. :0.000 Min. :1.000
## 1st Qu.: 40.24 1st Qu.:3.000 1st Qu.:6.000 1st Qu.:5.000
## Median : 52.45 Median :4.000 Median :7.000 Median :5.000
## Mean : 53.00 Mean :3.735 Mean :6.367 Mean :5.375
## 3rd Qu.: 65.36 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
K-means
set.seed(1234)
kmeans <- kmeans(C8$Ind_2_severidad, 3, iter.max = 1000, nstart = 10);kmeans
## K-means clustering with 3 clusters of sizes 362, 643, 438
##
## Cluster means:
## [,1]
## 1 76.82887
## 2 53.72342
## 3 32.23507
##
## Clustering vector:
## [1] 3 3 2 2 2 1 3 2 2 3 2 3 2 2 2 2 3 2 1 2 1 3 3 3 2 3 3 2 3 2 2 2 3 1 2 3 2
## [38] 1 1 2 1 3 2 2 2 3 3 3 3 2 3 3 3 1 2 2 3 2 3 3 1 3 3 3 2 3 2 2 1 1 2 3 2 3
## [75] 3 2 3 2 1 2 1 2 2 2 3 1 3 3 1 2 3 2 2 2 3 1 2 3 3 2 1 3 3 2 1 1 1 1 2 2 1
## [112] 1 2 1 3 3 3 2 3 2 1 1 2 2 3 2 1 2 1 3 1 2 1 3 2 3 3 1 2 3 1 2 2 2 3 2 2 3
## [149] 2 2 3 3 1 2 1 3 2 2 2 1 2 1 2 2 2 1 2 3 1 3 1 1 2 3 2 2 3 2 2 2 1 3 1 3 1
## [186] 2 3 2 2 2 2 2 3 3 1 3 2 2 3 2 3 2 2 1 2 1 2 2 3 1 1 3 2 2 2 2 2 1 3 1 2 2
## [223] 2 2 2 3 2 3 3 2 1 2 2 2 2 3 3 2 3 3 2 2 2 3 3 3 2 1 3 1 3 3 2 2 2 3 2 3 1
## [260] 2 1 2 2 3 3 2 3 2 1 3 1 3 3 3 3 2 2 1 1 2 2 2 1 2 3 2 1 3 2 2 2 2 3 2 3 3
## [297] 3 2 2 1 3 2 3 3 1 2 3 2 2 1 2 1 3 3 2 2 2 3 1 3 2 2 3 3 2 1 1 3 2 3 2 2 1
## [334] 1 2 3 2 1 3 3 3 2 2 3 2 2 3 2 2 1 1 2 3 2 2 3 1 3 3 1 2 1 3 3 2 3 1 3 1 3
## [371] 2 2 1 2 1 2 1 3 3 2 2 1 3 1 1 2 1 3 3 2 3 3 2 3 2 2 3 2 3 3 2 3 3 3 2 3 3
## [408] 3 1 2 1 2 3 2 1 1 2 2 3 2 2 3 1 1 1 2 1 2 2 2 2 1 3 2 1 1 2 3 2 3 3 3 2 1
## [445] 3 3 1 2 1 1 2 1 1 2 2 2 2 1 2 1 3 2 2 3 2 2 2 2 1 2 1 2 3 1 3 2 1 2 3 2 3
## [482] 1 2 2 2 3 1 1 1 3 2 2 3 3 1 2 2 2 2 1 3 1 2 3 2 2 2 3 3 3 3 2 2 2 3 3 1 2
## [519] 3 3 3 1 3 3 2 2 1 3 3 2 2 3 2 2 2 3 3 3 2 3 3 2 1 2 1 2 2 2 2 3 2 1 2 3 3
## [556] 1 3 3 2 3 3 3 2 3 2 3 2 3 2 3 2 1 2 2 3 2 2 2 2 2 2 2 1 3 3 3 3 2 3 2 2 3
## [593] 2 2 2 2 2 3 2 2 2 2 2 3 3 3 2 3 2 1 3 2 2 1 2 1 3 1 2 3 1 1 3 2 2 2 1 1 3
## [630] 3 1 2 3 1 2 2 3 1 3 3 3 3 2 2 3 2 2 2 3 1 1 1 2 2 2 1 2 1 2 2 1 3 1 2 1 2
## [667] 3 2 3 2 1 2 2 2 1 2 2 3 1 2 2 2 2 2 2 3 3 2 3 2 3 2 2 2 1 2 1 2 1 3 3 2 2
## [704] 2 1 1 2 2 3 2 2 1 3 3 2 2 3 2 2 2 2 3 2 2 2 1 3 3 2 3 3 2 3 2 2 3 3 2 2 2
## [741] 2 1 1 1 2 1 2 1 2 2 1 2 1 1 2 2 1 2 2 2 2 2 3 3 2 3 2 3 2 3 2 1 1 1 3 2 3
## [778] 2 2 1 1 2 2 2 1 3 2 2 3 1 2 2 2 1 3 1 3 2 3 3 2 1 3 3 2 2 1 1 3 3 3 2 1 1
## [815] 2 2 1 2 2 2 3 2 3 3 2 2 2 2 1 2 3 2 1 2 1 1 3 1 2 2 2 3 1 2 2 2 2 2 2 1 2
## [852] 2 3 1 1 2 3 3 2 3 1 3 2 1 1 3 3 1 3 3 2 2 2 3 1 1 3 1 3 2 2 1 2 3 2 2 2 1
## [889] 2 3 3 3 2 1 2 3 2 3 3 2 2 3 3 3 1 1 3 1 3 2 2 2 3 2 2 1 2 1 2 1 2 2 3 2 2
## [926] 1 2 2 3 1 2 3 2 1 2 3 1 2 3 1 3 3 1 1 1 2 2 3 3 1 1 1 1 2 2 1 3 1 3 2 1 2
## [963] 3 3 3 3 2 2 2 1 1 2 2 1 1 3 1 2 1 2 2 3 2 3 3 3 2 2 1 1 1 2 3 3 2 3 2 1 2
## [1000] 2 3 3 1 1 1 2 1 2 2 2 3 2 3 1 1 2 3 3 1 1 3 1 2 1 2 3 3 1 1 3 2 1 1 3 1 2
## [1037] 3 3 2 3 3 1 2 1 1 1 1 2 3 1 2 2 2 2 1 2 1 2 2 2 2 3 2 3 1 3 1 1 2 2 1 3 3
## [1074] 3 2 3 2 1 2 3 1 2 2 2 1 1 2 2 3 3 2 2 1 3 1 3 3 3 3 1 3 3 1 3 2 3 2 1 2 1
## [1111] 2 2 2 2 1 2 1 3 3 2 2 2 3 1 2 1 2 2 2 2 3 2 2 2 1 2 1 1 2 2 3 3 3 2 2 1 3
## [1148] 2 2 1 3 2 2 1 2 3 3 2 2 3 2 1 2 2 3 3 2 1 1 3 3 3 2 3 2 3 2 1 2 2 3 2 3 3
## [1185] 3 3 1 3 2 2 1 2 3 3 2 2 2 2 2 2 1 2 3 1 1 3 3 1 1 3 2 3 2 2 1 3 1 1 3 1 3
## [1222] 3 3 1 1 1 1 2 2 2 2 1 2 1 2 2 2 2 3 2 2 1 2 1 3 1 1 1 2 2 1 2 1 2 1 2 3 2
## [1259] 2 2 3 1 1 1 1 1 3 2 2 2 3 2 2 2 3 2 1 3 1 3 3 3 1 1 3 1 2 3 1 2 1 1 2 1 3
## [1296] 2 1 2 3 2 2 3 3 3 2 1 1 2 1 2 1 2 2 2 2 1 2 1 3 3 1 3 1 2 1 2 2 1 1 1 1 3
## [1333] 1 1 1 1 2 1 3 1 2 2 2 1 2 3 1 2 1 3 1 2 3 2 3 3 1 1 3 2 1 3 1 2 2 3 3 1 2
## [1370] 2 3 3 2 2 3 2 2 2 2 3 1 1 1 1 3 2 1 3 2 3 2 2 2 2 2 3 2 2 1 3 1 3 3 2 3 1
## [1407] 2 2 3 2 2 2 2 2 3 1 1 2 2 3 1 2 2 2 2 2 1 1 2 2 1 2 1 2 2 1 2 3 2 2 2 2 2
##
## Within cluster sum of squares by cluster:
## [1] 28951.78 24547.84 33413.90
## (between_SS / total_SS = 82.0 %)
##
## 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_severidad x11 x12 x21
## Min. : 0.00 Min. :0.000 Min. :0.000 Min. :1.000
## 1st Qu.: 40.24 1st Qu.:3.000 1st Qu.:6.000 1st Qu.:5.000
## Median : 52.45 Median :4.000 Median :7.000 Median :5.000
## Mean : 53.00 Mean :3.735 Mean :6.367 Mean :5.375
## 3rd Qu.: 65.36 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 :2.053
## 3rd Qu.:3.000
## Max. :3.000
kmeans$centers
## [,1]
## 1 76.82887
## 2 53.72342
## 3 32.23507
sum(kmeans$cluster==1)/1443#bajo
## [1] 0.2508663
sum(kmeans$cluster==2)/1443#alto
## [1] 0.4455994
sum(kmeans$cluster==3)/1443#medio
## [1] 0.3035343
tapply(Ind_2_severidad,kmeans$cluster,summary)
## $`1`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 65.35 69.53 74.64 76.83 81.84 100.00
##
## $`2`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 43.04 48.42 53.40 53.72 59.05 65.16
##
## $`3`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 27.94 34.34 32.24 38.73 42.90