Resultados
Laura Lucía Domínguez
2022-06-20
Todas las variables CALI Y PALMIRA
#factominer
library(bootstrap)
library(factoextra)## Loading required package: ggplot2## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(FactoMineR)
library(haven)
library(ade4)##
## Attaching package: 'ade4'## The following object is masked from 'package:FactoMineR':
##
## reconstlibrary(xtable)
library(readr)
library(data.table)
#library(ff)
#library(ffbase)
library(bigmemory)
library(foreach)
library(doParallel)## Loading required package: iterators## Loading required package: parallellibrary(biglm)## Loading required package: DBIlibrary(Factoshiny)## Loading required package: shiny## Loading required package: FactoInvestigatelibrary(readxl)
CaliyPalmira<-read_excel("C:/LAURA LUCIA/U/9/Tesis/MARZO/CaliyPalmira-TAINA.xlsx")
names(CaliyPalmira)## [1] "Total_act_sociales" "Total_lug_act_sociales" "conoce_enf"
## [4] "p26" "S_sintomas" "conoce_preven"
## [7] "S_prevención" "creenvirus" "contac_covid"
## [10] "dx_covid" "conf_presi" "conf_alcaldia"
## [13] "conf_gobern" "conf_mensgobierno" "p40"
## [16] "p42" "medios" "conf_mediocomu"
## [19] "p46" "p47" "p48"
## [22] "p49" "p50_1" "p50_2"
## [25] "p50_3" "p51" "p52"
## [28] "p53" "p54" "p55"
## [31] "p56" "p57" "p58"
## [34] "p60" "cumple_lavamanos" "cumple_tapaboca"
## [37] "cumple_distancia" "cumple_desinfecmano" "Total_tapaboca"
## [40] "Total_distancia" "ID" "Municipio"CaliyPalmira$creenvirus<-as.factor(CaliyPalmira$creenvirus)
CaliyPalmira$contac_covid<-as.factor(CaliyPalmira$contac_covid)
CaliyPalmira$dx_covid<-as.factor(CaliyPalmira$dx_covid)names(CaliyPalmira)<-c(
#1.voluntariedad
"x11",
"x12",
#2.conocimiento
"x21",
"x22", #p26
"x23",
"x24",
"x25", #p30
#3.incertidumbre
"x31", #p33
"x32", #p35
"x33", #p36
#4.gubernamental
"x41",
"x42",
"x43",
"x44",#"recomen_efectiva",
#5.salud
"x51",
#"p41", #factor
"x52",
#6.medios de comunicación
"x61", #total_medios_comu
"x62", #p43
#"mensaje", #categórica p72
#7.probabilidad de contagio
"x71",
"x72",
"x73",
"x74",
"x75",
"x76",
"x77",
#8.severidad
"x81",
"x82",
"x83",
"x84",
#9.susceptibilidad
"x91",
"x92",
"x93",
"x94",
#"p59_1", #factor
#"p59_2", #factor
#"p59_3", #factor
#"p59_4", #factor
"x95",
#10.cumplimiento
"x101", #p61
"x102",
"x103",
"x104",
"x105", #p76
"x106",
#otras
"id",
"Municipio"
)#recodificar la voluntariedad
library(car)## Loading required package: carDatasummary(CaliyPalmira$x11) #de 0 a 5## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 1.000 1.265 2.000 5.000CaliyPalmira$x11 <- recode(CaliyPalmira$x11,"5=0; 4=1; 3=2; 2=3; 1=4; 0=5")
summary(CaliyPalmira$x12) #de 0 a 8## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 1.000 1.633 2.000 8.000CaliyPalmira$x12 <- recode(CaliyPalmira$x12,"8=0; 7=1; 6=2; 5=3; 4=4; 3=5; 2=6; 1=7; 0=8")#x75 [23]
x75N<-as.factor(CaliyPalmira$x75)
summary(x75N)## 1 2 3 4 5
## 288 82 104 245 724#x77 [25]
x77N<-as.factor(CaliyPalmira$x77)
summary(x77N)## 1 2 3 4 5
## 43 42 151 269 938AFM CON TODAS LAS VARIABLES
##Análisis factorial múltiple
CaliyPalmira.FMA<-MFA(CaliyPalmira[,c(19:34)],
group=c(#2,
#5,
#3,
#4,
#2,
#2, #3
7,
4,
5
#6
),
type=c(#'s',
#'s',
#'n',
#'s', #n
#'s',
#'s', #n
's',
's',
's'#,
#'s'
),
name.group=c(#"Voluntariedad",
#"Conocimiento",
#"Incertidumbre",
#"Confianza gubernamental",
#"Confianza sector salud",
#"Confianza medios",
"Probabilidad de contagio",
"Severidad",
"Susceptibilidad"), #,
#"Cumplimiento"),
#num.group.sup=c(3),
graph=FALSE)
CaliyPalmira.FMA$eig## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 1.95371969 37.354849 37.35485
## comp 2 0.76278418 14.584328 51.93918
## comp 3 0.46061251 8.806847 60.74602
## comp 4 0.31307706 5.985990 66.73201
## comp 5 0.27980231 5.349781 72.08179
## comp 6 0.24472973 4.679198 76.76099
## comp 7 0.21919631 4.191003 80.95200
## comp 8 0.19355882 3.700818 84.65281
## comp 9 0.16004627 3.060062 87.71288
## comp 10 0.14217163 2.718302 90.43118
## comp 11 0.11679648 2.233132 92.66431
## comp 12 0.10867422 2.077836 94.74215
## comp 13 0.08312730 1.589382 96.33153
## comp 14 0.07144414 1.366002 97.69753
## comp 15 0.06704119 1.281818 98.97935
## comp 16 0.05338169 1.020651 100.00000CaliyPalmira.FMA$group$contrib## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## Probabilidad de contagio 22.07821 77.726865 15.65889 91.047532 84.042533
## Severidad 38.70329 13.287416 45.28145 6.939909 9.329046
## Susceptibilidad 39.21850 8.985719 39.05966 2.012559 6.628421CaliyPalmira.FMA$group$correlation[,1:3]## Dim.1 Dim.2 Dim.3
## Probabilidad de contagio 0.6636843 0.7801080 0.4438594
## Severidad 0.8746823 0.3278311 0.5848514
## Susceptibilidad 0.8775728 0.2757276 0.4690437Coordenadas<-round(CaliyPalmira.FMA$quanti.var$coord[,c(1,2,3)],3);Coordenadas## Dim.1 Dim.2 Dim.3
## x71 0.534 0.705 -0.037
## x72 0.529 0.644 -0.040
## x73 0.531 0.659 -0.043
## x74 0.451 0.444 0.230
## x75 0.178 0.067 0.180
## x76 0.396 0.570 0.063
## x77 0.354 -0.037 0.365
## x81 0.608 -0.274 0.570
## x82 0.792 -0.295 -0.026
## x83 0.641 -0.310 0.494
## x84 0.810 -0.135 0.003
## x91 0.342 -0.197 -0.528
## x92 0.795 -0.165 -0.369
## x93 0.793 -0.182 -0.252
## x94 0.818 -0.177 -0.297
## x95 0.652 -0.300 -0.117Contribu<-round(CaliyPalmira.FMA$quanti.var$contrib[,c(1,2,3)],3);Contribu## Dim.1 Dim.2 Dim.3
## x71 4.620 20.610 0.094
## x72 4.527 17.215 0.109
## x73 4.562 18.004 0.128
## x74 3.287 8.178 3.647
## x75 0.516 0.187 2.237
## x76 2.533 13.476 0.271
## x77 2.034 0.057 9.172
## x81 6.924 3.594 25.829
## x82 11.764 4.187 0.055
## x83 7.713 4.627 19.396
## x84 12.301 0.880 0.001
## x91 1.851 1.583 18.745
## x92 10.029 1.113 9.175
## x93 9.969 1.351 4.290
## x94 10.615 1.276 5.929
## x95 6.755 3.664 0.920Tabla<-cbind(Coordenadas,Contribu);Tabla## Dim.1 Dim.2 Dim.3 Dim.1 Dim.2 Dim.3
## x71 0.534 0.705 -0.037 4.620 20.610 0.094
## x72 0.529 0.644 -0.040 4.527 17.215 0.109
## x73 0.531 0.659 -0.043 4.562 18.004 0.128
## x74 0.451 0.444 0.230 3.287 8.178 3.647
## x75 0.178 0.067 0.180 0.516 0.187 2.237
## x76 0.396 0.570 0.063 2.533 13.476 0.271
## x77 0.354 -0.037 0.365 2.034 0.057 9.172
## x81 0.608 -0.274 0.570 6.924 3.594 25.829
## x82 0.792 -0.295 -0.026 11.764 4.187 0.055
## x83 0.641 -0.310 0.494 7.713 4.627 19.396
## x84 0.810 -0.135 0.003 12.301 0.880 0.001
## x91 0.342 -0.197 -0.528 1.851 1.583 18.745
## x92 0.795 -0.165 -0.369 10.029 1.113 9.175
## x93 0.793 -0.182 -0.252 9.969 1.351 4.290
## x94 0.818 -0.177 -0.297 10.615 1.276 5.929
## x95 0.652 -0.300 -0.117 6.755 3.664 0.920plot.MFA(CaliyPalmira.FMA, choix="group",title="Representación de grupos")
#plot.MFA(CaliyPalmira.FMA, choix="ind",lab.par=FALSE)
library(ggrepel)
options(ggrepel.max.overlaps = Inf)
#dim 1-2
plot.MFA(CaliyPalmira.FMA, choix="var",habillage='group',title="Círculo de correlación", repel = TRUE)
IPRG
#--------------------------ÍNDICE DE PERCEPCIÓN GLOBAL-----------------------------------------------#####
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Severidad=CaliyPalmira[,c(19:34)]
Coord1_severidad <-res.mfa_severidad$global.pca$var$coord[,1];Coord1_severidad## x71 x72 x73 x74 x75 x76 x77 x81
## 0.5341653 0.5287287 0.5308074 0.4505285 0.1784305 0.3955389 0.3544335 0.6076142
## x82 x83 x84 x91 x92 x93 x94 x95
## 0.7919927 0.6412808 0.8098680 0.3415608 0.7949994 0.7926213 0.8178959 0.6524851lp_severidad<-res.mfa_severidad$eig[1];lp_severidad #VALOR PROPIO## [1] 1.95372Vp_severidad<-Coord1_severidad/sqrt(lp_severidad);Vp_severidad #VECTOR PROPIO## x71 x72 x73 x74 x75 x76 x77 x81
## 0.3821594 0.3782699 0.3797571 0.3223229 0.1276550 0.2829815 0.2535734 0.4347071
## x82 x83 x84 x91 x92 x93 x94 x95
## 0.5666176 0.4587934 0.5794061 0.2443638 0.5687687 0.5670673 0.5851496 0.4668093Pesos_severidad<-(Vp_severidad/sum(Vp_severidad));Pesos_severidad # PESOS RELATIVOS DE LAS VARIABLES## x71 x72 x73 x74 x75 x76 x77
## 0.05791696 0.05732750 0.05755288 0.04884863 0.01934636 0.04288637 0.03842951
## x81 x82 x83 x84 x91 x92 x93
## 0.06588067 0.08587194 0.06953098 0.08781007 0.03703379 0.08619794 0.08594010
## x94 x95
## 0.08868050 0.07074581sum(Pesos_severidad)## [1] 1data.frame(round(Pesos_severidad,3))## round.Pesos_severidad..3.
## x71 0.058
## x72 0.057
## x73 0.058
## x74 0.049
## x75 0.019
## x76 0.043
## x77 0.038
## x81 0.066
## x82 0.086
## x83 0.070
## x84 0.088
## x91 0.037
## x92 0.086
## x93 0.086
## x94 0.089
## x95 0.071res.mfa_severidad$eig## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 1.95371969 37.354849 37.35485
## comp 2 0.76278418 14.584328 51.93918
## comp 3 0.46061251 8.806847 60.74602
## comp 4 0.31307706 5.985990 66.73201
## comp 5 0.27980231 5.349781 72.08179
## comp 6 0.24472973 4.679198 76.76099
## comp 7 0.21919631 4.191003 80.95200
## comp 8 0.19355882 3.700818 84.65281
## comp 9 0.16004627 3.060062 87.71288
## comp 10 0.14217163 2.718302 90.43118
## comp 11 0.11679648 2.233132 92.66431
## comp 12 0.10867422 2.077836 94.74215
## comp 13 0.08312730 1.589382 96.33153
## comp 14 0.07144414 1.366002 97.69753
## comp 15 0.06704119 1.281818 98.97935
## comp 16 0.05338169 1.020651 100.00000Ind_severidad<-as.matrix(Severidad)%*%Pesos_severidad
Imin_severidad<-min(Ind_severidad);Imin_severidad## [1] 0.9629662Imax_severidad<-max(Ind_severidad);Imax_severidad## [1] 6.613507Ind_2_severidad<-round(((Ind_severidad-Imin_severidad)/(Imax_severidad-Imin_severidad))*100,2) #con este índice se hace el cluster
min(Ind_2_severidad)## [1] 0max(Ind_2_severidad)## [1] 100C8<-cbind(Ind_2_severidad,CaliyPalmira)
summary(C8$Ind_2_severidad);sd(C8$Ind_2_severidad)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 41.24 53.47 54.10 66.20 100.00## [1] 17.83391K-means
set.seed(1234)
kmeans <- kmeans(C8$Ind_2_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. :3.000kmeans$centers## [,1]
## 1 54.69625
## 2 33.72363
## 3 77.28025sum(kmeans$cluster==1)/1443#bajo## [1] 0.4476784sum(kmeans$cluster==2)/1443#alto## [1] 0.3000693sum(kmeans$cluster==3)/1443#medio## [1] 0.2522523tapply(Ind_2_severidad,kmeans$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
##
## $`3`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 66.05 70.30 75.33 77.28 82.27 100.00ECM
#Nfinal<-c()
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
N1=CaliyPalmira[,c(19:34)]
Severidad=N1[sample(nrow(N1),size = 1155,replace = TRUE),]
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Coord1_severidad <-res.mfa_severidad$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(Ind_2_severidad)
}; NfinalMedia## [1] 53.75 54.54 52.41 54.17 52.33 53.60 53.73 53.31 53.08 53.75 54.14 52.99
## [13] 53.82 54.18 53.16 53.16 53.25 54.84 53.70 54.29 53.62 53.53 54.25 53.32
## [25] 52.06 52.42 53.54 54.55 52.76 53.85 52.14 54.18 51.73 53.91 52.93 53.83
## [37] 54.68 54.52 54.44 52.99 54.06 53.62 52.95 53.70 54.61 52.96 53.53 53.08
## [49] 52.74 53.18 53.11 53.97 51.53 50.06 52.56 53.08 52.76 53.04 53.81 53.81
## [61] 50.86 54.44 53.06 52.05 51.58 52.03 51.28 53.06 53.32 51.06 54.56 53.70
## [73] 53.20 50.97 53.48 53.29 52.57 54.29 52.03 53.81 53.16 52.83 54.15 50.58
## [85] 52.18 55.21 50.96 54.71 53.67 53.49 52.67 51.93 53.70 53.40 54.15 54.61
## [97] 52.63 54.17 54.50 53.67 52.69 53.18 53.32 53.16 54.76 53.52 53.46 52.02
## [109] 53.46 52.84 52.67 52.10 52.98 54.12 54.80 53.32 53.39 52.47 53.75 54.18
## [121] 54.31 53.49 54.42 53.46 52.40 53.55 54.18 51.28 53.92 52.94 53.18 54.29
## [133] 53.70 52.92 54.32 54.48 53.62 52.46 54.92 54.18 53.27 53.18 54.44 54.14
## [145] 52.83 52.59 52.93 53.11 51.49 52.05 53.20 52.61 53.49 53.46 54.64 54.17
## [157] 53.51 53.67 54.56 53.47 53.48 52.95 52.83 52.94 54.31 53.53 52.71 53.67
## [169] 53.40 53.75 54.47 54.61 50.97 51.32 53.94 52.76 54.31 53.67 51.79 53.20
## [181] 53.31 53.32 53.21 54.45 53.53 54.41 52.63 53.12 53.39 54.29 53.60 50.73
## [193] 53.49 52.63 54.44 54.02 54.02 53.08 53.48 53.92 53.31 54.06 52.99 54.06
## [205] 53.60 50.46 54.47 52.41 53.67 52.18 54.32 53.06 53.40 54.56 54.18 54.54
## [217] 53.67 53.31 52.86 53.31 52.84 52.76 53.49 52.59 52.94 53.49 53.04 53.76
## [229] 53.08 53.52 53.21 52.63 50.46 52.76 54.17 53.73 51.72 51.51 51.12 54.29
## [241] 54.41 53.76 53.81 53.46 54.17 53.49 54.41 53.83 53.85 53.49 52.57 54.29
## [253] 52.95 52.91 53.76 51.59 53.32 53.46 52.31 51.19 55.86 52.35 52.18 53.79
## [265] 52.95 53.52 54.10 54.02 52.86 51.16 52.83 51.42 52.95 53.20 53.51 53.60
## [277] 54.47 54.95 53.48 52.94 54.47 53.32 53.49 54.32 53.47 53.32 53.75 52.84
## [289] 52.92 53.70 52.76 54.42 53.11 52.91 52.86 53.06 53.65 54.25 53.85 49.04
## [301] 52.82 54.31 53.92 53.75 53.40 52.69 53.70 53.54 53.40 53.39 52.69 52.83
## [313] 55.02 52.84 53.53 54.61 54.18 51.98 52.63 53.49 52.09 54.18 53.27 53.48
## [325] 53.66 53.67 53.47 52.65 53.19 52.47 53.75 53.28 51.93 54.95 53.06 53.18
## [337] 53.47 53.39 53.62 53.95 51.10 53.08 53.31 54.29 52.46 52.37 54.81 53.47
## [349] 53.67 54.48 55.27 52.94 53.40 53.27 54.15 52.58 53.32 52.76 54.29 53.77
## [361] 51.99 54.44 53.60 53.31 51.71 53.76 53.11 53.70 53.13 51.30 52.63 53.91
## [373] 53.70 53.32 52.26 53.47 53.67 54.06 53.13 53.05 54.18 53.48 54.31 51.96
## [385] 51.47 54.31 53.08 53.75 53.66 51.77 53.52 53.52 52.54 51.28 54.44 53.46
## [397] 53.83 51.61 53.53 53.06 52.92 53.39 53.54 53.70 52.61 53.82 53.76 53.70
## [409] 52.19 54.85 53.60 53.83 51.31 53.06 53.70 50.74 53.53 54.15 54.15 53.48
## [421] 52.98 53.82 50.41 53.20 53.91 54.14 53.40 52.13 52.00 52.57 53.08 54.06
## [433] 53.47 54.14 53.47 51.44 53.32 53.24 50.47 54.45 54.32 53.47 54.76 51.11
## [445] 53.62 53.27 53.62 53.56 53.46 52.00 53.07 53.46 52.83 52.53 53.32 53.86
## [457] 53.47 52.09 53.92 54.47 53.29 52.37 53.92 52.47 53.40 54.45 52.45 53.73
## [469] 54.65 53.83 52.94 52.65 52.67 53.85 53.21 53.67 53.83 53.81 53.91 53.39
## [481] 53.40 53.11 52.76 53.40 53.16 51.10 53.60 53.52 53.48 51.00 52.92 53.21
## [493] 53.62 49.77 53.66 53.82 53.54 53.73 52.63 53.48 53.08 53.82 54.18 53.53
## [505] 52.84 53.73 53.46 52.84 52.18 54.15 54.76 53.88 53.53 53.62 51.58 53.49
## [517] 52.84 55.31 52.53 53.70 54.32 54.15 53.67 52.02 53.62 53.75 53.54 53.75
## [529] 53.43 53.52 52.92 53.75 53.16 54.02 53.27 54.02 54.02 52.77 52.58 53.62
## [541] 53.49 53.54 54.06 53.20 53.89 52.86 53.81 53.56 52.99 53.67 53.27 52.47
## [553] 52.67 52.86 53.40 52.83 54.71 52.02 53.46 53.43 52.28 54.17 52.22 53.53
## [565] 52.63 54.18 53.40 53.85 53.52 52.90 53.40 52.35 52.67 53.06 54.61 51.83
## [577] 51.85 53.62 53.27 53.70 51.53 53.54 51.75 53.27 53.30 51.98 53.39 52.26
## [589] 55.04 52.63 53.21 53.62 53.83 53.55 53.92 53.20 53.40 54.18 52.26 54.00
## [601] 53.49 53.40 52.05 53.11 53.49 50.60 54.54 52.69 53.49 54.06 53.62 54.15
## [613] 53.40 53.49 52.10 53.18 53.95 53.20 53.53 53.40 54.76 52.71 53.47 52.97
## [625] 54.80 52.35 53.41 53.11 53.40 53.70 53.40 54.68 53.11 47.74 53.27 53.20
## [637] 53.94 54.18 53.46 53.11 53.53 53.49 53.39 53.12 53.46 53.48 52.59 52.77
## [649] 53.91 53.97 52.76 53.49 53.60 51.10 53.53 53.46 53.06 54.76 53.32 54.29
## [661] 53.49 53.83 53.48 54.32 53.62 54.14 52.65 53.83 53.79 51.59 51.09 54.53
## [673] 50.70 53.49 53.29 52.63 55.03 53.73 53.43 53.76 52.83 52.14 53.75 53.01
## [685] 54.41 51.98 54.65 53.11 53.62 50.81 53.48 54.41 50.98 52.83 52.67 53.62
## [697] 54.18 53.31 54.06 52.28 53.85 54.15 53.53 53.92 53.75 53.40 54.29 54.80
## [709] 52.30 52.69 53.67 53.83 51.00 52.57 53.87 52.02 53.49 53.51 53.47 53.05
## [721] 53.67 54.26 54.48 54.29 53.62 53.54 53.92 53.60 52.27 54.06 52.71 53.91
## [733] 54.31 52.63 51.80 53.32 54.44 52.95 49.48 54.31 53.46 53.62 52.56 53.49
## [745] 54.97 50.76 51.83 51.93 53.18 53.87 52.91 54.45 50.59 51.06 53.53 53.62
## [757] 52.71 54.41 53.46 52.59 52.46 54.47 53.49 53.49 52.47 53.21 52.86 53.49
## [769] 53.85 51.62 54.17 51.98 51.15 51.59 54.18 52.35 53.70 53.67 52.63 53.60
## [781] 54.18 54.54 54.29 53.27 53.55 54.17 53.52 52.67 54.06 54.70 52.47 54.45
## [793] 54.71 54.18 52.59 52.82 53.73 52.76 53.52 53.66 54.50 53.67 54.15 53.18
## [805] 54.06 53.27 53.31 53.85 51.66 52.53 54.14 53.70 50.49 54.06 53.47 53.49
## [817] 55.73 53.62 53.81 52.30 49.65 54.81 51.43 53.49 53.48 53.91 53.67 52.89
## [829] 53.49 51.20 51.82 53.73 54.29 54.66 54.47 53.08 53.27 52.63 52.93 54.14
## [841] 53.46 52.61 53.73 52.67 51.51 53.31 52.69 52.46 53.21 53.97 53.10 54.56
## [853] 53.20 52.61 51.14 53.52 53.11 53.85 53.40 53.46 52.71 54.39 54.54 52.48
## [865] 53.51 53.21 54.18 53.82 54.52 52.94 54.31 53.82 53.76 54.14 54.06 54.06
## [877] 53.89 52.61 53.75 53.82 53.04 52.67 54.61 52.86 53.04 53.40 53.48 52.83
## [889] 52.59 53.73 52.76 54.18 52.61 51.77 54.91 54.29 53.53 53.40 52.42 54.80
## [901] 52.94 53.83 53.46 52.98 53.53 53.49 54.55 54.18 53.67 53.32 52.95 51.81
## [913] 53.21 53.70 52.18 52.94 53.47 53.85 54.26 53.41 53.91 52.61 51.73 52.86
## [925] 52.94 53.18 53.49 53.40 53.11 54.97 53.18 52.92 52.69 53.32 53.54 52.94
## [937] 53.46 54.05 54.67 53.06 53.21 53.20 54.18 53.48 54.95 53.20 53.18 53.49
## [949] 54.50 53.16 53.46 52.53 53.11 53.56 52.92 53.27 53.21 53.46 54.12 52.92
## [961] 53.75 51.33 53.08 53.91 54.95 52.63 53.04 53.55 54.44 52.26 53.18 52.76
## [973] 52.01 54.50 52.18 54.41 51.95 53.66 53.11 52.71 53.16 54.32 54.15 51.53
## [985] 53.47 54.39 53.70 54.76 50.61 52.10 51.98 53.52 52.30 54.71 53.91 53.20
## [997] 54.17 54.54 52.47 52.37x11();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## [1] 1.05Índices por separado
Probabilidad de contagio
#-----------------------------------probabilidad de contagio-----------------------------------
Proba<-CaliyPalmira.FMA$separate.analyses$`Probabilidad de contagio`$ind$coord[,1]
Imin_Proba<-min(Proba);Imin_Proba## [1] -5.561753Imax_Proba<-max(Proba);Imax_Proba## [1] 3.500155Ind_2_Proba<-round(((Proba-Imin_Proba)/(Imax_Proba-Imin_Proba))*100,2) #con este índice se hace el cluster
min(Ind_2_Proba)## [1] 0max(Ind_2_Proba)## [1] 100C8<-cbind(Ind_2_Proba,CaliyPalmira)
summary(C8$Ind_2_Proba);sd(C8$Ind_2_Proba)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 48.23 60.96 61.38 74.47 100.00## [1] 19.62697Severidad
#-----------------------------------severidad-----------------------------------
Sev<-CaliyPalmira.FMA$separate.analyses$Severidad$ind$coord[,1]
Imin_Sev<-min(Sev);Imin_Sev## [1] -4.142719Imax_Sev<-max(Sev);Imax_Sev## [1] 3.366186Ind_2_Sev<-round(((Sev-Imin_Sev)/(Imax_Sev-Imin_Sev))*100,2) #con este índice se hace el cluster
min(Ind_2_Sev)## [1] 0max(Ind_2_Sev)## [1] 100sd(Ind_2_Sev)## [1] 22.00786#rbind(summary(Ind_2_Sev))
#print(xtable(rbind(summary(Ind_2_Sev))), include.rownames = FALSE)
C8<-cbind(Ind_2_Sev,CaliyPalmira)
summary(C8$Ind_2_Sev)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 41.66 54.28 55.17 70.83 100.00Susceptibilidad
#-----------------------------------susceptibilidad-----------------------------------
SU<-CaliyPalmira.FMA$separate.analyses$Susceptibilidad$ind$coord[,1]
Imin_SU<-min(SU);Imin_SU## [1] -3.522199Imax_SU<-max(SU);Imax_SU## [1] 6.746319Ind_2_SU<-round(((SU-Imin_SU)/(Imax_SU-Imin_SU))*100,2) #con este índice se hace el cluster
min(Ind_2_SU)## [1] 0max(Ind_2_SU)## [1] 100sd(Ind_2_SU)## [1] 17.49717#rbind(summary(Ind_2_SU))
#print(xtable(rbind(summary(Ind_2_SU))), include.rownames = FALSE)
#-----------------------------------k-mean--------------------------------####
C8<-cbind(Ind_2_SU,CaliyPalmira)
summary(C8$Ind_2_SU)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 21.91 34.02 34.30 45.38 100.00CALI
#---------------------Índice de percepción por ciudad---------------------------####
library(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:car':
##
## recode## The following objects are masked from 'package:data.table':
##
## between, first, last## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionCaliyPalmira$Municipio<-as.factor(CaliyPalmira$Municipio)
Cali<-filter(CaliyPalmira, CaliyPalmira$Municipio=="Cali")
summary(Cali)## x11 x12 x21 x22
## Min. :0.000 Min. :2.000 Min. :1.000 Min. :2.000
## 1st Qu.:3.000 1st Qu.:6.000 1st Qu.:5.000 1st Qu.:3.000
## Median :4.000 Median :7.000 Median :5.000 Median :4.000
## Mean :3.565 Mean :6.294 Mean :5.386 Mean :3.656
## 3rd Qu.:4.000 3rd Qu.:7.000 3rd Qu.:6.000 3rd Qu.:4.000
## Max. :5.000 Max. :8.000 Max. :7.000 Max. :4.000
## x23 x24 x25 x31 x32
## Min. :0.000 Min. :1.000 Min. :3.000 No : 5 No :386
## 1st Qu.:7.000 1st Qu.:5.000 1st Qu.:6.000 No sabe: 9 No sabe: 61
## Median :8.000 Median :6.000 Median :7.000 Si :783 Si :350
## Mean :7.329 Mean :5.864 Mean :6.504
## 3rd Qu.:8.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :8.000 Max. :7.000 Max. :7.000
## x33 x41 x42 x43 x44
## 0: 4 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1:720 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:2.000
## 2: 73 Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.839 Mean :3.704 Mean :3.991 Mean :3.512
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x51 x52 x61 x62
## Min. :1.000 Min. :1.000 Min. :0.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:5.000 1st Qu.:1.000 1st Qu.:2.000
## Median :5.000 Median :6.000 Median :2.000 Median :4.000
## Mean :4.731 Mean :5.528 Mean :2.221 Mean :3.478
## 3rd Qu.:6.000 3rd Qu.:7.000 3rd Qu.:3.000 3rd Qu.:5.000
## Max. :7.000 Max. :7.000 Max. :6.000 Max. :7.000
## x71 x72 x73 x74
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:5.000
## Median :4.000 Median :5.000 Median :4.000 Median :6.000
## Mean :4.289 Mean :4.575 Mean :4.088 Mean :5.428
## 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x75 x76 x77 x81
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000
## Median :3.000 Median :3.000 Median :5.000 Median :5.000
## Mean :3.118 Mean :3.287 Mean :4.438 Mean :4.881
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :7.000
## x82 x83 x84 x91
## Min. :1.000 Min. :1.000 Min. :1.000 Min. : 0.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.: 0.000
## Median :4.000 Median :4.000 Median :4.000 Median : 0.000
## Mean :3.964 Mean :4.092 Mean :3.757 Mean : 0.601
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.: 1.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :13.000
## x92 x93 x94 x95
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.000
## Median :3.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.449 Mean :3.601 Mean :3.606 Mean :4.464
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## x101 x102 x103 x104
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.00 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :4.00 Median :5.000
## Mean :4.494 Mean :4.675 Mean :4.12 Mean :4.588
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.00 Max. :5.000
## x105 x106 id Municipio
## Min. : 0.000 Min. : 0.000 Min. : 1.0 Cali :797
## 1st Qu.: 4.000 1st Qu.: 2.000 1st Qu.:202.0 Palmira: 0
## Median : 8.000 Median : 6.000 Median :407.0
## Mean : 7.853 Mean : 5.762 Mean :406.9
## 3rd Qu.:12.000 3rd Qu.:10.000 3rd Qu.:611.0
## Max. :14.000 Max. :12.000 Max. :814.0## AFM CON TODAS LAS VARIABLES
##Análisis factorial múltiple
CaliyPalmira.FMA<-MFA(Cali[,c(19:34)],
group=c(#2,
#5,
#3,
#4,
#2,
#2, #3
7,
4,
5
#6
),
type=c(#'s',
#'s',
#'n',
#'s', #n
#'s',
#'s', #n
's',
's',
's'#,
#'s'
),
name.group=c(#"Voluntariedad",
#"Conocimiento",
#"Incertidumbre",
#"Confianza gubernamental",
#"Confianza sector salud",
#"Confianza medios",
"Probabilidad de contagio",
"Severidad",
"Susceptibilidad"), #,
#"Cumplimiento"),
#num.group.sup=c(3),
graph=FALSE)
CaliyPalmira.FMA$eig## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 1.88558352 35.330673 35.33067
## comp 2 0.80127659 15.013730 50.34440
## comp 3 0.46792772 8.767684 59.11209
## comp 4 0.33608339 6.297283 65.40937
## comp 5 0.31549687 5.911548 71.32092
## comp 6 0.26084766 4.887571 76.20849
## comp 7 0.23464033 4.396517 80.60501
## comp 8 0.19947998 3.737709 84.34272
## comp 9 0.16751433 3.138760 87.48148
## comp 10 0.14802976 2.773672 90.25515
## comp 11 0.12433215 2.329644 92.58479
## comp 12 0.10948924 2.051529 94.63632
## comp 13 0.08829007 1.654314 96.29063
## comp 14 0.07611270 1.426144 97.71678
## comp 15 0.06563418 1.229805 98.94658
## comp 16 0.05622042 1.053417 100.00000CaliyPalmira.FMA$group$contrib## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## Probabilidad de contagio 19.82260 79.665204 13.19394 91.207231 85.477448
## Severidad 39.72483 11.178689 50.39997 5.018541 7.488267
## Susceptibilidad 40.45257 9.156107 36.40609 3.774228 7.034285CaliyPalmira.FMA$group$correlation[,1:3]## Dim.1 Dim.2 Dim.3
## Probabilidad de contagio 0.6159570 0.8031239 0.4393622
## Severidad 0.8709080 0.3042487 0.6027138
## Susceptibilidad 0.8754346 0.2824650 0.4531235Coordenadas<-round(CaliyPalmira.FMA$quanti.var$coord[,c(1,2,3)],3);Coordenadas## Dim.1 Dim.2 Dim.3
## x71 0.519 0.714 -0.077
## x72 0.523 0.638 -0.060
## x73 0.490 0.679 -0.060
## x74 0.395 0.470 0.265
## x75 0.032 0.091 -0.035
## x76 0.349 0.585 0.061
## x77 0.282 0.034 0.316
## x81 0.592 -0.246 0.602
## x82 0.796 -0.288 -0.003
## x83 0.646 -0.272 0.525
## x84 0.793 -0.159 0.031
## x91 0.329 -0.209 -0.505
## x92 0.790 -0.173 -0.356
## x93 0.777 -0.200 -0.244
## x94 0.821 -0.191 -0.284
## x95 0.649 -0.289 -0.143Contribu<-round(CaliyPalmira.FMA$quanti.var$contrib[,c(1,2,3)],3);Contribu## Dim.1 Dim.2 Dim.3
## x71 4.676 20.813 0.418
## x72 4.744 16.620 0.248
## x73 4.174 18.858 0.254
## x74 2.715 9.028 4.929
## x75 0.018 0.336 0.088
## x76 2.113 13.964 0.262
## x77 1.382 0.047 6.994
## x81 6.868 2.781 28.610
## x82 12.399 3.808 0.001
## x83 8.165 3.418 21.713
## x84 12.293 1.171 0.077
## x91 1.804 1.707 17.124
## x92 10.378 1.178 8.505
## x93 10.053 1.559 4.005
## x94 11.210 1.433 5.395
## x95 7.008 3.278 1.377Tabla<-cbind(Coordenadas,Contribu);Tabla## Dim.1 Dim.2 Dim.3 Dim.1 Dim.2 Dim.3
## x71 0.519 0.714 -0.077 4.676 20.813 0.418
## x72 0.523 0.638 -0.060 4.744 16.620 0.248
## x73 0.490 0.679 -0.060 4.174 18.858 0.254
## x74 0.395 0.470 0.265 2.715 9.028 4.929
## x75 0.032 0.091 -0.035 0.018 0.336 0.088
## x76 0.349 0.585 0.061 2.113 13.964 0.262
## x77 0.282 0.034 0.316 1.382 0.047 6.994
## x81 0.592 -0.246 0.602 6.868 2.781 28.610
## x82 0.796 -0.288 -0.003 12.399 3.808 0.001
## x83 0.646 -0.272 0.525 8.165 3.418 21.713
## x84 0.793 -0.159 0.031 12.293 1.171 0.077
## x91 0.329 -0.209 -0.505 1.804 1.707 17.124
## x92 0.790 -0.173 -0.356 10.378 1.178 8.505
## x93 0.777 -0.200 -0.244 10.053 1.559 4.005
## x94 0.821 -0.191 -0.284 11.210 1.433 5.395
## x95 0.649 -0.289 -0.143 7.008 3.278 1.377plot.MFA(CaliyPalmira.FMA, choix="group",title="Representación de grupos")
#plot.MFA(CaliyPalmira.FMA, choix="ind",lab.par=FALSE)
library(ggrepel)
options(ggrepel.max.overlaps = Inf)
#dim 1-2
plot.MFA(CaliyPalmira.FMA, choix="var",habillage='group',title="Círculo de correlación", repel = TRUE)
IPRG
#--------------------------ÍNDICE DE PERCEPCIÓN GLOBAL-----------------------------------------------#####
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Severidad=Cali[,c(19:34)]
Coord1_severidad <-res.mfa_severidad$global.pca$var$coord[,1];Coord1_severidad## x71 x72 x73 x74 x75 x76 x77
## 0.51897714 0.52273671 0.49032346 0.39540629 0.03201124 0.34883299 0.28217312
## x81 x82 x83 x84 x91 x92 x93
## 0.59244118 0.79605796 0.64597488 0.79262979 0.32918363 0.78957486 0.77708632
## x94 x95
## 0.82059472 0.64882058lp_severidad<-res.mfa_severidad$eig[1];lp_severidad #VALOR PROPIO## [1] 1.885584Vp_severidad<-Coord1_severidad/sqrt(lp_severidad);Vp_severidad #VECTOR PROPIO## x71 x72 x73 x74 x75 x76 x77 x81
## 0.3779422 0.3806800 0.3570753 0.2879524 0.0233120 0.2540356 0.2054910 0.4314419
## x82 x83 x84 x91 x92 x93 x94 x95
## 0.5797247 0.4704275 0.5772282 0.2397261 0.5750034 0.5659087 0.5975935 0.4724999Pesos_severidad<-(Vp_severidad/sum(Vp_severidad));Pesos_severidad # PESOS RELATIVOS DE LAS VARIABLES## x71 x72 x73 x74 x75 x76
## 0.059090002 0.059518062 0.055827535 0.045020400 0.003644754 0.039717630
## x77 x81 x82 x83 x84 x91
## 0.032127832 0.067454514 0.090638033 0.073549784 0.090247705 0.037480382
## x92 x93 x94 x95
## 0.089899876 0.088477947 0.093431752 0.073873792sum(Pesos_severidad)## [1] 1data.frame(round(Pesos_severidad,3))## round.Pesos_severidad..3.
## x71 0.059
## x72 0.060
## x73 0.056
## x74 0.045
## x75 0.004
## x76 0.040
## x77 0.032
## x81 0.067
## x82 0.091
## x83 0.074
## x84 0.090
## x91 0.037
## x92 0.090
## x93 0.088
## x94 0.093
## x95 0.074res.mfa_severidad$eig## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 1.88558352 35.330673 35.33067
## comp 2 0.80127659 15.013730 50.34440
## comp 3 0.46792772 8.767684 59.11209
## comp 4 0.33608339 6.297283 65.40937
## comp 5 0.31549687 5.911548 71.32092
## comp 6 0.26084766 4.887571 76.20849
## comp 7 0.23464033 4.396517 80.60501
## comp 8 0.19947998 3.737709 84.34272
## comp 9 0.16751433 3.138760 87.48148
## comp 10 0.14802976 2.773672 90.25515
## comp 11 0.12433215 2.329644 92.58479
## comp 12 0.10948924 2.051529 94.63632
## comp 13 0.08829007 1.654314 96.29063
## comp 14 0.07611270 1.426144 97.71678
## comp 15 0.06563418 1.229805 98.94658
## comp 16 0.05622042 1.053417 100.00000Ind_severidad<-as.matrix(Severidad)%*%Pesos_severidad
Imin_severidad<-min(Ind_severidad);Imin_severidad## [1] 0.9625196Imax_severidad<-max(Ind_severidad);Imax_severidad## [1] 6.661618Ind_2_severidad<-round(((Ind_severidad-Imin_severidad)/(Imax_severidad-Imin_severidad))*100,2) #con este índice se hace el cluster
min(Ind_2_severidad)## [1] 0max(Ind_2_severidad)## [1] 100C8<-cbind(Ind_2_severidad,Cali)
summary(C8$Ind_2_severidad);sd(C8$Ind_2_severidad)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 39.76 50.89 52.05 63.37 100.00## [1] 17.58605ECM
n=round(797*0.80,0);n## [1] 638#Nfinal<-c()
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
N1=Cali[,c(19:34)]
Severidad=N1[sample(nrow(N1),size = n,replace = TRUE),]
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(Ind_2_severidad)
}; NfinalMedia## [1] 49.380 51.065 52.640 49.575 51.640 49.760 52.525 50.555 51.390 50.490
## [11] 52.300 46.940 49.810 50.250 47.950 49.140 48.385 50.150 50.335 51.710
## [21] 48.210 49.910 50.005 50.460 47.960 47.865 47.220 48.545 45.850 51.405
## [31] 50.530 49.645 50.770 49.575 49.210 52.295 47.700 47.315 49.445 52.560
## [41] 50.390 50.190 47.310 49.710 51.280 52.165 49.895 51.010 51.225 51.735
## [51] 51.640 47.740 53.590 50.855 50.330 50.460 52.010 52.110 49.710 48.100
## [61] 48.530 49.520 50.785 49.720 51.840 51.340 49.085 50.270 51.065 47.770
## [71] 50.900 51.350 49.695 48.385 48.930 45.745 49.980 52.100 49.100 48.530
## [81] 49.460 48.270 51.250 50.030 47.330 46.785 48.820 48.345 48.915 48.840
## [91] 49.435 51.310 48.230 50.380 48.090 49.265 51.540 51.200 50.035 51.200
## [101] 50.775 47.445 52.770 52.510 51.710 48.410 47.370 47.745 52.040 48.360
## [111] 52.165 51.000 51.510 52.100 51.350 52.470 51.155 49.845 51.020 51.600
## [121] 52.690 51.600 49.335 49.240 47.310 51.495 52.100 49.150 47.060 50.040
## [131] 47.210 47.960 49.645 46.890 49.400 48.715 51.440 49.680 50.985 50.095
## [141] 51.710 52.340 44.695 47.425 48.985 49.400 49.280 49.900 50.800 48.820
## [151] 51.215 50.775 48.030 51.690 49.960 50.220 49.050 48.905 51.860 49.770
## [161] 51.630 50.290 50.360 49.950 47.840 51.820 51.955 50.385 48.190 44.835
## [171] 51.475 52.360 51.780 49.730 51.370 49.630 48.260 50.540 49.870 49.845
## [181] 47.720 49.855 51.400 51.675 48.765 49.820 48.900 50.980 50.830 47.380
## [191] 49.580 50.095 50.960 48.500 47.510 49.200 50.990 50.735 48.565 48.565
## [201] 51.600 49.530 48.895 48.530 51.420 48.545 49.435 49.625 52.110 50.960
## [211] 51.610 49.270 51.800 51.510 51.240 51.675 49.825 50.030 50.770 49.365
## [221] 48.490 45.090 51.405 51.930 49.710 51.670 51.020 49.180 50.090 46.210
## [231] 48.860 51.220 51.665 50.120 52.850 49.920 49.710 53.640 51.110 49.105
## [241] 51.405 52.580 47.215 49.490 49.860 50.540 49.820 51.350 50.545 49.060
## [251] 50.110 50.485 50.200 51.225 51.155 49.910 50.210 48.930 50.570 48.960
## [261] 50.525 52.985 48.250 48.750 47.655 50.120 49.350 52.100 51.780 49.445
## [271] 47.920 53.690 51.110 51.110 50.630 51.280 51.440 47.495 50.880 47.950
## [281] 50.340 50.030 50.570 50.585 52.455 49.715 50.890 49.755 51.510 50.315
## [291] 50.120 52.340 47.620 44.435 51.110 49.435 50.955 49.520 52.645 51.290
## [301] 49.765 46.790 51.510 50.775 53.180 48.230 46.780 50.895 50.460 46.570
## [311] 50.385 51.480 52.570 51.290 50.770 50.385 49.260 52.360 51.405 50.310
## [321] 50.135 51.760 48.740 50.225 50.555 52.645 50.195 50.890 51.000 52.470
## [331] 48.775 48.705 52.510 49.955 50.990 47.295 50.545 49.265 49.265 52.000
## [341] 51.780 47.435 51.780 47.110 48.660 50.250 49.620 49.470 48.805 49.465
## [351] 51.290 50.900 49.815 50.555 49.660 52.450 50.840 49.710 52.125 51.220
## [361] 49.365 48.400 47.520 51.220 51.200 47.610 51.715 48.660 52.930 51.735
## [371] 50.895 49.680 50.150 50.075 49.260 51.200 49.875 52.510 49.615 51.770
## [381] 48.660 45.730 49.465 49.050 49.820 50.280 48.880 49.960 49.240 49.310
## [391] 48.485 49.120 50.180 49.605 49.710 52.580 51.025 49.265 52.005 50.200
## [401] 52.450 53.580 48.520 52.295 52.455 51.220 50.480 50.830 50.990 50.855
## [411] 50.845 51.340 50.470 47.940 49.805 51.660 53.170 50.640 49.735 52.750
## [421] 47.820 48.410 52.100 48.580 50.950 46.900 49.905 52.395 51.105 48.300
## [431] 48.765 49.940 48.130 46.230 50.055 48.785 51.115 50.540 52.220 44.335
## [441] 51.630 50.890 49.800 51.520 50.965 48.345 47.960 52.230 48.310 49.780
## [451] 51.220 48.165 49.530 47.820 48.680 50.445 50.150 50.250 49.730 49.820
## [461] 49.535 49.845 52.445 52.495 49.110 48.850 51.960 49.720 49.665 49.110
## [471] 47.780 46.800 51.430 51.585 52.110 50.555 50.990 48.850 51.010 51.045
## [481] 48.590 50.770 51.710 50.210 51.920 52.185 52.635 52.450 48.580 51.760
## [491] 44.335 49.265 50.085 50.460 50.555 51.300 49.620 51.950 52.075 51.280
## [501] 48.510 48.300 50.740 48.320 51.495 52.005 51.920 51.330 51.220 50.085
## [511] 50.810 50.890 52.000 48.055 50.120 52.610 51.330 49.920 49.320 48.920
## [521] 48.930 52.395 50.100 50.600 47.415 49.140 50.650 51.855 50.990 49.140
## [531] 50.745 52.150 50.570 50.885 48.240 50.900 51.220 50.195 51.840 47.345
## [541] 50.710 52.540 46.875 48.945 47.895 51.495 50.740 50.160 52.395 48.760
## [551] 46.990 52.640 49.760 49.290 52.670 51.665 52.705 48.055 48.805 51.660
## [561] 48.900 49.720 52.985 52.360 48.430 48.280 52.120 49.420 49.155 50.610
## [571] 50.955 48.075 51.330 51.215 49.575 48.580 47.480 47.150 49.985 49.910
## [581] 50.560 50.725 49.630 47.340 47.320 51.690 47.840 50.555 45.140 49.880
## [591] 48.985 51.290 48.945 47.995 50.190 52.845 49.760 49.015 47.355 51.155
## [601] 52.450 52.580 47.370 49.805 50.890 52.150 49.580 51.960 52.450 52.100
## [611] 52.295 48.830 45.425 51.020 50.095 52.360 52.045 45.865 52.035 50.515
## [621] 48.630 49.840 50.390 48.805 48.760 49.335 49.820 50.490 50.030 51.670
## [631] 51.245 50.180 50.220 51.860 48.785 50.235 50.890 50.070 50.500 50.095
## [641] 51.985 49.685 51.535 51.495 51.860 49.820 48.615 51.890 52.510 49.580
## [651] 49.350 50.570 50.615 52.210 47.605 49.530 51.065 51.710 49.870 49.260
## [661] 49.855 49.940 49.940 48.915 49.630 48.765 50.450 50.340 50.895 50.730
## [671] 48.500 50.405 49.325 47.660 50.230 50.480 51.350 47.610 48.970 53.340
## [681] 49.485 48.975 51.670 52.705 51.005 51.155 52.100 48.390 51.155 50.255
## [691] 50.350 51.350 52.890 51.320 50.730 46.535 47.920 48.060 44.595 51.780
## [701] 50.035 48.130 51.670 51.960 52.100 48.920 50.505 50.530 51.290 51.490
## [711] 50.240 47.850 47.800 52.230 48.830 48.130 48.490 47.865 47.210 47.440
## [721] 49.820 48.210 50.965 48.825 48.385 48.095 51.475 48.870 49.395 52.185
## [731] 50.740 49.600 51.230 51.775 52.150 51.225 52.965 50.500 49.800 50.810
## [741] 47.710 49.790 52.105 50.080 51.010 49.680 50.030 48.830 47.770 48.140
## [751] 47.880 52.690 48.135 47.840 47.600 50.890 46.410 52.110 51.600 50.460
## [761] 51.170 50.945 51.100 50.615 45.630 49.050 49.810 48.385 50.480 48.775
## [771] 47.480 49.050 46.830 51.955 52.260 48.155 53.300 49.720 47.940 49.920
## [781] 47.860 45.085 52.575 52.405 48.150 50.555 51.670 48.385 50.585 49.660
## [791] 51.435 49.265 52.950 49.900 49.990 51.350 49.380 51.350 51.540 49.250
## [801] 52.850 52.045 50.195 50.585 51.530 50.050 50.445 49.075 50.075 51.710
## [811] 50.615 48.580 52.340 51.840 50.895 51.670 51.065 52.520 43.400 49.225
## [821] 52.580 53.415 49.210 51.410 50.600 48.445 52.575 49.670 47.510 49.620
## [831] 47.195 47.555 48.760 50.320 51.405 50.050 49.675 51.710 47.770 50.950
## [841] 49.640 48.510 49.645 50.380 50.780 51.745 49.750 52.295 48.595 50.105
## [851] 50.130 49.390 50.950 50.940 50.440 49.540 49.260 51.475 51.670 51.220
## [861] 50.890 51.330 47.050 47.400 51.450 51.540 47.690 50.150 49.325 51.555
## [871] 49.665 50.585 51.405 51.665 47.405 49.540 51.220 51.780 50.660 52.140
## [881] 49.830 51.435 49.310 50.950 49.745 47.585 51.050 50.290 48.055 50.220
## [891] 51.085 50.525 51.480 52.995 51.920 48.105 48.520 48.700 47.675 47.705
## [901] 47.320 52.580 48.835 48.675 52.940 51.615 50.850 48.660 50.950 51.215
## [911] 52.230 49.865 48.510 52.100 49.730 51.000 50.075 50.715 50.710 48.080
## [921] 52.570 48.150 48.660 50.905 50.145 44.160 50.660 52.450 52.220 51.220
## [931] 49.180 51.105 49.990 50.595 49.170 48.995 48.535 47.690 49.355 49.820
## [941] 51.800 50.900 52.820 50.510 49.820 50.950 50.085 51.245 50.385 50.135
## [951] 48.075 49.415 49.720 50.855 48.245 51.850 50.855 47.340 50.260 52.575
## [961] 51.665 48.110 47.595 49.810 45.065 46.945 48.045 51.035 49.730 48.485
## [971] 52.565 48.260 51.405 48.915 49.165 48.690 49.215 51.620 52.110 48.230
## [981] 51.260 51.955 51.525 48.135 50.990 48.170 49.165 50.460 50.580 51.200
## [991] 51.770 49.380 51.950 50.840 49.695 50.310 51.400 49.820 48.625 50.870x11();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## [1] 2.79Índices por separado
Probabilidad de contagio
#-----------------------------------probabilidad de contagio-----------------------------------
Proba<-CaliyPalmira.FMA$separate.analyses$`Probabilidad de contagio`$ind$coord[,1]
Imin_Proba<-min(Proba);Imin_Proba## [1] -5.543756Imax_Proba<-max(Proba);Imax_Proba## [1] 3.539925Ind_2_Proba<-round(((Proba-Imin_Proba)/(Imax_Proba-Imin_Proba))*100,2) #con este índice se hace el cluster
min(Ind_2_Proba)## [1] 0max(Ind_2_Proba)## [1] 100C8<-cbind(Ind_2_Proba,Cali)
summary(C8$Ind_2_Proba);sd(C8$Ind_2_Proba)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 49.02 60.59 61.03 73.73 100.00## [1] 19.25214ECM
Hago 1000 AFM para construir los índices y así sacar las medianas de casa muestra?
Severidad
#-----------------------------------severidad-----------------------------------
Sev<-CaliyPalmira.FMA$separate.analyses$Severidad$ind$coord[,1]
Imin_Sev<-min(Sev);Imin_Sev## [1] -3.969254Imax_Sev<-max(Sev);Imax_Sev## [1] 3.521764Ind_2_Sev<-round(((Sev-Imin_Sev)/(Imax_Sev-Imin_Sev))*100,2) #con este índice se hace el cluster
min(Ind_2_Sev)## [1] 0max(Ind_2_Sev)## [1] 100sd(Ind_2_Sev)## [1] 21.99115#rbind(summary(Ind_2_Sev))
#print(xtable(rbind(summary(Ind_2_Sev))), include.rownames = FALSE)
C8<-cbind(Ind_2_Sev,Cali)
summary(C8$Ind_2_Sev)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 37.60 50.26 52.99 66.90 100.00Susceptibilidad
#-----------------------------------susceptibilidad-----------------------------------
SU<-CaliyPalmira.FMA$separate.analyses$Susceptibilidad$ind$coord[,1]
Imin_SU<-min(SU);Imin_SU## [1] -3.393132Imax_SU<-max(SU);Imax_SU## [1] 6.640584Ind_2_SU<-round(((SU-Imin_SU)/(Imax_SU-Imin_SU))*100,2) #con este índice se hace el cluster
min(Ind_2_SU)## [1] 0max(Ind_2_SU)## [1] 100sd(Ind_2_SU)## [1] 17.79983#rbind(summary(Ind_2_SU))
#print(xtable(rbind(summary(Ind_2_SU))), include.rownames = FALSE)
#-----------------------------------k-mean--------------------------------####
C8<-cbind(Ind_2_SU,Cali)
summary(C8$Ind_2_SU)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 21.30 32.61 33.82 45.43 100.00PALMIRA
library(dplyr)
CaliyPalmira$Municipio<-as.factor(CaliyPalmira$Municipio)
Palmira<-filter(CaliyPalmira, CaliyPalmira$Municipio=="Palmira")AFM CON TODAS LAS VARIABLES
##Análisis factorial múltiple
CaliyPalmira.FMA<-MFA(Palmira[,c(19:34)],
group=c(#2,
#5,
#3,
#4,
#2,
#2, #3
7,
4,
5
#6
),
type=c(#'s',
#'s',
#'n',
#'s', #n
#'s',
#'s', #n
's',
's',
's'#,
#'s'
),
name.group=c(#"Voluntariedad",
#"Conocimiento",
#"Incertidumbre",
#"Confianza gubernamental",
#"Confianza sector salud",
#"Confianza medios",
"Probabilidad de contagio",
"Severidad",
"Susceptibilidad"), #,
#"Cumplimiento"),
#num.group.sup=c(3),
graph=FALSE)
CaliyPalmira.FMA$eig## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 2.04287583 40.5263204 40.52632
## comp 2 0.70280965 13.9422517 54.46857
## comp 3 0.48852501 9.6912993 64.15987
## comp 4 0.32139975 6.3758888 70.53576
## comp 5 0.22235609 4.4110729 74.94683
## comp 6 0.21048293 4.1755345 79.12237
## comp 7 0.18525508 3.6750675 82.79744
## comp 8 0.16106274 3.1951430 85.99258
## comp 9 0.13244381 2.6274041 88.61998
## comp 10 0.11596779 2.3005548 90.92054
## comp 11 0.10876158 2.1575989 93.07814
## comp 12 0.09656076 1.9155604 94.99370
## comp 13 0.07852095 1.5576890 96.55139
## comp 14 0.06938844 1.3765194 97.92790
## comp 15 0.05687026 1.1281852 99.05609
## comp 16 0.04758121 0.9439101 100.00000CaliyPalmira.FMA$group$contrib## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## Probabilidad de contagio 25.03689 75.462789 32.35813 60.09339 20.54200
## Severidad 37.54348 15.833327 31.50396 25.29624 26.00522
## Susceptibilidad 37.41964 8.703884 36.13791 14.61037 53.45278CaliyPalmira.FMA$group$correlation[,1:3]## Dim.1 Dim.2 Dim.3
## Probabilidad de contagio 0.7225955 0.7538494 0.5811008
## Severidad 0.8805261 0.3520768 0.5469880
## Susceptibilidad 0.8769671 0.2667586 0.4634630Coordenadas<-round(CaliyPalmira.FMA$quanti.var$coord[,c(1,2,3)],3);Coordenadas## Dim.1 Dim.2 Dim.3
## x71 0.545 0.685 -0.043
## x72 0.531 0.650 -0.050
## x73 0.567 0.627 -0.084
## x74 0.519 0.420 0.237
## x75 0.409 0.167 0.495
## x76 0.451 0.559 0.070
## x77 0.457 -0.113 0.472
## x81 0.626 -0.296 0.499
## x82 0.788 -0.295 -0.069
## x83 0.632 -0.347 0.402
## x84 0.825 -0.094 -0.067
## x91 0.359 -0.188 -0.503
## x92 0.793 -0.149 -0.389
## x93 0.808 -0.156 -0.264
## x94 0.811 -0.148 -0.313
## x95 0.656 -0.310 -0.069Contribu<-round(CaliyPalmira.FMA$quanti.var$contrib[,c(1,2,3)],3);Contribu## Dim.1 Dim.2 Dim.3
## x71 4.257 19.529 0.111
## x72 4.027 17.544 0.152
## x73 4.604 16.356 0.423
## x74 3.848 7.338 3.364
## x75 2.398 1.154 14.671
## x76 2.911 13.014 0.289
## x77 2.991 0.528 13.348
## x81 7.034 4.569 18.684
## x82 11.140 4.536 0.353
## x83 7.163 6.265 12.135
## x84 12.206 0.463 0.332
## x91 1.933 1.545 15.850
## x92 9.422 0.966 9.470
## x93 9.784 1.060 4.377
## x94 9.836 0.958 6.140
## x95 6.444 4.175 0.301Tabla<-cbind(Coordenadas,Contribu);Tabla## Dim.1 Dim.2 Dim.3 Dim.1 Dim.2 Dim.3
## x71 0.545 0.685 -0.043 4.257 19.529 0.111
## x72 0.531 0.650 -0.050 4.027 17.544 0.152
## x73 0.567 0.627 -0.084 4.604 16.356 0.423
## x74 0.519 0.420 0.237 3.848 7.338 3.364
## x75 0.409 0.167 0.495 2.398 1.154 14.671
## x76 0.451 0.559 0.070 2.911 13.014 0.289
## x77 0.457 -0.113 0.472 2.991 0.528 13.348
## x81 0.626 -0.296 0.499 7.034 4.569 18.684
## x82 0.788 -0.295 -0.069 11.140 4.536 0.353
## x83 0.632 -0.347 0.402 7.163 6.265 12.135
## x84 0.825 -0.094 -0.067 12.206 0.463 0.332
## x91 0.359 -0.188 -0.503 1.933 1.545 15.850
## x92 0.793 -0.149 -0.389 9.422 0.966 9.470
## x93 0.808 -0.156 -0.264 9.784 1.060 4.377
## x94 0.811 -0.148 -0.313 9.836 0.958 6.140
## x95 0.656 -0.310 -0.069 6.444 4.175 0.301plot.MFA(CaliyPalmira.FMA, choix="group",title="Representación de grupos")
#plot.MFA(CaliyPalmira.FMA, choix="ind",lab.par=FALSE)
library(ggrepel)
options(ggrepel.max.overlaps = Inf)
#dim 1-2
plot.MFA(CaliyPalmira.FMA, choix="var",habillage='group',title="Círculo de correlación", repel = TRUE)
IPRG
#--------------------------ÍNDICE DE PERCEPCIÓN GLOBAL-----------------------------------------------#####
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Severidad=Palmira[,c(19:34)]
Coord1_severidad <-res.mfa_severidad$global.pca$var$coord[,1];Coord1_severidad## x71 x72 x73 x74 x75 x76 x77 x81
## 0.5454594 0.5305513 0.5672769 0.5186316 0.4093872 0.4510565 0.4572500 0.6262629
## x82 x83 x84 x91 x92 x93 x94 x95
## 0.7881412 0.6319853 0.8249893 0.3593309 0.7932819 0.8083693 0.8105135 0.6560303lp_severidad<-res.mfa_severidad$eig[1];lp_severidad #VALOR PROPIO## [1] 2.042876Vp_severidad<-Coord1_severidad/sqrt(lp_severidad);Vp_severidad #VECTOR PROPIO## x71 x72 x73 x74 x75 x76 x77 x81
## 0.3816290 0.3711987 0.3968936 0.3628591 0.2864265 0.3155803 0.3199136 0.4381630
## x82 x83 x84 x91 x92 x93 x94 x95
## 0.5514207 0.4421667 0.5772013 0.2514048 0.5550173 0.5655732 0.5670734 0.4589897Pesos_severidad<-(Vp_severidad/sum(Vp_severidad));Pesos_severidad # PESOS RELATIVOS DE LAS VARIABLES## x71 x72 x73 x74 x75 x76 x77
## 0.05578140 0.05425683 0.05801257 0.05303785 0.04186598 0.04612728 0.04676066
## x81 x82 x83 x84 x91 x92 x93
## 0.06404477 0.08059925 0.06462997 0.08436752 0.03674697 0.08112496 0.08266788
## x94 x95
## 0.08288716 0.06708894sum(Pesos_severidad)## [1] 1data.frame(round(Pesos_severidad,3))## round.Pesos_severidad..3.
## x71 0.056
## x72 0.054
## x73 0.058
## x74 0.053
## x75 0.042
## x76 0.046
## x77 0.047
## x81 0.064
## x82 0.081
## x83 0.065
## x84 0.084
## x91 0.037
## x92 0.081
## x93 0.083
## x94 0.083
## x95 0.067res.mfa_severidad$eig## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 2.04287583 40.5263204 40.52632
## comp 2 0.70280965 13.9422517 54.46857
## comp 3 0.48852501 9.6912993 64.15987
## comp 4 0.32139975 6.3758888 70.53576
## comp 5 0.22235609 4.4110729 74.94683
## comp 6 0.21048293 4.1755345 79.12237
## comp 7 0.18525508 3.6750675 82.79744
## comp 8 0.16106274 3.1951430 85.99258
## comp 9 0.13244381 2.6274041 88.61998
## comp 10 0.11596779 2.3005548 90.92054
## comp 11 0.10876158 2.1575989 93.07814
## comp 12 0.09656076 1.9155604 94.99370
## comp 13 0.07852095 1.5576890 96.55139
## comp 14 0.06938844 1.3765194 97.92790
## comp 15 0.05687026 1.1281852 99.05609
## comp 16 0.04758121 0.9439101 100.00000Ind_severidad<-as.matrix(Severidad)%*%Pesos_severidad
Imin_severidad<-min(Ind_severidad);Imin_severidad## [1] 0.963253Imax_severidad<-max(Ind_severidad);Imax_severidad## [1] 6.546757Ind_2_severidad<-round(((Ind_severidad-Imin_severidad)/(Imax_severidad-Imin_severidad))*100,2) #con este índice se hace el cluster
min(Ind_2_severidad)## [1] 0max(Ind_2_severidad)## [1] 100C8<-cbind(Ind_2_severidad,Palmira)
summary(C8$Ind_2_severidad);sd(C8$Ind_2_severidad)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 44.15 57.09 57.03 69.84 100.00## [1] 18.0409ECM
n=round(646*0.80,0);n## [1] 517#Nfinal<-c()
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
N1=Palmira[,c(19:34)]
Severidad=N1[sample(nrow(N1),size = n,replace = TRUE),]
res.mfa_severidad=CaliyPalmira.FMA
#Datos
Coord1_severidad <-res.mfa_severidad$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(Ind_2_severidad)
}; NfinalMedia## [1] 57.35 57.97 58.24 57.46 57.38 56.32 56.50 58.10 57.52 56.29 56.78 56.56
## [13] 57.04 57.30 59.09 57.30 57.70 56.52 57.31 58.35 57.38 56.73 56.85 58.70
## [25] 55.84 55.96 58.10 56.59 55.54 55.35 57.07 56.07 56.55 52.90 57.11 56.45
## [37] 57.84 57.13 56.48 55.49 52.40 55.39 56.92 56.35 57.27 56.70 58.04 58.04
## [49] 56.15 55.55 57.43 54.00 56.15 57.72 57.01 58.35 56.98 58.10 57.39 56.82
## [61] 57.34 56.04 58.82 56.15 58.29 56.66 57.96 55.12 54.87 56.27 55.43 56.29
## [73] 54.97 55.11 57.45 58.70 55.88 56.55 56.75 56.17 57.34 57.51 57.46 57.10
## [85] 57.12 58.73 55.58 55.86 57.11 57.34 57.21 55.39 58.24 56.18 57.32 53.52
## [97] 58.17 58.43 58.24 57.14 56.06 57.49 57.86 55.49 55.99 57.42 56.36 56.51
## [109] 57.46 53.00 58.08 56.16 55.97 57.21 57.00 54.23 56.78 54.57 56.73 58.50
## [121] 56.45 57.10 57.99 56.50 57.96 55.09 56.13 55.54 56.37 57.22 56.45 54.87
## [133] 57.51 58.47 57.23 55.47 57.49 57.39 58.82 55.88 57.49 55.38 49.41 57.39
## [145] 54.44 57.38 56.36 53.15 56.92 57.46 58.10 56.10 58.97 57.52 55.07 57.60
## [157] 56.85 57.32 56.05 55.09 56.07 57.08 55.00 56.48 52.53 55.98 55.81 56.92
## [169] 57.17 53.28 51.69 56.29 52.99 55.81 55.62 56.76 58.10 54.60 57.22 55.01
## [181] 57.38 56.06 57.45 55.19 56.75 54.67 56.84 54.98 55.11 56.51 58.03 55.43
## [193] 57.44 56.18 53.86 57.13 56.27 50.08 57.13 57.70 57.97 57.11 52.40 55.49
## [205] 56.44 56.92 57.39 57.45 55.12 56.35 57.49 57.11 58.73 57.11 57.68 53.32
## [217] 56.01 55.18 55.41 57.49 58.06 47.35 56.18 57.08 56.15 54.63 52.63 55.14
## [229] 57.39 57.01 58.28 55.92 58.47 57.25 57.49 54.10 57.87 56.13 53.55 59.35
## [241] 56.76 57.13 49.65 56.71 57.46 56.98 55.79 55.88 55.79 55.85 57.10 55.85
## [253] 57.22 58.35 57.59 55.02 51.63 55.99 56.73 57.21 57.65 57.46 57.21 54.87
## [265] 57.08 56.32 56.64 57.42 58.98 58.70 54.47 55.77 56.52 56.78 54.63 57.74
## [277] 56.15 53.79 56.15 56.85 52.06 55.98 57.12 55.63 57.04 57.11 56.33 55.53
## [289] 56.27 58.45 54.60 57.44 56.72 55.52 57.86 54.70 57.60 56.15 56.32 56.93
## [301] 58.89 56.76 55.53 55.52 54.64 56.85 57.30 55.70 56.29 58.43 57.17 57.65
## [313] 58.70 57.10 57.46 56.31 56.17 54.44 57.91 56.75 57.91 58.48 54.00 55.20
## [325] 57.60 56.45 55.70 58.04 57.10 56.56 56.26 58.43 56.92 55.54 56.75 58.43
## [337] 55.92 58.89 57.21 55.26 55.05 56.48 58.10 54.60 57.59 58.37 57.49 56.76
## [349] 54.17 56.85 58.35 58.04 56.27 58.50 56.37 57.00 58.89 55.48 58.37 56.07
## [361] 55.54 58.59 56.12 57.12 57.46 57.49 53.53 56.60 57.87 57.34 57.60 57.21
## [373] 54.68 57.38 56.29 57.22 55.92 56.76 54.72 57.65 58.08 56.85 57.60 56.52
## [385] 57.11 55.99 58.10 54.70 57.17 57.22 55.09 55.52 57.22 56.48 54.98 57.86
## [397] 55.95 54.87 58.74 57.49 55.99 57.86 53.94 52.73 58.74 57.39 57.70 56.76
## [409] 57.00 55.89 58.22 58.43 56.84 55.24 58.47 57.77 56.55 54.57 57.17 58.45
## [421] 52.59 56.43 58.49 57.39 56.27 57.98 57.51 54.87 53.24 57.14 56.10 55.72
## [433] 58.04 55.52 55.55 58.74 56.21 58.04 56.65 58.24 55.48 58.84 57.59 56.33
## [445] 57.65 54.74 57.50 55.83 56.26 55.44 57.01 57.08 55.97 57.36 57.27 58.36
## [457] 53.95 57.99 58.29 58.04 58.45 49.84 58.04 55.99 58.08 58.43 55.95 56.21
## [469] 56.15 57.09 57.13 56.05 57.17 56.07 55.99 56.78 58.96 55.71 59.13 56.45
## [481] 58.35 57.60 54.22 57.22 55.78 57.22 57.08 57.00 56.52 57.49 57.46 56.82
## [493] 57.08 54.81 58.24 56.84 56.55 56.93 56.52 53.99 57.65 52.13 54.15 57.08
## [505] 56.44 55.99 53.63 55.89 55.41 57.72 56.93 52.46 58.24 59.09 57.46 57.96
## [517] 54.24 57.01 57.66 56.26 58.29 52.67 57.54 57.96 56.55 57.59 57.59 57.10
## [529] 58.84 55.81 57.14 57.39 56.65 57.21 56.15 55.47 58.08 57.13 57.27 56.64
## [541] 57.21 53.49 57.51 56.93 56.85 57.79 52.67 54.51 57.14 56.92 56.92 56.73
## [553] 50.89 58.50 57.65 56.26 57.70 56.13 55.24 57.27 56.05 56.26 52.60 56.92
## [565] 55.97 58.24 51.74 58.79 57.38 54.25 57.13 57.42 53.38 57.22 57.49 58.59
## [577] 56.04 57.21 57.21 54.65 57.01 55.54 57.00 54.15 56.43 54.31 56.82 57.34
## [589] 56.19 58.24 59.07 58.50 57.14 56.55 57.65 55.55 55.38 55.99 57.22 55.55
## [601] 57.27 57.01 53.63 58.24 57.22 59.57 56.98 56.07 56.64 56.36 55.02 55.85
## [613] 56.51 57.38 56.85 54.24 56.19 55.11 55.89 57.10 56.40 56.75 55.71 56.66
## [625] 51.54 58.47 55.68 57.86 56.73 56.75 55.79 57.60 52.40 51.46 56.85 58.47
## [637] 57.10 55.77 58.87 58.35 56.35 58.10 57.64 52.45 57.51 55.18 57.90 54.70
## [649] 57.13 57.65 58.74 55.24 57.42 55.47 57.65 58.73 57.34 54.39 58.56 55.18
## [661] 57.91 56.55 57.27 57.11 58.40 58.08 57.85 57.45 56.76 54.15 56.73 56.40
## [673] 57.72 55.99 56.43 57.10 57.27 54.83 58.10 56.52 55.14 58.43 56.36 54.99
## [685] 56.15 57.59 57.10 59.57 58.48 56.92 57.77 54.81 57.55 57.59 58.76 56.15
## [697] 58.35 57.00 55.70 53.69 56.26 56.17 54.02 58.36 54.22 57.34 54.83 56.10
## [709] 56.78 56.17 56.36 55.54 56.76 56.48 54.63 56.59 57.49 56.18 56.84 57.45
## [721] 57.32 53.79 57.31 56.92 55.41 57.42 57.86 57.74 57.07 58.36 56.45 56.72
## [733] 56.07 58.36 59.07 55.48 55.71 57.27 56.83 57.38 55.92 55.52 55.52 55.55
## [745] 57.49 55.79 57.52 57.64 57.65 58.35 57.21 56.13 58.36 56.40 56.15 56.76
## [757] 56.44 55.44 55.04 57.00 56.07 57.65 55.88 57.27 56.45 57.45 56.84 57.82
## [769] 56.17 51.63 56.07 57.60 52.57 58.76 54.49 57.72 57.22 57.12 54.15 57.32
## [781] 58.24 56.75 56.52 56.52 57.21 52.60 57.12 57.01 55.00 58.04 53.91 54.37
## [793] 56.56 56.60 57.22 57.01 57.82 54.16 54.83 56.48 50.45 57.14 58.24 56.92
## [805] 57.60 57.30 57.13 54.97 53.50 58.68 51.65 55.78 55.86 54.53 57.49 58.35
## [817] 55.78 56.48 57.17 58.91 55.32 57.11 57.25 53.00 57.49 57.07 56.13 57.60
## [829] 58.04 57.21 58.24 55.86 56.98 57.86 55.44 53.59 55.32 58.94 57.64 57.01
## [841] 56.31 57.13 56.78 56.82 58.84 56.38 55.11 56.66 54.31 57.51 57.17 56.82
## [853] 51.54 57.70 56.37 57.11 56.83 56.52 57.60 57.00 58.73 54.15 58.17 58.70
## [865] 57.07 56.13 57.39 58.10 57.87 57.91 57.86 55.52 57.12 54.72 57.87 55.89
## [877] 57.11 55.52 58.35 57.12 57.49 56.40 57.54 56.72 57.00 55.43 53.88 56.52
## [889] 51.64 48.21 58.92 55.79 55.97 57.65 57.00 56.56 54.60 52.82 57.95 55.95
## [901] 56.76 58.08 55.95 58.24 57.34 56.84 54.15 58.24 57.32 57.13 52.90 55.70
## [913] 53.04 56.66 58.35 52.10 56.05 57.49 50.88 57.39 57.21 52.95 55.95 57.21
## [925] 58.35 53.69 58.73 56.52 57.72 55.62 58.74 53.90 54.72 57.14 57.17 57.39
## [937] 55.43 57.87 58.48 58.24 56.45 58.04 56.13 54.50 56.13 57.49 57.86 56.64
## [949] 57.49 57.65 56.17 55.35 55.20 58.24 55.48 56.65 55.92 56.21 56.15 56.05
## [961] 57.08 59.11 59.91 53.41 57.49 57.44 56.13 52.69 56.04 58.59 59.25 56.29
## [973] 57.08 58.04 51.51 56.65 56.45 57.77 56.40 57.41 54.24 58.73 55.79 56.82
## [985] 57.32 57.36 57.32 55.49 56.83 58.24 53.49 56.15 56.76 59.09 56.94 56.83
## [997] 55.99 56.50 57.17 57.34x11();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## [1] 3.03Índices por separado
Probabilidad de contagio
#-----------------------------------probabilidad de contagio-----------------------------------
Proba<-CaliyPalmira.FMA$separate.analyses$`Probabilidad de contagio`$ind$coord[,1]
Imin_Proba<-min(Proba);Imin_Proba## [1] -6.214496Imax_Proba<-max(Proba);Imax_Proba## [1] 3.395533Ind_2_Proba<-round(((Proba-Imin_Proba)/(Imax_Proba-Imin_Proba))*100,2) #con este índice se hace el cluster
min(Ind_2_Proba)## [1] 0max(Ind_2_Proba)## [1] 100C8<-cbind(Ind_2_Proba,Palmira)
summary(C8$Ind_2_Proba);sd(C8$Ind_2_Proba)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 51.83 64.84 64.67 77.22 100.00## [1] 19.26239Severidad
#-----------------------------------severidad-----------------------------------
Sev<-CaliyPalmira.FMA$separate.analyses$Severidad$ind$coord[,1]
Imin_Sev<-min(Sev);Imin_Sev## [1] -4.397929Imax_Sev<-max(Sev);Imax_Sev## [1] 3.203421Ind_2_Sev<-round(((Sev-Imin_Sev)/(Imax_Sev-Imin_Sev))*100,2) #con este índice se hace el cluster
min(Ind_2_Sev)## [1] 0max(Ind_2_Sev)## [1] 100sd(Ind_2_Sev)## [1] 21.75114#rbind(summary(Ind_2_Sev))
#print(xtable(rbind(summary(Ind_2_Sev))), include.rownames = FALSE)
C8<-cbind(Ind_2_Sev,Palmira)
summary(C8$Ind_2_Sev)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 45.23 58.40 57.86 71.08 100.00Susceptibilidad
#-----------------------------------susceptibilidad-----------------------------------
SU<-CaliyPalmira.FMA$separate.analyses$Susceptibilidad$ind$coord[,1]
Imin_SU<-min(SU);Imin_SU## [1] -3.712545Imax_SU<-max(SU);Imax_SU## [1] 4.942211Ind_2_SU<-round(((SU-Imin_SU)/(Imax_SU-Imin_SU))*100,2) #con este índice se hace el cluster
min(Ind_2_SU)## [1] 0max(Ind_2_SU)## [1] 100sd(Ind_2_SU)## [1] 20.9077#rbind(summary(Ind_2_SU))
#print(xtable(rbind(summary(Ind_2_SU))), include.rownames = FALSE)
#-----------------------------------k-mean--------------------------------####
C8<-cbind(Ind_2_SU,Palmira)
summary(C8$Ind_2_SU)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 26.64 41.91 42.90 57.20 100.00