Prevalencia del VIH en mujeres a nivel mundial
###Limpieza de datas
Data Prevalencia VIH (2013-2017)
link1="https://docs.google.com/spreadsheets/d/e/2PACX-1vQ51CAVKCjF_48ylMXr4FJkuVOHpXlUhaGmIA44cdQeWt4cBNjfekSgrPMjZMXrZg/pub?gid=14954305&single=true&output=csv"
DataVIH=read.csv(link1, stringsAsFactors = F)
DataVIH2 = DataVIH[,c(1,58:62)]
names(DataVIH2) = c("Pais","2013","2014","2015", "2016", "2017")
DataVIH2$`2013` = gsub("\\,", ".", DataVIH2$`2013`)
DataVIH2$`2014` = gsub("\\,", ".", DataVIH2$`2014`)
DataVIH2$`2015` = gsub("\\,", ".", DataVIH2$`2015`)
DataVIH2$`2016` = gsub("\\,", ".", DataVIH2$`2016`)
DataVIH2$`2017` = gsub("\\,", ".", DataVIH2$`2017`)
DataVIH2[,c(2:6)]=lapply(DataVIH2[,c(2:6)],as.numeric) #volver numerico en grupo
DataVIH2 = DataVIH2[complete.cases(DataVIH2),]
row.names(DataVIH2) = NULL
DataVIH2$VIH = rowMeans(DataVIH2[,2:6])
DataVIH2 = DataVIH2[,c (1,7)]Data Población Activa Mujeres
link2="https://docs.google.com/spreadsheets/d/e/2PACX-1vQ-T56gOlA6lVVHHnrqUR6bc_doAmUewuvSlG4CcNgYyeZVUbPiozpcciPpDS3SyQ/pub?gid=1993384276&single=true&output=csv"
DataAct=read.csv(link2,stringsAsFactors = F)
DataAct2 = DataAct[,c(1,53:57)]
names(DataAct2) = c("Pais","2008","2009", "2010", "2011", "2012")
DataAct2$`2008` = gsub("\\,", ".", DataAct2$`2008`)
DataAct2$`2009` = gsub("\\,", ".", DataAct2$`2009`)
DataAct2$`2010` = gsub("\\,", ".", DataAct2$`2010`)
DataAct2$`2011` = gsub("\\,", ".", DataAct2$`2011`)
DataAct2$`2012` = gsub("\\,", ".", DataAct2$`2012`)
DataAct2[,c(2:6)]=lapply(DataAct2[,c(2:6)],as.numeric) #volver numerico en grupo
DataAct2 = DataAct2[complete.cases(DataAct2),]
row.names(DataAct2) = NULL
DataAct2$PoblacionActiva = rowMeans(DataAct2[,2:6])
DataAct2= DataAct2[,c (1,7)]Data Participación en la Fuerza Laboral Mujeres
link3="https://docs.google.com/spreadsheets/d/e/2PACX-1vTDcvi_z6RrnoATATdOGBLj2WlKRmVxqvx2hc4lUqkMwCcF3j9BLklmz0VjjIX4vA/pub?gid=1181245938&single=true&output=csv"
DataFLM= read.csv(link3, stringsAsFactors = F)
DataFLM2 = DataFLM[,c(1,53:57)]
names(DataFLM2) = c("Pais","2008","2009","2010", "2011", "2012")
DataFLM2$`2008` = gsub("\\,", ".", DataFLM2$`2008`)
DataFLM2$`2009` = gsub("\\,", ".", DataFLM2$`2009`)
DataFLM2$`2010` = gsub("\\,", ".", DataFLM2$`2010`)
DataFLM2$`2011` = gsub("\\,", ".", DataFLM2$`2011`)
DataFLM2$`2012` = gsub("\\,", ".", DataFLM2$`2012`)
DataFLM2[,c(2:6)]=lapply(DataFLM2[,c(2:6)],as.numeric)
DataFLM2$FLM = rowMeans(DataFLM2[,2:6],na.rm = TRUE)
DataFLM2= DataFLM2[,c (1,7)]
DataFLM2 = DataFLM2[complete.cases(DataFLM2),]
row.names(DataFLM2) = NULLData Prevalencia de Métodos anticoceptivos
link4="https://docs.google.com/spreadsheets/d/e/2PACX-1vRI-hkw-v7fdeFY_c1aS0c_DD86WJ-0k_G9Ti2lE_0_P3nGcPHagckLPhVM9SzD5g/pub?gid=932337199&single=true&output=csv"
DataMetodos= read.csv(link4, stringsAsFactors = F)
DataMetodos2 = DataMetodos[,c(1,53:57)]
names(DataMetodos2) = c("Pais","2008","2009","2010", "2011", "2012")
DataMetodos2$`2008` = gsub("\\,", ".", DataMetodos2$`2008`)
DataMetodos2$`2009` = gsub("\\,", ".", DataMetodos2$`2009`)
DataMetodos2$`2010` = gsub("\\,", ".", DataMetodos2$`2010`)
DataMetodos2$`2011` = gsub("\\,", ".", DataMetodos2$`2011`)
DataMetodos2$`2012` = gsub("\\,", ".", DataMetodos2$`2012`)
DataMetodos2[,c(2:6)]=lapply(DataMetodos2[,c(2:6)],as.numeric)
DataMetodos2$Metodos = rowMeans(DataMetodos2[,2:6],na.rm = TRUE)
DataMetodos2= DataMetodos2[,c (1,7)]
DataMetodos2 = DataMetodos2[complete.cases(DataMetodos2),]
row.names(DataMetodos2) = NULLData de población en barrios de tugurios
link5="https://docs.google.com/spreadsheets/d/e/2PACX-1vQnghOacrnZH200jpcJc-Vym7n8rrfuQlupJ470spsBTvZ0WUWvl63x2AYL1W92sw/pub?gid=1538383881&single=true&output=csv"
DataTugurios= read.csv(link5, stringsAsFactors = F)
DataTugurios2 = DataTugurios[,c(1,53:57)]
names(DataTugurios2) = c("Pais","2008","2009","2010", "2011", "2012")
DataTugurios2$`2008` = gsub("\\,", ".", DataTugurios2$`2008`)
DataTugurios2$`2009` = gsub("\\,", ".", DataTugurios2$`2009`)
DataTugurios2$`2010` = gsub("\\,", ".", DataTugurios2$`2010`)
DataTugurios2$`2011` = gsub("\\,", ".", DataTugurios2$`2011`)
DataTugurios2$`2012` = gsub("\\,", ".", DataTugurios2$`2012`)
DataTugurios2[,c(2:6)]=lapply(DataTugurios2[,c(2:6)],as.numeric)
DataTugurios2$BarriosTugurios= rowMeans(DataTugurios2[,2:6],na.rm = TRUE)
DataTugurios2= DataTugurios2[,c (1,7)]
DataTugurios2 = DataTugurios2[complete.cases(DataTugurios2),]
row.names(DataTugurios2) = NULLData de Gini
link6="https://docs.google.com/spreadsheets/d/e/2PACX-1vSOdz-vUkw9_yctGztLL_PS87cCS7GoU10PiLA3ywnO8-iNXG1OBi_8OBOpZ0r3AQ/pub?gid=826191890&single=true&output=csv"
DataGini= read.csv(link6, stringsAsFactors = F)
DataGini2 = DataGini[,c(1,53:57)]
names(DataGini2) = c("Pais","2008","2009","2010", "2011", "2012")
DataGini2$`2008` = gsub("\\,", ".", DataGini2$`2008`)
DataGini2$`2009` = gsub("\\,", ".", DataGini2$`2009`)
DataGini2$`2010` = gsub("\\,", ".", DataGini2$`2010`)
DataGini2$`2011` = gsub("\\,", ".", DataGini2$`2011`)
DataGini2$`2012` = gsub("\\,", ".", DataGini2$`2012`)
DataGini2[,c(2:6)]=lapply(DataGini2[,c(2:6)],as.numeric)
DataGini2$Gini= rowMeans(DataGini2[,2:6],na.rm = TRUE)
DataGini2= DataGini2[,c (1,7)]
DataGini2 = DataGini2[complete.cases(DataGini2),]
row.names(DataGini2) = NULLDATA ALFABETIZACIÓN
link7="https://docs.google.com/spreadsheets/d/e/2PACX-1vTTojodRJwkAu-98fKnnuzUCJZE-Wj4tDFm7F2XQLDeT3CSifu-yWiHZuZv-uzL2Q/pub?gid=1142139153&single=true&output=csv"
EDU=read.csv(link7,stringsAsFactors = F)
EDU2=EDU[,c(1,53:57)]
names(EDU2)=c("Pais", "2008","2009","2010","2011","2012")
EDU2$`2008`= gsub("\\,", ".",EDU2$`2008`)
EDU2$`2009`= gsub("\\,", ".",EDU2$`2009`)
EDU2$`2010`= gsub("\\,", ".",EDU2$`2010`)
EDU2$`2011`= gsub("\\,", ".",EDU2$`2011`)
EDU2$`2012`= gsub("\\,", ".",EDU2$`2012`)
EDU2[c(2:6)] = lapply(EDU2[c(2:6)], as.numeric)
EDU2$EDU = rowMeans(EDU2[,2:6],na.rm = TRUE)
EDU2= EDU2[,c (1,7)]
EDU2= EDU2[complete.cases(EDU2),]
row.names(EDU2) = NULLDATA DE ACCESO A LA ELECTRICIDAD
link8="https://docs.google.com/spreadsheets/d/e/2PACX-1vQrlQtEYBGaf0IhPz_H9oaM8uD1UaVoR1J_xP6bYe8ZqNAVSRJiLh4DXq52KomGsQ/pub?gid=2059415238&single=true&output=csv"
ENER=read.csv(link8,stringsAsFactors = F)
ENER2=ENER[,c(1,53:57)]
names(ENER2)=c("Pais","2008","2009","2010","2011","2012")
ENER2$`2008`= gsub("\\,", ".",ENER2$`2008`)
ENER2$`2009`= gsub("\\,", ".",ENER2$`2009`)
ENER2$`2010`= gsub("\\,", ".",ENER2$`2010`)
ENER2$`2011`= gsub("\\,", ".",ENER2$`2011`)
ENER2$`2012`= gsub("\\,", ".",ENER2$`2012`)
ENER2[c(2:6)] = lapply(ENER2[c(2:6)], as.numeric)
ENER2$ENER = rowMeans(ENER2[,2:6],na.rm = TRUE)
ENER2= ENER2[,c (1,7)]
ENER2= ENER2[complete.cases(ENER2),]
row.names(ENER2) = NULLDATA DE GASTO
link9="https://docs.google.com/spreadsheets/d/e/2PACX-1vQ6hJgrir9ZBg6Qo5dfwQGluWp4oZLEfSVgz-sVzHEqav1pulMwix_2jNcjxcCkjw/pub?gid=1845220649&single=true&output=csv"
GAST=read.csv(link9,stringsAsFactors = F)
GAST2=GAST[,c(1,53:57)]
names(GAST2)=c("Pais","2008","2009","2010","2011","2012")
GAST2$`2008`= gsub("\\,", ".",GAST2$`2008`)
GAST2$`2009`= gsub("\\,", ".",GAST2$`2009`)
GAST2$`2010`= gsub("\\,", ".",GAST2$`2010`)
GAST2$`2011`= gsub("\\,", ".",GAST2$`2011`)
GAST2$`2012`= gsub("\\,", ".",GAST2$`2012`)
GAST2[c(2:6)] = lapply(GAST2[c(2:6)], as.numeric)
GAST2$GAST = rowMeans(GAST2[,2:6],na.rm = TRUE)
GAST2= GAST2[,c (1,7)]
GAST2= GAST2[complete.cases(GAST2),]
row.names(GAST2) = NULLBANDA ANCHA
link10="https://docs.google.com/spreadsheets/d/e/2PACX-1vTJo2T8oxMssc3utiol3H4IZDOw4jq1sNu12vGh4LUz2aZU-BTtDvkhDaESiIwCJQ/pub?gid=508597872&single=true&output=csv"
ban=read.csv(link10,stringsAsFactors = F)
ban2=ban[,c(1,53:57)]
names(ban2)=c("Pais","2008","2009","2010","2011","2012")
ban2$`2008`= gsub("\\,", ".",ban2$`2008`)
ban2$`2009`= gsub("\\,", ".",ban2$`2009`)
ban2$`2010`= gsub("\\,", ".",ban2$`2010`)
ban2$`2011`= gsub("\\,", ".",ban2$`2011`)
ban2$`2012`= gsub("\\,", ".",ban2$`2012`)
ban2[c(2:6)] = lapply(ban2[c(2:6)], as.numeric)
ban2$ban = rowMeans(ban2[,2:6],na.rm = TRUE)
ban2= ban2[,c (1,7)]
ban2= ban2[complete.cases(ban2),]
row.names(ban2) = NULLCobertura de tratamiento antirretroviral
link11="https://docs.google.com/spreadsheets/d/e/2PACX-1vQ7mf0BRsmhcre-RKQseWRY_aGZrVve25Wmmm85m4OMF2Eb8_NyqGhDjrRmePcuWg/pub?gid=26033417&single=true&output=csv"
antiRetrov=read.csv(link11, stringsAsFactors = F)
antiRetrov2=antiRetrov[,c(1,53:57)]
names(antiRetrov2) = c("Pais","2008","2009","2010","2011","2012")
antiRetrov2[,c(2:6)]=lapply(antiRetrov2[,c(2:6)],as.numeric) #volver numerico en grupo## Warning in lapply(antiRetrov2[, c(2:6)], as.numeric): NAs introduced by
## coercion
## Warning in lapply(antiRetrov2[, c(2:6)], as.numeric): NAs introduced by
## coercion
## Warning in lapply(antiRetrov2[, c(2:6)], as.numeric): NAs introduced by
## coercion
## Warning in lapply(antiRetrov2[, c(2:6)], as.numeric): NAs introduced by
## coercion
## Warning in lapply(antiRetrov2[, c(2:6)], as.numeric): NAs introduced by
## coercionantiRetrov2$CobARet = rowMeans(antiRetrov2[,2:6], na.rm = TRUE)
antiRetrov2= antiRetrov2[,c (1,7)]
antiRetrov2 = antiRetrov2[complete.cases(antiRetrov2),]
row.names(antiRetrov2) = NULLEsperanza de vida en mujeres
link12="https://docs.google.com/spreadsheets/d/e/2PACX-1vQoWu6HPrX8qbiqsewwImM89BxWol-bI-b_ubT6v_hxbPG9JTxXFxaxX5nUJTm-bg/pub?gid=72965045&single=true&output=csv"
EspVida=read.csv(link12, stringsAsFactors = F)
EspVida2=EspVida[,c(1,53:57)]
names(EspVida2) = c("Pais","2008","2009","2010","2011","2012")
EspVida2$`2008` = gsub("\\,", ".", EspVida2$`2008`)
EspVida2$`2009` = gsub("\\,", ".", EspVida2$`2009`)
EspVida2$`2010` = gsub("\\,", ".", EspVida2$`2010`)
EspVida2$`2011` = gsub("\\,", ".", EspVida2$`2011`)
EspVida2$`2012` = gsub("\\,", ".", EspVida2$`2012`)
EspVida2[,c(2:6)]=lapply(EspVida2[,c(2:6)],as.numeric) #volver numerico en grupo
EspVida2$VidaM = rowMeans(EspVida2[,2:6], na.rm = TRUE)
EspVida2= EspVida2[,c (1,7)]
EspVida2 = EspVida2[complete.cases(EspVida2),]
row.names(EspVida2) = NULLMigracion neta
link13="https://docs.google.com/spreadsheets/d/e/2PACX-1vSri2T-73zRzhVczOzNqkAKmQ_qLcWrQHzuVl7QIFUE7fJRUbtKoDCeJ1zixpCjRA/pub?gid=1968467072&single=true&output=csv"
neta=read.csv(link13, stringsAsFactors = F)
migra2=neta[,c(1,57)]
names(migra2) = c("Pais","Migracion")
migra2 = migra2[complete.cases(migra2),]
row.names(migra2) = NULL
migra2[,c(2)]=as.numeric(migra2[,c(2)]) #volver numerico ODA
link14="https://docs.google.com/spreadsheets/d/e/2PACX-1vQMbPO51JV-DhQLOOX9GnTSN7Z4hlKuFjX6Ft9QRS07q1i28GeqAsAAhdfXkNq-uA/pub?gid=1457611805&single=true&output=csv"
ODA=read.csv(link14, stringsAsFactors = F)
ODA2=ODA[,c(1,53:57)]
names(ODA2) = c("Pais","2008","2009","2010", "2011", "2012")
ODA2$`2008` = gsub("\\,", ".", ODA2$`2008`)
ODA2$`2009` = gsub("\\,", ".", ODA2$`2009`)
ODA2$`2010` = gsub("\\,", ".", ODA2$`2010`)
ODA2$`2011` = gsub("\\,", ".", ODA2$`2011`)
ODA2$`2012` = gsub("\\,", ".", ODA2$`2012`)
ODA2[,c(2:6)]=lapply(ODA2[,c(2:6)],as.numeric) #volver numerico en grupo
ODA2$ODA = rowMeans(ODA2[,2:6], na.rm = TRUE)
ODA2= ODA2[,c (1,7)]
ODA2 = ODA2[complete.cases(ODA2),]
row.names(ODA2) = NULLPAIS AFRICANO
link15="https://docs.google.com/spreadsheets/d/e/2PACX-1vT-0H84cFoinLuU8HZaFS1Yln8HhKYcVtaKCbcKn3iShculk-vuU3QJ888lCbUd_Q/pub?gid=1165629081&single=true&output=csv"
AFRICA=read.csv(link15, stringsAsFactors = F)
names(AFRICA)=c("Pais", "Africa")Población total de mujeres
link16="https://docs.google.com/spreadsheets/d/e/2PACX-1vTnOQNQQsmZQ1Ru-bqF5mBAlnpbxQOOH_IUuMv7XylSyOIvQ_Ij1OB64-a-pXaSgg/pub?gid=871901844&single=true&output=csv"
women=read.csv(link16, stringsAsFactors = F)
women2=women[,c(1,53:57)]
names(women2) = c("Pais","2008","2009","2010", "2011", "2012")
women2$`2008` = gsub("\\,", ".", women2$`2008`)
women2$`2009` = gsub("\\,", ".", women2$`2009`)
women2$`2010` = gsub("\\,", ".", women2$`2010`)
women2$`2011` = gsub("\\,", ".", women2$`2011`)
women2$`2012` = gsub("\\,", ".", women2$`2012`)
women2[,c(2:6)]=lapply(women2[,c(2:6)],as.numeric) #volver numerico en grupo
women2$Women = rowMeans(women2[,2:6], na.rm = TRUE)
women2= women2[,c (1,7)]
women2 = women2[complete.cases(women2),]
row.names(women2) = NULLMerge
Act2FLM2=merge(DataAct2,DataFLM2,all.x=T,all.y=T)
desigualdad2=merge(DataTugurios2,DataGini2,all.x=T,all.y=T)
est2=merge(EDU2,ENER2,all.x=T,all.y=T)
estado2=merge(est2,GAST2,all.x=T,all.y=T)
densidad2=merge(estado2,ban2,all.x=T,all.y=T)
Calidad2=merge(desigualdad2,densidad2,all.x=T,all.y=T)
salud2=merge(EspVida2,antiRetrov2, all.x=T,all.y=T)Análisis Factorial
Análisis factorial empoderamiento
Act2FLM2_x=Act2FLM2
row.names(Act2FLM2) = Act2FLM2$Pais
Act2FLM2$Pais = NULL
head(Act2FLM2)## PoblacionActiva FLM
## Afghanistan 43.15240 29.90230
## Albania 46.56340 47.99584
## Algeria 14.75860 14.63667
## Angola 75.30100 80.61500
## Arab World 20.13167 20.88708
## Argentina 48.21300 48.44430Act2FLM2[is.na(Act2FLM2$PoblacionActiva), "PoblacionActiva"]=mean(Act2FLM2$PoblacionActiva, na.rm=T)
Act2FLM2[is.na(Act2FLM2$FLM), "FLM"]=mean(Act2FLM2$FLM, na.rm=T)Act2FLM2=as.data.frame(scale(Act2FLM2[,c(1,2)]))
head(Act2FLM2)## PoblacionActiva FLM
## Afghanistan -0.5011848 -1.4197678
## Albania -0.2705588 -0.1427890
## Algeria -2.4209582 -2.4971627
## Angola 1.6724597 2.1593574
## Arab World -2.0576717 -2.0560302
## Argentina -0.1590254 -0.1111382library(psych)pearson2 = cor(Act2FLM2) #sacar la correlación de los puntajes estandarizadas
pearson2## PoblacionActiva FLM
## PoblacionActiva 1.000000 0.864506
## FLM 0.864506 1.000000cor.plot(pearson2,
numbers=T,
upper=FALSE,
main = "Correlation",
show.legend = FALSE) #verlo en un gráfico
KMO(Act2FLM2) #nos indica que mientras más cercano a uno hay una división subyacente, a partir de 0.7. Que tan buena idea es juntarlos en un indice. ver el Overall MSA(más cercano a 1)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = Act2FLM2)
## Overall MSA = 0.5
## MSA for each item =
## PoblacionActiva FLM
## 0.5 0.5#Prueba de esfericidad de bartlett
library(psych)
cortest.bartlett(Act2FLM2, n=nrow(Act2FLM2)) ## R was not square, finding R from data## $chisq
## [1] 333.6392
##
## $p.value
## [1] 1.549413e-74
##
## $df
## [1] 1fa.parallel(pearson2, fm="pa", fa="fa", main = "Scree Plot",n.obs = nrow(Act2FLM2)) #cuantos indices deberia formar
## Parallel analysis suggests that the number of factors = 1 and the number of components = NAAct2FLM2 = fa(Act2FLM2,
nfactors=1,
rotate="varimax") #codigo para el analisis factorial solo cambiar la data y el numero de factoresAct2FLM2$loadings##
## Loadings:
## MR1
## PoblacionActiva 0.93
## FLM 0.93
##
## MR1
## SS loadings 1.729
## Proportion Var 0.865fa.diagram(Act2FLM2)
#Para ver el tipo de análisis factorial:
# mientras mas grande mejor (lo que aporta)
sort(Act2FLM2$communalities)## PoblacionActiva FLM
## 0.864506 0.864506# mientras mas grande peor (lo que mantiene)
sort(Act2FLM2$uniquenesses)## PoblacionActiva FLM
## 0.135494 0.135494sort(Act2FLM2$complexity)## PoblacionActiva FLM
## 1 1Act2FLM2$scores## MR1
## Afghanistan -0.957936814
## Albania -0.206127451
## Algeria -2.452558785
## Angola 1.910842948
## Arab World -2.051412705
## Argentina -0.134724627
## Armenia -0.003203348
## Aruba 0.321716349
## Australia 0.583245223
## Austria 0.248409257
## Azerbaijan 0.724065533
## Bahamas, The 1.293475038
## Bahrain -0.504468467
## Bangladesh -1.208854798
## Barbados 0.853973567
## Belarus 0.410514455
## Belgium -0.223791856
## Belize 0.176865679
## Benin 1.388095882
## Bermuda 1.055179121
## Bhutan 0.972773007
## Bolivia 0.758604626
## Bosnia and Herzegovina -1.081725092
## Botswana 0.363650970
## Brazil 0.290391135
## Brunei Darussalam 0.439111288
## Bulgaria -0.169381193
## Burkina Faso 0.308629824
## Burundi 1.713822749
## Cabo Verde 0.276720231
## Cambodia 1.913624610
## Cameroon 1.456609287
## Canada 0.811835599
## Caribbean small states 0.322600325
## Cayman Islands 1.049759068
## Central African Republic 0.505677673
## Central Europe and the Baltics -0.174184424
## Chad 0.455223801
## Channel Islands -0.027021182
## Chile -0.249707952
## China 0.935866021
## Colombia 0.344824362
## Comoros -0.522685446
## Congo, Dem. Rep. 0.889777222
## Congo, Rep. 0.380742695
## Costa Rica -0.264711634
## Cote d'Ivoire 0.359120294
## Croatia -0.324163072
## Cuba -0.469537629
## Cyprus 0.445661568
## Czech Republic -0.052299602
## Denmark 0.671106942
## Djibouti 0.026426746
## Dominican Republic -0.545707012
## Early-demographic dividend -1.148137378
## East Asia & Pacific 0.702412513
## East Asia & Pacific (excluding high income) 0.799843359
## East Asia & Pacific (IDA & IBRD countries) 0.794178274
## Ecuador -0.025008402
## Egypt, Arab Rep. -1.923753954
## El Salvador -0.246904148
## Equatorial Guinea 0.111581975
## Eritrea 0.774818825
## Estonia 0.333192186
## Eswatini -0.438737337
## Ethiopia 0.784165191
## Euro area -0.032651759
## Europe & Central Asia 0.024644777
## Europe & Central Asia (excluding high income) 0.003059226
## Europe & Central Asia (IDA & IBRD countries) -0.019337885
## European Union 0.005345216
## Fiji -0.430651323
## Finland 0.424036791
## Fragile and conflict affected situations -0.121270395
## France 0.057534593
## French Polynesia -0.110637088
## Gabon -0.717002018
## Gambia, The 0.004341844
## Georgia 0.371760800
## Germany 0.196612219
## Ghana 1.229293064
## Greece -0.438754594
## Greenland 0.749668323
## Guam 0.334856760
## Guatemala -0.558469088
## Guinea 0.427164715
## Guinea-Bissau 0.521484133
## Guyana -0.445049081
## Haiti 0.495107476
## Heavily indebted poor countries (HIPC) 0.444674059
## High income 0.137513064
## Honduras -0.601406995
## Hong Kong SAR, China 0.164568363
## Hungary -0.459137089
## IBRD only -0.350545563
## Iceland 1.614808242
## IDA & IBRD total -0.222378554
## IDA blend -0.659863816
## IDA only 0.137474868
## IDA total -0.008244770
## India -1.727627330
## Indonesia 0.038596582
## Iran, Islamic Rep. -2.399518052
## Iraq -2.639221453
## Ireland 0.368276158
## Isle of Man 0.262940846
## Israel 0.467166412
## Italy -0.816413641
## Jamaica 0.533835201
## Japan -0.125779127
## Jordan -2.444445892
## Kazakhstan 1.085802316
## Kenya 1.132218882
## Kiribati 0.079575052
## Korea, Dem. People’s Rep. 0.820720957
## Korea, Rep. -0.041486864
## Kosovo -0.927406405
## Kuwait 0.342453034
## Kyrgyz Republic 0.147533753
## Lao PDR 1.826360223
## Late-demographic dividend 0.725821087
## Latin America & Caribbean 0.051031735
## Latin America & Caribbean (excluding high income) 0.082640191
## Latin America & the Caribbean (IDA & IBRD countries) 0.067917711
## Latvia 0.263889255
## Least developed countries: UN classification 0.198790933
## Lebanon -1.935468052
## Lesotho -0.008994052
## Liberia 0.385196962
## Libya -1.499071019
## Liechtenstein 0.117515231
## Lithuania 0.137755199
## Low & middle income -0.221585361
## Low income 0.461308488
## Lower middle income -1.062838706
## Luxembourg -0.054823107
## Macao SAR, China 1.063540554
## Madagascar 2.162356901
## Malawi 1.392729132
## Malaysia -0.305094473
## Maldives -0.218579937
## Mali 0.345872300
## Malta -1.055523687
## Marshall Islands -0.738355520
## Mauritania -1.528646123
## Mauritius -0.546916305
## Mexico -0.492061217
## Middle East & North Africa -2.099511388
## Middle East & North Africa (excluding high income) -2.212285671
## Middle East & North Africa (IDA & IBRD countries) -2.210458196
## Middle income -0.268700119
## Moldova -0.754335065
## Mongolia 0.264793622
## Montenegro -0.515949470
## Morocco -1.749565070
## Mozambique 1.087663848
## Myanmar 0.104144013
## Namibia 0.134322117
## Nauru -0.025657774
## Nepal 2.004082360
## Netherlands 0.609649229
## New Caledonia 0.657928604
## New Zealand 0.782031149
## Nicaragua 0.064029970
## Niger 1.221846237
## Nigeria 0.196544383
## North America 0.566246572
## North Macedonia -0.508392052
## Northern Mariana Islands 0.582158901
## Norway 1.066801298
## OECD members 0.043796360
## Oman -1.680785687
## Other small states 0.005153535
## Pacific island small states -0.071744448
## Pakistan -1.994420826
## Panama -0.226029029
## Papua New Guinea -0.146946725
## Paraguay 0.504846850
## Peru 1.142832351
## Philippines -0.142069509
## Poland -0.178585272
## Portugal 0.347406419
## Post-demographic dividend 0.198455654
## Pre-demographic dividend 0.236305410
## Puerto Rico -1.056886094
## Qatar 0.067587282
## Romania -0.276402251
## Russian Federation 0.631587290
## Rwanda 1.903235199
## Samoa -1.808000681
## Sao Tome and Principe -0.303977896
## Saudi Arabia -2.178148313
## Senegal -0.747793201
## Serbia -0.586548626
## Seychelles 0.418502462
## Sierra Leone 0.334665957
## Singapore 0.441575795
## Slovak Republic 0.018743478
## Slovenia 0.186890300
## Small states 0.018817799
## Solomon Islands 0.852451946
## Somalia -1.113485168
## South Africa -0.304471943
## South Asia -1.673057853
## South Asia (IDA & IBRD) -1.673057853
## South Sudan 1.441090425
## Spain 0.097710499
## Sri Lanka -1.079302538
## St. Lucia 0.586376124
## St. Vincent and the Grenadines 0.199225502
## Sub-Saharan Africa 0.360199399
## Sub-Saharan Africa (excluding high income) 0.360199399
## Sub-Saharan Africa (IDA & IBRD countries) 0.360199399
## Sudan -1.714188636
## Suriname -0.408659681
## Sweden 0.808392777
## Switzerland 0.729499639
## Syrian Arab Republic -2.526839852
## Tajikistan -1.449616001
## Tanzania 2.207699847
## Thailand 1.016285752
## Timor-Leste -1.619543488
## Togo 1.826017425
## Tonga -0.174323234
## Trinidad and Tobago 0.046667065
## Tunisia -1.756480858
## Turkey -1.619744506
## Turkmenistan 0.063090399
## Uganda 1.396774817
## Ukraine 0.207602776
## United Arab Emirates -0.512920396
## United Kingdom 0.382580295
## United States 0.538173274
## Upper middle income 0.436073738
## Uruguay 0.371744132
## Uzbekistan 0.073360587
## Vanuatu 0.716404137
## Venezuela, RB -0.007453655
## Vietnam 1.465705136
## Virgin Islands (U.S.) 0.240124002
## West Bank and Gaza -2.362713114
## World -0.140505894
## Yemen, Rep. -2.781120503
## Zambia 1.011156411
## Zimbabwe 1.530300799scores2=as.data.frame(Act2FLM2$scores)
names(scores2) = c("Empoderamiento")
head(scores2)## Empoderamiento
## Afghanistan -0.9579368
## Albania -0.2061275
## Algeria -2.4525588
## Angola 1.9108429
## Arab World -2.0514127
## Argentina -0.1347246scores2$Pais=row.names(scores2)
row.names(scores2) = NULLAnálisis factorial calidad de vida
#Ponemos las variables en forma intuitiva restando con el mayor valor
Calidad2$BarriosTugurios= 100 - Calidad2$BarriosTugurios
Calidad2$Gini= 65 - Calidad2$GiniCalidad2_X=Calidad2
row.names(Calidad2) = Calidad2$Pais
Calidad2$Pais = NULL
head(Calidad2)## BarriosTugurios Gini EDU ENER GAST
## Afghanistan NA NA 31.74112 48.45538 NA
## Albania NA 35.5 96.67697 100.00000 0.15412
## Algeria NA 37.4 75.13605 98.94692 NA
## Andorra NA NA NA 100.00000 NA
## Angola 34.2 22.3 NA 33.51748 NA
## Antigua and Barbuda NA NA NA 94.22346 NA
## ban
## Afghanistan 0.003874254
## Albania 3.739381479
## Algeria 2.385431981
## Andorra 28.845289134
## Angola 0.070203059
## Antigua and Barbuda 9.959628841Calidad2[is.na(Calidad2$BarriosTugurios), "BarriosTugurios"]=mean(Calidad2$BarriosTugurios, na.rm=T)
Calidad2[is.na(Calidad2$Gini), "Gini"]=mean(Calidad2$Gini, na.rm=T)
Calidad2[is.na(Calidad2$EDU), "EDU"]=mean(Calidad2$EDU, na.rm=T)
Calidad2[is.na(Calidad2$ENER), "ENER"]=mean(Calidad2$ENER, na.rm=T)
Calidad2[is.na(Calidad2$GAST), "GAST"]=mean(Calidad2$GAST, na.rm=T)
Calidad2[is.na(Calidad2$ban), "ban"]=mean(Calidad2$ban, na.rm=T)Calidad2=as.data.frame(scale(Calidad2[,c(1:6)]))
head(Calidad2)## BarriosTugurios Gini EDU ENER
## Afghanistan 0.000000 0.000000 -3.3708303 -1.0591265
## Albania 0.000000 1.468706 1.0350593 0.7089823
## Algeria 0.000000 1.788523 -0.4264896 0.6728591
## Andorra 0.000000 0.000000 0.0000000 0.7089823
## Angola -1.762928 -0.753182 0.0000000 -1.5715337
## Antigua and Barbuda 0.000000 0.000000 0.0000000 0.5108327
## GAST ban
## Afghanistan 0.000000 -0.83285151
## Albania -1.122095 -0.49709541
## Algeria 0.000000 -0.61879155
## Andorra 0.000000 1.75948211
## Angola 0.000000 -0.82688972
## Antigua and Barbuda 0.000000 0.06199493library(psych)promedio2 = cor(Calidad2) #sacar la correlación de los puntajes estandarizadas
promedio2## BarriosTugurios Gini EDU ENER
## BarriosTugurios 1.000000000 -0.003586325 0.38641647 0.5086595
## Gini -0.003586325 1.000000000 0.06969314 0.3248744
## EDU 0.386416467 0.069693141 1.00000000 0.6184280
## ENER 0.508659451 0.324874426 0.61842801 1.0000000
## GAST 0.037442304 0.283556377 -0.01961126 0.1962350
## ban 0.092071248 0.364626587 0.28765543 0.5218063
## GAST ban
## BarriosTugurios 0.03744230 0.09207125
## Gini 0.28355638 0.36462659
## EDU -0.01961126 0.28765543
## ENER 0.19623497 0.52180626
## GAST 1.00000000 0.56726309
## ban 0.56726309 1.00000000cor.plot(promedio2,
numbers=T,
upper=FALSE,
main = "Correlation",
show.legend = FALSE) #verlo en un gráfico
KMO(Calidad2) #nos indica que mientras más cercano a uno hay una división subyacente, a partir de 0.7. Que tan buena idea es juntarlos en un indice. ver el Overall MSA(más cercano a 1)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = Calidad2)
## Overall MSA = 0.62
## MSA for each item =
## BarriosTugurios Gini EDU ENER
## 0.58 0.69 0.68 0.61
## GAST ban
## 0.56 0.61fa.parallel(promedio2, fm="pa", fa="fa", main = "Scree Plot",n.obs = nrow(Calidad2)) #cuantos indices deberia formar
## Parallel analysis suggests that the number of factors = 2 and the number of components = NACalidad2 = fa(Calidad2,
nfactors=3,
rotate="varimax") #codigo para el analisis factorial solo cambiar la data y el numero de factoresCalidad2$loadings##
## Loadings:
## MR3 MR1 MR2
## BarriosTugurios 0.688 -0.107
## Gini 0.477 0.126
## EDU 0.616 0.222
## ENER 0.812 0.554
## GAST 0.360 0.931
## ban 0.208 0.664 0.352
##
## MR3 MR1 MR2
## SS loadings 1.558 1.165 1.023
## Proportion Var 0.260 0.194 0.171
## Cumulative Var 0.260 0.454 0.624fa.diagram(Calidad2)
#Para ver el tipo de análisis factorial:
# mientras mas grande mejor (lo que aporta)
sort(Calidad2$communalities)## Gini EDU BarriosTugurios ban
## 0.2449321 0.4355344 0.4945847 0.6070800
## ENER GAST
## 0.9664973 0.9950000# mientras mas grande peor (lo que mantiene)
sort(Calidad2$uniquenesses)## GAST ENER ban BarriosTugurios
## 0.00227872 0.03348006 0.39290797 0.50540898
## EDU Gini
## 0.56449554 0.75505748sort(Calidad2$complexity)## BarriosTugurios Gini GAST EDU
## 1.088113 1.150353 1.293427 1.294300
## ban ENER
## 1.746668 1.765751Calidad2$scores## MR3
## Afghanistan -0.993305602
## Albania 0.556809873
## Algeria 0.299975584
## Andorra 0.252986939
## Angola -1.438560842
## Antigua and Barbuda 0.366987825
## Arab World 0.221388549
## Argentina 1.449641471
## Armenia 0.558001662
## Aruba 0.338724444
## Australia 0.129894542
## Austria 0.019625590
## Azerbaijan 0.736531222
## Bahamas, The 0.503567519
## Bahrain 0.524838401
## Bangladesh -1.160950879
## Barbados 0.362390275
## Belarus 0.315826637
## Belgium -0.091418977
## Belize 0.380781036
## Benin -1.843074939
## Bermuda -0.113291382
## Bhutan -0.062651701
## Bolivia 0.658008875
## Bosnia and Herzegovina 0.566287612
## Botswana -0.095999955
## Brazil 1.466063511
## British Virgin Islands -0.239280527
## Brunei Darussalam 0.678715761
## Bulgaria 0.505826784
## Burkina Faso -1.417071793
## Burundi -1.907279196
## Cabo Verde 0.168104606
## Cambodia -1.051328345
## Cameroon -0.589921625
## Canada 0.039924754
## Caribbean small states 0.438466119
## Cayman Islands 0.188505250
## Central African Republic -2.830856878
## Central Europe and the Baltics 0.415185142
## Chad -2.749890419
## Channel Islands 0.522521449
## Chile 0.880136616
## China 1.131437774
## Colombia 1.874162605
## Comoros -0.496698005
## Congo, Dem. Rep. -1.838395977
## Congo, Rep. -0.693009279
## Costa Rica 0.930482904
## Cote d'Ivoire -0.869622784
## Croatia 0.398081111
## Cuba 0.805285926
## Curacao 0.344658284
## Cyprus 0.354216365
## Czech Republic 0.040394714
## Denmark -0.283597989
## Djibouti -0.373915005
## Dominica 0.363291655
## Dominican Republic 1.692675587
## Early-demographic dividend 0.486193642
## East Asia & Pacific 0.873495612
## East Asia & Pacific (excluding high income) 0.947711801
## East Asia & Pacific (IDA & IBRD countries) 0.968542817
## Ecuador 0.874883217
## Egypt, Arab Rep. 1.339479705
## El Salvador 0.627613424
## Equatorial Guinea -0.222626034
## Eritrea -0.987722449
## Estonia 0.232281711
## Eswatini -0.337674563
## Ethiopia -2.128535629
## Euro area 0.309667609
## Europe & Central Asia 0.442869426
## Europe & Central Asia (excluding high income) 0.645011128
## Europe & Central Asia (IDA & IBRD countries) 0.638315085
## European Union 0.335229949
## Faroe Islands 0.200402288
## Fiji 0.407275596
## Finland -0.209670348
## Fragile and conflict affected situations -0.949121485
## France -0.022968843
## French Polynesia 0.479426657
## Gabon 0.311505835
## Gambia, The -0.672134790
## Georgia 0.579831546
## Germany -0.101621673
## Ghana 0.001456106
## Gibraltar 0.205927538
## Greece 0.418641738
## Greenland 0.357443979
## Grenada 0.212545396
## Guam 0.625084092
## Guatemala 0.491550070
## Guinea -1.723150699
## Guinea-Bissau -1.366503654
## Guyana 0.510562956
## Haiti -1.524380661
## Heavily indebted poor countries (HIPC) -1.764245364
## High income 0.187355349
## Honduras 0.548356424
## Hong Kong SAR, China 0.244013511
## Hungary 0.119088882
## IBRD only 0.882198560
## Iceland -0.179831114
## IDA & IBRD total 0.460780493
## IDA blend -0.455510273
## IDA only -1.535301480
## IDA total -1.142803932
## India 0.318408283
## Indonesia 1.254892139
## Iran, Islamic Rep. 0.744472277
## Iraq 0.312552834
## Ireland 0.165250064
## Isle of Man 0.522521449
## Israel 0.166680931
## Italy 0.366969411
## Jamaica 0.396231423
## Japan -0.026389200
## Jordan 1.343718276
## Kazakhstan 0.513377302
## Kenya -1.438600040
## Kiribati -0.136556737
## Korea, Dem. People’s Rep. -1.160831840
## Korea, Rep. -0.162558484
## Kosovo -0.187126863
## Kuwait 0.781079184
## Kyrgyz Republic 0.599987225
## Lao PDR -0.310078661
## Late-demographic dividend 1.063823481
## Latin America & Caribbean 0.540014087
## Latin America & Caribbean (excluding high income) 0.532743402
## Latin America & the Caribbean (IDA & IBRD countries) 0.534634556
## Latvia 0.453619845
## Least developed countries: UN classification -1.721510883
## Lebanon 0.472772342
## Lesotho -1.182849504
## Liberia -2.348347542
## Libya 0.599629859
## Liechtenstein 0.078099840
## Lithuania 0.402500680
## Low & middle income 0.440360974
## Low income -1.858847293
## Lower middle income 0.268341483
## Luxembourg 0.027788484
## Macao SAR, China 0.472851143
## Madagascar -2.187618184
## Malawi -2.244658042
## Malaysia 0.757899375
## Maldives 0.537408905
## Mali -2.197177804
## Malta 0.147922914
## Marshall Islands 0.244770233
## Mauritania -1.112023043
## Mauritius 0.574643105
## Mexico 0.776953126
## Micronesia, Fed. Sts. -0.265861925
## Middle East & North Africa 0.478335285
## Middle East & North Africa (excluding high income) 0.505280150
## Middle East & North Africa (IDA & IBRD countries) 0.502744220
## Middle income 0.661117949
## Moldova 0.565760045
## Monaco 0.111440230
## Mongolia 0.089499832
## Montenegro 0.490261142
## Morocco 1.193238998
## Mozambique -2.348241930
## Myanmar -0.583797097
## Namibia 0.262582928
## Nauru 0.498431095
## Nepal -0.686578970
## Netherlands -0.169116510
## New Caledonia 0.445320164
## New Zealand 0.281653410
## Nicaragua 0.273569873
## Niger -2.995522085
## Nigeria -1.089503739
## North America 0.137662158
## North Macedonia 0.565436963
## Northern Mariana Islands 0.522521449
## Norway -0.173069668
## OECD members 0.201783467
## Oman 0.714386655
## Other small states -0.305156929
## Pacific island small states -0.164003040
## Pakistan 0.086119172
## Palau 0.583399471
## Panama 0.713531238
## Papua New Guinea -1.455381739
## Paraguay 0.991506609
## Peru 0.624417696
## Philippines 0.637047559
## Poland 0.451122157
## Portugal 0.377399239
## Post-demographic dividend 0.170133707
## Pre-demographic dividend -1.616796100
## Puerto Rico 0.565351121
## Qatar 0.655600754
## Romania 0.543585092
## Russian Federation 0.662906718
## Rwanda -1.978360800
## Samoa 0.776339110
## San Marino 0.361253293
## Sao Tome and Principe -0.581297151
## Saudi Arabia 0.591031499
## Senegal -0.413850975
## Serbia 0.382385707
## Seychelles 0.566501802
## Sierra Leone -1.765810521
## Singapore 0.305212636
## Sint Maarten (Dutch part) 0.522521449
## Slovak Republic 0.186512553
## Slovenia -0.067180853
## Small states -0.148307712
## Solomon Islands -1.114750783
## Somalia -2.089650043
## South Africa 1.534396398
## South Asia 0.204050215
## South Asia (IDA & IBRD) 0.204050215
## South Sudan -1.991113231
## Spain 0.349783728
## Sri Lanka 0.382798518
## St. Kitts and Nevis 0.249692399
## St. Lucia 0.344042230
## St. Martin (French part) -0.369993714
## St. Vincent and the Grenadines 0.327405838
## Sub-Saharan Africa -1.458099943
## Sub-Saharan Africa (excluding high income) -1.458283881
## Sub-Saharan Africa (IDA & IBRD countries) -1.458099943
## Sudan -1.269064654
## Suriname 0.460643116
## Sweden -0.214220543
## Switzerland -0.142890217
## Syrian Arab Republic 0.524541704
## Tajikistan 0.519095524
## Tanzania -2.016430913
## Thailand 1.257290138
## Timor-Leste -0.901660775
## Togo -0.918545795
## Tonga 0.536819893
## Trinidad and Tobago 0.656836860
## Tunisia 0.526463449
## Turkey 1.544914394
## Turkmenistan 0.646424274
## Turks and Caicos Islands 0.367305383
## Tuvalu 0.557480813
## Uganda -1.811624312
## Ukraine 0.399455881
## United Arab Emirates 0.552132395
## United Kingdom 0.068890053
## United States 0.187433949
## Upper middle income 1.093940651
## Uruguay 0.741389302
## Uzbekistan 0.697015720
## Vanuatu -0.972011451
## Venezuela, RB 0.690521782
## Vietnam 0.957084121
## Virgin Islands (U.S.) 0.540144513
## West Bank and Gaza 0.641702032
## World 0.426631333
## Yemen, Rep. -0.286498349
## Zambia -1.104017100
## Zimbabwe -0.179454559
## MR1
## Afghanistan -0.591262933
## Albania 0.351893454
## Algeria 0.481970830
## Andorra 1.049147223
## Angola -0.623255388
## Antigua and Barbuda 0.311058811
## Arab World -0.035970831
## Argentina -0.805512605
## Armenia 0.330053265
## Aruba 0.485919986
## Australia 1.013366336
## Austria 1.094166481
## Azerbaijan 0.183476762
## Bahamas, The 0.401944542
## Bahrain 0.528200811
## Bangladesh 0.102265670
## Barbados 0.785648295
## Belarus 0.818143544
## Belgium 1.393793112
## Belize 0.008416537
## Benin -0.165646191
## Bermuda 2.047504685
## Bhutan -0.145072210
## Bolivia -0.464157411
## Bosnia and Herzegovina 0.435114481
## Botswana -1.499395505
## Brazil -0.885041175
## British Virgin Islands 0.576309326
## Brunei Darussalam 0.173159892
## Bulgaria 0.577205724
## Burkina Faso -1.605882980
## Burundi -1.531695708
## Cabo Verde -0.247174935
## Cambodia -1.136093806
## Cameroon -0.785753582
## Canada 1.274240665
## Caribbean small states 0.119924619
## Cayman Islands 1.204451958
## Central African Republic -0.059554902
## Central Europe and the Baltics 0.661879235
## Chad -0.306309344
## Channel Islands 0.399970906
## Chile 0.048725291
## China -0.461290705
## Colombia -1.486466659
## Comoros -0.410345071
## Congo, Dem. Rep. -1.122314728
## Congo, Rep. -1.222841493
## Costa Rica -0.094725505
## Cote d'Ivoire -0.246266867
## Croatia 0.735442339
## Cuba -0.028397660
## Curacao 0.828355955
## Cyprus 0.897308243
## Czech Republic 1.130642152
## Denmark 1.672979461
## Djibouti -0.959683348
## Dominica 0.354677763
## Dominican Republic -1.312356635
## Early-demographic dividend -0.802086975
## East Asia & Pacific -0.409196669
## East Asia & Pacific (excluding high income) -0.498324967
## East Asia & Pacific (IDA & IBRD countries) -0.481591078
## Ecuador -0.205406034
## Egypt, Arab Rep. -0.842202061
## El Salvador -0.153723666
## Equatorial Guinea -0.571657335
## Eritrea -0.967437043
## Estonia 0.974508415
## Eswatini -1.270900389
## Ethiopia -0.161823873
## Euro area 0.905453242
## Europe & Central Asia 0.612116190
## Europe & Central Asia (excluding high income) 0.267326050
## Europe & Central Asia (IDA & IBRD countries) 0.289862329
## European Union 0.858067380
## Faroe Islands 1.175797834
## Fiji -0.104461302
## Finland 1.399515937
## Fragile and conflict affected situations -0.889958835
## France 1.355778873
## French Polynesia 0.503765103
## Gabon -0.191368078
## Gambia, The -1.219220864
## Georgia 0.246936709
## Germany 1.375680703
## Ghana -0.897647106
## Gibraltar 1.162490219
## Greece 0.732386188
## Greenland 0.797561494
## Grenada 0.311749365
## Guam 0.152947846
## Guatemala -0.463518774
## Guinea -0.870845085
## Guinea-Bissau -2.012624099
## Guyana -0.757110632
## Haiti -0.373029819
## Heavily indebted poor countries (HIPC) -0.575008964
## High income 0.952911046
## Honduras -0.740341903
## Hong Kong SAR, China 1.101716693
## Hungary 1.062834308
## IBRD only -0.636475994
## Iceland 1.523542708
## IDA & IBRD total -0.649845215
## IDA blend -0.233201001
## IDA only -0.359912470
## IDA total -0.365272091
## India -0.873979768
## Indonesia -0.914804923
## Iran, Islamic Rep. 0.019493015
## Iraq 0.506948224
## Ireland 0.987710709
## Isle of Man 0.399970906
## Israel 0.823455899
## Italy 0.795310030
## Jamaica 0.074236337
## Japan 1.154275756
## Jordan -0.755865970
## Kazakhstan 0.424489436
## Kenya -1.066756422
## Kiribati -0.476055464
## Korea, Dem. People’s Rep. -1.103210438
## Korea, Rep. 1.427828848
## Kosovo 0.207315661
## Kuwait 0.051030098
## Kyrgyz Republic 0.224318498
## Lao PDR -0.288821029
## Late-demographic dividend -0.428277212
## Latin America & Caribbean 0.172030392
## Latin America & Caribbean (excluding high income) 0.151205799
## Latin America & the Caribbean (IDA & IBRD countries) 0.173620653
## Latvia 0.739153812
## Least developed countries: UN classification -0.358717092
## Lebanon 0.425043691
## Lesotho -1.638076222
## Liberia -0.976738266
## Libya 0.099233047
## Liechtenstein 1.470364393
## Lithuania 0.792223316
## Low & middle income -0.656002152
## Low income -0.430520508
## Lower middle income -0.726510583
## Luxembourg 1.317142963
## Macao SAR, China 0.810382289
## Madagascar -0.490309104
## Malawi -0.959483179
## Malaysia 0.073085555
## Maldives 0.194440231
## Mali -0.215774851
## Malta 1.216886292
## Marshall Islands -0.007249052
## Mauritania -1.134535147
## Mauritius 0.324836588
## Mexico 0.101304748
## Micronesia, Fed. Sts. -0.566532637
## Middle East & North Africa 0.143452450
## Middle East & North Africa (excluding high income) 0.107316453
## Middle East & North Africa (IDA & IBRD countries) 0.107491049
## Middle income -0.612770694
## Moldova 0.367645548
## Monaco 1.390063760
## Mongolia -0.252499549
## Montenegro 0.516176985
## Morocco -1.146194878
## Mozambique -0.292991260
## Myanmar -0.850289620
## Namibia -2.115041881
## Nauru 0.390551480
## Nepal 0.172879619
## Netherlands 1.628090383
## New Caledonia 0.585910903
## New Zealand 0.925724425
## Nicaragua -0.449471106
## Niger 0.188496139
## Nigeria -0.198042289
## North America 1.031023916
## North Macedonia 0.430727410
## Northern Mariana Islands 0.399970906
## Norway 1.602456262
## OECD members 0.915899348
## Oman 0.119291066
## Other small states -0.401373205
## Pacific island small states -0.481179872
## Pakistan 0.283043811
## Palau 0.122346885
## Panama -0.369067440
## Papua New Guinea -1.452655641
## Paraguay -0.323851933
## Peru -0.318598192
## Philippines -0.571520910
## Poland 0.635974870
## Portugal 0.721730716
## Post-demographic dividend 0.987861711
## Pre-demographic dividend -0.486083186
## Puerto Rico 0.483801966
## Qatar 0.286175874
## Romania 0.533231454
## Russian Federation 0.277190290
## Rwanda -1.115834012
## Samoa -0.174810412
## San Marino 0.788386725
## Sao Tome and Principe -0.481630647
## Saudi Arabia 0.296762788
## Senegal -0.934979564
## Serbia 0.616504036
## Seychelles 0.215088989
## Sierra Leone -1.160545419
## Singapore 0.858763293
## Sint Maarten (Dutch part) 0.399970906
## Slovak Republic 0.929322301
## Slovenia 1.234030951
## Small states -0.304312453
## Solomon Islands -1.240941318
## Somalia -0.461274158
## South Africa -1.815412757
## South Asia -0.737978021
## South Asia (IDA & IBRD) -0.737978021
## South Sudan -1.663700128
## Spain 0.823208167
## Sri Lanka -0.202607236
## St. Kitts and Nevis 0.878014068
## St. Lucia 0.367348772
## St. Martin (French part) -0.283216548
## St. Vincent and the Grenadines 0.327261617
## Sub-Saharan Africa -0.676698974
## Sub-Saharan Africa (excluding high income) -0.676821470
## Sub-Saharan Africa (IDA & IBRD countries) -0.676698974
## Sudan -0.923127239
## Suriname -0.062619783
## Sweden 1.460991887
## Switzerland 1.494547122
## Syrian Arab Republic 0.016678736
## Tajikistan 0.275847979
## Tanzania -0.915373775
## Thailand -0.635154952
## Timor-Leste -0.826547346
## Togo -1.229092651
## Tonga -0.121803923
## Trinidad and Tobago 0.333699857
## Tunisia 0.295701521
## Turkey -0.946846947
## Turkmenistan 0.090332510
## Turks and Caicos Islands 0.281158730
## Tuvalu 0.108542052
## Uganda -1.155196713
## Ukraine 0.521739283
## United Arab Emirates 0.402313943
## United Kingdom 1.237145144
## United States 0.958407737
## Upper middle income -0.448402996
## Uruguay 0.211183429
## Uzbekistan 0.099719546
## Vanuatu -1.114037365
## Venezuela, RB 0.159171344
## Vietnam -0.297938653
## Virgin Islands (U.S.) 0.357525596
## West Bank and Gaza 0.227442333
## World -0.474578088
## Yemen, Rep. -0.608442477
## Zambia -1.498676888
## Zimbabwe -2.089189133
## MR2
## Afghanistan 0.15655800
## Albania -1.29724367
## Algeria -0.16886720
## Andorra -0.42797285
## Angola 0.23195033
## Antigua and Barbuda -0.11688492
## Arab World -0.55597791
## Argentina -0.21942395
## Armenia -1.12779718
## Aruba -0.17342774
## Australia 1.83550252
## Austria 2.36603925
## Azerbaijan -1.14990252
## Bahamas, The -0.15003091
## Bahrain -0.18717855
## Bangladesh -0.06542994
## Barbados -0.31455997
## Belarus -0.66827818
## Belgium 1.26984333
## Belize 0.00954316
## Benin -0.02120005
## Bermuda -1.93805257
## Bhutan 0.02945341
## Bolivia -1.00403376
## Bosnia and Herzegovina -1.42218929
## Botswana -0.49138536
## Brazil 0.69027585
## British Virgin Islands -0.24804995
## Brunei Darussalam -0.03243604
## Bulgaria -0.81362678
## Burkina Faso -0.53302491
## Burundi -0.69872575
## Cabo Verde -1.22589946
## Cambodia 0.43046464
## Cameroon 0.29400825
## Canada 0.92797080
## Caribbean small states -0.02462369
## Cayman Islands -0.49481773
## Central African Republic -0.08244552
## Central Europe and the Baltics -0.29279454
## Chad 0.09563859
## Channel Islands -0.14856013
## Chile -0.89338200
## China 1.43099561
## Colombia -0.53347648
## Comoros 0.11777139
## Congo, Dem. Rep. -0.91362596
## Congo, Rep. 0.45956014
## Costa Rica -0.63000796
## Cote d'Ivoire 0.03226354
## Croatia -0.45689284
## Cuba -0.63531670
## Curacao -0.33294182
## Cyprus -1.09492378
## Czech Republic 0.38485263
## Denmark 2.50262409
## Djibouti 0.36817939
## Dominica -0.13516515
## Dominican Republic 0.53091507
## Early-demographic dividend -0.02262031
## East Asia & Pacific 2.48797077
## East Asia & Pacific (excluding high income) 1.26842256
## East Asia & Pacific (IDA & IBRD countries) 1.26209538
## Ecuador -0.81585144
## Egypt, Arab Rep. -0.41999279
## El Salvador -1.26268229
## Equatorial Guinea 0.23028840
## Eritrea 0.35007628
## Estonia 0.92144121
## Eswatini 0.48367224
## Ethiopia -1.01183672
## Euro area 1.35949933
## Europe & Central Asia 1.15125094
## Europe & Central Asia (excluding high income) -0.15620745
## Europe & Central Asia (IDA & IBRD countries) -0.19920842
## European Union 1.27119368
## Faroe Islands -0.48248468
## Fiji 0.05160349
## Finland 3.69315578
## Fragile and conflict affected situations 0.31514396
## France 1.42667595
## French Polynesia -0.19323431
## Gabon -0.50190611
## Gambia, The -0.89360890
## Georgia -0.08487381
## Germany 2.31611246
## Ghana -0.52005787
## Gibraltar -0.47675693
## Greece -0.68975422
## Greenland -0.31968755
## Grenada -0.12246369
## Guam -0.04223865
## Guatemala -1.18203725
## Guinea 0.25284920
## Guinea-Bissau 0.75738925
## Guyana 0.31196831
## Haiti 0.13686521
## Heavily indebted poor countries (HIPC) 0.17734179
## High income 1.90825260
## Honduras 0.28755261
## Hong Kong SAR, China -0.73849001
## Hungary -0.06588613
## IBRD only 0.51520626
## Iceland 1.95584895
## IDA & IBRD total 0.49483531
## IDA blend 0.08043269
## IDA only 0.10154647
## IDA total 0.11392352
## India 0.21125914
## Indonesia -0.92600551
## Iran, Islamic Rep. -0.85251972
## Iraq -1.56011335
## Ireland 0.59074763
## Isle of Man -0.14856013
## Israel 4.70174563
## Italy 0.17956914
## Jamaica -0.01732078
## Japan 3.18294935
## Jordan -0.42651384
## Kazakhstan -1.27545847
## Kenya 0.19536080
## Kiribati 0.19344907
## Korea, Dem. People’s Rep. 0.44011626
## Korea, Rep. 3.53587989
## Kosovo -0.07193001
## Kuwait -1.27692202
## Kyrgyz Republic -1.22124127
## Lao PDR 0.08863294
## Late-demographic dividend 0.76659584
## Latin America & Caribbean -0.37100947
## Latin America & Caribbean (excluding high income) -0.28892178
## Latin America & the Caribbean (IDA & IBRD countries) -0.36528984
## Latvia -0.76967720
## Least developed countries: UN classification 0.09643968
## Lebanon -0.13258305
## Lesotho -0.81867448
## Liberia 0.36436394
## Libya -0.02075539
## Liechtenstein -0.60926941
## Lithuania -0.41385395
## Low & middle income 0.55320728
## Low income 0.12074292
## Lower middle income -0.27971513
## Luxembourg 0.39533401
## Macao SAR, China -1.66441548
## Madagascar -1.10107094
## Malawi 0.31986047
## Malaysia 0.10374786
## Maldives -0.06297874
## Mali -0.52465934
## Malta -0.92119861
## Marshall Islands 0.03021435
## Mauritania 0.44084404
## Mauritius -1.27187215
## Mexico -0.67894120
## Micronesia, Fed. Sts. 0.22667426
## Middle East & North Africa -0.03533436
## Middle East & North Africa (excluding high income) -0.98633168
## Middle East & North Africa (IDA & IBRD countries) -0.98785596
## Middle income 0.54722805
## Moldova -0.81728958
## Monaco -0.57470712
## Mongolia -0.88628659
## Montenegro -1.11543562
## Morocco 0.10981329
## Mozambique -0.95190692
## Myanmar 0.33387713
## Namibia -0.41814942
## Nauru -0.14546526
## Nepal -1.21478258
## Netherlands 0.69859449
## New Caledonia -0.22859077
## New Zealand 0.13086289
## Nicaragua -1.12399715
## Niger -0.15752639
## Nigeria 0.02669046
## North America 2.33146965
## North Macedonia -1.24404921
## Northern Mariana Islands -0.14856013
## Norway 0.48518411
## OECD members 1.92565125
## Oman -1.20059757
## Other small states 0.15633406
## Pacific island small states 0.19464143
## Pakistan -0.96347710
## Palau -0.03093112
## Panama -1.07184331
## Papua New Guinea 0.52273006
## Paraguay -1.19790149
## Peru -1.19702793
## Philippines -1.01666690
## Poland -0.53092223
## Portugal 0.61337055
## Post-demographic dividend 2.01878162
## Pre-demographic dividend 0.14539089
## Puerto Rico -0.91138804
## Qatar -0.78029968
## Romania -0.86269126
## Russian Federation 0.19424966
## Rwanda 0.38914564
## Samoa 0.10884222
## San Marino -0.31573862
## Sao Tome and Principe 0.19865752
## Saudi Arabia -0.67885181
## Senegal -0.41506886
## Serbia -0.40433079
## Seychelles -0.05611129
## Sierra Leone 0.43382385
## Singapore 1.66745824
## Sint Maarten (Dutch part) -0.14856013
## Slovak Republic -0.84956907
## Slovenia 1.38016227
## Small states 0.12366034
## Solomon Islands 0.47826984
## Somalia 0.16820031
## South Africa 0.49819478
## South Asia 0.08533137
## South Asia (IDA & IBRD) 0.08533137
## South Sudan 0.54159040
## Spain 0.33581297
## Sri Lanka -1.14608176
## St. Kitts and Nevis -0.35686248
## St. Lucia -0.14109816
## St. Martin (French part) 0.10519437
## St. Vincent and the Grenadines -0.12498442
## Sub-Saharan Africa 0.22561122
## Sub-Saharan Africa (excluding high income) 0.22565029
## Sub-Saharan Africa (IDA & IBRD countries) 0.22561122
## Sudan 0.31711736
## Suriname 0.05595306
## Sweden 3.22871390
## Switzerland 2.59284323
## Syrian Arab Republic 0.01102996
## Tajikistan -1.37897589
## Tanzania -0.52007600
## Thailand -0.74719994
## Timor-Leste 0.28729210
## Togo -0.64307414
## Tonga 0.09109121
## Trinidad and Tobago -1.48138735
## Tunisia -0.47282805
## Turkey 0.18423974
## Turkmenistan -0.01544788
## Turks and Caicos Islands -0.10443004
## Tuvalu -0.02841669
## Uganda -0.42599633
## Ukraine -0.33048447
## United Arab Emirates -0.83459848
## United Kingdom 0.65604183
## United States 2.49967168
## Upper middle income 0.59479279
## Uruguay -0.93996996
## Uzbekistan -1.15895170
## Vanuatu 0.43202009
## Venezuela, RB -1.14319770
## Vietnam -0.99994752
## Virgin Islands (U.S.) -0.13029119
## West Bank and Gaza -1.00085515
## World 1.94014852
## Yemen, Rep. 0.24340952
## Zambia -0.45471833
## Zimbabwe 0.81150227promedio2=as.data.frame(Calidad2$scores)
names(promedio2) = c("Densidad", "Desigualdad", "Informacion")
head(promedio2)## Densidad Desigualdad Informacion
## Afghanistan -0.9933056 -0.5912629 0.1565580
## Albania 0.5568099 0.3518935 -1.2972437
## Algeria 0.2999756 0.4819708 -0.1688672
## Andorra 0.2529869 1.0491472 -0.4279728
## Angola -1.4385608 -0.6232554 0.2319503
## Antigua and Barbuda 0.3669878 0.3110588 -0.1168849promedio2$Pais=row.names(promedio2)
row.names(promedio2) = NULLAnálisis factorial de salud
salud2_X=salud2
row.names(salud2) = salud2$Pais
salud2$Pais = NULL
head(salud2)## VidaM CobARet
## Afghanistan 62.44780 NA
## Albania 79.21420 18.0
## Algeria 75.89040 28.8
## Angola 60.80120 9.8
## Antigua and Barbuda 77.79520 NA
## Arab World 71.90649 NAsalud2[is.na(salud2$VidaM), "VidaM"]=mean(salud2$VidaM, na.rm=T)
salud2[is.na(salud2$CobARet), "CobARet"]=mean(salud2$CobARet, na.rm=T)salud2=as.data.frame(scale(salud2[,c(1,2)]))
head(salud2)## VidaM CobARet
## Afghanistan -1.1656446 0.0000000
## Albania 0.7174271 -0.7262289
## Algeria 0.3441237 0.0631689
## Angola -1.3505779 -1.3255865
## Antigua and Barbuda 0.5580561 0.0000000
## Arab World -0.1033177 0.0000000library(psych)puntaje2 = cor(salud2) #sacar la correlación de los puntajes estandarizadas
puntaje2## VidaM CobARet
## VidaM 1.0000000 0.3953312
## CobARet 0.3953312 1.0000000cor.plot(puntaje2,
numbers=T,
upper=FALSE,
main = "Correlation",
show.legend = FALSE) #verlo en un gráfico
KMO(salud2) #nos indica que mientras más cercano a uno hay una división subyacente, a partir de 0.7. Que tan buena idea es juntarlos en un indice. ver el Overall MSA(más cercano a 1)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = salud2)
## Overall MSA = 0.5
## MSA for each item =
## VidaM CobARet
## 0.5 0.5fa.parallel(puntaje2, fm="pa", fa="fa", main = "Scree Plot",n.obs = nrow(salud2)) #cuantos indices deberia formar
## Parallel analysis suggests that the number of factors = 1 and the number of components = NAsalud2 = fa(salud2,
nfactors=1,
rotate="varimax") #codigo para el analisis factorial solo cambiar la data y el numero de factoressalud2$loadings##
## Loadings:
## MR1
## VidaM 0.629
## CobARet 0.629
##
## MR1
## SS loadings 0.791
## Proportion Var 0.395fa.diagram(salud2)
#Para ver el tipo de análisis factorial:
# mientras mas grande mejor (lo que aporta)
sort(salud2$communalities)## VidaM CobARet
## 0.3953312 0.3953312# mientras mas grande peor (lo que mantiene)
sort(salud2$uniquenesses)## VidaM CobARet
## 0.6046688 0.6046688sort(salud2$complexity)## VidaM CobARet
## 1 1salud2$scores## MR1
## Afghanistan -0.525254033
## Albania -0.003966211
## Algeria 0.183531157
## Angola -1.205913178
## Antigua and Barbuda 0.251467044
## Arab World -0.046556236
## Argentina 1.113839189
## Armenia -0.493835927
## Aruba 0.232539168
## Australia 1.668689360
## Austria 1.766518457
## Azerbaijan -0.489522683
## Bahamas, The 0.242197633
## Bahrain 0.164941789
## Bangladesh -0.835551393
## Barbados 0.702308356
## Belarus 0.114545236
## Belgium 0.512853925
## Belize -0.150894145
## Benin -0.440830430
## Bermuda 0.531680583
## Bhutan -0.249382772
## Bolivia -0.873357083
## Bosnia and Herzegovina 0.284150526
## Botswana 0.171549232
## Brazil 0.565174203
## Brunei Darussalam 0.284201136
## Bulgaria 0.091200473
## Burkina Faso -0.670685296
## Burundi -0.847462831
## Cabo Verde 0.012044930
## Cambodia 0.623487436
## Cameroon -1.171192539
## Canada 0.537146387
## Caribbean small states 0.113243573
## Cayman Islands 0.600914097
## Central African Republic -1.759328051
## Central Europe and the Baltics 0.329657888
## Chad -1.111819538
## Channel Islands 0.468186163
## Chile 1.696529561
## China 0.202021764
## Colombia -0.154708810
## Comoros -1.108943528
## Congo, Dem. Rep. -1.346127167
## Congo, Rep. -0.932693059
## Costa Rica 0.804023461
## Cote d'Ivoire -1.358032295
## Croatia 0.350904185
## Cuba 0.688019445
## Curacao 0.370641810
## Cyprus 0.698208218
## Czech Republic 1.187016106
## Denmark 1.579167337
## Djibouti -1.136876101
## Dominican Republic -0.175246244
## Early-demographic dividend -0.156507262
## East Asia & Pacific 0.188080249
## East Asia & Pacific (excluding high income) 0.133967444
## East Asia & Pacific (IDA & IBRD countries) 0.135679689
## Ecuador 0.103322520
## Egypt, Arab Rep. -0.686768677
## El Salvador 0.511151781
## Equatorial Guinea -1.369142395
## Eritrea -0.315059970
## Estonia 0.681322714
## Eswatini -0.748431840
## Ethiopia -0.359709550
## Euro area 0.553190188
## Europe & Central Asia 0.344983535
## Europe & Central Asia (excluding high income) 0.135216023
## Europe & Central Asia (IDA & IBRD countries) 0.159348217
## European Union 0.502147007
## Faroe Islands 0.523987970
## Fiji -0.020962785
## Finland 0.533097643
## Fragile and conflict affected situations -0.539849697
## France 2.000510738
## French Polynesia 0.263299497
## Gabon -0.508556573
## Gambia, The -1.152878942
## Georgia -0.228357151
## Germany 1.715893041
## Ghana -1.005406067
## Greece 1.251217360
## Greenland 0.021943774
## Grenada 0.110854182
## Guam 0.410400471
## Guatemala 0.181174461
## Guinea -1.100175699
## Guinea-Bissau -1.385799035
## Guyana 0.393568629
## Haiti -0.628226762
## Heavily indebted poor countries (HIPC) -0.626624262
## High income 0.491239739
## Honduras 0.181340532
## Hong Kong SAR, China 0.671767108
## Hungary 0.658155294
## IBRD only 0.037009168
## Iceland 0.561438848
## IDA & IBRD total -0.094607364
## IDA blend -0.619924032
## IDA only -0.464057657
## IDA total -0.514983179
## India -0.573839043
## Indonesia -0.924028440
## Iran, Islamic Rep. -0.648707880
## Iraq -0.079102742
## Ireland 1.544109887
## Israel 0.535122015
## Italy 1.636732956
## Jamaica -0.123547729
## Japan 1.687808037
## Jordan 0.114052689
## Kazakhstan -0.551478399
## Kenya -0.302403525
## Kiribati -0.217782329
## Korea, Dem. People’s Rep. 0.004493690
## Korea, Rep. 0.547268245
## Kosovo -0.037775192
## Kuwait 1.236783463
## Kyrgyz Republic -0.699030005
## Lao PDR -0.719986319
## Late-demographic dividend 0.209792546
## Latin America & Caribbean 0.235081499
## Latin America & Caribbean (excluding high income) 0.218152799
## Latin America & the Caribbean (IDA & IBRD countries) 0.229846952
## Latvia 0.283087731
## Least developed countries: UN classification -0.484638836
## Lebanon 0.562233592
## Lesotho -0.844990251
## Liberia -1.246536137
## Libya 0.081763960
## Liechtenstein 0.593828796
## Lithuania -0.236733382
## Low & middle income -0.102124406
## Low income -0.565529443
## Lower middle income -0.250244406
## Luxembourg 1.653522694
## Macao SAR, China 0.644539308
## Madagascar -1.277307191
## Malawi -0.640374086
## Malaysia -0.200674050
## Maldives 0.222275604
## Mali -1.082199825
## Malta 0.510829554
## Mauritania -0.992083276
## Mauritius 0.198975085
## Mexico 0.633065449
## Micronesia, Fed. Sts. -0.169045579
## Middle East & North Africa 0.068101592
## Middle East & North Africa (excluding high income) 0.047793570
## Middle East & North Africa (IDA & IBRD countries) 0.047466511
## Middle income -0.045670142
## Moldova -0.489751547
## Mongolia -0.746627289
## Montenegro 0.394644098
## Morocco -0.164814912
## Mozambique -1.334538185
## Myanmar -0.706189861
## Namibia 0.116469833
## Nepal -0.614896695
## Netherlands 1.664207073
## New Caledonia 0.356477122
## New Zealand 1.550199124
## Nicaragua -0.209627773
## Niger -1.043872103
## Nigeria -1.595619350
## North America 0.424023693
## North Macedonia -0.032712836
## Norway 2.487583484
## OECD members 0.473009210
## Oman 0.269514318
## Other small states -0.330597205
## Pacific island small states -0.041122400
## Pakistan -1.168774803
## Panama 0.389814639
## Papua New Guinea -0.532040252
## Paraguay -0.278430351
## Peru 0.155803629
## Philippines -0.642988063
## Poland 0.392403806
## Portugal 1.320158854
## Post-demographic dividend 0.493120513
## Pre-demographic dividend -0.735041435
## Puerto Rico 0.489249751
## Qatar 1.091449637
## Romania 1.259171501
## Russian Federation -0.351325370
## Rwanda 0.075983856
## Samoa 0.182041215
## San Marino 0.707193614
## Sao Tome and Principe -0.253927487
## Saudi Arabia 0.120621776
## Senegal -0.490444081
## Serbia 1.030159539
## Seychelles 0.281063359
## Sierra Leone -1.811352162
## Singapore 0.330975672
## Sint Maarten (Dutch part) 0.236527181
## Slovak Republic 0.685966222
## Slovenia 0.802286988
## Small states -0.222874152
## Solomon Islands -0.142222653
## Somalia -1.599207394
## South Africa -0.809724055
## South Asia -0.241342515
## South Asia (IDA & IBRD) -0.241342515
## South Sudan -1.794605027
## Spain 1.794297141
## Sri Lanka -0.452913069
## St. Lucia 0.219411117
## St. Martin (French part) 0.465281189
## St. Vincent and the Grenadines 0.081794325
## Sub-Saharan Africa -0.730856374
## Sub-Saharan Africa (excluding high income) -0.730954905
## Sub-Saharan Africa (IDA & IBRD countries) -0.730856374
## Sudan -1.177303955
## Suriname 0.084241723
## Sweden 0.540182944
## Switzerland 0.597877539
## Syrian Arab Republic 0.199977149
## Tajikistan -0.798790681
## Tanzania -0.796048513
## Thailand 0.642156574
## Timor-Leste -0.207771811
## Togo -1.078055316
## Tonga 0.117807899
## Trinidad and Tobago 0.438948077
## Tunisia 0.039449652
## Turkey 0.239573860
## Turkmenistan -0.132809324
## Uganda -0.923976989
## Ukraine -0.484908812
## United Arab Emirates 0.251021682
## United Kingdom 0.484512721
## United States 0.411635338
## Upper middle income 0.176679969
## Uruguay 0.264041151
## Uzbekistan -0.751807255
## Vanuatu 0.001912616
## Venezuela, RB 0.259250754
## Vietnam 0.140702909
## Virgin Islands (U.S.) 0.439976542
## West Bank and Gaza 0.076449984
## World -0.101237568
## Yemen, Rep. -0.402303815
## Zambia -0.423634121
## Zimbabwe -0.911656024puntaje2=as.data.frame(salud2$scores)
names(puntaje2) = c("Salud")
head(puntaje2)## Salud
## Afghanistan -0.525254033
## Albania -0.003966211
## Algeria 0.183531157
## Angola -1.205913178
## Antigua and Barbuda 0.251467044
## Arab World -0.046556236puntaje2$Pais=row.names(puntaje2)
row.names(puntaje2) = NULLMerge final e imputación a las variables faltantes
responsabilidad2 = merge(scores2, DataMetodos2, by= "Pais")
dendes2 = merge(responsabilidad2, promedio2, by= "Pais")
sal2 = merge(dendes2, puntaje2, by= "Pais")
movilidad2 = merge(sal2, migra2, by= "Pais")
movi2 = merge(movilidad2, ODA2, by= "Pais")
control2 = merge(movi2, women2, by= "Pais")
final2 = merge(control2, AFRICA, by= "Pais")
VIH2 = merge(final2, DataVIH2,by= "Pais")VIH2$Metodos=scale(VIH2$Metodos)
VIH2$Migracion=scale(VIH2$Migracion)
VIH2$ODA=scale(VIH2$ODA)
VIH2$Women=scale(VIH2$Women)VIH2=VIH2[-grep("San|high|Pacific|French|Caribbean|Early|Late|Island|Small|West|Sint|Other|OECD|North|World|Euro|Latin|Upper|High|Heavily|IBR|IDA|Least|Low|Middle|East|Central|Fragile|Post|Pre",VIH2$Pais),] #Eliminamos los casos que no son paÃses
row.names(VIH2)=NULLRegresión
VIH2$VIH=VIH2$VIH/100summary(VIH2)## Pais Empoderamiento Metodos.V1
## Length:88 Min. :-2.4526 Min. :-1.7383877
## Class :character 1st Qu.:-0.4512 1st Qu.:-0.9363038
## Mode :character Median : 0.2362 Median : 0.2352182
## Mean : 0.1914 Mean : 0.0160288
## 3rd Qu.: 1.0337 3rd Qu.: 0.7278023
## Max. : 2.2077 Max. : 1.5920042
## Densidad Desigualdad Informacion
## Min. :-2.99552 Min. :-2.08919 Min. :-1.4814
## 1st Qu.:-1.20440 1st Qu.:-0.96147 1st Qu.:-0.9167
## Median : 0.08781 Median :-0.42991 Median :-0.4494
## Mean :-0.28312 Mean :-0.49460 Mean :-0.3844
## 3rd Qu.: 0.58487 3rd Qu.: 0.01119 3rd Qu.: 0.1515
## Max. : 1.87416 Max. : 0.81814 Max. : 0.8115
## Salud Migracion.V1 ODA.V1
## Min. :-1.81135 Min. :-5.464267 Min. :-0.922033
## 1st Qu.:-0.94754 1st Qu.:-0.065585 1st Qu.:-0.826862
## Median :-0.59437 Median : 0.211923 Median :-0.271479
## Mean :-0.47062 Mean : 0.002483 Mean :-0.008051
## 3rd Qu.: 0.07805 3rd Qu.: 0.331108 3rd Qu.: 0.436841
## Max. : 1.11384 Max. : 3.164738 Max. : 4.243441
## Women.V1 Africa VIH
## Min. :-4.301060 Length:88 Min. :0.00100
## 1st Qu.:-0.449604 Class :character 1st Qu.:0.00100
## Median :-0.107809 Mode :character Median :0.00200
## Mean : 0.000852 Mean :0.01207
## 3rd Qu.: 0.475179 3rd Qu.:0.00940
## Max. : 3.155620 Max. :0.19240table(VIH2$Africa)##
## NO SI
## 49 39library(betareg)
modelo2=betareg(VIH ~ Empoderamiento + Metodos + Densidad + Desigualdad + Salud + Informacion + Migracion + ODA + Women + Africa,data=VIH2)
summary(modelo2)##
## Call:
## betareg(formula = VIH ~ Empoderamiento + Metodos + Densidad + Desigualdad +
## Salud + Informacion + Migracion + ODA + Women + Africa, data = VIH2)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -2.8137 -0.4102 -0.0515 0.4928 3.1127
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.00737 0.24032 -20.836 < 2e-16 ***
## Empoderamiento -0.03791 0.10616 -0.357 0.72104
## Metodos 0.34909 0.17049 2.048 0.04061 *
## Densidad -0.41825 0.16082 -2.601 0.00930 **
## Desigualdad -0.57193 0.18364 -3.114 0.00184 **
## Salud -0.21155 0.20413 -1.036 0.30005
## Informacion 0.13282 0.17979 0.739 0.46007
## Migracion 0.16478 0.10889 1.513 0.13024
## ODA -0.16588 0.13155 -1.261 0.20733
## Women 0.14116 0.10789 1.308 0.19074
## AfricaSI 0.02626 0.28660 0.092 0.92699
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 59.02 10.96 5.383 7.33e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 328.3 on 12 Df
## Pseudo R-squared: 0.3601
## Number of iterations: 51 (BFGS) + 16 (Fisher scoring)library(margins)
(modelo2M = margins(modelo2))## Average marginal effects## betareg(formula = VIH ~ Empoderamiento + Metodos + Densidad + Desigualdad + Salud + Informacion + Migracion + ODA + Women + Africa, data = VIH2)## Empoderamiento Metodos Densidad Desigualdad Salud Informacion
## -0.0004601 0.004237 -0.005076 -0.006941 -0.002567 0.001612
## Migracion ODA Women AfricaSI
## 0.002 -0.002013 0.001713 0.0003181resultado = summary(modelo2M)#salen los limites de su error
bet=summary(modelo2M)
library(ggplot2)##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alphaggplot(bet,aes(x=factor, y=AME)) + geom_point() + geom_errorbar(aes(ymin=lower, ymax=upper))
AnalÃsis de conglomerados
Mapa de todas las variables significativas y la dependiente
VIH2_x=VIH2
row.names(VIH2_x) = VIH2_x$Pais
VIH2_x$Pais=NULL
VIH2_x$Africa=NULL
VIH2_x$Woman=NULL
VIH2_x$ODA=NULL
head(VIH2_x) #resultado final## Empoderamiento Metodos Densidad Desigualdad Informacion
## Albania -0.206127451 1.1081383 0.5568099 0.3518935 -1.2972437
## Algeria -2.452558785 0.5240120 0.2999756 0.4819708 -0.1688672
## Angola 1.910842948 -1.1411839 -1.4385608 -0.6232554 0.2319503
## Argentina -0.134724627 0.5022162 1.4496415 -0.8055126 -0.2194240
## Armenia -0.003203348 0.4804205 0.5580017 0.3300533 -1.1277972
## Azerbaijan 0.724065533 0.4804205 0.7365312 0.1834768 -1.1499025
## Salud Migracion Women VIH
## Albania -0.003966211 0.13658302 -0.53518863 0.0010
## Algeria 0.183531157 0.02423413 -0.80647133 0.0010
## Angola -1.205913178 0.54399679 0.62464746 0.0096
## Argentina 1.113839189 0.41478982 0.63572400 0.0010
## Armenia -0.493835927 0.27834057 2.55751915 0.0010
## Azerbaijan -0.489522683 0.34716815 0.05606804 0.0010Solo consideraremos las variables independientes, no las vairbles de control
VIH2_d = as.data.frame(scale(VIH2_x)) #Estandarizamos y eliminamos la variable no significativa y la categórica
set.seed(15) #Para que todos los grupos tengan el mismo punto de inicio, el 15 es inventadoPedimos las distancias como un criterio de agrupamiento
#Para pedir la distancia
VIH2_d=dist(VIH2_d[c(1:9)]) #Pedimos el numero de grupos:
VIH2_clus=kmeans(VIH2_d,centers = 5) #El 5 hace referencia al numero de grupos que se piden
VIH2_clus$cluster## Albania Algeria Angola
## 4 2 1
## Argentina Armenia Azerbaijan
## 3 3 4
## Bangladesh Barbados Belarus
## 5 3 3
## Belize Benin Bolivia
## 4 1 4
## Botswana Burkina Faso Burundi
## 5 1 1
## Cambodia Cameroon Chad
## 1 1 1
## Colombia Comoros Congo, Dem. Rep.
## 3 1 1
## Congo, Rep. Costa Rica Cote d'Ivoire
## 1 3 1
## Cuba Dominican Republic Ecuador
## 3 2 4
## Egypt, Arab Rep. El Salvador Equatorial Guinea
## 2 3 5
## Eritrea Eswatini Ethiopia
## 1 5 1
## Gabon Gambia, The Georgia
## 4 1 3
## Ghana Guatemala Guinea
## 1 4 1
## Guinea-Bissau Guyana Haiti
## 1 4 1
## Honduras India Indonesia
## 4 5 4
## Iran, Islamic Rep. Kazakhstan Kenya
## 2 3 1
## Kyrgyz Republic Lao PDR Lebanon
## 4 1 2
## Lesotho Madagascar Malawi
## 5 1 1
## Mali Mauritania Mexico
## 1 1 3
## Moldova Mongolia Morocco
## 3 4 2
## Mozambique Myanmar Nepal
## 1 1 4
## Nicaragua Niger Nigeria
## 4 1 1
## Pakistan Panama Paraguay
## 2 4 4
## Peru Philippines Rwanda
## 4 4 1
## Senegal Serbia Sierra Leone
## 1 3 1
## South Sudan Sudan Suriname
## 1 1 4
## Tajikistan Tanzania Thailand
## 2 1 3
## Togo Trinidad and Tobago Tunisia
## 1 3 4
## Uganda Ukraine Vietnam
## 1 3 3
## Zimbabwe
## 5#Para ver la cantidad de paises en cada grupo:
table(VIH2_clus$cluster)##
## 1 2 3 4 5
## 36 8 17 20 7#Graficamos el mapa:
library(rgdal)## Loading required package: sp## rgdal: version: 1.4-4, (SVN revision 833)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 2.1.3, released 2017/20/01
## Path to GDAL shared files: /usr/share/gdal/2.1
## GDAL binary built with GEOS: TRUE
## Loaded PROJ.4 runtime: Rel. 4.9.2, 08 September 2015, [PJ_VERSION: 492]
## Path to PROJ.4 shared files: (autodetected)
## Linking to sp version: 1.3-1folderMap='MapaMundo' #Nombre del archivo que contiene al mapa
fileName='world_map.shp'
fileToRead=file.path(folderMap,fileName)
mapamundo = readOGR(fileToRead,stringsAsFactors=FALSE)## OGR data source with driver: ESRI Shapefile
## Source: "/cloud/project/MapaMundo/world_map.shp", layer: "world_map"
## with 246 features
## It has 11 fields
## Integer64 fields read as strings: POP2005plot(mapamundo, border='grey')
#Creamos un objeto (cluster) que mezcla la informacion de los grupos con el mapa:
grupos2=as.data.frame(VIH2_clus$cluster)
grupos2## VIH2_clus$cluster
## Albania 4
## Algeria 2
## Angola 1
## Argentina 3
## Armenia 3
## Azerbaijan 4
## Bangladesh 5
## Barbados 3
## Belarus 3
## Belize 4
## Benin 1
## Bolivia 4
## Botswana 5
## Burkina Faso 1
## Burundi 1
## Cambodia 1
## Cameroon 1
## Chad 1
## Colombia 3
## Comoros 1
## Congo, Dem. Rep. 1
## Congo, Rep. 1
## Costa Rica 3
## Cote d'Ivoire 1
## Cuba 3
## Dominican Republic 2
## Ecuador 4
## Egypt, Arab Rep. 2
## El Salvador 3
## Equatorial Guinea 5
## Eritrea 1
## Eswatini 5
## Ethiopia 1
## Gabon 4
## Gambia, The 1
## Georgia 3
## Ghana 1
## Guatemala 4
## Guinea 1
## Guinea-Bissau 1
## Guyana 4
## Haiti 1
## Honduras 4
## India 5
## Indonesia 4
## Iran, Islamic Rep. 2
## Kazakhstan 3
## Kenya 1
## Kyrgyz Republic 4
## Lao PDR 1
## Lebanon 2
## Lesotho 5
## Madagascar 1
## Malawi 1
## Mali 1
## Mauritania 1
## Mexico 3
## Moldova 3
## Mongolia 4
## Morocco 2
## Mozambique 1
## Myanmar 1
## Nepal 4
## Nicaragua 4
## Niger 1
## Nigeria 1
## Pakistan 2
## Panama 4
## Paraguay 4
## Peru 4
## Philippines 4
## Rwanda 1
## Senegal 1
## Serbia 3
## Sierra Leone 1
## South Sudan 1
## Sudan 1
## Suriname 4
## Tajikistan 2
## Tanzania 1
## Thailand 3
## Togo 1
## Trinidad and Tobago 3
## Tunisia 4
## Uganda 1
## Ukraine 3
## Vietnam 3
## Zimbabwe 5names(grupos2)='cluster'
grupos2$NAME=row.names(grupos2)
head(grupos2)## cluster NAME
## Albania 4 Albania
## Algeria 2 Algeria
## Angola 1 Angola
## Argentina 3 Argentina
## Armenia 3 Armenia
## Azerbaijan 4 Azerbaijan#Creamos el objeto final:
mapamundo_VIH2=merge(mapamundo,grupos2)library("RColorBrewer")
plot(mapamundo,col='gray',border=NA)
plot(mapamundo,
col=brewer.pal(n = 5, name = "Set2")[mapamundo_VIH2$cluster],
border='gray',add=T)
#Para tener un mapa interactivo asignando colores a cada grupo:
library(leaflet)
#newMaps!
c1=mapamundo_VIH2[!is.na(mapamundo_VIH2$cluster) & mapamundo_VIH2$cluster==1,]
c2=mapamundo_VIH2[!is.na(mapamundo_VIH2$cluster) & mapamundo_VIH2$cluster==2,]
c3=mapamundo_VIH2[!is.na(mapamundo_VIH2$cluster) & mapamundo_VIH2$cluster==3,]
c4=mapamundo_VIH2[!is.na(mapamundo_VIH2$cluster) & mapamundo_VIH2$cluster==4,]
c5=mapamundo_VIH2[!is.na(mapamundo_VIH2$cluster) & mapamundo_VIH2$cluster==5,]
title="Clusters"
# base Layer
base= leaflet() %>% addProviderTiles("CartoDB.Positron")
layer1= base %>%
addPolygons(data=c1,color="yellow"
,fillOpacity = 1,stroke = F,
group = "1")
layer_12= layer1%>%addPolygons(data=c2,color="lightcoral",fillOpacity = 1,stroke = F,
group = "2")
layer_123= layer_12%>%addPolygons(data=c3,color="red",fillOpacity = 1,stroke = F,
group = "3")
layer_1234= layer_123%>%addPolygons(data=c4,color="sandybrown",fillOpacity = 1,stroke = F,
group = "4")
layer_12345= layer_1234%>%addPolygons(data=c5,color="powderblue",fillOpacity = 1,stroke = F,
group = "5")
layer_12345#Pedimos el mapa interactivo graficado:
layer_12345%>% addLayersControl(
overlayGroups = c("1", "2","3", "4", "5"),
options = layersControlOptions(collapsed = TRUE)) #No quedarse solo con el mapa, preguntarse cu?les son las caracter?sticas de los grupos formados (en que se asemejan y en qu? se diferencian).Mapa de las variables significativas
Hacemos una data aparte donde solo se encuentren las variables significativas.
VIH2_Sig=VIH2_x
VIH2_Sig=VIH2_Sig[c(2:4)]VIH2_dS = as.data.frame(scale(VIH2_Sig)) #Estandarizamos y eliminamos la variable no significativa y la categóricaset.seed(15) #Para que todos los grupos tengan el mismo punto de inicio, el 15 es inventadoPedimos las distancias como un criterio de agrupamiento
#Para pedir la distancia
VIH2_dS=dist(VIH2_dS[c(1:3)]) #Pedimos el numero de grupos:
VIH2s_clus=kmeans(VIH2_dS,centers = 5) #El 5 hace referencia al numero de grupos que se piden
VIH2s_clus$cluster## Albania Algeria Angola
## 5 5 2
## Argentina Armenia Azerbaijan
## 3 5 5
## Bangladesh Barbados Belarus
## 4 5 5
## Belize Benin Bolivia
## 4 1 4
## Botswana Burkina Faso Burundi
## 3 1 1
## Cambodia Cameroon Chad
## 2 2 1
## Colombia Comoros Congo, Dem. Rep.
## 3 2 1
## Congo, Rep. Costa Rica Cote d'Ivoire
## 2 5 2
## Cuba Dominican Republic Ecuador
## 5 3 5
## Egypt, Arab Rep. El Salvador Equatorial Guinea
## 3 5 2
## Eritrea Eswatini Ethiopia
## 1 3 2
## Gabon Gambia, The Georgia
## 4 1 4
## Ghana Guatemala Guinea
## 2 4 1
## Guinea-Bissau Guyana Haiti
## 1 4 2
## Honduras India Indonesia
## 3 4 3
## Iran, Islamic Rep. Kazakhstan Kenya
## 5 5 2
## Kyrgyz Republic Lao PDR Lebanon
## 4 4 5
## Lesotho Madagascar Malawi
## 2 2 2
## Mali Mauritania Mexico
## 1 1 5
## Moldova Mongolia Morocco
## 5 4 3
## Mozambique Myanmar Nepal
## 1 2 4
## Nicaragua Niger Nigeria
## 5 1 2
## Pakistan Panama Paraguay
## 4 4 5
## Peru Philippines Rwanda
## 5 4 2
## Senegal Serbia Sierra Leone
## 2 5 1
## South Sudan Sudan Suriname
## 1 1 4
## Tajikistan Tanzania Thailand
## 4 2 3
## Togo Trinidad and Tobago Tunisia
## 1 4 5
## Uganda Ukraine Vietnam
## 1 5 5
## Zimbabwe
## 3#Para ver la cantidad de paises en cada grupo:
table(VIH2s_clus$cluster)##
## 1 2 3 4 5
## 18 19 11 18 22#Creamos un objeto (cluster) que mezcla la informacion de los grupos con el mapa:
grupos2s=as.data.frame(VIH2s_clus$cluster)
grupos2s## VIH2s_clus$cluster
## Albania 5
## Algeria 5
## Angola 2
## Argentina 3
## Armenia 5
## Azerbaijan 5
## Bangladesh 4
## Barbados 5
## Belarus 5
## Belize 4
## Benin 1
## Bolivia 4
## Botswana 3
## Burkina Faso 1
## Burundi 1
## Cambodia 2
## Cameroon 2
## Chad 1
## Colombia 3
## Comoros 2
## Congo, Dem. Rep. 1
## Congo, Rep. 2
## Costa Rica 5
## Cote d'Ivoire 2
## Cuba 5
## Dominican Republic 3
## Ecuador 5
## Egypt, Arab Rep. 3
## El Salvador 5
## Equatorial Guinea 2
## Eritrea 1
## Eswatini 3
## Ethiopia 2
## Gabon 4
## Gambia, The 1
## Georgia 4
## Ghana 2
## Guatemala 4
## Guinea 1
## Guinea-Bissau 1
## Guyana 4
## Haiti 2
## Honduras 3
## India 4
## Indonesia 3
## Iran, Islamic Rep. 5
## Kazakhstan 5
## Kenya 2
## Kyrgyz Republic 4
## Lao PDR 4
## Lebanon 5
## Lesotho 2
## Madagascar 2
## Malawi 2
## Mali 1
## Mauritania 1
## Mexico 5
## Moldova 5
## Mongolia 4
## Morocco 3
## Mozambique 1
## Myanmar 2
## Nepal 4
## Nicaragua 5
## Niger 1
## Nigeria 2
## Pakistan 4
## Panama 4
## Paraguay 5
## Peru 5
## Philippines 4
## Rwanda 2
## Senegal 2
## Serbia 5
## Sierra Leone 1
## South Sudan 1
## Sudan 1
## Suriname 4
## Tajikistan 4
## Tanzania 2
## Thailand 3
## Togo 1
## Trinidad and Tobago 4
## Tunisia 5
## Uganda 1
## Ukraine 5
## Vietnam 5
## Zimbabwe 3names(grupos2s)='cluster'
grupos2s$NAME=row.names(grupos2s)
head(grupos2s)## cluster NAME
## Albania 5 Albania
## Algeria 5 Algeria
## Angola 2 Angola
## Argentina 3 Argentina
## Armenia 5 Armenia
## Azerbaijan 5 Azerbaijan#Creamos el objeto final:
mapamundo_VIH2s=merge(mapamundo,grupos2s)library("RColorBrewer")
plot(mapamundo,col='gray',border=NA)
plot(mapamundo,
col=brewer.pal(n = 5, name = "Accent")[mapamundo_VIH2s$cluster],
border='gray',add=T)
Mapa de las hipotesis
Hacemos una data aparte donde solo se encuentre la relación entre las hipotesis
Reforzamiento de las capacidades femeninas
En esta hipotesis encontraremos las varibales de Empoderamiento y acceso a metodos anticonceptivos
VIH2_H1=VIH2_x[c(1,2)]
VIH2_dH1 = as.data.frame(scale(VIH2_H1)) #Estandarizamos y eliminamos la variable no significativa y la categóricaset.seed(15) #Para que todos los grupos tengan el mismo punto de inicio, el 15 es inventado
#Para pedir la distancia
VIH2_dH1=dist(VIH2_dH1[c(1,2)])
#Pedimos las distancias como un criterio de agrupamiento#Pedimos el numero de grupos:
VIH2H_clus=kmeans(VIH2_dH1,centers = 5) #El 5 hace referencia al numero de grupos que se piden
VIH2H_clus$cluster## Albania Algeria Angola
## 5 3 1
## Argentina Armenia Azerbaijan
## 4 4 4
## Bangladesh Barbados Belarus
## 3 4 4
## Belize Benin Bolivia
## 4 1 4
## Botswana Burkina Faso Burundi
## 4 1 1
## Cambodia Cameroon Chad
## 1 1 1
## Colombia Comoros Congo, Dem. Rep.
## 5 2 1
## Congo, Rep. Costa Rica Cote d'Ivoire
## 4 5 1
## Cuba Dominican Republic Ecuador
## 5 5 5
## Egypt, Arab Rep. El Salvador Equatorial Guinea
## 3 5 1
## Eritrea Eswatini Ethiopia
## 1 5 1
## Gabon Gambia, The Georgia
## 2 1 4
## Ghana Guatemala Guinea
## 1 4 1
## Guinea-Bissau Guyana Haiti
## 1 4 4
## Honduras India Indonesia
## 5 3 4
## Iran, Islamic Rep. Kazakhstan Kenya
## 3 4 4
## Kyrgyz Republic Lao PDR Lebanon
## 4 1 3
## Lesotho Madagascar Malawi
## 4 1 4
## Mali Mauritania Mexico
## 1 2 5
## Moldova Mongolia Morocco
## 5 4 3
## Mozambique Myanmar Nepal
## 1 4 1
## Nicaragua Niger Nigeria
## 5 1 1
## Pakistan Panama Paraguay
## 2 4 5
## Peru Philippines Rwanda
## 5 4 1
## Senegal Serbia Sierra Leone
## 2 5 1
## South Sudan Sudan Suriname
## 1 2 4
## Tajikistan Tanzania Thailand
## 2 1 5
## Togo Trinidad and Tobago Tunisia
## 1 4 3
## Uganda Ukraine Vietnam
## 1 5 5
## Zimbabwe
## 4#Para ver la cantidad de paises en cada grupo:
table(VIH2H_clus$cluster)##
## 1 2 3 4 5
## 29 7 8 26 18#Creamos un objeto (cluster) que mezcla la informacion de los grupos con el mapa:
grupos2H=as.data.frame(VIH2H_clus$cluster)
grupos2H## VIH2H_clus$cluster
## Albania 5
## Algeria 3
## Angola 1
## Argentina 4
## Armenia 4
## Azerbaijan 4
## Bangladesh 3
## Barbados 4
## Belarus 4
## Belize 4
## Benin 1
## Bolivia 4
## Botswana 4
## Burkina Faso 1
## Burundi 1
## Cambodia 1
## Cameroon 1
## Chad 1
## Colombia 5
## Comoros 2
## Congo, Dem. Rep. 1
## Congo, Rep. 4
## Costa Rica 5
## Cote d'Ivoire 1
## Cuba 5
## Dominican Republic 5
## Ecuador 5
## Egypt, Arab Rep. 3
## El Salvador 5
## Equatorial Guinea 1
## Eritrea 1
## Eswatini 5
## Ethiopia 1
## Gabon 2
## Gambia, The 1
## Georgia 4
## Ghana 1
## Guatemala 4
## Guinea 1
## Guinea-Bissau 1
## Guyana 4
## Haiti 4
## Honduras 5
## India 3
## Indonesia 4
## Iran, Islamic Rep. 3
## Kazakhstan 4
## Kenya 4
## Kyrgyz Republic 4
## Lao PDR 1
## Lebanon 3
## Lesotho 4
## Madagascar 1
## Malawi 4
## Mali 1
## Mauritania 2
## Mexico 5
## Moldova 5
## Mongolia 4
## Morocco 3
## Mozambique 1
## Myanmar 4
## Nepal 1
## Nicaragua 5
## Niger 1
## Nigeria 1
## Pakistan 2
## Panama 4
## Paraguay 5
## Peru 5
## Philippines 4
## Rwanda 1
## Senegal 2
## Serbia 5
## Sierra Leone 1
## South Sudan 1
## Sudan 2
## Suriname 4
## Tajikistan 2
## Tanzania 1
## Thailand 5
## Togo 1
## Trinidad and Tobago 4
## Tunisia 3
## Uganda 1
## Ukraine 5
## Vietnam 5
## Zimbabwe 4names(grupos2H)='cluster'
grupos2H$NAME=row.names(grupos2H)
head(grupos2H)## cluster NAME
## Albania 5 Albania
## Algeria 3 Algeria
## Angola 1 Angola
## Argentina 4 Argentina
## Armenia 4 Armenia
## Azerbaijan 4 Azerbaijan#Creamos el objeto final:
mapamundo_VIH2H=merge(mapamundo,grupos2H)library("RColorBrewer")
plot(mapamundo,col='gray',border=NA)
plot(mapamundo,
col=brewer.pal(n = 5, name = "PiYG")[mapamundo_VIH2H$cluster],
border='gray',add=T)
Calidad de vida
En esta hipotesis encontraremos las varibales de Densidad Estatal, Salud y Desigualdad.
VIH2_H2=VIH2_x[c(3,4,6)]
VIH2_dH2 = as.data.frame(scale(VIH2_H2)) #Estandarizamos y eliminamos la variable no significativa y la categóricaset.seed(15) #Para que todos los grupos tengan el mismo punto de inicio, el 15 es inventado
#Para pedir la distancia
VIH2_dH2=dist(VIH2_dH2[c(1,2,3)])
#Pedimos las distancias como un criterio de agrupamiento#Pedimos el numero de grupos:
VIH2H2_clus=kmeans(VIH2_dH2,centers = 5) #El 5 hace referencia al numero de grupos que se piden
VIH2H2_clus$cluster## Albania Algeria Angola
## 4 5 1
## Argentina Armenia Azerbaijan
## 5 4 4
## Bangladesh Barbados Belarus
## 2 5 5
## Belize Benin Bolivia
## 4 2 2
## Botswana Burkina Faso Burundi
## 3 1 1
## Cambodia Cameroon Chad
## 3 2 1
## Colombia Comoros Congo, Dem. Rep.
## 3 2 1
## Congo, Rep. Costa Rica Cote d'Ivoire
## 2 5 2
## Cuba Dominican Republic Ecuador
## 5 3 4
## Egypt, Arab Rep. El Salvador Equatorial Guinea
## 3 5 2
## Eritrea Eswatini Ethiopia
## 2 2 2
## Gabon Gambia, The Georgia
## 4 1 4
## Ghana Guatemala Guinea
## 2 4 1
## Guinea-Bissau Guyana Haiti
## 1 3 2
## Honduras India Indonesia
## 3 2 3
## Iran, Islamic Rep. Kazakhstan Kenya
## 4 4 2
## Kyrgyz Republic Lao PDR Lebanon
## 4 2 5
## Lesotho Madagascar Malawi
## 1 1 1
## Mali Mauritania Mexico
## 1 1 5
## Moldova Mongolia Morocco
## 4 2 3
## Mozambique Myanmar Nepal
## 1 2 2
## Nicaragua Niger Nigeria
## 4 1 1
## Pakistan Panama Paraguay
## 2 4 4
## Peru Philippines Rwanda
## 4 2 1
## Senegal Serbia Sierra Leone
## 2 5 1
## South Sudan Sudan Suriname
## 1 1 4
## Tajikistan Tanzania Thailand
## 4 1 5
## Togo Trinidad and Tobago Tunisia
## 1 5 4
## Uganda Ukraine Vietnam
## 1 4 4
## Zimbabwe
## 1#Para ver la cantidad de paises en cada grupo:
table(VIH2H2_clus$cluster)##
## 1 2 3 4 5
## 24 22 9 21 12#Creamos un objeto (cluster) que mezcla la informacion de los grupos con el mapa:
grupos2H2=as.data.frame(VIH2H2_clus$cluster)
grupos2H2## VIH2H2_clus$cluster
## Albania 4
## Algeria 5
## Angola 1
## Argentina 5
## Armenia 4
## Azerbaijan 4
## Bangladesh 2
## Barbados 5
## Belarus 5
## Belize 4
## Benin 2
## Bolivia 2
## Botswana 3
## Burkina Faso 1
## Burundi 1
## Cambodia 3
## Cameroon 2
## Chad 1
## Colombia 3
## Comoros 2
## Congo, Dem. Rep. 1
## Congo, Rep. 2
## Costa Rica 5
## Cote d'Ivoire 2
## Cuba 5
## Dominican Republic 3
## Ecuador 4
## Egypt, Arab Rep. 3
## El Salvador 5
## Equatorial Guinea 2
## Eritrea 2
## Eswatini 2
## Ethiopia 2
## Gabon 4
## Gambia, The 1
## Georgia 4
## Ghana 2
## Guatemala 4
## Guinea 1
## Guinea-Bissau 1
## Guyana 3
## Haiti 2
## Honduras 3
## India 2
## Indonesia 3
## Iran, Islamic Rep. 4
## Kazakhstan 4
## Kenya 2
## Kyrgyz Republic 4
## Lao PDR 2
## Lebanon 5
## Lesotho 1
## Madagascar 1
## Malawi 1
## Mali 1
## Mauritania 1
## Mexico 5
## Moldova 4
## Mongolia 2
## Morocco 3
## Mozambique 1
## Myanmar 2
## Nepal 2
## Nicaragua 4
## Niger 1
## Nigeria 1
## Pakistan 2
## Panama 4
## Paraguay 4
## Peru 4
## Philippines 2
## Rwanda 1
## Senegal 2
## Serbia 5
## Sierra Leone 1
## South Sudan 1
## Sudan 1
## Suriname 4
## Tajikistan 4
## Tanzania 1
## Thailand 5
## Togo 1
## Trinidad and Tobago 5
## Tunisia 4
## Uganda 1
## Ukraine 4
## Vietnam 4
## Zimbabwe 1names(grupos2H2)='cluster'
grupos2H2$NAME=row.names(grupos2H2)
head(grupos2H2)## cluster NAME
## Albania 4 Albania
## Algeria 5 Algeria
## Angola 1 Angola
## Argentina 5 Argentina
## Armenia 4 Armenia
## Azerbaijan 4 Azerbaijan#Creamos el objeto final:
mapamundo_VIH2H2=merge(mapamundo,grupos2H2)library("RColorBrewer")
plot(mapamundo,col='gray',border=NA)
plot(mapamundo,
col=brewer.pal(n = 5, name = "BrBG")[mapamundo_VIH2H2$cluster],
border='gray',add=T)
Movilidazación para el desarrollo en la población
En este analisis de conglomerados pondremos los componentes la hipoteisis tres que son el indice de Desarrollo tecnológico y migración
VIH2_H3=VIH2_x[c(5,7)]
VIH2_dH3 = as.data.frame(scale(VIH2_H3)) #Estandarizamos y eliminamos la variable no significativa y la categóricaset.seed(15) #Para que todos los grupos tengan el mismo punto de inicio, el 15 es inventado
#Para pedir la distancia
VIH2_dH3=dist(VIH2_dH3[c(1,2)])
#Pedimos las distancias como un criterio de agrupamiento#Pedimos el numero de grupos:
VIH2H3_clus=kmeans(VIH2_dH3,centers = 5) #El 5 hace referencia al numero de grupos que se piden
VIH2H3_clus$cluster## Albania Algeria Angola
## 5 1 2
## Argentina Armenia Azerbaijan
## 1 5 5
## Bangladesh Barbados Belarus
## 4 1 1
## Belize Benin Bolivia
## 2 2 5
## Botswana Burkina Faso Burundi
## 1 1 1
## Cambodia Cameroon Chad
## 2 2 2
## Colombia Comoros Congo, Dem. Rep.
## 1 2 5
## Congo, Rep. Costa Rica Cote d'Ivoire
## 2 1 2
## Cuba Dominican Republic Ecuador
## 1 2 5
## Egypt, Arab Rep. El Salvador Equatorial Guinea
## 1 5 2
## Eritrea Eswatini Ethiopia
## 2 2 5
## Gabon Gambia, The Georgia
## 1 5 2
## Ghana Guatemala Guinea
## 1 5 2
## Guinea-Bissau Guyana Haiti
## 2 2 2
## Honduras India Indonesia
## 2 4 3
## Iran, Islamic Rep. Kazakhstan Kenya
## 5 5 2
## Kyrgyz Republic Lao PDR Lebanon
## 5 2 3
## Lesotho Madagascar Malawi
## 5 5 2
## Mali Mauritania Mexico
## 1 2 1
## Moldova Mongolia Morocco
## 5 5 2
## Mozambique Myanmar Nepal
## 5 2 5
## Nicaragua Niger Nigeria
## 5 1 2
## Pakistan Panama Paraguay
## 3 5 5
## Peru Philippines Rwanda
## 5 5 2
## Senegal Serbia Sierra Leone
## 1 1 2
## South Sudan Sudan Suriname
## 2 2 2
## Tajikistan Tanzania Thailand
## 5 1 1
## Togo Trinidad and Tobago Tunisia
## 1 5 1
## Uganda Ukraine Vietnam
## 1 1 5
## Zimbabwe
## 2#Para ver la cantidad de paises en cada grupo:
table(VIH2H3_clus$cluster)##
## 1 2 3 4 5
## 24 32 3 2 27#Creamos un objeto (cluster) que mezcla la informacion de los grupos con el mapa:
grupos2H3=as.data.frame(VIH2H3_clus$cluster)
grupos2H3## VIH2H3_clus$cluster
## Albania 5
## Algeria 1
## Angola 2
## Argentina 1
## Armenia 5
## Azerbaijan 5
## Bangladesh 4
## Barbados 1
## Belarus 1
## Belize 2
## Benin 2
## Bolivia 5
## Botswana 1
## Burkina Faso 1
## Burundi 1
## Cambodia 2
## Cameroon 2
## Chad 2
## Colombia 1
## Comoros 2
## Congo, Dem. Rep. 5
## Congo, Rep. 2
## Costa Rica 1
## Cote d'Ivoire 2
## Cuba 1
## Dominican Republic 2
## Ecuador 5
## Egypt, Arab Rep. 1
## El Salvador 5
## Equatorial Guinea 2
## Eritrea 2
## Eswatini 2
## Ethiopia 5
## Gabon 1
## Gambia, The 5
## Georgia 2
## Ghana 1
## Guatemala 5
## Guinea 2
## Guinea-Bissau 2
## Guyana 2
## Haiti 2
## Honduras 2
## India 4
## Indonesia 3
## Iran, Islamic Rep. 5
## Kazakhstan 5
## Kenya 2
## Kyrgyz Republic 5
## Lao PDR 2
## Lebanon 3
## Lesotho 5
## Madagascar 5
## Malawi 2
## Mali 1
## Mauritania 2
## Mexico 1
## Moldova 5
## Mongolia 5
## Morocco 2
## Mozambique 5
## Myanmar 2
## Nepal 5
## Nicaragua 5
## Niger 1
## Nigeria 2
## Pakistan 3
## Panama 5
## Paraguay 5
## Peru 5
## Philippines 5
## Rwanda 2
## Senegal 1
## Serbia 1
## Sierra Leone 2
## South Sudan 2
## Sudan 2
## Suriname 2
## Tajikistan 5
## Tanzania 1
## Thailand 1
## Togo 1
## Trinidad and Tobago 5
## Tunisia 1
## Uganda 1
## Ukraine 1
## Vietnam 5
## Zimbabwe 2names(grupos2H3)='cluster'
grupos2H3$NAME=row.names(grupos2H3)
head(grupos2H3)## cluster NAME
## Albania 5 Albania
## Algeria 1 Algeria
## Angola 2 Angola
## Argentina 1 Argentina
## Armenia 5 Armenia
## Azerbaijan 5 Azerbaijan#Creamos el objeto final:
mapamundo_VIH2H3=merge(mapamundo,grupos2H3)library("RColorBrewer")
plot(mapamundo,col='gray',border=NA)
plot(mapamundo,
col=brewer.pal(n = 5, name = "Greens")[mapamundo_VIH2H$cluster],
border='gray',add=T)