PROYECTO MULTIVARIADA - ANÁLISIS DE ENFERMEDADES CONTINENTALES (2020-2024)
Santiago Herrera - nelsonsa.herrerapi@ecci.edu.co
2025-11-19
1 Resumen
Este estudio presenta un análisis estadístico descriptivo multivariado de datos relacionados con enfermedades en países de Latinoamérica y Europa, en el período comprendido entre 2020 y 2024. El conjunto de datos incluye variables cuantitativas y cualitativas, lo que permite explorar la prevalencia, incidencia, mortalidad, costos de tratamiento y acceso a la salud. El objetivo principal es caracterizar la población de estudio e identificar patrones generales en la información de salud.
Palabras clave: Análisis multivariado, estadística descriptiva, salud pública, Latinoamérica, medidas de dispersión.
2 Abstract
This study presents a descriptive multivariate statistical analysis of disease-related data collected in Latin American countries between 2020 and 2024. The dataset includes both quantitative and qualitative variables, allowing the exploration of prevalence, incidence, mortality, treatment costs, and healthcare access indicators. The main objective is to characterize the population under study and identify general patterns in the health data.
Key words Multivariate analysis, descriptive statistics, public health, Latin America, dispersion measures.
3 Introducción
En la actualidad, el análisis multivariado constituye una herramienta
fundamental en el estudio de datos complejos que involucran múltiples
variables. El presente trabajo se centra en la descripción de un
conjunto de datos de salud provenientes de países latinoamericanos y
europeos entre los años 2020 y 2024.
La población de estudio corresponde a los registros disponibles en la
base de datos filtrada, considerando únicamente los países de
Latinoamérica y europa, las variables relevantes para el análisis de
enfermedades y su contexto socioeconómico.
4 Marco teórico
El análisis estadístico multivariado permite estudiar la relación entre varias variables de manera simultánea. Entre las técnicas más utilizadas se encuentran la obtención de medidas de tendencia central (media, mediana, moda), medidas de dispersión (varianza, desviación estándar), así como la construcción de matrices de correlación y covarianza.
En el caso de datos cualitativos, se utilizan frecuencias absolutas y relativas para describir la distribución de categorías. La combinación de variables cualitativas y cuantitativas permite realizar un análisis integral de la población de estudio.
5 Metodología
El análisis se llevó a cabo en varias etapas:
5.1 Filtrado del dataset: Se seleccionaron únicamente países de Latinoamérica y el período comprendido entre 2020 y 2024.
5.2 Clasificación de variables: Se diferenciaron variables cualitativas (ej. país, género, categoría de enfermedad) y cuantitativas (ej. tasas de incidencia, mortalidad, ingresos per cápita).
5.3 Muestreo: Para el análisis multivariado se tomó una muestra representativa de 300 registros, con el fin de evitar sobrecarga computacional.
5.4 Análisis descriptivo: Se calcularon medidas de tendencia central y dispersión para las variables cuantitativas, así como tablas de frecuencias y proporciones para las variables cualitativas.
5.5 Visualización: Se realizaron gráficos de barras, histogramas y diagramas de caja para complementar la descripción de los datos.
5.6 Análisis de Componentes Principales (ACP): El Análisis de Componentes Principales (ACP) se aplicó con el propósito de reducir la dimensionalidad del conjunto de variables cuantitativas y detectar patrones subyacentes en los datos. Previamente, las variables fueron estandarizadas para eliminar el efecto de las diferentes escalas de medida. Posteriormente, se calculó la matriz de correlaciones y se extrajeron los valores propios y vectores propios, que determinan las direcciones de máxima varianza.
5.7 Análisis de Componentes Multiples (ACM): El Análisis de Componentes Múltiples (ACM) se realizó con el fin de estudiar las relaciones entre las variables cualitativas y reducir la dimensionalidad del conjunto categórico. Para ello, se calcularon los componentes principales a partir de las categorías incluidas en la muestra, y posteriormente se generaron representaciones gráficas de los individuos, variables y su relación conjunta mediante un biplot. Finalmente, se examinaron los valores propios y las coordenadas de los ejes con el objetivo de identificar las dimensiones más relevantes y evaluar la variabilidad explicada por el modelo.
5.8 Análisis y generacion de K-means y Clusters: Se aplicó el algoritmo de k-means con el objetivo de agrupar los registros según su similitud en las variables cuantitativas seleccionadas. Previamente, los datos fueron estandarizados para evitar que las diferencias de escala influyeran en la conformación de los grupos. Posteriormente, se definió un número óptimo de clústeres mediante criterios visuales y métricos, y se ejecutó el proceso iterativo de asignación y actualización de centroides hasta alcanzar la convergencia. Finalmente, se analizaron las características de cada grupo y se generaron visualizaciones para identificar patrones comunes y diferencias entre los clústeres resultantes.
6 IV. Resultados y análisis
6.1 CARGA DE DATOS
Como primer proceso se realizo la carga y exploracion de los datos para poder realizar analisis descriptivo a las variables y asi poder continuar con otros procesos
library(readr)
library(ggplot2)
library(PASWR)## Cargando paquete requerido: latticelibrary(dplyr)##
## Adjuntando el paquete: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(psych)##
## Adjuntando el paquete: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(corrplot)## corrplot 0.95 loadedlibrary(grid)
library(gridExtra)##
## Adjuntando el paquete: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinelibrary(gridGraphics)
library(ade4)
library(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(FactoMineR)##
## Adjuntando el paquete: 'FactoMineR'## The following object is masked from 'package:ade4':
##
## reconstlibrary(FactoClass)## Cargando paquete requerido: ggrepel## Cargando paquete requerido: xtable## Cargando paquete requerido: scatterplot3dlibrary(RODBC)
library(reshape2)
library(hexbin)
library(cluster)
library(NbClust)
library(tidyr)##
## Adjuntando el paquete: 'tidyr'## The following object is masked from 'package:reshape2':
##
## smithspar(mfrow = c(1, 1))
# ------------------ CORTE 1 --------------------
# ---------------- CARGAR DATA ------------------
DATAP <- read.csv2("DATAFINAL_filtrado.csv", check.names = FALSE)
View(DATAP)
# Vectores de variables
vars_cuali <- c("Country","Disease Name","Disease Category","Age Group","Gender",
"Treatment Type","Availability of Vaccines/Treatment")
vars_cuanti <- c("Prevalence Rate (%)","Incidence Rate (%)","Mortality Rate (%)",
"Population Affected","Healthcare Access (%)","Doctors per 1000",
"Hospital Beds per 1000","Average Treatment Cost (USD)",
"Recovery Rate (%)","DALYs","Improvement in 5 Years (%)",
"Per Capita Income (USD)","Education Index","Urbanization Rate (%)","Year")
# Cualitativas como factor
DATAP[vars_cuali] <- lapply(DATAP[vars_cuali], as.factor)
# ---------------- MUESTREO ----------------
set.seed(123) # para reproducibilidad
DATAP_muestra <- DATAP[sample(1:nrow(DATAP), 300), ]
# ---------------- ANALISIS DESCRIPTIVO ----------------
# Resumen cuantitativas
summary(DATAP[vars_cuanti])## Prevalence Rate (%) Incidence Rate (%) Mortality Rate (%) Population Affected
## Min. : 1.0 Min. : 1 Min. : 1.0 Min. : 1011
## 1st Qu.: 339.0 1st Qu.: 262 1st Qu.:174.0 1st Qu.:250984
## Median : 892.0 Median : 669 Median :452.0 Median :501568
## Mean : 912.3 Mean : 686 Mean :460.8 Mean :501930
## 3rd Qu.:1444.8 3rd Qu.:1084 3rd Qu.:728.0 3rd Qu.:754429
## Max. :1999.0 Max. :1499 Max. :999.0 Max. :999998
## Healthcare Access (%) Doctors per 1000 Hospital Beds per 1000
## Min. : 500 Min. : 5 Min. : 5.0
## 1st Qu.:5818 1st Qu.:124 1st Qu.:208.0
## Median :7212 Median :249 Median :472.0
## Mean :6813 Mean :250 Mean :477.5
## 3rd Qu.:8607 3rd Qu.:374 3rd Qu.:736.0
## Max. :9999 Max. :499 Max. :999.0
## Average Treatment Cost (USD) Recovery Rate (%) DALYs
## Min. : 100 Min. : 500 Min. : 1
## 1st Qu.:12613 1st Qu.:5817 1st Qu.:1251
## Median :25138 Median :7175 Median :2494
## Mean :25059 Mean :6780 Mean :2499
## 3rd Qu.:37498 3rd Qu.:8541 3rd Qu.:3744
## Max. :50000 Max. :9899 Max. :5000
## Improvement in 5 Years (%) Per Capita Income (USD) Education Index
## Min. : 0.0 Min. : 500 Min. : 4.00
## 1st Qu.:169.0 1st Qu.:25489 1st Qu.:48.00
## Median :446.0 Median :50060 Median :62.00
## Mean :456.1 Mean :50180 Mean :59.17
## 3rd Qu.:723.0 3rd Qu.:74984 3rd Qu.:76.00
## Max. :999.0 Max. :99999 Max. :89.00
## Urbanization Rate (%) Year
## Min. : 200 Min. :2020
## 1st Qu.:3168 1st Qu.:2021
## Median :5136 Median :2022
## Mean :5011 Mean :2022
## 3rd Qu.:7053 3rd Qu.:2023
## Max. :8999 Max. :2024describe(DATAP[vars_cuanti])## vars n mean sd median trimmed
## Prevalence Rate (%) 1 80074 912.33 608.64 892.0 897.07
## Incidence Rate (%) 2 80074 685.99 455.50 669.0 674.89
## Mortality Rate (%) 3 80074 460.81 304.86 452.0 453.89
## Population Affected 4 80074 501930.03 288844.70 501567.5 502089.06
## Healthcare Access (%) 5 80074 6813.39 2451.79 7212.0 7208.28
## Doctors per 1000 6 80074 249.97 144.14 249.0 249.62
## Hospital Beds per 1000 7 80074 477.46 296.23 472.0 472.75
## Average Treatment Cost (USD) 8 80074 25059.44 14395.99 25138.0 25062.74
## Recovery Rate (%) 9 80074 6779.67 2418.77 7175.0 7178.05
## DALYs 10 80074 2498.51 1442.28 2494.0 2497.96
## Improvement in 5 Years (%) 11 80074 456.10 305.10 446.0 448.77
## Per Capita Income (USD) 12 80074 50179.77 28684.83 50060.0 50169.10
## Education Index 13 80074 59.17 22.20 62.0 62.26
## Urbanization Rate (%) 14 80074 5011.44 2422.94 5136.0 5118.83
## Year 15 80074 2022.00 1.41 2022.0 2022.00
## mad min max range skew kurtosis
## Prevalence Rate (%) 819.88 1 1999 1998 0.12 -1.28
## Incidence Rate (%) 610.83 1 1499 1498 0.12 -1.28
## Mortality Rate (%) 410.68 1 999 998 0.11 -1.29
## Population Affected 373154.11 1011 999998 998987 0.00 -1.21
## Healthcare Access (%) 2066.74 500 9999 9499 -1.25 1.14
## Doctors per 1000 185.32 5 499 494 0.01 -1.21
## Hospital Beds per 1000 391.41 5 999 994 0.07 -1.26
## Average Treatment Cost (USD) 18445.03 100 50000 49900 0.00 -1.20
## Recovery Rate (%) 2017.82 500 9899 9399 -1.28 1.23
## DALYs 1848.80 1 5000 4999 0.00 -1.20
## Improvement in 5 Years (%) 410.68 0 999 999 0.12 -1.28
## Per Capita Income (USD) 36704.73 500 99999 99499 0.00 -1.19
## Education Index 20.76 4 89 85 -1.02 0.61
## Urbanization Rate (%) 2877.73 200 8999 8799 -0.24 -0.90
## Year 1.48 2020 2024 4 0.00 -1.30
## se
## Prevalence Rate (%) 2.15
## Incidence Rate (%) 1.61
## Mortality Rate (%) 1.08
## Population Affected 1020.75
## Healthcare Access (%) 8.66
## Doctors per 1000 0.51
## Hospital Beds per 1000 1.05
## Average Treatment Cost (USD) 50.87
## Recovery Rate (%) 8.55
## DALYs 5.10
## Improvement in 5 Years (%) 1.08
## Per Capita Income (USD) 101.37
## Education Index 0.08
## Urbanization Rate (%) 8.56
## Year 0.00# Frecuencias cualitativas
lapply(DATAP[vars_cuali], table)## $Country
##
## Argentina Brazil France Germany Italy Mexico Russia Turkey
## 9717 9962 10022 10009 10083 10085 10136 10060
##
## $`Disease Name`
##
## Alzheimer's Disease Asthma Cancer Cholera
## 4011 4047 4028 3933
## COVID-19 Dengue Diabetes Ebola
## 3998 4022 4021 4010
## Hepatitis HIV/AIDS Hypertension Influenza
## 4090 4056 3985 3975
## Leprosy Malaria Measles Parkinson's Disease
## 3982 4032 3990 3935
## Polio Rabies Tuberculosis Zika
## 4053 3970 3921 4015
##
## $`Disease Category`
##
## Autoimmune Bacterial Cardiovascular Chronic Genetic
## 7416 7217 7281 7232 7204
## Infectious Metabolic Neurological Parasitic Respiratory
## 7306 7051 7437 7451 7209
## Viral
## 7270
##
## $`Age Group`
##
## 0-18 19-35 36-60 61+
## 19838 20061 20097 20078
##
## $Gender
##
## Female Male Other
## 26562 26830 26682
##
## $`Treatment Type`
##
## Medication Surgery Therapy Vaccination
## 19808 20090 19996 20180
##
## $`Availability of Vaccines/Treatment`
##
## No Yes
## 40069 40005lapply(DATAP[vars_cuali], function(x) round(prop.table(table(x))*100, 2))## $Country
## x
## Argentina Brazil France Germany Italy Mexico Russia Turkey
## 12.14 12.44 12.52 12.50 12.59 12.59 12.66 12.56
##
## $`Disease Name`
## x
## Alzheimer's Disease Asthma Cancer Cholera
## 5.01 5.05 5.03 4.91
## COVID-19 Dengue Diabetes Ebola
## 4.99 5.02 5.02 5.01
## Hepatitis HIV/AIDS Hypertension Influenza
## 5.11 5.07 4.98 4.96
## Leprosy Malaria Measles Parkinson's Disease
## 4.97 5.04 4.98 4.91
## Polio Rabies Tuberculosis Zika
## 5.06 4.96 4.90 5.01
##
## $`Disease Category`
## x
## Autoimmune Bacterial Cardiovascular Chronic Genetic
## 9.26 9.01 9.09 9.03 9.00
## Infectious Metabolic Neurological Parasitic Respiratory
## 9.12 8.81 9.29 9.31 9.00
## Viral
## 9.08
##
## $`Age Group`
## x
## 0-18 19-35 36-60 61+
## 24.77 25.05 25.10 25.07
##
## $Gender
## x
## Female Male Other
## 33.17 33.51 33.32
##
## $`Treatment Type`
## x
## Medication Surgery Therapy Vaccination
## 24.74 25.09 24.97 25.20
##
## $`Availability of Vaccines/Treatment`
## x
## No Yes
## 50.04 49.96Al cargar la data se realizaron dos vectores uno de variables cualitativas y otro de variables cuantitativas para poder revisar datos nulos, valores, medias, varianzas, entre otras medidas de tendencia. Adicional se realizo un muestreo de 300 datos para poder facilitar algunos procesos.
6.2 ANALISIS DESCRIPTIVO BIVARIADO
# --------------------------- PARTE 1 --------------------------
# --------------------------------------------------------------
# ANALISIS BIVARIADO CUALI-CUALI
# --------------------------------------------------------------
#LISTO
tabla_cat_genero <- table(DATAP$`Disease Category`, DATAP$Gender)
tabla_cat_genero##
## Female Male Other
## Autoimmune 2485 2456 2475
## Bacterial 2383 2427 2407
## Cardiovascular 2351 2512 2418
## Chronic 2407 2433 2392
## Genetic 2400 2420 2384
## Infectious 2423 2423 2460
## Metabolic 2366 2404 2281
## Neurological 2552 2435 2450
## Parasitic 2434 2518 2499
## Respiratory 2415 2397 2397
## Viral 2346 2405 2519#LISTO
tabla_prob <- round(prop.table(tabla_cat_genero, 1) * 100, 2)
tabla_prob##
## Female Male Other
## Autoimmune 33.51 33.12 33.37
## Bacterial 33.02 33.63 33.35
## Cardiovascular 32.29 34.50 33.21
## Chronic 33.28 33.64 33.08
## Genetic 33.31 33.59 33.09
## Infectious 33.16 33.16 33.67
## Metabolic 33.56 34.09 32.35
## Neurological 34.31 32.74 32.94
## Parasitic 32.67 33.79 33.54
## Respiratory 33.50 33.25 33.25
## Viral 32.27 33.08 34.65#LISTO
conteo_pais <- DATAP %>%
group_by(Country) %>%
summarise(Conteo = n()) %>%
arrange(desc(Conteo))
print(conteo_pais)## # A tibble: 8 × 2
## Country Conteo
## <fct> <int>
## 1 Russia 10136
## 2 Mexico 10085
## 3 Italy 10083
## 4 Turkey 10060
## 5 France 10022
## 6 Germany 10009
## 7 Brazil 9962
## 8 Argentina 9717#LISTO
conteo_anio <- DATAP %>%
group_by(Year) %>%
summarise(Conteo = n()) %>%
arrange(Year)
print(conteo_anio)## # A tibble: 5 × 2
## Year Conteo
## <int> <int>
## 1 2020 15919
## 2 2021 16070
## 3 2022 15962
## 4 2023 16040
## 5 2024 16083#LISTO
colores_genero <- c(
"Female" = "pink",
"Male" = "lightblue",
"Other" = "red"
)
par(mar = c(10, 4, 4, 2)) # más espacio inferior
barplot(t(tabla_cat_genero), # <--- nota el transpose “t()”
beside = TRUE, # barras lado a lado
col = colores_genero[colnames(tabla_cat_genero)],
main = "Categoría de Enfermedad vs Género",
las = 2,
legend.text = F)
# Ahora sí, agregamos el título del eje X
mtext("Categoría de Enfermedad", side = 1, line = 7)
par(mar = c(5, 4, 4, 2) + 0.1)El gráfico muestra que la distribución de enfermedades entre géneros es bastante uniforme. Para cada categoría (autoinmunes, bacterianas, cardiovasculares, etc.), las barras de hombres y mujeres tienen alturas muy similares, lo que sugiere que no existe un sesgo notable hacia alguno de los dos géneros. Aunque hay ligeras variaciones, estas no parecen ser estadísticamente significativas. En general, esto indica que las enfermedades estudiadas afectan de forma relativamente equivalente a hombres y mujeres.
#LISTO
tabla_pais_categoria <- table(DATAP$Country, DATAP$`Disease Category`)
tabla_pais_categoria##
## Autoimmune Bacterial Cardiovascular Chronic Genetic Infectious
## Argentina 953 862 882 848 838 899
## Brazil 940 889 954 867 907 897
## France 920 882 897 918 891 934
## Germany 918 943 858 885 934 899
## Italy 863 893 898 927 892 975
## Mexico 937 939 891 934 910 898
## Russia 941 940 926 969 929 886
## Turkey 944 869 975 884 903 918
##
## Metabolic Neurological Parasitic Respiratory Viral
## Argentina 836 947 895 881 876
## Brazil 943 935 885 861 884
## France 920 939 930 888 903
## Germany 846 961 946 886 933
## Italy 877 935 1002 903 918
## Mexico 879 943 914 914 926
## Russia 869 868 918 977 913
## Turkey 881 909 961 899 917#LISTO
# ---- Dividir la pantalla en 2 áreas ----
layout(matrix(c(1,2), nrow=1), widths = c(4,1)) # 4/5 gráfico, 1/5 leyenda
# ---- Panel 1: Gráfico ----
# Transponer para que el eje X muestre las categorías
tabla_transpuesta <- t(tabla_pais_categoria)
par(mar = c(10, 4, 4, 2))
barplot(tabla_transpuesta,
beside = TRUE,
col = rainbow(nrow(tabla_transpuesta)),
main = "Categorías de Enfermedad según País",
ylab = "Frecuencia",
las = 2)
#Ahora sí, agregamos el título del eje X
mtext("Categoría de Enfermedad", side = 1, line = 7)
# ---- Panel 2: Solo la leyenda ----
par(mar = c(10, 0, 4, 0)) # sin margen derecho
plot.new() # panel vacío
legend("right",
legend = rownames(tabla_transpuesta),
fill = rainbow(nrow(tabla_transpuesta)),
xpd = TRUE)
Este gráfico compara la frecuencia de cada tipo de enfermedad entre
países. Se observa que las diferencias entre países son pequeñas: la
mayoría de las barras están en rangos similares. Aunque hay ligeras
variaciones donde ciertos países presentan mayores frecuencias para
categorías como Neurológicas o Genéticas, la tendencia general muestra
un patrón homogéneo. Esto puede indicar que los países analizados
comparten condiciones epidemiológicas semejantes o que el dataset está
simulado con variaciones controladas.
# --------------------------- PARTE 2 ----------------------------
# ----------------------------------------------------------------
# ANALISIS BIVARIADO CUANTI-CUANTI
# ----------------------------------------------------------------
# Evitar errores de acentos en R Markdown
main_utf8 <- function(txt) enc2utf8(txt)
#LISTO
# Prevalencia vs Incidencia
bin <- hexbin(
DATAP$`Prevalence Rate (%)`,
DATAP$`Incidence Rate (%)`
)
plot(
bin,
xlab = enc2utf8("Prevalencia"),
ylab = "",
main = main_utf8("Relación entre Prevalencia e Incidencia")
)
# Etiqueta "Incidencia" en vertical
grid.text(
enc2utf8("Incidencia"),
x = unit(0.02, "npc"),
y = unit(0.5, "npc"),
rot = 90,
gp = gpar(fontsize = 14)
)
El gráfico hexagonal revela una distribución dispersa entre prevalencia
e incidencia. La mayor concentración de puntos se ubica en valores bajos
de incidencia (por debajo de 200), independientemente del nivel de
prevalencia. Esto sugiere que, aunque una enfermedad pueda ser muy
prevalente, su incidencia reciente no necesariamente es elevada. No se
observa una relación lineal clara entre ambas variables, por lo que se
podría concluir que no existe correlación fuerte entre prevalencia
histórica y casos nuevos en este conjunto de datos.
# --- Correlación lineal ---
REL_LIN <- cor(
DATAP$`Prevalence Rate (%)`,
DATAP$`Incidence Rate (%)`,
use = "complete.obs"
)
REL_LIN## [1] -0.006077847par(mfrow = c(1, 1))
# --- Histogramas ---
hist(
DATAP$`Prevalence Rate (%)`,
main = main_utf8("Histograma Prevalencia"),
col = "#DCCCFF",
xlab = enc2utf8("Prevalencia")
)
El histograma muestra que la prevalencia presenta una distribución
sesgada hacia la izquierda: un número importante de países o registros
tiene prevalencias bajas, mientras que valores muy altos son menos
frecuentes pero están distribuidos de forma relativamente uniforme. Esto
sugiere que la mayoría de enfermedades afectan a una proporción pequeña
a moderada de la población, mientras que los valores más extremos
representan casos específicos de alta prevalencia.
hist(
DATAP$`Incidence Rate (%)`,
main = main_utf8("Histograma Incidencia"),
col = "#8093F1",
xlab = enc2utf8("Incidencia")
)
La distribución de incidencia es similar a la de prevalencia, con un
sesgo hacia valores bajos. Los primeros intervalos contienen la mayor
cantidad de registros, indicando que la mayoría de las enfermedades
presentan una incidencia reciente relativamente baja. Sin embargo,
también hay casos con incidencias moderadas y altas, aunque en menor
cantidad, lo que refleja heterogeneidad entre enfermedades o países.
6.3 ANALISIS MULTIVARIADO DE DISTANCIAS
# --------------------- PARTE 3 -------------------------
# ---------------- ANALISIS MULTIVARIADO ----------------
X <- as.matrix(DATAP_muestra[vars_cuanti]) # solo numéricas
n <- nrow(X)
p <- ncol(X)
# 1. VECTOR DE MEDIAS
V1 <- rep(1, n)
TRV1 <- t(V1)
K <- 1/n
VDM <- K * (TRV1 %*% X) # vector de medias fila
TVDM <- t(VDM) # vector de medias columna
# 2. MATRIZ DE VARIANZA-COVARIANZA
I <- diag(n)
J <- 1/(n-1)
L <- 1/n
TX <- t(X)
S_formula <- J * (TX %*% (I - L * (V1 %*% TRV1)) %*% X)
S <- cov(X) # método directo
S - S_formula # verificación## Prevalence Rate (%) Incidence Rate (%)
## Prevalence Rate (%) -5.820766e-11 3.637979e-11
## Incidence Rate (%) 3.674359e-10 4.947651e-10
## Mortality Rate (%) -7.753442e-11 -7.185008e-11
## Population Affected 5.960464e-08 4.190952e-08
## Healthcare Access (%) 2.473826e-10 1.891749e-10
## Doctors per 1000 1.068656e-11 6.593837e-12
## Hospital Beds per 1000 -2.992238e-10 -2.573870e-10
## Average Treatment Cost (USD) -1.484295e-09 -8.149073e-10
## Recovery Rate (%) 1.746230e-09 1.542503e-09
## DALYs -5.456968e-11 -5.820766e-11
## Improvement in 5 Years (%) 8.594725e-11 4.774847e-11
## Per Capita Income (USD) -4.423782e-09 -2.153683e-09
## Education Index -1.227818e-11 -1.006129e-11
## Urbanization Rate (%) -4.656613e-10 -2.764864e-10
## Year 1.900169e-09 1.560331e-09
## Mortality Rate (%) Population Affected
## Prevalence Rate (%) 2.523848e-11 4.190952e-08
## Incidence Rate (%) 1.937224e-10 2.030283e-07
## Mortality Rate (%) -1.018634e-10 -3.702007e-08
## Population Affected 3.166497e-08 4.577637e-05
## Healthcare Access (%) 8.731149e-11 1.378357e-07
## Doctors per 1000 5.911716e-12 5.355105e-09
## Hospital Beds per 1000 -1.582521e-10 -1.722947e-07
## Average Treatment Cost (USD) -4.074536e-10 -8.940697e-07
## Recovery Rate (%) 9.604264e-10 1.050532e-06
## DALYs -5.820766e-11 -1.862645e-09
## Improvement in 5 Years (%) 3.251444e-11 4.004687e-08
## Per Capita Income (USD) -2.153683e-09 -1.788139e-06
## Education Index -6.167511e-12 -6.315531e-09
## Urbanization Rate (%) -8.731149e-11 -1.341105e-07
## Year 9.999894e-10 1.071634e-06
## Healthcare Access (%) Doctors per 1000
## Prevalence Rate (%) 5.238689e-10 1.659828e-11
## Incidence Rate (%) 2.852175e-09 9.964651e-11
## Mortality Rate (%) -6.111804e-10 -2.000888e-11
## Population Affected 4.954636e-07 1.047738e-08
## Healthcare Access (%) 4.656613e-09 7.912604e-11
## Doctors per 1000 9.003998e-11 3.637979e-12
## Hospital Beds per 1000 -2.340130e-09 -8.367351e-11
## Average Treatment Cost (USD) -9.313226e-09 -1.891749e-10
## Recovery Rate (%) 1.396984e-08 5.047696e-10
## DALYs -9.313226e-10 -3.183231e-11
## Improvement in 5 Years (%) 6.402843e-10 2.091838e-11
## Per Capita Income (USD) -2.747402e-08 -9.531504e-10
## Education Index -8.856205e-11 -3.353762e-12
## Urbanization Rate (%) -2.561137e-09 -6.548362e-11
## Year 1.462243e-08 5.380940e-10
## Hospital Beds per 1000
## Prevalence Rate (%) 2.455636e-11
## Incidence Rate (%) 1.828084e-10
## Mortality Rate (%) -4.183676e-11
## Population Affected 2.374873e-08
## Healthcare Access (%) 7.639755e-11
## Doctors per 1000 5.456968e-12
## Hospital Beds per 1000 -1.600711e-10
## Average Treatment Cost (USD) -4.365575e-10
## Recovery Rate (%) 9.313226e-10
## DALYs -7.821654e-11
## Improvement in 5 Years (%) 3.410605e-11
## Per Capita Income (USD) -1.455192e-09
## Education Index -4.888534e-12
## Urbanization Rate (%) -1.673470e-10
## Year 9.726691e-10
## Average Treatment Cost (USD) Recovery Rate (%)
## Prevalence Rate (%) 1.309672e-09 7.130438e-10
## Incidence Rate (%) 9.313226e-09 2.677552e-09
## Mortality Rate (%) -2.444722e-09 -5.529728e-10
## Population Affected 1.281500e-06 4.023314e-07
## Healthcare Access (%) 6.286427e-09 1.746230e-09
## Doctors per 1000 3.055902e-10 4.092726e-11
## Hospital Beds per 1000 -8.498318e-09 -2.270099e-09
## Average Treatment Cost (USD) 8.940697e-08 -8.847564e-09
## Recovery Rate (%) 5.168840e-08 1.210719e-08
## DALYs -5.122274e-09 -8.149073e-10
## Improvement in 5 Years (%) 1.920853e-09 5.165930e-10
## Per Capita Income (USD) -6.332994e-08 -2.374873e-08
## Education Index -3.055902e-10 -8.083134e-11
## Urbanization Rate (%) -1.094304e-08 -1.280569e-09
## Year 5.228333e-08 1.443053e-08
## DALYs Improvement in 5 Years (%)
## Prevalence Rate (%) 1.782610e-10 3.319656e-11
## Incidence Rate (%) 1.069566e-09 1.732587e-10
## Mortality Rate (%) -2.401066e-10 -4.138201e-11
## Population Affected 1.527369e-07 3.213063e-08
## Healthcare Access (%) 8.731149e-10 1.527951e-10
## Doctors per 1000 2.523848e-11 4.547474e-12
## Hospital Beds per 1000 -8.672032e-10 -1.614353e-10
## Average Treatment Cost (USD) -4.190952e-09 -7.566996e-10
## Recovery Rate (%) 5.020411e-09 9.622454e-10
## DALYs -1.629815e-09 -9.185896e-11
## Improvement in 5 Years (%) 1.728040e-10 2.910383e-11
## Per Capita Income (USD) -9.313226e-09 -8.149073e-10
## Education Index -3.169021e-11 -5.741185e-12
## Urbanization Rate (%) -1.091394e-09 -1.600711e-10
## Year 5.254620e-09 9.954686e-10
## Per Capita Income (USD) Education Index
## Prevalence Rate (%) 4.656613e-09 4.320100e-12
## Incidence Rate (%) 2.107117e-08 2.488321e-11
## Mortality Rate (%) -2.910383e-09 -5.030643e-12
## Population Affected 2.861023e-06 4.263711e-09
## Healthcare Access (%) 1.024455e-08 1.870148e-11
## Doctors per 1000 6.257324e-10 3.979039e-13
## Hospital Beds per 1000 -1.851004e-08 -2.148681e-11
## Average Treatment Cost (USD) -5.029142e-08 -8.003553e-11
## Recovery Rate (%) 9.918585e-08 1.217018e-10
## DALYs -2.793968e-09 -3.730349e-12
## Improvement in 5 Years (%) 4.307367e-09 5.286438e-12
## Per Capita Income (USD) -2.384186e-07 -2.000888e-10
## Education Index -6.148184e-10 -6.821210e-13
## Urbanization Rate (%) -1.172157e-08 -1.955414e-11
## Year 1.087496e-07 1.293796e-10
## Urbanization Rate (%) Year
## Prevalence Rate (%) 2.473826e-10 8.020606e-11
## Incidence Rate (%) 2.095476e-09 8.254197e-10
## Mortality Rate (%) -4.583853e-10 -1.919531e-10
## Population Affected 3.352761e-07 1.313197e-07
## Healthcare Access (%) 1.338776e-09 4.936993e-10
## Doctors per 1000 6.184564e-11 1.207212e-11
## Hospital Beds per 1000 -1.760782e-09 -7.489120e-10
## Average Treatment Cost (USD) -8.381903e-09 -2.504294e-09
## Recovery Rate (%) 1.059379e-08 4.207301e-09
## DALYs -7.348717e-10 -2.480149e-10
## Improvement in 5 Years (%) 4.074536e-10 1.526246e-10
## Per Capita Income (USD) -2.238085e-08 -9.269570e-09
## Education Index -5.684342e-11 -2.562406e-11
## Urbanization Rate (%) -6.519258e-09 -6.774599e-10
## Year 1.093287e-08 4.364790e-09# 3. MEDIDAS DE DISPERSIÓN
tr_S <- sum(diag(S)) # traza
det_S <- det(S) # determinante
# 4. MATRIZ DE CORRELACIONES
D_desv <- diag(sqrt(diag(S)))
MC <- solve(D_desv) %*% S %*% solve(D_desv)
MC - cor(X) # comparación con cor()## Prevalence Rate (%) Incidence Rate (%)
## Prevalence Rate (%) 2.220446e-16 0.000000e+00
## Incidence Rate (%) 0.000000e+00 2.220446e-16
## Mortality Rate (%) -3.469447e-18 -6.938894e-18
## Population Affected 6.938894e-18 -6.938894e-18
## Healthcare Access (%) 0.000000e+00 -6.938894e-18
## Doctors per 1000 -3.469447e-18 0.000000e+00
## Hospital Beds per 1000 0.000000e+00 6.938894e-18
## Average Treatment Cost (USD) 3.469447e-18 0.000000e+00
## Recovery Rate (%) 0.000000e+00 0.000000e+00
## DALYs 0.000000e+00 1.387779e-17
## Improvement in 5 Years (%) 0.000000e+00 -3.469447e-18
## Per Capita Income (USD) 0.000000e+00 0.000000e+00
## Education Index 0.000000e+00 8.673617e-19
## Urbanization Rate (%) 0.000000e+00 0.000000e+00
## Year 0.000000e+00 0.000000e+00
## Mortality Rate (%) Population Affected
## Prevalence Rate (%) -1.734723e-18 6.938894e-18
## Incidence Rate (%) -6.938894e-18 0.000000e+00
## Mortality Rate (%) 0.000000e+00 0.000000e+00
## Population Affected 0.000000e+00 0.000000e+00
## Healthcare Access (%) -1.387779e-17 0.000000e+00
## Doctors per 1000 -6.938894e-18 6.938894e-18
## Hospital Beds per 1000 -1.387779e-17 0.000000e+00
## Average Treatment Cost (USD) 2.775558e-17 6.938894e-18
## Recovery Rate (%) 0.000000e+00 0.000000e+00
## DALYs 0.000000e+00 0.000000e+00
## Improvement in 5 Years (%) -3.469447e-18 0.000000e+00
## Per Capita Income (USD) 0.000000e+00 0.000000e+00
## Education Index -6.938894e-18 0.000000e+00
## Urbanization Rate (%) -6.938894e-18 -1.387779e-17
## Year 0.000000e+00 0.000000e+00
## Healthcare Access (%) Doctors per 1000
## Prevalence Rate (%) 0.000000e+00 -3.469447e-18
## Incidence Rate (%) -6.938894e-18 0.000000e+00
## Mortality Rate (%) -1.387779e-17 -6.938894e-18
## Population Affected -3.469447e-18 6.938894e-18
## Healthcare Access (%) 0.000000e+00 -1.734723e-18
## Doctors per 1000 0.000000e+00 2.220446e-16
## Hospital Beds per 1000 0.000000e+00 -1.387779e-17
## Average Treatment Cost (USD) 3.469447e-18 -6.938894e-18
## Recovery Rate (%) 0.000000e+00 0.000000e+00
## DALYs -1.387779e-17 -8.673617e-19
## Improvement in 5 Years (%) -1.387779e-17 -1.387779e-17
## Per Capita Income (USD) 3.469447e-18 0.000000e+00
## Education Index 8.673617e-19 -6.938894e-18
## Urbanization Rate (%) 0.000000e+00 0.000000e+00
## Year -3.469447e-18 -1.387779e-17
## Hospital Beds per 1000
## Prevalence Rate (%) 0.000000e+00
## Incidence Rate (%) 0.000000e+00
## Mortality Rate (%) 0.000000e+00
## Population Affected 0.000000e+00
## Healthcare Access (%) -8.673617e-19
## Doctors per 1000 -1.387779e-17
## Hospital Beds per 1000 -2.220446e-16
## Average Treatment Cost (USD) 0.000000e+00
## Recovery Rate (%) 0.000000e+00
## DALYs 0.000000e+00
## Improvement in 5 Years (%) 0.000000e+00
## Per Capita Income (USD) -3.469447e-18
## Education Index -3.469447e-18
## Urbanization Rate (%) 0.000000e+00
## Year 1.387779e-17
## Average Treatment Cost (USD) Recovery Rate (%)
## Prevalence Rate (%) 3.469447e-18 0.000000e+00
## Incidence Rate (%) 0.000000e+00 1.387779e-17
## Mortality Rate (%) 2.775558e-17 0.000000e+00
## Population Affected 0.000000e+00 0.000000e+00
## Healthcare Access (%) 3.469447e-18 1.387779e-17
## Doctors per 1000 -6.938894e-18 -3.469447e-18
## Hospital Beds per 1000 0.000000e+00 0.000000e+00
## Average Treatment Cost (USD) 0.000000e+00 0.000000e+00
## Recovery Rate (%) 0.000000e+00 -1.110223e-16
## DALYs 0.000000e+00 -3.469447e-18
## Improvement in 5 Years (%) 1.387779e-17 1.734723e-18
## Per Capita Income (USD) 6.938894e-18 -6.938894e-18
## Education Index -6.938894e-18 0.000000e+00
## Urbanization Rate (%) 0.000000e+00 1.387779e-17
## Year 0.000000e+00 1.734723e-18
## DALYs Improvement in 5 Years (%)
## Prevalence Rate (%) 3.469447e-18 0.000000e+00
## Incidence Rate (%) 0.000000e+00 -3.469447e-18
## Mortality Rate (%) 6.938894e-18 0.000000e+00
## Population Affected 0.000000e+00 -6.938894e-18
## Healthcare Access (%) -1.387779e-17 -6.938894e-18
## Doctors per 1000 0.000000e+00 0.000000e+00
## Hospital Beds per 1000 -8.673617e-19 0.000000e+00
## Average Treatment Cost (USD) 2.775558e-17 1.387779e-17
## Recovery Rate (%) -3.469447e-18 -1.734723e-18
## DALYs -1.110223e-16 0.000000e+00
## Improvement in 5 Years (%) 0.000000e+00 0.000000e+00
## Per Capita Income (USD) 0.000000e+00 0.000000e+00
## Education Index 1.084202e-19 0.000000e+00
## Urbanization Rate (%) 1.734723e-18 -1.387779e-17
## Year 0.000000e+00 0.000000e+00
## Per Capita Income (USD) Education Index
## Prevalence Rate (%) 0.000000e+00 0.000000e+00
## Incidence Rate (%) 0.000000e+00 1.734723e-18
## Mortality Rate (%) 0.000000e+00 -3.469447e-18
## Population Affected 0.000000e+00 0.000000e+00
## Healthcare Access (%) 0.000000e+00 8.673617e-19
## Doctors per 1000 1.734723e-18 0.000000e+00
## Hospital Beds per 1000 -3.469447e-18 0.000000e+00
## Average Treatment Cost (USD) 0.000000e+00 -6.938894e-18
## Recovery Rate (%) -6.938894e-18 0.000000e+00
## DALYs 0.000000e+00 1.084202e-19
## Improvement in 5 Years (%) 0.000000e+00 0.000000e+00
## Per Capita Income (USD) -1.110223e-16 0.000000e+00
## Education Index 0.000000e+00 0.000000e+00
## Urbanization Rate (%) 1.084202e-19 0.000000e+00
## Year 0.000000e+00 -3.469447e-18
## Urbanization Rate (%) Year
## Prevalence Rate (%) 0.000000e+00 0.000000e+00
## Incidence Rate (%) 0.000000e+00 -1.734723e-18
## Mortality Rate (%) 0.000000e+00 0.000000e+00
## Population Affected 0.000000e+00 0.000000e+00
## Healthcare Access (%) 0.000000e+00 -3.469447e-18
## Doctors per 1000 0.000000e+00 0.000000e+00
## Hospital Beds per 1000 0.000000e+00 1.387779e-17
## Average Treatment Cost (USD) 6.938894e-18 0.000000e+00
## Recovery Rate (%) 1.387779e-17 1.734723e-18
## DALYs 1.734723e-18 -3.469447e-18
## Improvement in 5 Years (%) 1.387779e-17 0.000000e+00
## Per Capita Income (USD) 1.084202e-19 0.000000e+00
## Education Index 6.938894e-18 0.000000e+00
## Urbanization Rate (%) -2.220446e-16 0.000000e+00
## Year 0.000000e+00 -2.220446e-16# 5. DISTANCIA EUCLIDIANA PONDERADA
M <- diag(diag(S))
IM <- solve(M)
Q_p <- X %*% IM %*% t(X)
d2_p <- outer(diag(Q_p), diag(Q_p), "+") - 2*Q_p
D_p <- sqrt(d2_p)
# 6. DISTANCIA DE MAHALANOBIS
S_inv <- solve(S)
Q_M <- X %*% S_inv %*% t(X)
d2_M <- outer(diag(Q_M), diag(Q_M), "+") - 2*Q_M
D_M <- sqrt(d2_M)
# 7. COMPARACIÓN
diff <- D_M - D_p
diff## 51663 57870 2986 29925 68293
## 51663 0.000000000 -0.2482475806 -0.241466355 -0.1522952166 -0.050063987
## 57870 -0.248247581 0.0000000000 -0.214460050 0.0962775308 -0.275818859
## 2986 -0.241466355 -0.2144600495 0.000000000 0.0484209138 0.100075953
## 29925 -0.152295217 0.0962775308 0.048420914 0.0000000000 0.048808599
## 68293 -0.050063987 -0.2758188591 0.100075953 0.0488085986 0.000000000
## 62555 -0.249253338 -0.1711151836 -0.013828040 -0.0036913105 0.017107731
## 45404 -0.203272022 0.1678003852 0.026543980 0.2282485835 0.385013378
## 65161 -0.387966127 -0.3905677904 -0.081358343 -0.0295836713 0.069323326
## 46435 0.110288992 0.1092632975 -0.179541329 0.0671049606 0.434210723
## 9642 0.190648620 0.1311995563 0.138360979 0.1797205923 0.270146828
## 59134 -0.140694932 -0.3677989181 -0.335121843 0.0171181563 0.384412752
## 52132 -0.011421074 0.0950869622 -0.309594690 0.1138759109 0.234850962
## 14183 0.236071942 0.1647915888 0.111065838 0.0912961833 0.373650919
## 15180 0.099498995 0.0575746001 0.026810079 0.2124024074 0.180916135
## 27168 -0.299944116 -0.0961192709 0.086254318 0.1532318672 0.330438920
## 9097 -0.304225860 -0.1769006910 -0.401575737 -0.0273793264 0.120932457
## 30538 0.252473532 -0.0022415225 -0.112505330 0.0340143918 0.152286804
## 56219 -0.094340586 0.1476007297 0.089854363 0.3710852638 0.347491651
## 7989 -0.287931180 -0.1241173474 0.056535018 0.0734706870 0.401537863
## 13536 -0.211168230 -0.2941098524 -0.088381883 0.1210382402 0.066378806
## 6216 -0.081280427 -0.1331580329 -0.090680459 0.2190076125 0.306624887
## 29394 -0.038777716 -0.0127380309 0.102992046 0.2580501646 0.103414269
## 53241 -0.090782489 -0.3476083187 -0.003182825 -0.0471130264 -0.042250221
## 28825 -0.407986368 -0.1432774430 -0.280003056 -0.1952725509 0.008077955
## 41 -0.375917877 -0.2381278337 0.018853677 0.1890274044 0.094133103
## 14426 -0.081505821 0.1560745347 -0.163590233 0.0591193139 0.155148412
## 72820 -0.416650224 -0.0771608952 -0.072629030 0.1961788390 0.114328640
## 51656 -0.070057235 0.2216735952 -0.076704814 0.1194763110 0.187464155
## 58527 -0.340879840 -0.1966890817 -0.062147528 -0.0708008197 -0.033281627
## 77009 0.069209558 0.1590085409 -0.178471891 -0.0296486808 0.127744650
## 43042 -0.333373045 0.1318514471 -0.000453769 0.1130043186 -0.025049556
## 77837 -0.418123093 -0.1089596772 -0.060038790 0.0727276133 -0.031903337
## 6134 -0.614438363 -0.2805995370 -0.287145116 -0.2208013143 -0.265164724
## 33523 -0.492980064 -0.0848078046 -0.267057086 -0.0928919266 -0.271937242
## 21812 -0.013671378 0.0286277989 0.004331282 -0.0506975610 0.151805524
## 39895 -0.243154657 -0.0437334599 -0.177557929 0.1139380539 -0.009135594
## 9640 -0.229766924 -0.3250317801 -0.373711194 -0.0037653710 0.314399342
## 9326 -0.155642680 -0.1445442572 -0.146343039 0.1088263812 0.178590266
## 26510 -0.236407055 -0.1908436739 -0.099749488 -0.2084360558 0.153172398
## 20960 -0.261695102 -0.2367656895 0.090595642 -0.0083831723 -0.023845917
## 14403 -0.165208336 0.1453667878 -0.066660474 0.0170078708 0.059434395
## 64502 -0.165385881 -0.0084495466 -0.214062009 0.2379013659 0.096494028
## 71503 -0.194130160 0.0305660190 -0.033842070 0.1361572872 0.326106395
## 45221 -0.154095568 -0.0818759272 -0.095166111 -0.0418440274 -0.205299717
## 77585 0.327267450 0.1488184193 0.130217052 0.2323936741 0.261345475
## 70563 -0.284203527 -0.3855498875 0.057910646 0.0704471358 0.268306196
## 32606 -0.387660626 -0.2790072564 -0.176648546 -0.0683497278 0.105080883
## 16152 -0.349345964 -0.2059433588 -0.233174417 0.1027784004 0.203277301
## 25559 -0.364659880 -0.1228270577 -0.031492242 0.0349726741 -0.090124063
## 14215 -0.228647112 0.0345119837 0.004594326 -0.0500178373 0.065939943
## 14287 -0.401809211 -0.1839308840 0.008312913 0.0530337972 -0.020880822
## 23194 0.002892048 0.1220988984 0.056817341 -0.0806398636 0.305488901
## 34976 -0.200003194 -0.0816107996 -0.444676548 -0.1926948193 0.004875989
## 14491 -0.045337533 0.0784967540 -0.076274070 0.2486024254 0.062319786
## 77584 -0.283048303 -0.1998648449 -0.020139083 0.1193991561 -0.085176174
## 40503 -0.208466215 -0.1139390132 -0.383668302 0.0451285703 0.148227366
## 34724 -0.301034574 -0.2030019018 -0.076468047 0.1208861595 0.238109947
## 17369 -0.187511144 -0.0157031793 0.009733724 0.0393247178 0.107829336
## 31542 -0.474073754 -0.2569153543 -0.099837902 0.0603944009 0.062963181
## 59106 0.117127122 -0.2098101340 -0.216390487 -0.1377506808 0.097663154
## 40632 -0.071955788 0.1363025724 -0.177803502 -0.0304959627 -0.131268068
## 43204 -0.153198914 -0.2080969708 0.013251505 -0.0125192917 0.054337298
## 37544 -0.253376130 -0.1108727849 -0.052146123 -0.0442835075 0.269652537
## 76820 -0.383497283 -0.3693821014 -0.102916063 -0.0208652059 0.107765342
## 44196 -0.452472372 0.0703624530 -0.040400596 0.1414863581 -0.046062694
## 44983 -0.347765852 0.0749024861 -0.002774184 0.4107343198 0.160365546
## 36717 -0.287452706 -0.0252522925 -0.130820285 -0.0972537170 0.058650627
## 28078 -0.042460791 -0.1117482095 -0.076884950 -0.0393573402 0.154300683
## 69369 -0.195372914 -0.1329146776 -0.083212524 -0.0201910457 0.289413933
## 43406 -0.137441567 -0.2012452605 -0.160714769 0.0532553195 -0.035531223
## 41488 -0.044717403 -0.0734857773 -0.055528348 0.2723066553 0.373955353
## 47485 -0.308705910 -0.0636607610 -0.064661899 0.0327160450 0.106108395
## 63528 -0.224333105 -0.3959185616 0.050100868 0.1183500428 0.175188171
## 14536 -0.413471018 -0.1915334205 -0.294273364 -0.0838669630 -0.196325141
## 51465 -0.450408589 -0.2524253907 0.159484074 -0.0172050431 -0.027823685
## 26503 -0.318101409 -0.1638713670 -0.179513452 0.0528449841 0.097960886
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## 11667 0.1432201587 0.031004249 -0.0217592740 0.057303922 -0.091370102
## 33027 -0.0573975898 -0.085597488 -0.2467184348 -0.209448371 -0.248103047
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## 65615 0.1067240096 0.073496229 0.0564602823 0.101208211 0.024653984
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## 34599 0.0488289706 -0.160538825 0.0214606271 -0.090894858 -0.116404943
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## 53289 0.1296841472 -0.203396448 0.1905171770 0.195987871 -0.154152586
## 16436 -0.0281483652 0.291289683 -0.1594435773 -0.036862138 0.126441940
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## 42887 0.0181579587 0.136855552 -0.1600720379 -0.065825960 0.125974499
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## 23977 -0.0258950943 -0.019928955 -0.1060286793 -0.173913902 0.081295368
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## 63481 0.1771307568 0.072989290 -0.1529551297 0.187229224 0.218280554
## 31315 -0.0282098588 -0.344780576 0.0983010062 -0.168536066 -0.304583768
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## 45555 -0.1136944088 0.158918629 -0.5835166089 0.107572714 0.138253095
## 57943 -0.0481625104 -0.050908684 -0.0394469527 -0.021139143 -0.143488902
## 74659 0.1201487857 0.125448187 0.0557181983 -0.053784707 -0.172813012
## 73799 0.2864796265 -0.010660105 0.1998620394 0.384218164 0.273837240
## 20400 0.0079458383 -0.090111769 -0.2466318298 0.237231498 0.016003467
## 4972 -0.0345111627 -0.154350607 -0.1154514349 0.079024519 -0.130461789
## 69045 0.1290779986 0.131666525 0.0561903885 0.094604190 0.110032636
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## 6373 -0.1651677167 0.113441913 -0.2610660974 -0.109346275 -0.228977390
## 73396 -0.0083278107 -0.359786054 0.0767090486 -0.013609728 -0.300451959
## 602 -0.1172477544 -0.107431627 -0.1605958263 -0.138932375 -0.269113144
## 74059 0.0675793506 -0.054657921 -0.1520146143 0.107656745 0.162325855
## 47606 -0.2446347740 -0.146158308 -0.3068656813 -0.306614028 -0.462715196
## 28577 -0.0455821593 0.140451304 -0.2481101744 0.160069045 0.297014778
## 7877 0.0378431561 -0.122561058 0.1555001702 -0.042691099 -0.177399387
## 58263 -0.0125075478 0.090268472 0.1374049145 -0.135023186 -0.171279010
## 2777 0.1440676455 -0.157129306 0.2340818586 -0.129778223 -0.215274768
## 30712 0.0469246892 0.029144756 -0.1313613866 0.011039649 0.101610148
## 17749 -0.0056501511 -0.102647133 -0.0440284180 -0.186440718 -0.129861728
## 7488 -0.0590722054 -0.083250310 -0.0530201092 -0.094065007 -0.338285453
## 76662 -0.1408774961 0.146338104 -0.3465149402 -0.050609429 -0.135109682
## 45430 -0.0327202278 -0.117822947 -0.1658475863 0.168623333 0.076609378
## 9739 0.0258617268 0.029218347 0.0005384834 -0.097309846 -0.320189903
## 61775 -0.0706161397 0.029680736 -0.0678086936 -0.040185999 -0.106492301
## 19512 -0.2418127300 -0.015768764 -0.5714219911 0.084203279 -0.122282738
## 49588 -0.0367327005 0.191563227 -0.1462311668 -0.103321348 -0.133311258
## 49753 -0.0916186045 -0.195349659 -0.1243798959 -0.300447775 -0.271413933
## 52292 -0.1971493333 -0.125764501 -0.2822411603 -0.374949334 -0.337164062
## 38245 -0.1571080527 -0.015623314 -0.1360254782 -0.252564621 -0.454819173
## 4682 0.0351408812 0.092753732 -0.1908349312 -0.018163619 -0.131112974
## 68957 0.1185869071 -0.127052227 0.0018351858 0.147813357 -0.114482750
## 37765 -0.0113145242 0.028764539 0.0499664567 -0.247453206 -0.215685919
## 34827 -0.0672774292 -0.074815301 -0.0448525641 -0.258674193 -0.223387423
## 2748 0.0376344361 0.083806904 0.0390532191 0.031686453 -0.115900245
## 56138 -0.0286712072 -0.215441654 -0.4036863337 0.120435662 -0.073438187
## 44859 -0.0960441986 -0.157784389 -0.0426212961 -0.009997081 -0.346858310
## 51442 -0.0823953619 -0.347772122 -0.1117808618 0.044822841 -0.266012185
## 78912 -0.0770049557 -0.232066956 -0.2682556411 -0.083470127 -0.307555091
## 75936 0.0051771276 0.033932569 -0.0939250936 -0.079969965 -0.135255669
## 57223 -0.1369252456 -0.053394289 -0.1165769177 -0.202010653 -0.208896009
## 67089 0.0738225142 -0.147004817 -0.2613219303 0.255713407 0.008623244
## 27365 -0.1031552433 0.050592283 -0.4112693498 -0.062166965 -0.113867260
## 76141 0.0030766002 -0.117578022 -0.1767170767 -0.137449351 -0.100257815
## 19927 -0.3342819696 -0.069084431 -0.4677035154 -0.397885268 -0.447261069
## 10216 -0.1223466882 -0.165831966 -0.2143811293 -0.215909269 -0.318390178
## 2882 -0.1358992136 0.295305172 -0.4722514663 0.001218142 0.130767368
## 4015 -0.1550079336 -0.264281900 -0.0054762633 -0.314595067 -0.442793120
## 27913 0.0000000000 0.005467691 -0.1833239555 -0.085848315 -0.157576734
## 29465 0.0054676907 0.000000000 -0.1819324151 -0.027327251 0.101037321
## 9174 -0.1833239555 -0.181932415 0.0000000000 -0.282675638 -0.365466724
## 7657 -0.0858483145 -0.027327251 -0.2826756376 0.000000000 -0.076638496
## 31351 -0.1575767339 0.101037321 -0.3654667237 -0.076638496 0.000000000# HISTOGRAMA
hist(diff, breaks=75, main="Diferencias Mahalanobis - Ponderada", col = "#7C77B9")
Este histograma tiene forma aproximadamente normal, centrada cerca de 0.
Esto confirma que las diferencias entre las distancias ponderadas siguen
una distribución simétrica, donde la mayoría de registros se ubican en
valores cercanos al promedio. Los valores extremos son pocos, lo que
indica baja presencia de outliers y sugiere que los datos son
relativamente homogéneos bajo la métrica de Mahalanobis
# LINEAS
plot(density(D_M), col="red", lwd=2,
main="Comparación de Distancias", xlab="Distancia")
lines(density(D_p), col="blue", lwd=2)
legend("topright", legend=c("Mahalanobis", "Euclidiana Ponderada"),
col=c("red","blue"), lwd=2)
Ambas curvas de densidad (roja y azul) tienen formas muy similares y
coinciden en gran parte del rango, lo que indica que ambas métricas
generan distribuciones de distancia parecidas. Esto implica que las
ponderaciones aplicadas en la distancia euclidiana replican de forma
cercana el comportamiento de la distancia Mahalanobis. También sugiere
que las variables del dataset pueden no estar altamente correlacionadas
entre sí, dado que Mahalanobis ajusta por correlación y aun así la
diferencia es mínima.
6.4 ACP
# ------------------------ CORTE 2 --------------------------
# ------------------------ PARTE 1 --------------------------
colores_pastel <- colorRampPalette(c("#C7CEEA", "#B5EAD7", "#FFDAC1", "#FFB7B2", "#E2F0CB"))(200)
cor_matrix <- cor(DATAP[vars_cuanti], use = "pairwise.complete.obs") # Matriz de correlaciones
cor_data <- melt(cor_matrix) # Reestructurar matriz
corrplot(cor_matrix, method = "number", type = "upper", col = colores_pastel, tl.cex = 0.7) # Mapa de calor
La matriz muestra que todas las variables tienen correlaciones
extremadamente bajas entre sí (cerca de 0). Esto es poco común en datos
reales y sugiere que el dataset fue construido de forma que cada
variable es prácticamente independiente. En consecuencia:
No existen relaciones lineales destacables.
Ninguna variable predice a otra de manera significativa.
Técnicas como ACP no captarán componentes dominantes fuertes.
Esto también explica resultados posteriores del PCA.
# Pares de variables altamente correlacionadas (|r| >= 0.9)
cor_high <- subset(cor_data, abs(value) >= 0.9 & Var1 != Var2)
cor_high # Mostrar los pares## [1] Var1 Var2 value
## <0 rows> (o 0- extensión row.names)# Media de correlaciones por variable (para evaluar redundancia)
mean_cor <- apply(abs(cor_matrix), 1, mean)
sort(mean_cor)## Population Affected Mortality Rate (%)
## 0.06834896 0.06867606
## Education Index Hospital Beds per 1000
## 0.06892589 0.06894896
## Incidence Rate (%) Improvement in 5 Years (%)
## 0.06916874 0.06923061
## Healthcare Access (%) Year
## 0.06927523 0.06938782
## DALYs Urbanization Rate (%)
## 0.06942559 0.06945000
## Recovery Rate (%) Prevalence Rate (%)
## 0.06945954 0.06962686
## Average Treatment Cost (USD) Doctors per 1000
## 0.06963677 0.06964317
## Per Capita Income (USD)
## 0.06965392# ------------------------ PARTE 2 --------------------------
# Vector con las variables conservadas
vars_conservadas <- c(
"Improvement in 5 Years (%)",
"Healthcare Access (%)",
"Mortality Rate (%)",
"Year", "Average Treatment Cost (USD)",
"DALYs",
"Hospital Beds per 1000",
"Doctors per 1000",
"Urbanization Rate (%)",
"Education Index",
"Recovery Rate (%)"
)
# Si luego tienes un data frame llamado 'datos', puedes filtrarlo así:
datos_filtrado <- DATAP_muestra[, vars_conservadas]
# ------------------------- PARTE 3 -----------------------------------
A <- datos_filtrado %>%
mutate(across(everything(), as.numeric))
n_1 <- nrow(A)
p_1 <- ncol(A)
# ACP
ACP <- dudi.pca(A, scale = TRUE, scannf = FALSE, nf = p_1)# Analisis Componentes Principales. Coordenadas
aux.ACP <- inertia.dudi(ACP,T,T) # Valores Propios y Contribuciones
# -------------------------- PARTE 4 --------------------------------
# Diagrama de barras de los valores propios (lambda)
barplot(ACP$eig,
main = "Valores propios (lambda) - Análisis de Componentes Principales",
col = "skyblue",
names.arg = paste0("CP", 1:length(ACP$eig)),
ylab = "Valor propio (lambda)")
Los valores propios son muy similares entre sí (entre 0.7 y 1.3). Esto
refleja que ninguna componente principal captura una proporción
sustancialmente mayor de la varianza total del dataset. Esto es
consistente con la matriz de correlación sin relaciones fuertes. Cada
componente explica aproximadamente la misma fracción de la variabilidad,
lo que indica un sistema multidimensional sin variables dominantes.
# Cálculo de varianza explicada y acumulada
var_exp <- ACP$eig / sum(ACP$eig) * 100 # Porcentaje de varianza explicada
var_acum <- cumsum(var_exp) # Varianza acumulada
num_comp_opt <- which(var_acum >= 80)[1] # Primer punto que supera el 80%
# -------------------------- PARTE 5 ----------------------------------
plot(var_acum,
type = "b",
pch = 19,
col = "steelblue",
xlab = "Número de componentes principales",
ylab = "Varianza acumulada (%)",
main = "Gráfico del Codo - Análisis de Componentes Principales")
# Línea vertical indicando el punto óptimo
abline(v = num_comp_opt, lty = 2, col = "darkgreen")
El gráfico del codo muestra un incremento gradual y lineal de la
varianza acumulada, sin un punto claro donde la pendiente cambie
abruptamente. Esto significa que no hay un número óptimo “natural” de
componentes principales, lo cual coincide con el hecho de que las
variables no están correlacionadas. La línea verde indica la elección de
9 componentes para explicar aproximadamente el 85% de la variabilidad,
una decisión adecuada dado el comportamiento uniforme de los datos
# -------------------------- PARTE 6 -----------------------------------
ACP$eig # Valores propios (eigenvalues)## [1] 1.3137887 1.2443960 1.2190195 1.0805174 1.0320177 0.9864252 0.9420955
## [8] 0.8580392 0.8444443 0.7948086 0.6844480ACP$c1 # Eigenvectores (coeficientes de las variables)## CS1 CS2 CS3 CS4
## Improvement in 5 Years (%) -0.29846295 -0.06248397 -0.30771457 0.49715375
## Healthcare Access (%) 0.24686762 -0.21443771 0.31508680 0.56402733
## Mortality Rate (%) -0.40679483 0.32284534 0.26367215 -0.25521222
## Year 0.37598086 0.20751690 -0.19355258 0.11102214
## Average Treatment Cost (USD) -0.21842655 0.45947274 -0.22175207 0.39686484
## DALYs -0.27431885 0.44905888 -0.08397067 -0.12790670
## Hospital Beds per 1000 -0.36078936 -0.19403850 0.35923114 0.05435042
## Doctors per 1000 0.43774966 0.22378909 0.03376477 -0.18934935
## Urbanization Rate (%) -0.29499234 -0.43629150 -0.35995808 -0.04021255
## Education Index -0.07396553 -0.31441641 0.07099197 -0.30722416
## Recovery Rate (%) -0.07672361 0.11450034 0.61483708 0.22040740
## CS5 CS6 CS7 CS8
## Improvement in 5 Years (%) 0.42628455 -0.19074261 0.07182762 0.32197661
## Healthcare Access (%) -0.41935792 0.07659733 -0.07825887 -0.03844763
## Mortality Rate (%) 0.13857206 0.22954055 0.38132865 0.01365970
## Year 0.30245787 -0.51765792 0.29647580 -0.35841328
## Average Treatment Cost (USD) -0.34806406 -0.02574382 0.01493227 -0.19274048
## DALYs -0.30207928 -0.33212760 -0.45440767 0.09518820
## Hospital Beds per 1000 0.25075893 -0.16934721 -0.31457068 -0.68939006
## Doctors per 1000 0.25503710 -0.04035233 -0.53057260 0.12809586
## Urbanization Rate (%) 0.06069615 -0.00920742 -0.32413081 0.10521085
## Education Index -0.37579113 -0.64582736 0.24504037 0.12785712
## Recovery Rate (%) 0.21566174 -0.28042019 -0.05594679 0.44823267
## CS9 CS10 CS11
## Improvement in 5 Years (%) -0.06622823 0.36396531 -0.318400979
## Healthcare Access (%) -0.14249655 -0.19962522 -0.475583785
## Mortality Rate (%) -0.37356846 -0.26366096 -0.411184708
## Year 0.05223477 -0.41998698 -0.104214253
## Average Treatment Cost (USD) -0.43090002 0.03335232 0.434198755
## DALYs 0.38936607 -0.09383849 -0.349520731
## Hospital Beds per 1000 -0.04149700 0.18078286 -0.001672663
## Doctors per 1000 -0.56604001 0.14629052 -0.122107163
## Urbanization Rate (%) -0.23672337 -0.63879260 0.086310728
## Education Index -0.33572750 0.21910622 -0.028639149
## Recovery Rate (%) 0.06979770 -0.24841082 0.398285583ACP$co # Coordenadas de las variables## Comp1 Comp2 Comp3 Comp4
## Improvement in 5 Years (%) -0.34210008 -0.06970243 -0.33974524 0.51678106
## Healthcare Access (%) 0.28296120 -0.23921063 0.34788487 0.58629477
## Mortality Rate (%) -0.46627076 0.36014204 0.29111836 -0.26528784
## Year 0.43095160 0.23149028 -0.21369989 0.11540523
## Average Treatment Cost (USD) -0.25036187 0.51255332 -0.24483472 0.41253282
## DALYs -0.31442597 0.50093640 -0.09271135 -0.13295637
## Hospital Beds per 1000 -0.41353901 -0.21645479 0.39662428 0.05649614
## Doctors per 1000 0.50175138 0.24964232 0.03727941 -0.19682474
## Urbanization Rate (%) -0.33812205 -0.48669407 -0.39742689 -0.04180011
## Education Index -0.08477975 -0.35073936 0.07838168 -0.31935317
## Recovery Rate (%) -0.08794108 0.12772799 0.67883680 0.22910894
## Comp5 Comp6 Comp7 Comp8
## Improvement in 5 Years (%) 0.43305510 -0.189443536 0.06971705 0.29824825
## Healthcare Access (%) -0.42601845 0.076075655 -0.07595932 -0.03561420
## Mortality Rate (%) 0.14077296 0.227977234 0.37012371 0.01265304
## Year 0.30726172 -0.514132352 0.28776417 -0.33199968
## Average Treatment Cost (USD) -0.35359226 -0.025568488 0.01449350 -0.17853629
## DALYs -0.30687711 -0.329865604 -0.44105539 0.08817322
## Hospital Beds per 1000 0.25474166 -0.168193849 -0.30532735 -0.63858482
## Doctors per 1000 0.25908778 -0.040077510 -0.51498229 0.11865572
## Urbanization Rate (%) 0.06166017 -0.009144711 -0.31460657 0.09745724
## Education Index -0.38175971 -0.641428882 0.23784012 0.11843458
## Recovery Rate (%) 0.21908703 -0.278510360 -0.05430285 0.41519975
## Comp9 Comp10 Comp11
## Improvement in 5 Years (%) -0.06085954 0.32448250 -0.263417497
## Healthcare Access (%) -0.13094528 -0.17796996 -0.393456987
## Mortality Rate (%) -0.34328570 -0.23505913 -0.340178748
## Year 0.04800044 -0.37442697 -0.086217881
## Average Treatment Cost (USD) -0.39596976 0.02973427 0.359218584
## DALYs 0.35780269 -0.08365893 -0.289163294
## Hospital Beds per 1000 -0.03813311 0.16117160 -0.001383817
## Doctors per 1000 -0.52015483 0.13042098 -0.101020931
## Urbanization Rate (%) -0.21753375 -0.56949663 0.071406049
## Education Index -0.30851225 0.19533767 -0.023693561
## Recovery Rate (%) 0.06413965 -0.22146331 0.329507125aux.ACP$col.abs # Contribución absoluta de las variables## Axis1 Axis2 Axis3 Axis4
## Improvement in 5 Years (%) 8.9080130 0.3904246 9.4688254 24.7161851
## Healthcare Access (%) 6.0943624 4.5983534 9.9279694 31.8126829
## Mortality Rate (%) 16.5482033 10.4229112 6.9523004 6.5133279
## Year 14.1361608 4.3063262 3.7462603 1.2325916
## Average Treatment Cost (USD) 4.7710157 21.1115196 4.9173981 15.7501704
## DALYs 7.5250829 20.1653875 0.7051073 1.6360124
## Hospital Beds per 1000 13.0168963 3.7650938 12.9047012 0.2953968
## Doctors per 1000 19.1624764 5.0081558 0.1140059 3.5853176
## Urbanization Rate (%) 8.7020479 19.0350271 12.9569819 0.1617049
## Education Index 0.5470900 9.8857681 0.5039860 9.4386683
## Recovery Rate (%) 0.5886513 1.3110327 37.8024640 4.8579420
## Axis5 Axis6 Axis7 Axis8
## Improvement in 5 Years (%) 18.1718519 3.638274361 0.51592077 10.36689384
## Healthcare Access (%) 17.5861065 0.586715091 0.61244514 0.14782203
## Mortality Rate (%) 1.9202216 5.268886270 14.54115397 0.01865874
## Year 9.1480764 26.796972360 8.78978973 12.84600765
## Average Treatment Cost (USD) 12.1148592 0.066274423 0.02229726 3.71488907
## DALYs 9.1251891 11.030874087 20.64863320 0.90607939
## Hospital Beds per 1000 6.2880042 2.867847681 9.89547100 47.52586503
## Doctors per 1000 6.5043924 0.162831088 28.15072861 1.64085495
## Urbanization Rate (%) 0.3684023 0.008477658 10.50607798 1.10693228
## Education Index 14.1218976 41.709298462 6.00447807 1.63474444
## Recovery Rate (%) 4.6509987 7.863548520 0.31300428 20.09125259
## Axis9 Axis10 Axis11
## Improvement in 5 Years (%) 0.4386179 13.2470749 1.013792e+01
## Healthcare Access (%) 2.0305266 3.9850230 2.261799e+01
## Mortality Rate (%) 13.9553398 6.9517103 1.690729e+01
## Year 0.2728471 17.6389067 1.086061e+00
## Average Treatment Cost (USD) 18.5674827 0.1112377 1.885286e+01
## DALYs 15.1605937 0.8805662 1.221647e+01
## Hospital Beds per 1000 0.1722001 3.2682441 2.797803e-04
## Doctors per 1000 32.0401297 2.1400916 1.491016e+00
## Urbanization Rate (%) 5.6037954 40.8055984 7.449542e-01
## Education Index 11.2712951 4.8007538 8.202009e-02
## Recovery Rate (%) 0.4871719 6.1707934 1.586314e+01aux.ACP$col.rel/10000 # Calidad de las variables## Axis1 Axis2 Axis3
## Improvement in 5 Years (%) -1.170325e-03 -4.858428e-05 -1.154268e-03
## Healthcare Access (%) 8.006704e-04 -5.722173e-04 1.210239e-03
## Mortality Rate (%) -2.174084e-03 1.297023e-03 8.474990e-04
## Year 1.857193e-03 5.358775e-04 -4.566764e-04
## Average Treatment Cost (USD) -6.268106e-04 2.627109e-03 -5.994404e-04
## DALYs -9.886369e-04 2.509373e-03 -8.595395e-05
## Hospital Beds per 1000 -1.710145e-03 -4.685268e-04 1.573108e-03
## Doctors per 1000 2.517544e-03 6.232129e-04 1.389755e-05
## Urbanization Rate (%) -1.143265e-03 -2.368711e-03 -1.579481e-03
## Education Index -7.187606e-05 -1.230181e-03 6.143688e-05
## Recovery Rate (%) -7.733634e-05 1.631444e-04 4.608194e-03
## Axis4 Axis5 Axis6
## Improvement in 5 Years (%) 2.670627e-03 1.875367e-03 -3.588885e-04
## Healthcare Access (%) 3.437416e-03 -1.814917e-03 5.787505e-05
## Mortality Rate (%) -7.037764e-04 1.981703e-04 5.197362e-04
## Year 1.331837e-04 9.440976e-04 -2.643321e-03
## Average Treatment Cost (USD) 1.701833e-03 -1.250275e-03 -6.537476e-06
## DALYs -1.767740e-04 -9.417356e-04 -1.088113e-03
## Hospital Beds per 1000 3.191814e-05 6.489331e-04 -2.828917e-04
## Doctors per 1000 -3.873998e-04 6.712648e-04 -1.606207e-05
## Urbanization Rate (%) -1.747250e-05 3.801977e-05 -8.362575e-07
## Education Index -1.019864e-03 -1.457405e-03 -4.114310e-03
## Recovery Rate (%) 5.249091e-04 4.799913e-04 -7.756802e-04
## Axis7 Axis8 Axis9
## Improvement in 5 Years (%) 4.860467e-05 8.895202e-04 -3.703884e-05
## Healthcare Access (%) -5.769818e-05 -1.268371e-05 -1.714667e-04
## Mortality Rate (%) 1.369916e-03 1.600993e-06 -1.178451e-03
## Year 8.280822e-04 -1.102238e-03 2.304042e-05
## Average Treatment Cost (USD) 2.100615e-06 -3.187521e-04 -1.567920e-03
## DALYs -1.945299e-03 7.774517e-05 1.280228e-03
## Hospital Beds per 1000 -9.322479e-04 -4.077906e-03 -1.454134e-05
## Doctors per 1000 -2.652068e-03 1.407918e-04 -2.705610e-03
## Urbanization Rate (%) -9.897729e-04 9.497913e-05 -4.732093e-04
## Education Index 5.656792e-04 1.402675e-04 -9.517981e-04
## Recovery Rate (%) -2.948799e-05 1.723908e-03 4.113895e-05
## Axis10 Axis11
## Improvement in 5 Years (%) 1.052889e-03 -6.938878e-04
## Healthcare Access (%) -3.167331e-04 -1.548084e-03
## Mortality Rate (%) -5.525279e-04 -1.157216e-03
## Year -1.401956e-03 -7.433523e-05
## Average Treatment Cost (USD) 8.841270e-06 1.290380e-03
## DALYs -6.998816e-05 -8.361541e-04
## Hospital Beds per 1000 2.597629e-04 -1.914951e-08
## Doctors per 1000 1.700963e-04 -1.020523e-04
## Urbanization Rate (%) -3.243264e-03 5.098824e-05
## Education Index 3.815681e-04 -5.613848e-06
## Recovery Rate (%) -4.904600e-04 1.085749e-03aux.ACP$col.cum/10000 # Calidad acumulada## Axis1 Axis1:2 Axis1:3 Axis1:4
## Improvement in 5 Years (%) 1.170325e-03 0.0012189089 0.002373177 0.005043804
## Healthcare Access (%) 8.006704e-04 0.0013728877 0.002583127 0.006020542
## Mortality Rate (%) 2.174084e-03 0.0034711071 0.004318606 0.005022382
## Year 1.857193e-03 0.0023930703 0.002849747 0.002982930
## Average Treatment Cost (USD) 6.268106e-04 0.0032539197 0.003853360 0.005555193
## DALYs 9.886369e-04 0.0034980096 0.003583964 0.003760738
## Hospital Beds per 1000 1.710145e-03 0.0021786719 0.003751780 0.003783698
## Doctors per 1000 2.517544e-03 0.0031407574 0.003154655 0.003542055
## Urbanization Rate (%) 1.143265e-03 0.0035119764 0.005091458 0.005108930
## Education Index 7.187606e-05 0.0013020571 0.001363494 0.002383358
## Recovery Rate (%) 7.733634e-05 0.0002404807 0.004848675 0.005373584
## Axis1:5 Axis1:6 Axis1:7 Axis1:8
## Improvement in 5 Years (%) 0.006919171 0.007278060 0.007326664 0.008216184
## Healthcare Access (%) 0.007835459 0.007893334 0.007951033 0.007963716
## Mortality Rate (%) 0.005220553 0.005740289 0.007110205 0.007111806
## Year 0.003927028 0.006570349 0.007398431 0.008500669
## Average Treatment Cost (USD) 0.006805468 0.006812006 0.006814106 0.007132858
## DALYs 0.004702473 0.005790586 0.007735885 0.007813630
## Hospital Beds per 1000 0.004432631 0.004715523 0.005647771 0.009725677
## Doctors per 1000 0.004213319 0.004229382 0.006881449 0.007022241
## Urbanization Rate (%) 0.005146950 0.005147786 0.006137559 0.006232538
## Education Index 0.003840763 0.007955073 0.008520753 0.008661020
## Recovery Rate (%) 0.005853575 0.006629255 0.006658743 0.008382652
## Axis1:9 Axis1:10 Axis1:11 Axis12:11
## Improvement in 5 Years (%) 0.008253223 0.009306112 0.01 5.551115e-18
## Healthcare Access (%) 0.008135183 0.008451916 0.01 -1.998401e-17
## Mortality Rate (%) 0.008290256 0.008842784 0.01 7.771561e-18
## Year 0.008523709 0.009925665 0.01 -2.220446e-18
## Average Treatment Cost (USD) 0.008700779 0.008709620 0.01 -2.220446e-17
## DALYs 0.009093858 0.009163846 0.01 7.771561e-18
## Hospital Beds per 1000 0.009740218 0.009999981 0.01 6.661338e-18
## Doctors per 1000 0.009727851 0.009897948 0.01 -1.332268e-17
## Urbanization Rate (%) 0.006705748 0.009949012 0.01 -2.220446e-18
## Education Index 0.009612818 0.009994386 0.01 2.220446e-18
## Recovery Rate (%) 0.008423791 0.008914251 0.01 6.661338e-18ACP$li # Coordenadas de los individuos## Axis1 Axis2 Axis3 Axis4 Axis5
## 51663 0.180855760 -1.511926523 -2.372533676 -0.22713793 -1.169408909
## 57870 1.782808336 0.029443264 0.202590859 -0.52408947 0.496146644
## 2986 0.607923349 -1.308394583 0.906248787 -0.43711037 0.459636306
## 29925 0.944180149 -0.547782501 0.672660323 0.06327991 -0.340615226
## 68293 -1.047476851 0.550239684 -1.664822332 0.78656649 -0.347117088
## 62555 1.258077933 -0.297734305 0.863113565 -1.76660992 0.511671638
## 45404 -0.598690548 0.879170853 1.197368787 1.59843391 0.281446290
## 65161 -1.380398738 1.373289110 0.301199082 1.16460248 0.065931190
## 46435 0.019399247 -0.604437072 -1.458984177 1.55245253 1.419461173
## 9642 0.521074932 -0.202769316 -1.516096538 -0.79734194 -0.667489005
## 59134 -1.878532766 1.072634383 -1.969960664 -1.09474575 0.245280537
## 52132 1.036118187 -0.189708515 -0.375750314 -2.13380525 2.075359268
## 14183 0.103550003 -0.403870972 -1.052424301 0.81695052 1.224600766
## 15180 1.172468394 -1.673283786 0.272325357 -0.20967323 1.141301309
## 27168 -1.217860443 1.062982668 0.436548655 0.15402138 -0.302014932
## 9097 -0.330836078 -0.668746589 -0.569209485 2.10976467 0.387962633
## 30538 1.445139036 -0.919608776 -1.481196652 -0.42035098 -0.155540038
## 56219 0.609075625 -0.671699853 0.113354636 1.40330262 -0.651184865
## 7989 -1.213289640 1.044125996 -0.926953508 0.57670450 -1.496619368
## 13536 -0.973330899 -0.988045316 0.323536816 0.81360899 -0.771690282
## 6216 -1.594680929 -0.640272339 0.361561115 1.00103242 -0.181315492
## 29394 0.277998267 -1.707841678 1.388875968 -1.12665226 0.161668095
## 53241 -0.241572966 0.168380463 -0.539251948 -0.50786780 0.969411271
## 28825 -2.250206497 1.766594386 1.581387815 -1.38564064 1.204880692
## 41 0.394914677 0.946659503 0.026034598 0.75187945 -1.593992838
## 14426 0.419946278 -1.541882578 0.078267122 0.19504254 0.767322984
## 72820 1.033924161 1.459149892 1.383090707 0.51719858 -0.474728936
## 51656 2.126924717 1.573027503 -0.560897576 -0.48665006 -0.171966671
## 58527 -0.931685280 1.158266629 -0.127112034 1.54161047 -0.192371664
## 77009 -0.320945187 0.626783603 -1.441374490 -0.88960655 2.224356561
## 43042 1.284696112 -1.299249686 2.926519655 -0.69246417 0.151680640
## 77837 -0.664527514 -0.264816327 1.239365154 -0.44439179 -2.348175784
## 6134 0.118926718 3.284081924 0.673414306 0.95255288 0.219814978
## 33523 2.339697212 0.424049214 1.575074329 -0.73409259 0.309990006
## 21812 -0.784115939 -1.906332829 0.257282302 0.82632373 0.876013407
## 39895 1.906863222 -1.110118300 0.893157562 -0.32999977 0.868897018
## 9640 -1.784544452 1.440516873 -1.143601273 -0.43584652 0.800234041
## 9326 -1.511245703 1.600132588 -0.692110016 -1.39763797 1.263521578
## 26510 -1.431243269 0.936761208 -0.312673666 0.03841469 0.476014965
## 20960 -0.198086156 -0.278246543 0.898248273 0.28618686 0.215529613
## 14403 1.740850281 0.798120969 0.856410777 0.02708359 0.645021297
## 64502 -0.012232168 -1.471420084 1.525700875 -0.57558843 -0.182753477
## 71503 -0.804945782 0.321363862 0.146822112 0.93818017 0.304990417
## 45221 2.462066957 -0.885958581 -1.292218384 -0.80403237 -0.252125306
## 77585 -0.109359697 -1.716157915 -1.227805313 0.05777790 -0.636604680
## 70563 -1.496491148 0.114127555 0.174575062 -0.06664197 -2.017418298
## 32606 0.185991703 1.189956605 -1.009208397 1.54890141 -0.903217562
## 16152 -1.010916257 1.471163243 -0.081470325 0.32779652 0.374606670
## 25559 0.344099716 -0.954158469 2.713957089 -0.21988741 0.912031061
## 14215 0.620548648 0.929813755 -0.607734289 2.29477450 0.370862850
## 14287 1.162139546 0.654172613 0.306628817 -1.10111968 -0.639072269
## 23194 0.046540310 0.186467849 -1.309806431 -1.61367707 0.363578663
## 34976 -1.337093205 -2.232154242 0.384510291 0.75710255 1.391459860
## 14491 0.749445362 0.166568310 -0.043897459 -0.30078243 0.690974408
## 77584 0.422373566 1.622310930 -0.688500121 -1.24537488 -1.437455961
## 40503 -0.632448385 -0.536205041 -0.385989167 0.61559398 0.313939368
## 34724 -0.110454161 0.755413009 0.449523316 0.38303280 -0.734339788
## 17369 0.252947796 0.493777584 1.078771544 -2.51536673 1.448111725
## 31542 1.571837903 0.780604251 -0.007343075 0.64871478 -2.628778352
## 59106 -0.188138498 -0.822299136 -2.700660941 0.70774788 0.213837130
## 40632 1.156339277 -1.375098430 -0.209246243 0.72533628 2.546822791
## 43204 -1.173381705 -1.635241487 -0.398020318 0.77916613 0.163390979
## 37544 -1.406320264 -1.080698764 0.860439211 1.95110226 -0.764762097
## 76820 -1.955088808 0.166048994 0.723627211 -0.34270667 -0.518342820
## 44196 0.099425595 1.745556312 2.100232898 -0.80844299 -0.095934132
## 44983 1.166580273 0.458817071 0.773804054 0.40117399 -1.069790069
## 36717 0.916400223 1.557678241 -0.888972389 0.38036351 0.967959036
## 28078 0.685281014 -1.069392227 -1.271516582 -0.21297977 0.406564220
## 69369 -0.890333286 0.539131289 -0.561531255 1.04116453 -0.411370923
## 43406 0.886263116 -2.383459753 0.097731821 -0.40616507 0.199901300
## 41488 -1.178077305 -0.093239289 0.298783646 -0.12568673 0.293695086
## 47485 -0.704211709 0.193555961 2.005373861 -0.19411789 -0.313379171
## 63528 -0.836573720 0.165899328 0.252861610 0.17639897 -0.725377172
## 14536 1.091810645 -0.330095437 -0.312360116 1.50428539 -0.002324422
## 51465 0.414201211 -0.533072484 1.174425533 -1.03223906 -1.758041847
## 26503 -0.163161734 2.218289403 1.309019852 1.14596028 0.398677583
## 32953 1.008291446 0.094431388 -1.145900175 -1.34781859 -1.017501915
## 413 1.417391611 0.674323026 -0.070720732 -0.33777849 -0.030047210
## 10762 1.187041766 -0.625491435 -0.018342408 -1.13486341 -1.650520463
## 48182 0.101627432 -1.496301687 -1.118004702 1.48964819 0.312689923
## 30571 -0.101625026 -1.818522646 -0.470037955 -1.97307463 0.855436432
## 42521 0.731361588 -0.026702555 -0.051942361 0.38942467 0.892607368
## 74522 -0.541894348 -0.164316407 -2.095008238 -0.27343803 -1.046016546
## 14745 -2.128209809 0.945202601 -1.354988568 -0.50843568 2.214145737
## 25946 0.645163090 -0.398224432 -2.181901756 -0.48536330 -1.266178014
## 43928 0.315625028 -0.293102224 -1.411755080 -0.45119464 -1.102493169
## 6601 0.238350122 0.500478882 -1.213114583 -1.45169515 2.166677736
## 57701 1.953382513 1.051298992 0.439307805 -0.90540442 0.428058866
## 53518 0.393270106 -0.551701371 0.953838835 1.09850751 1.454176334
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## 18388 -1.761793382 1.536167919 -1.198230595 0.29098939 1.881746812
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## 43755 0.478650995 -0.538026487 0.419030565 2.33889251 -0.740711806
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## 4972 -1.063777320 -0.830224890 0.604845811 0.24485375 1.902390020
## 69045 0.459575963 0.344141366 2.185518332 -1.14839895 -0.230998476
## 37492 1.111232612 -1.142887073 0.913081170 -0.43892947 -0.474274064
## 6373 0.836695265 -1.538667112 -0.663404393 -0.24046037 -0.296630819
## 73396 1.264667531 -0.404732446 -0.247046155 -0.67839860 1.305967783
## 602 -0.031199836 1.569435973 0.976115792 -2.70905090 2.163338235
## 74059 -0.968075707 -0.551272458 -0.227958039 -0.17851024 -0.553357335
## 47606 0.331363591 0.024138161 -2.470542477 -1.72895926 1.426075299
## 28577 -2.032071395 -0.220575400 1.284660806 0.08704669 -1.484416841
## 7877 1.167759254 -0.599972997 1.347978904 -1.39856510 1.083593845
## 58263 0.736343013 0.759042731 1.946410177 0.28648332 -0.941150469
## 2777 1.272051803 2.007117471 1.033386571 -0.78588125 1.623009672
## 30712 -0.089299398 -0.335184258 1.193909033 -1.20897931 -1.684221074
## 17749 1.542661523 -0.412215090 -1.193595299 -1.28708476 -1.847411384
## 7488 1.843808222 1.461895616 -0.598034340 1.49548530 0.821702353
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## 45430 -1.588118745 -0.217876301 1.909223442 1.08164334 0.249139908
## 9739 1.432146020 0.633251421 -0.125201484 0.81329343 0.947900655
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## 49588 -0.163493205 1.753369748 -0.374653106 -1.16567674 -0.664384551
## 49753 1.769959666 0.545084260 -0.126602622 0.16870773 -0.097403424
## 52292 0.570915973 1.572096396 -1.015531155 0.96969795 -0.866246366
## 38245 1.475141463 -0.671262423 -2.268775925 1.01919618 -0.983508165
## 4682 0.009489693 1.549293582 0.126072057 0.64220597 -0.531577515
## 68957 -0.729895360 1.662636883 -1.485132787 1.56262391 0.152981886
## 37765 0.501000200 1.171595005 -0.014264114 0.54234864 0.550854374
## 34827 1.200427635 0.081499616 0.523178908 2.14431328 0.566311197
## 2748 0.751111419 -0.155966973 -1.008927765 1.88832643 0.004129821
## 56138 -2.111761303 0.616916571 -1.033711319 -0.12242649 -0.063116113
## 44859 0.703205700 -1.828137612 -0.083183145 0.27250806 1.551833442
## 51442 0.068007942 1.756792457 0.461509529 1.55166181 2.203934288
## 78912 0.314823932 -0.164268562 -1.652719981 1.39794218 -0.604889983
## 75936 0.343879712 0.863006501 -0.913355455 -1.60458792 -0.150859974
## 57223 1.498831484 -1.260081076 0.692386826 -0.57233339 -0.883146958
## 67089 -0.644058816 -0.778864107 -0.599366436 1.14289086 0.482449106
## 27365 -0.512050743 0.187321045 -2.092062643 0.57665664 -0.838312006
## 76141 -0.837114108 0.967476268 1.482130772 -1.01736919 -0.447141747
## 19927 0.559737547 -0.044606859 -3.657467971 -1.93537323 0.522514216
## 10216 1.340305348 -0.483408359 -1.359969154 -1.67734697 1.860750863
## 2882 -1.653318464 -1.324000978 0.323868351 1.23226453 -1.440864747
## 4015 2.194133018 -0.941321422 -2.295362621 -0.04257623 -0.152384157
## 27913 0.307409249 0.549196641 0.202699001 -0.90773934 -2.053834217
## 29465 -1.146731512 -0.157446706 0.192411891 0.65611573 0.080212112
## 9174 2.636755470 0.273694581 1.382949328 0.10833428 -1.298900769
## 7657 -1.376221038 -0.400819374 0.628600646 2.18806299 0.653476580
## 31351 -1.980348958 -0.490025238 0.894942813 -0.92350529 -1.049885159
## Axis6 Axis7 Axis8 Axis9 Axis10
## 51663 1.183299278 -0.152504860 -0.04661427 -0.0029692065 -0.222975798
## 57870 1.154303940 1.097189824 -0.14076918 -1.0358990322 -2.177726486
## 2986 -0.744455479 -1.048024893 -0.02046079 0.7936918040 -0.184067593
## 29925 0.950980421 -1.277685418 1.36836461 1.5527620564 0.417508538
## 68293 -0.587709523 0.017339412 -0.23745102 0.7789864357 -1.939445303
## 62555 0.716447877 0.668613455 -0.05486463 0.0205629375 0.171882686
## 45404 0.090561578 2.006770547 -0.38115696 -1.1799483324 1.393303376
## 65161 -0.744325982 -0.857320053 -0.45268975 -0.0778345269 -1.111743253
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## 32102 2.773932322 -1.212936192 -2.17844119 -0.6583690161 -0.398308390
## 78889 -0.474430648 2.272495445 -0.12873852 -0.3749665407 -1.300378781
## 74984 0.083823137 0.438255819 -0.07195297 0.4026137732 -0.426713782
## 53289 -1.079294611 0.962843077 0.66771408 -1.2249356502 -1.776619188
## 16436 -0.101819913 -1.696147155 0.04309950 0.9757021843 1.556548219
## 26691 -0.797217182 1.095000782 0.27470903 1.0939475959 0.318780228
## 53705 -1.250497868 0.809179558 -0.60780670 -2.4640352770 0.437402983
## 42887 -0.543792340 -0.882506082 -0.06030075 2.7528119398 0.412023319
## 30237 1.223762688 -1.119109429 -0.13552021 0.7053029549 -0.553627639
## 66766 1.572491746 -0.741847920 0.55668207 1.9644837785 -0.390440730
## 35556 0.025424981 1.160175820 -0.31668789 -1.5898703345 0.107415581
## 32076 -0.274148989 0.235368065 0.58852421 0.2526182751 1.048767278
## 26715 -0.918667126 0.446553017 1.07283436 0.3956488388 0.055283233
## 18388 1.034164991 -2.402857103 0.46265037 0.8886383096 0.990987652
## 23977 -0.967234543 0.116714809 1.14450825 1.7098065589 -0.048853716
## 68616 1.053394424 -1.327231636 -0.10370682 -0.6087221686 0.460585353
## 63481 0.042501568 -0.779560114 1.71766838 -1.4037620623 -0.534788893
## 31315 -0.538762344 1.437945627 0.15860034 0.4962760799 0.266650736
## 43755 0.080656768 1.639292726 -0.72420123 0.3894855707 1.634300930
## 45555 -0.233278030 -0.314007419 -0.06599905 0.1792433771 -0.336787616
## 57943 0.359986952 0.256074915 0.65334072 0.5101695521 -1.230274081
## 74659 2.396811761 1.789709592 -0.64893669 0.1509879367 -1.433899115
## 73799 0.066638034 0.031365850 1.12955838 -0.4121014184 -1.273803319
## 20400 0.340960439 1.063891741 0.68646357 -1.6926478045 -0.113573135
## 4972 0.706010185 1.423426295 0.24622260 0.3186620967 -0.041691805
## 69045 -1.467318619 -0.539647462 -0.67343172 0.8819371254 -0.225201526
## 37492 -0.380267265 -0.686476324 0.11359870 -0.0963347400 -0.379947290
## 6373 1.083503446 -1.180818406 0.37396586 -1.0683566764 0.458203998
## 73396 -0.447962137 0.821078671 0.28950132 -0.2449924095 -0.399823675
## 602 0.432847507 -0.101432055 -1.65790533 0.2310982649 0.361682603
## 74059 1.242137176 1.055134854 1.43625547 0.1000785656 0.082946825
## 47606 1.167372729 0.591283494 -1.12634689 -0.7334342300 0.066745635
## 28577 -0.030192519 0.172335380 -0.31330198 -0.4964181350 0.401143100
## 7877 -0.357989322 0.605632372 0.40840196 -1.1196488492 -2.330654165
## 58263 0.992939959 0.486707247 -0.92301407 -0.6349087694 0.867381808
## 2777 1.960206947 0.257298813 0.70895239 0.2995723274 -1.847915755
## 30712 -0.692689669 -0.904962893 -0.29851106 0.6641156587 0.057688321
## 17749 0.403663786 -0.359990622 -0.43144541 1.5609065195 0.249150483
## 7488 0.663339134 0.221557747 0.60288517 -1.1418891612 2.021408119
## 76662 1.651755579 0.829392546 -2.37330375 -0.7677261169 1.165375165
## 45430 1.176431537 1.450094301 0.14258219 0.0008396184 0.893947970
## 9739 -0.127631268 1.172535626 -0.11016699 -0.2741339540 -0.075003634
## 61775 -1.287557085 1.227120571 -2.04146106 0.0510341883 1.312151379
## 19512 -0.322391505 0.117998413 0.31775918 -0.0417338654 0.213191585
## 49588 -1.878937798 -0.976210568 -1.38950546 -1.3248973595 -0.597689348
## 49753 -1.603978556 0.115346009 1.06939960 0.6996664081 -0.123084443
## 52292 -0.850493064 0.132254348 0.35894926 0.2976652843 0.649527566
## 38245 1.390387604 0.922130334 -1.52439333 0.0699887173 0.005669116
## 4682 -0.301522202 0.795736145 0.55212495 -0.6351498464 0.717215361
## 68957 -0.442438672 -0.106419148 0.59935109 -0.7396754956 -1.726002780
## 37765 -0.386437376 1.215630408 -0.99282294 1.2110211653 0.767362045
## 34827 -0.684489031 1.028936207 0.68684485 1.3205296537 1.278151998
## 2748 0.517381678 -1.395797781 1.20675017 -1.3091867330 0.351667415
## 56138 1.012902601 0.628566126 1.06848159 0.1154683526 -1.079918135
## 44859 0.596734646 -0.232673988 -0.37089443 0.0244587495 -1.233352877
## 51442 1.170771310 -0.595075400 -0.72549832 -0.2280221713 0.108791264
## 78912 0.025823968 0.179038711 0.06172863 0.9538690383 -0.639010379
## 75936 -0.393547022 -0.615412914 1.35038111 -1.1106011581 -0.424231194
## 57223 -0.661221723 -1.253874205 -0.01258898 0.1351346700 -0.449572705
## 67089 1.271756217 0.283271040 2.16689955 0.3525240161 0.077051211
## 27365 -1.635066953 -1.237019065 -0.19429573 -0.3951949838 -0.420073391
## 76141 -0.077601806 1.710434329 0.49271207 1.3153344144 -0.469331592
## 19927 -0.834116957 -1.090037858 0.05840554 -0.0320751433 0.571810826
## 10216 -2.064318790 0.193093568 1.47302879 0.2522804421 0.464973700
## 2882 -0.560502130 -0.488571612 0.46045159 0.2236553592 0.274209791
## 4015 0.445027504 -0.146119629 -0.72964119 0.1918141162 -0.717136863
## 27913 0.881034131 0.278451353 0.28006990 -0.1967759895 0.767339845
## 29465 -0.674710993 0.213733173 1.21527481 0.7797028706 -0.637180526
## 9174 0.397145154 -0.312566059 1.09297805 0.2562633947 0.796512009
## 7657 1.910352429 -0.428767813 -0.39393579 1.4985500194 0.311621904
## 31351 0.744248970 0.886745925 0.11034866 -0.2482428423 -0.650403851
## Axis11
## 51663 -0.134498444
## 57870 -0.289824956
## 2986 0.699476696
## 29925 -0.409153470
## 68293 -1.183767271
## 62555 -0.204392649
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## 59134 1.174474063
## 52132 1.034073822
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## 15180 -0.297195836
## 27168 -0.208601407
## 9097 0.733097338
## 30538 -1.230251671
## 56219 1.528347773
## 7989 0.690687483
## 13536 0.322285275
## 6216 0.086596838
## 29394 -0.480434487
## 53241 -0.846446352
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## 41 0.074979627
## 14426 0.758647482
## 72820 0.114126265
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## 58527 -1.206918344
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## 77837 -0.152108123
## 6134 -1.132916473
## 33523 -0.502886780
## 21812 -0.455257738
## 39895 0.310129581
## 9640 1.447282489
## 9326 0.546203365
## 26510 -0.720971653
## 20960 -1.134803993
## 14403 -0.299079850
## 64502 0.578272701
## 71503 0.564062693
## 45221 -1.060326189
## 77585 -0.772253615
## 70563 -0.680744360
## 32606 0.829665596
## 16152 0.220422648
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## 14491 -0.034292799
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## 40503 0.728156386
## 34724 -0.064929194
## 17369 0.349327620
## 31542 1.168495399
## 59106 -1.490577774
## 40632 -1.230289121
## 43204 0.301630333
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## 76820 -0.436285620
## 44196 0.124591713
## 44983 1.456609120
## 36717 -0.859095937
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## 32953 -0.791835916
## 413 -1.304328606
## 10762 0.458085203
## 48182 1.127762124
## 30571 1.718604394
## 42521 -0.176044605
## 74522 -0.582419033
## 14745 -0.420374816
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## 6601 0.235740962
## 57701 0.243075624
## 53518 -0.879635067
## 72326 2.577931078
## 52353 0.574259722
## 66154 -0.793150219
## 47802 0.398528106
## 31517 -0.116964269
## 66075 1.506063274
## 32263 2.204316183
## 69161 0.440865013
## 56157 -1.147774460
## 19392 -0.744624165
## 46137 -1.608203034
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## 2286 0.529464119
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## 63405 -0.227098120
## 76223 -0.471117991
## 64616 1.486447268
## 27455 0.368409671
## 54740 1.359916045
## 78173 1.502708036
## 20193 -0.709881791
## 66701 -0.486494704
## 22467 0.249170437
## 63167 -1.108730439
## 79225 0.183894581
## 63658 0.399580629
## 49862 0.142000844
## 79331 0.488860055
## 51491 0.414295244
## 15703 -0.144092904
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## 8983 0.406453588
## 37769 0.508131464
## 29479 -0.348257038
## 70943 0.988691657
## 78121 -0.006910561
## 1835 0.572472660
## 42221 0.010088806
## 21099 0.312409163
## 41043 -1.758240615
## 56529 -0.180069883
## 36940 -1.486030173
## 76099 0.686990078
## 38265 -1.061455884
## 76384 -0.051856067
## 46438 -0.173868120
## 75176 0.497116684
## 69350 -0.206790837
## 8176 -0.721259291
## 76369 0.656097875
## 17746 2.338422453
## 53873 1.299770288
## 29133 0.644182983
## 31 0.031165437
## 7588 0.618035308
## 4620 0.054265708
## 45980 -0.471145502
## 68783 0.918926169
## 51573 0.215010218
## 70778 0.577043934
## 11667 0.445097425
## 33027 0.683000805
## 70945 -0.208341705
## 76792 -0.617794241
## 2450 1.673032183
## 44500 -0.195452741
## 1673 -1.178928665
## 19851 1.884033695
## 48434 0.296335346
## 6007 1.448011374
## 55992 -1.015167323
## 78967 -0.289224872
## 66596 -1.130811570
## 60906 -0.203486280
## 78715 -0.549116261
## 46468 -0.259355668
## 1204 -1.420839087
## 53272 0.182818841
## 24111 0.701409688
## 75455 1.465892744
## 43694 0.229514433
## 3984 -0.211031182
## 7880 -0.823859332
## 57722 0.197341976
## 22119 0.484421190
## 67632 -1.863893607
## 52523 -0.513407730
## 53918 -0.190465776
## 24522 0.403300941
## 6333 -0.382671591
## 68430 -1.156712504
## 55737 -1.007701839
## 19955 -0.004924334
## 55368 1.257253010
## 47818 1.059412759
## 14417 1.153785975
## 49286 0.455973480
## 25244 0.847060635
## 5596 -0.056876302
## 40276 -0.262830509
## 60101 0.012276035
## 37167 -0.089340089
## 69232 -0.488873664
## 22974 0.590923081
## 52851 0.723315434
## 4913 -0.651524170
## 10301 -0.831043936
## 76908 0.238255651
## 18547 -1.294678310
## 78774 1.088096384
## 29370 1.149977607
## 46843 -0.542949343
## 58102 -1.830724373
## 36086 -0.815065370
## 76723 -0.635029826
## 9182 -1.358436812
## 71848 0.208244015
## 56519 -1.039909806
## 24671 0.348839371
## 34134 -0.599827177
## 47800 0.138805698
## 25196 0.030878792
## 65778 1.419950434
## 7071 0.806262316
## 40160 -0.798734590
## 1498 0.392626269
## 57337 2.441596228
## 60587 0.170506874
## 20698 -1.336185805
## 65615 -0.717078651
## 9723 -0.054125521
## 34599 -0.686699191
## 4518 0.408349003
## 62170 -0.418082599
## 4695 -1.117026865
## 32102 -0.323543005
## 78889 0.764006633
## 74984 0.923794679
## 53289 -0.269765574
## 16436 -1.036074931
## 26691 0.450136407
## 53705 0.119176054
## 42887 -0.500125322
## 30237 1.286444391
## 66766 1.362006939
## 35556 -0.274631233
## 32076 -0.436694967
## 26715 0.104003990
## 18388 1.001946165
## 23977 -0.903377757
## 68616 -0.114362388
## 63481 0.063872872
## 31315 -1.018760992
## 43755 0.581736375
## 45555 0.483449384
## 57943 0.199220998
## 74659 1.040182130
## 73799 -1.240585256
## 20400 -0.115998456
## 4972 -0.808839121
## 69045 -0.897925015
## 37492 -0.778433422
## 6373 1.074909327
## 73396 -1.077709370
## 602 -0.193591500
## 74059 -0.940417342
## 47606 0.542224883
## 28577 -1.104889391
## 7877 -0.316919885
## 58263 0.647124432
## 2777 -0.013211077
## 30712 -0.682415320
## 17749 -1.538415478
## 7488 1.194926259
## 76662 1.030729123
## 45430 -0.230192318
## 9739 1.376617807
## 61775 -0.727693585
## 19512 1.116205661
## 49588 0.092062042
## 49753 -0.347122264
## 52292 -0.214148505
## 38245 0.694921334
## 4682 0.680378839
## 68957 -1.451467916
## 37765 -0.764367221
## 34827 0.218034928
## 2748 0.239594865
## 56138 -0.584606203
## 44859 -0.229726509
## 51442 -1.337467271
## 78912 0.126728810
## 75936 -0.778951810
## 57223 0.079671742
## 67089 0.173568341
## 27365 0.650809813
## 76141 -0.120282247
## 19927 -0.008326207
## 10216 0.587425754
## 2882 0.160591838
## 4015 -0.728320562
## 27913 0.574609760
## 29465 -1.137645813
## 9174 0.839863220
## 7657 0.852629200
## 31351 0.544243214aux.ACP$row.abs/10000 # Contribución absoluta de los individuos## Axis1 Axis2 Axis3 Axis4 Axis5
## 51663 8.298850e-07 6.123243e-05 1.539192e-04 1.591572e-06 4.416970e-05
## 57870 8.064223e-05 2.322160e-08 1.122297e-06 8.473403e-06 7.950817e-06
## 2986 9.376719e-06 4.585615e-05 2.245758e-05 5.894259e-06 6.823705e-06
## 29925 2.261846e-05 8.037786e-06 1.237256e-05 1.235318e-07 3.747311e-06
## 68293 2.783826e-05 8.110058e-06 7.578860e-05 1.908613e-05 3.891738e-06
## 62555 4.015765e-05 2.374531e-06 2.037061e-05 9.627828e-05 8.456182e-06
## 45404 9.094065e-06 2.070459e-05 3.920342e-05 7.881999e-05 2.558484e-06
## 65161 4.834620e-05 5.051776e-05 2.480706e-06 4.184103e-05 1.404021e-07
## 46435 9.548232e-09 9.786386e-06 5.820620e-05 7.435046e-05 6.507867e-05
## 9642 6.888959e-06 1.101348e-06 6.285239e-05 1.961265e-05 1.439063e-05
## 59134 8.953458e-05 3.081936e-05 1.061166e-04 3.697205e-05 1.943201e-06
## 52132 2.723779e-05 9.640372e-07 3.860707e-06 1.404613e-04 1.391164e-04
## 14183 2.720530e-07 4.369235e-06 3.028655e-05 2.058915e-05 4.843738e-05
## 15180 3.487832e-05 7.499967e-05 2.027889e-06 1.356229e-06 4.207191e-05
## 27168 3.763122e-05 3.026722e-05 5.211148e-06 7.318280e-07 2.946107e-06
## 9097 2.777020e-06 1.197963e-05 8.859564e-06 1.373141e-04 4.861513e-06
## 30538 5.298739e-05 2.265303e-05 5.999203e-05 5.450937e-06 7.814047e-07
## 56219 9.412298e-06 1.208567e-05 3.513554e-07 6.075047e-05 1.369620e-05
## 7989 3.734928e-05 2.920290e-05 2.349546e-05 1.026015e-05 7.234597e-05
## 13536 2.403667e-05 2.615013e-05 2.862302e-06 2.042107e-05 1.923436e-05
## 6216 6.452096e-05 1.098120e-05 3.574634e-06 3.091315e-05 1.061846e-06
## 29394 1.960819e-06 7.812956e-05 5.274667e-05 3.915857e-05 8.441901e-07
## 53241 1.480641e-06 7.594576e-07 7.951545e-06 7.956982e-06 3.035343e-05
## 28825 1.284689e-04 8.359760e-05 6.838248e-05 5.923089e-05 4.688994e-05
## 41 3.956943e-06 2.400533e-05 1.853403e-08 1.743988e-05 8.206621e-05
## 14426 4.474461e-06 6.368289e-05 1.675046e-07 1.173561e-06 1.901726e-05
## 72820 2.712256e-05 5.703218e-05 5.230816e-05 8.252046e-06 7.279190e-06
## 51656 1.147777e-04 6.628157e-05 8.602709e-06 7.306015e-06 9.551689e-07
## 58527 2.202377e-05 3.593662e-05 4.418160e-07 7.331558e-05 1.195291e-06
## 77009 2.613455e-06 1.052338e-05 5.680960e-05 2.441422e-05 1.598087e-04
## 43042 4.187493e-05 4.521738e-05 2.341914e-04 1.479250e-05 7.431080e-07
## 77837 1.120416e-05 1.878493e-06 4.200168e-05 6.092269e-06 1.780955e-04
## 6134 3.588493e-07 2.889004e-04 1.240032e-05 2.799144e-05 1.560652e-06
## 33523 1.388905e-04 4.816734e-06 6.783756e-05 1.662450e-05 3.103752e-06
## 21812 1.559961e-05 9.734588e-05 1.810039e-06 2.106432e-05 2.478638e-05
## 39895 9.225551e-05 3.301100e-05 2.181344e-05 3.359497e-06 2.438531e-05
## 9640 8.079937e-05 5.558490e-05 3.576163e-05 5.860223e-06 2.068358e-05
## 9326 5.794599e-05 6.858546e-05 1.309840e-05 6.026101e-05 5.156523e-05
## 26510 5.197328e-05 2.350596e-05 2.673319e-06 4.552414e-08 7.318681e-06
## 20960 9.955463e-07 2.073861e-06 2.206281e-05 2.526658e-06 1.500395e-06
## 14403 7.689110e-05 1.706309e-05 2.005545e-05 2.262869e-08 1.343816e-05
## 64502 3.796296e-09 5.799539e-05 6.365124e-05 1.022048e-05 1.078755e-06
## 71503 1.643942e-05 2.766395e-06 5.894555e-07 2.715311e-05 3.004443e-06
## 45221 1.537988e-04 2.102553e-05 4.566042e-05 1.994316e-05 2.053168e-06
## 77585 3.034365e-07 7.889230e-05 4.122182e-05 1.029842e-07 1.308975e-05
## 70563 5.682004e-05 3.489002e-07 8.333597e-07 1.370070e-07 1.314569e-04
## 32606 8.776884e-07 3.792996e-05 2.785030e-05 7.401071e-05 2.634974e-05
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## 53289 6.040190e-06 3.460163e-05 9.458409e-06 2.506306e-06 2.782883e-05
## 16436 1.926621e-07 1.399639e-05 1.750284e-05 8.544825e-08 9.000782e-06
## 26691 1.990025e-05 3.524956e-06 2.515922e-05 9.161304e-06 4.316026e-05
## 53705 1.970959e-05 3.913976e-10 1.158034e-05 1.134337e-05 3.383445e-05
## 42887 3.218658e-06 3.314482e-05 4.740277e-05 1.085776e-05 1.551311e-05
## 30237 1.924704e-06 8.336987e-05 3.225172e-05 1.188496e-06 9.685450e-06
## 66766 1.642476e-05 8.047551e-05 5.655594e-06 1.390203e-04 5.650011e-05
## 35556 1.445206e-04 1.089794e-04 2.513524e-06 9.724076e-07 1.198984e-05
## 32076 1.765740e-06 7.266632e-05 1.473230e-05 1.607705e-06 2.977403e-05
## 26715 1.421145e-04 7.374174e-06 4.483467e-07 1.737428e-05 3.154470e-05
## 18388 7.875229e-05 6.321171e-05 3.925987e-05 2.612169e-06 1.143705e-04
## 23977 4.398896e-07 7.879742e-06 9.368040e-05 1.135584e-05 1.027304e-04
## 68616 5.296828e-06 9.311217e-05 1.156649e-04 2.603025e-05 4.679188e-05
## 63481 2.937213e-06 2.449691e-05 1.928144e-05 2.653313e-06 1.065310e-04
## 31315 1.418080e-04 5.214441e-06 7.204524e-06 2.952716e-05 6.508567e-07
## 43755 5.812877e-06 7.754030e-06 4.801307e-06 1.687592e-04 1.772108e-05
## 45555 2.488657e-04 2.019374e-05 2.005012e-05 7.328852e-06 6.859262e-05
## 57943 3.024207e-06 1.127793e-04 5.530734e-05 1.604384e-06 1.526561e-07
## 74659 4.503015e-06 5.609473e-05 8.217275e-07 1.620953e-05 1.010938e-05
## 73799 1.309194e-05 1.412682e-05 4.460134e-06 5.388432e-08 2.800070e-05
## 20400 7.921562e-05 4.841864e-08 7.555361e-06 3.049364e-06 2.522238e-07
## 4972 2.871142e-05 1.846340e-05 1.000363e-05 1.849526e-06 1.168936e-04
## 69045 5.358804e-06 3.172444e-06 1.306102e-04 4.068484e-05 1.723494e-06
## 37492 3.133020e-05 3.498862e-05 2.279748e-05 5.943421e-06 7.265247e-06
## 6373 1.776184e-05 6.341755e-05 1.203441e-05 1.783750e-06 2.842001e-06
## 73396 4.057943e-05 4.387895e-06 1.668877e-06 1.419766e-05 5.508794e-05
## 602 2.469778e-08 6.597925e-05 2.605378e-05 2.264026e-04 1.511612e-04
## 74059 2.377781e-05 8.140531e-06 1.420947e-06 9.830447e-07 9.890152e-06
## 47606 2.785883e-06 1.560733e-08 1.668986e-04 9.221817e-05 6.568656e-05
## 28577 1.047686e-04 1.303270e-06 4.512789e-05 2.337499e-07 7.117105e-05
## 7877 3.459871e-05 9.642365e-06 4.968603e-05 6.034098e-05 3.792492e-05
## 58263 1.375668e-05 1.543308e-05 1.035945e-04 2.531895e-06 2.860947e-05
## 2777 4.105469e-05 1.079110e-04 2.920073e-05 1.905289e-05 8.508124e-05
## 30712 2.023253e-07 3.009452e-06 3.897719e-05 4.509047e-05 9.161990e-05
## 17749 6.038020e-05 4.551640e-06 3.895671e-05 5.110475e-05 1.102348e-04
## 7488 8.625509e-05 5.724702e-05 9.779583e-06 6.899400e-05 2.180824e-05
## 76662 8.606095e-05 7.618209e-06 4.672963e-05 4.975010e-05 9.757542e-06
## 45430 6.399104e-05 1.271570e-06 9.967394e-05 3.609235e-05 2.004833e-06
## 9739 5.203887e-05 1.074169e-05 4.286344e-07 2.040523e-05 2.902133e-05
## 61775 3.000473e-07 1.125584e-04 1.055057e-04 5.477373e-05 4.339734e-07
## 19512 5.183594e-05 1.857454e-04 2.899110e-08 2.271201e-05 3.085954e-05
## 49588 6.781920e-07 8.235067e-05 3.838193e-06 4.191826e-05 1.425708e-05
## 49753 7.948405e-05 7.958797e-06 4.382819e-07 8.780454e-07 3.064362e-07
## 52292 8.269850e-06 6.620312e-05 2.820036e-05 2.900814e-05 2.423675e-05
## 38245 5.521036e-05 1.206993e-05 1.407509e-04 3.204517e-05 3.124263e-05
## 4682 2.284849e-09 6.429654e-05 4.346161e-07 1.272318e-05 9.126932e-06
## 68957 1.351682e-05 7.404828e-05 6.031130e-05 7.532791e-05 7.559127e-07
## 37765 6.368381e-06 3.676844e-05 5.563623e-09 9.074111e-06 9.800883e-06
## 34827 3.656160e-05 1.779227e-07 7.484599e-06 1.418481e-04 1.035862e-05
## 2748 1.431403e-05 6.516065e-07 2.783481e-05 1.100021e-04 5.508763e-10
## 56138 1.131470e-04 1.019467e-05 2.921909e-05 4.623787e-07 1.286685e-07
## 44859 1.254637e-05 8.952368e-05 1.892077e-07 2.290898e-06 7.778249e-05
## 51442 1.173471e-07 8.267250e-05 5.824108e-06 7.427474e-05 1.568877e-04
## 78912 2.514715e-06 7.228181e-07 7.469072e-05 6.028724e-05 1.181801e-05
## 75936 3.000311e-06 1.995025e-05 2.281118e-05 7.942808e-05 7.350886e-07
## 57223 5.699790e-05 4.253213e-05 1.310888e-05 1.010521e-05 2.519171e-05
## 67089 1.052457e-05 1.624963e-05 9.823199e-06 4.029550e-05 7.517867e-06
## 27365 6.652413e-06 9.399252e-07 1.196789e-04 1.025845e-05 2.269880e-05
## 76141 1.777962e-05 2.507268e-05 6.006772e-05 3.193039e-05 6.457762e-06
## 19927 7.949176e-06 5.329954e-08 3.657878e-04 1.155517e-04 8.818360e-06
## 10216 4.557857e-05 6.259627e-06 5.057387e-05 8.679462e-05 1.118325e-04
## 2882 6.935316e-05 4.695661e-05 2.868171e-06 4.684410e-05 6.705606e-05
## 4015 1.221460e-04 2.373537e-05 1.440691e-04 5.592183e-08 7.500173e-07
## 27913 2.397657e-06 8.079340e-06 1.123495e-06 2.541964e-05 1.362456e-04
## 29465 3.336389e-05 6.640294e-07 1.012353e-06 1.328030e-05 2.078124e-07
## 9174 1.763977e-04 2.006562e-06 5.229746e-05 3.620585e-07 5.449336e-05
## 7657 4.805401e-05 4.303458e-06 1.080483e-05 1.476953e-04 1.379278e-05
## 31351 9.950312e-05 6.432163e-06 2.190073e-05 2.631030e-05 3.560207e-05
## Axis6 Axis7 Axis8 Axis9 Axis10
## 51663 4.731554e-05 8.229078e-07 8.441302e-08 3.480075e-10 2.085123e-06
## 57870 4.502513e-05 4.259389e-05 7.698157e-07 4.235870e-05 1.988945e-04
## 2986 1.872803e-05 3.886216e-05 1.626359e-08 2.486632e-05 1.420924e-06
## 29925 3.056031e-05 5.776060e-05 7.274033e-05 9.517383e-05 7.310497e-06
## 68293 1.167186e-05 1.063782e-08 2.190381e-06 2.395342e-05 1.577507e-04
## 62555 1.734538e-05 1.581736e-05 1.169382e-07 1.669083e-08 1.239026e-06
## 45404 2.771421e-07 1.424883e-04 5.643900e-06 5.495835e-05 8.141559e-05
## 65161 1.872151e-05 2.600577e-05 7.961097e-06 2.391401e-07 5.183525e-05
## 46435 1.914404e-04 1.802259e-05 4.202614e-05 3.450174e-05 2.188245e-05
## 9642 4.285540e-06 5.854810e-06 3.518835e-07 8.440883e-05 9.864396e-06
## 59134 5.989401e-07 3.297134e-05 2.074649e-05 4.676663e-05 3.319677e-05
## 52132 2.174771e-06 1.730562e-06 5.256955e-06 2.375999e-06 8.628085e-07
## 14183 9.581071e-07 1.078771e-04 5.454447e-07 7.439933e-05 9.358949e-06
## 15180 5.931008e-05 1.616489e-05 3.138722e-05 8.496783e-06 1.160089e-05
## 27168 3.631692e-06 6.853350e-06 2.292407e-04 9.247613e-05 3.312813e-05
## 9097 1.652204e-06 1.381294e-06 3.478727e-05 1.250402e-07 3.562501e-05
## 30538 5.634789e-05 5.699431e-05 2.686326e-05 5.538671e-05 4.325222e-06
## 56219 2.423400e-06 1.137656e-06 3.793123e-05 9.315638e-07 1.107434e-07
## 7989 1.793346e-05 5.270801e-06 1.463736e-04 1.690714e-07 1.066372e-04
## 13536 8.202423e-05 7.465080e-06 2.123496e-05 2.803039e-07 7.712997e-05
## 6216 7.040496e-05 2.638218e-06 9.237609e-05 1.238024e-04 4.276934e-05
## 29394 7.144292e-06 1.600615e-05 1.470965e-04 1.035816e-04 3.190127e-05
## 53241 2.302920e-05 9.227878e-05 3.615844e-05 2.380038e-05 1.130630e-04
## 28825 8.190636e-06 5.899878e-06 7.783776e-06 3.155217e-05 1.063096e-04
## 41 5.257916e-05 2.405162e-05 8.232868e-05 8.961309e-05 1.679196e-05
## 14426 4.970131e-05 1.138306e-06 7.623341e-05 5.612394e-05 1.793914e-05
## 72820 2.319894e-05 2.164402e-06 1.245914e-06 9.827422e-05 7.355265e-08
## 51656 6.753009e-05 1.268953e-04 1.182999e-04 8.352826e-05 9.988915e-06
## 58527 9.548409e-05 4.858175e-05 1.104535e-05 4.689206e-07 1.072999e-05
## 77009 3.832328e-05 2.664930e-04 3.618553e-07 1.062347e-04 1.669081e-05
## 43042 4.804552e-07 1.082045e-05 2.056264e-06 1.634528e-06 1.543474e-04
## 77837 6.214591e-06 1.942812e-05 1.374213e-06 2.537683e-06 6.198709e-06
## 6134 3.787152e-05 5.653670e-06 3.250127e-06 1.703587e-05 6.651162e-08
## 33523 2.090202e-05 2.241619e-05 1.787088e-05 3.639925e-07 3.446593e-05
## 21812 7.793003e-05 6.876878e-06 2.364539e-06 4.946590e-05 5.168625e-05
## 39895 1.492389e-06 2.631086e-07 9.576534e-06 1.890951e-06 6.032249e-06
## 9640 3.407960e-05 3.916695e-07 3.267356e-07 5.212655e-09 1.943741e-05
## 9326 1.022722e-05 2.227674e-05 7.737174e-05 4.017983e-06 2.448942e-05
## 26510 3.356055e-06 5.154518e-05 1.295640e-04 5.032420e-06 7.814435e-06
## 20960 4.380023e-11 1.250074e-05 2.293068e-07 5.979763e-05 7.896148e-05
## 14403 1.767055e-04 4.430234e-05 5.690079e-05 1.480041e-04 4.169919e-06
## 64502 2.445546e-06 1.726675e-05 3.475046e-05 4.684417e-05 5.348226e-06
## 71503 1.624264e-06 1.274893e-04 4.784987e-06 9.819915e-07 5.419585e-05
## 45221 4.587141e-05 8.353070e-07 1.679056e-05 1.305340e-05 3.092559e-06
## 77585 8.907764e-05 1.387339e-04 6.880322e-05 7.774749e-06 6.011565e-06
## 70563 2.003080e-06 1.883188e-07 1.441953e-05 1.007600e-04 3.872329e-06
## 32606 1.425154e-04 7.318452e-08 3.044918e-05 3.350954e-05 1.683618e-05
## 16152 1.044404e-05 1.013957e-05 6.897682e-05 9.055048e-05 1.329828e-05
## 25559 5.815791e-05 4.631867e-06 3.745054e-05 4.211753e-06 5.766580e-06
## 14215 2.993296e-05 9.434454e-05 5.870028e-06 7.485175e-05 1.461877e-05
## 14287 1.654838e-07 1.006414e-04 2.997469e-06 2.222350e-05 4.083916e-06
## 23194 6.266546e-06 9.713260e-06 1.653135e-04 1.047414e-04 2.649629e-04
## 34976 4.448934e-05 1.441440e-05 6.359725e-06 1.083655e-05 1.653208e-05
## 14491 1.460645e-04 4.500987e-05 5.159625e-06 9.449364e-06 2.433485e-04
## 77584 5.812915e-07 2.400781e-05 5.571033e-06 8.183973e-06 3.706524e-05
## 40503 4.256875e-05 3.960272e-05 8.248797e-06 3.777370e-06 2.215940e-05
## 34724 3.281934e-05 9.155608e-05 1.847698e-05 3.438933e-07 4.393849e-05
## 17369 3.786325e-05 4.274838e-05 1.641345e-05 5.200898e-06 6.878401e-05
## 31542 3.019400e-05 3.537509e-05 1.293624e-07 7.229107e-09 7.452322e-08
## 59106 3.019842e-05 4.570314e-05 2.677025e-08 1.903143e-05 2.116120e-05
## 40632 5.875714e-06 3.447618e-08 3.946919e-07 4.150013e-05 3.780170e-07
## 43204 3.879145e-05 3.608465e-07 6.386555e-06 7.923961e-05 5.040885e-06
## 37544 1.558757e-05 2.604081e-06 4.980977e-07 1.896527e-05 1.457249e-04
## 76820 3.290153e-06 1.465466e-05 5.964673e-06 7.664986e-06 2.764309e-06
## 44196 6.307436e-05 1.359081e-05 4.367928e-09 4.475904e-06 2.989384e-05
## 44983 5.901191e-06 1.425621e-05 1.523615e-05 2.603339e-06 2.300359e-08
## 36717 1.412689e-06 1.406347e-05 5.735531e-05 1.792377e-08 5.409949e-07
## 28078 2.268222e-08 2.792292e-05 7.925098e-05 7.232258e-05 2.314647e-05
## 69369 6.538593e-06 2.014656e-05 2.924638e-05 2.213017e-05 2.254645e-05
## 43406 9.310274e-07 1.042924e-05 5.035832e-09 7.990633e-06 1.852182e-05
## 41488 3.456731e-06 3.291939e-05 2.408590e-05 2.185180e-04 1.312745e-05
## 47485 7.308290e-07 1.114103e-04 7.893605e-05 2.231668e-06 1.985998e-05
## 63528 3.215331e-05 7.829847e-07 1.287512e-04 4.131198e-05 1.656389e-04
## 14536 2.711646e-06 2.747075e-05 9.341094e-07 2.120572e-05 4.881371e-05
## 51465 1.175735e-06 2.831026e-08 3.463354e-05 1.860620e-05 7.083104e-06
## 26503 3.222229e-05 1.824501e-05 9.817502e-05 3.097317e-05 4.541390e-05
## 32953 4.321904e-07 9.060090e-06 9.157619e-05 1.001529e-04 1.753785e-05
## 413 3.869305e-05 8.015196e-11 1.331766e-07 4.962466e-05 2.829687e-08
## 10762 3.769890e-08 4.389901e-05 8.228696e-05 7.741474e-06 9.423254e-06
## 48182 2.244344e-08 4.974869e-06 1.255756e-05 3.807153e-06 2.145716e-05
## 30571 7.789639e-05 8.427974e-06 1.288071e-04 1.026005e-04 8.998137e-06
## 42521 7.218068e-05 3.458191e-05 7.552724e-05 1.124316e-05 9.907815e-06
## 74522 7.308483e-05 5.520966e-05 2.793630e-04 5.201181e-05 1.195817e-05
## 14745 4.121628e-05 1.859516e-06 1.491294e-05 4.998797e-05 6.254824e-06
## 25946 1.058176e-06 3.073396e-06 1.067258e-04 2.111668e-04 5.181182e-05
## 43928 4.557189e-05 7.132435e-06 1.201967e-05 3.062591e-05 5.599172e-05
## 6601 3.559809e-05 1.551427e-05 7.829172e-05 2.705439e-06 1.703096e-05
## 57701 4.163897e-05 1.145131e-05 2.316482e-05 8.744830e-05 2.872014e-07
## 53518 2.033113e-05 3.245678e-05 9.212870e-08 2.276312e-05 1.089436e-04
## 72326 4.237637e-05 8.681889e-07 1.361050e-07 1.296422e-05 1.350680e-06
## 52353 2.132776e-05 1.630723e-05 1.646052e-06 1.637116e-04 1.986419e-05
## 66154 7.833714e-06 9.648000e-06 4.011435e-06 1.930344e-05 6.696946e-09
## 47802 2.436989e-08 1.502874e-04 4.888319e-05 1.972722e-06 6.000796e-06
## 31517 7.451261e-06 6.220189e-06 2.754113e-05 2.279213e-05 4.838964e-05
## 66075 7.653990e-05 1.493294e-07 2.523264e-05 1.886339e-06 2.837269e-05
## 32263 2.307757e-05 6.552235e-05 4.349803e-05 3.546386e-06 1.068808e-06
## 69161 1.051839e-04 6.554063e-06 5.356268e-05 2.175486e-06 8.568071e-06
## 56157 9.415375e-06 4.895565e-08 1.450470e-05 8.802584e-07 3.132743e-05
## 19392 6.446005e-05 2.045877e-06 9.640131e-05 1.972345e-05 1.147970e-04
## 46137 5.429102e-05 2.708919e-07 6.747392e-05 6.594490e-06 8.220646e-05
## 67747 1.420219e-05 4.674215e-08 5.436729e-07 9.172040e-06 1.196966e-04
## 2286 6.555953e-05 1.154405e-05 1.394067e-04 2.869743e-06 4.961729e-06
## 59970 6.657030e-08 7.804165e-06 7.617073e-07 4.000569e-07 4.783996e-05
## 54661 1.275652e-05 1.025359e-04 4.964693e-05 2.087897e-06 9.552292e-09
## 32281 1.600837e-06 4.148223e-06 7.425458e-08 4.951058e-06 4.248450e-07
## 64085 7.534196e-06 2.359096e-04 9.441594e-07 7.128526e-07 7.524625e-06
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## 25449 2.793178e-05 2.303339e-06 4.919033e-06 8.991314e-05 2.184870e-06
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## 64616 3.973993e-05 6.016376e-07 1.375675e-05 1.514904e-06 9.181380e-05
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## 20193 5.250548e-07 2.028311e-04 6.134094e-05 9.777578e-06 6.998236e-06
## 66701 2.958750e-05 4.446782e-06 4.136754e-05 4.476414e-05 8.682999e-05
## 22467 3.378156e-06 6.455687e-06 1.114359e-05 3.100223e-05 1.293930e-06
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## 79225 2.229669e-06 1.358485e-06 3.246660e-06 4.505154e-06 9.302380e-05
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## 7880 4.986503e-05 4.585025e-05 1.577428e-05 4.254984e-05 3.210312e-06
## 57722 3.542564e-05 2.280186e-05 1.020429e-04 1.894070e-05 8.341764e-05
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## 52523 1.149478e-05 8.767554e-06 7.136174e-08 2.834121e-06 9.095499e-05
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## 45430 4.676791e-05 7.440057e-05 7.897727e-07 2.782733e-11 3.351511e-05
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## 31351 1.871764e-05 2.782161e-05 4.730486e-07 2.432547e-06 1.774117e-05
## Axis11
## 51663 8.809937e-07
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## 29925 8.152875e-06
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## 52132 5.207645e-05
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## 9097 2.617349e-05
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## 53241 3.489291e-05
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## 41 2.737946e-07
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## Axis6 Axis7 Axis8 Axis9 Axis10
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## 2882 2.833483e-05
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## 29465 -2.121802e-03
## 9174 5.227167e-04
## 7657 4.937813e-04
## 31351 3.272501e-04# Fórmulas de los primeros 3 componentes principales
ACP$c1[, 1:9]## CS1 CS2 CS3 CS4
## Improvement in 5 Years (%) -0.29846295 -0.06248397 -0.30771457 0.49715375
## Healthcare Access (%) 0.24686762 -0.21443771 0.31508680 0.56402733
## Mortality Rate (%) -0.40679483 0.32284534 0.26367215 -0.25521222
## Year 0.37598086 0.20751690 -0.19355258 0.11102214
## Average Treatment Cost (USD) -0.21842655 0.45947274 -0.22175207 0.39686484
## DALYs -0.27431885 0.44905888 -0.08397067 -0.12790670
## Hospital Beds per 1000 -0.36078936 -0.19403850 0.35923114 0.05435042
## Doctors per 1000 0.43774966 0.22378909 0.03376477 -0.18934935
## Urbanization Rate (%) -0.29499234 -0.43629150 -0.35995808 -0.04021255
## Education Index -0.07396553 -0.31441641 0.07099197 -0.30722416
## Recovery Rate (%) -0.07672361 0.11450034 0.61483708 0.22040740
## CS5 CS6 CS7 CS8
## Improvement in 5 Years (%) 0.42628455 -0.19074261 0.07182762 0.32197661
## Healthcare Access (%) -0.41935792 0.07659733 -0.07825887 -0.03844763
## Mortality Rate (%) 0.13857206 0.22954055 0.38132865 0.01365970
## Year 0.30245787 -0.51765792 0.29647580 -0.35841328
## Average Treatment Cost (USD) -0.34806406 -0.02574382 0.01493227 -0.19274048
## DALYs -0.30207928 -0.33212760 -0.45440767 0.09518820
## Hospital Beds per 1000 0.25075893 -0.16934721 -0.31457068 -0.68939006
## Doctors per 1000 0.25503710 -0.04035233 -0.53057260 0.12809586
## Urbanization Rate (%) 0.06069615 -0.00920742 -0.32413081 0.10521085
## Education Index -0.37579113 -0.64582736 0.24504037 0.12785712
## Recovery Rate (%) 0.21566174 -0.28042019 -0.05594679 0.44823267
## CS9
## Improvement in 5 Years (%) -0.06622823
## Healthcare Access (%) -0.14249655
## Mortality Rate (%) -0.37356846
## Year 0.05223477
## Average Treatment Cost (USD) -0.43090002
## DALYs 0.38936607
## Hospital Beds per 1000 -0.04149700
## Doctors per 1000 -0.56604001
## Urbanization Rate (%) -0.23672337
## Education Index -0.33572750
## Recovery Rate (%) 0.06979770# ------------------------- PARTE 7 ------------------------------------
par(mfrow = c(1, 1))
# grupo 1
# Gráfico de correlaciones (círculo de correlación)
s.corcircle(ACP$co, xax = 1, yax = 2)
Este gráfico muestra cómo se relacionan las variables originales con los
dos primeros factores. Las flechas largas indican variables que
contribuyen fuertemente a la dimensión, mientras que flechas cortas
indican baja carga.
Interpretación clave:
- Factor 1 (11.9%) está asociado positivamente con:
– Doctors per 1000
– Healthcare Access (%)
– Recovery Rate (%) Es decir, este factor captura calidad y disponibilidad de servicios de salud.
- Negativamente se asocia con:
– Average Treatment Cost (USD)
– DALYs
– Mortality Rate (%) Esto sugiere un eje de mejor desempeño sanitario vs. peor salud general.
- Factor 2 (11.3%) se relaciona con:
– Urbanization Rate (%)
– Improvement in 5 Years (%) Este segundo eje captura desarrollo social, urbano y mejora del sistema de salud.
La distribución angular indica que no hay colinealidad alta entre muchas variables, lo cual es deseable.
# Gráfico de observaciones (filas)
plot.dudi(ACP, ex = 1, ey = 2, Tcol = FALSE)
Este gráfico muestra los individuos posicionados según sus coordenadas
en los F1 y F2.
Interpretación clave:
Los puntos están muy concentrados hacia el centro: esto indica que los individuos no presentan variabilidad extrema, por lo que los factores extraídos capturan diferencias moderadas.
Los puntos dispersos hacia los extremos (por ejemplo ID 70945) representan individuos con valores muy altos o muy bajos en las variables del factor.
No se observan grupos definidos, lo que indica que los dos primeros factores no separan claramente clusters naturales.
# grupo 2
# Gráfico de correlaciones (círculo de correlación)
s.corcircle(ACP$co, xax = 1, yax = 3)
Aquí se comparan las cargas entre F1 (calidad sanitaria) y F3.
Interpretación clave:
- Factor 3 (11.1%) está influido por:
– Average Treatment Cost (USD)
– Year
– DALYs Esto sugiere que F3 captura tendencias temporales y costos, asociado a carga de enfermedad.
- F1 se mantiene como eje de calidad del sistema de salud.
Las direcciones de las variables refuerzan que Cost y DALYs suelen estar correlacionadas positivamente.
# Gráfico de observaciones (filas)
plot.dudi(ACP, ex = 1, ey = 3, Tcol = FALSE)
Interpretación clave:
Nuevamente se muestra una nube densa alrededor del origen → los individuos no presentan valores extremos simultáneos en F1 y F3.
Los pocos puntos alejados (como 19927) corresponden a individuos con costos o DALYs altos.
No se revelan grupos naturales en este plano.
# grupo 3
# Gráfico de correlaciones (círculo de correlación)
s.corcircle(ACP$co, xax = 2, yax = 3)
Interpretación clave:
- F2 (desarrollo urbano/social) se asocia con:
– Urbanization Rate (%)
– Improvement in 5 Years (%)
- F3 (costo y carga de enfermedad) se asocia con:
– Average Treatment Cost (USD)
– DALYs
– Year
Este plano revela que la modernización y el desarrollo no necesariamente reducen los costos ni la carga de enfermedad, ya que no están alineados en la misma dirección.
# Gráfico de observaciones (filas)
plot.dudi(ACP, ex = 2, ey = 3, Tcol = FALSE)
Interpretación clave:
Individuos dispersos en forma circular indican que los ejes F2 y F3 capturan información distinta y complementaria.
No aparecen grupos claros: los patrones sociales/urbanos y los costos/carga de enfermedad no separan a los individuos en clusters definidos.
6.5 ACM
# ---------------------------- CORTE 3 ---------------------------------
# ---------------------------- PARTE 1 ---------------------------------
res.mca <- MCA(DATAP_muestra[vars_cuali], graph = FALSE)
fviz_mca_ind(res.mca, repel = TRUE)
Este gráfico muestra las coordenadas de los individuos según el Análisis
de Correspondencias Múltiples (para variables categóricas).
Interpretación clave:
Los individuos están distribuidos en forma de nube sin agrupaciones naturales claras.
La varianza explicada es baja (3.8% y 3.6%), típico del MCA.
La cercanía al centro significa que la mayoría de los individuos tienen combinaciones comunes de categorías.
Los que están alejados representan perfiles poco frecuentes.
fviz_mca_var(res.mca, repel = TRUE)
Aquí se observan las categorías que definen la estructura del
espacio.
Interpretaciones clave:
COVID-19 está extremadamente separado → es una categoría muy distintiva.
Enfermedades como Malaria, Cardiovascular y Bacterial se agrupan juntas, indicando perfiles similares.
Edad 19–35, No, Crónicas y Neurológicas se sitúan cerca: este conjunto de categorías aparece frecuentemente juntas.
Países como Italy, Brazil, Russia forman otro grupo.
La cercanía entre categorías indica fuerte asociación; la distancia indica comportamientos muy distintos.
fviz_mca_biplot(res.mca, repel = TRUE)
Interpretación clave:
Las categorías (rojo) orientan la estructura general, mientras que los individuos (azul) se distribuyen alrededor según las combinaciones observadas.
COVID-19 nuevamente se ubica lejos → fuerte capacidad discriminante.
Se observa que varios individuos se concentran hacia categorías como Viral, Autoimmune, Metabolic, etc.
Esto confirma que algunas categorías explican mejor la variación que otras.
res.mca$eig## eigenvalue percentage of variance cumulative percentage of variance
## dim 1 0.24404699 3.7962865 3.796287
## dim 2 0.23371196 3.6355194 7.431806
## dim 3 0.22877508 3.5587235 10.990529
## dim 4 0.21684182 3.3730949 14.363624
## dim 5 0.20835186 3.2410290 17.604653
## dim 6 0.20473233 3.1847251 20.789378
## dim 7 0.19917046 3.0982072 23.887586
## dim 8 0.19558017 3.0423581 26.929944
## dim 9 0.19192198 2.9854530 29.915397
## dim 10 0.18524729 2.8816245 32.797021
## dim 11 0.18416916 2.8648535 35.661875
## dim 12 0.18149508 2.8232568 38.485132
## dim 13 0.17693997 2.7523995 41.237531
## dim 14 0.17047411 2.6518195 43.889351
## dim 15 0.16336336 2.5412078 46.430558
## dim 16 0.16200439 2.5200683 48.950627
## dim 17 0.16001988 2.4891981 51.439825
## dim 18 0.15608717 2.4280226 53.867847
## dim 19 0.15192136 2.3632211 56.231069
## dim 20 0.14937637 2.3236325 58.554701
## dim 21 0.14712161 2.2885584 60.843259
## dim 22 0.14490178 2.2540276 63.097287
## dim 23 0.13848352 2.1541880 65.251475
## dim 24 0.13664640 2.1256106 67.377086
## dim 25 0.13513248 2.1020607 69.479146
## dim 26 0.13264547 2.0633739 71.542520
## dim 27 0.12737635 1.9814099 73.523930
## dim 28 0.12207783 1.8989885 75.422919
## dim 29 0.11898984 1.8509531 77.273872
## dim 30 0.11665507 1.8146344 79.088506
## dim 31 0.11216485 1.7447866 80.833293
## dim 32 0.11049124 1.7187526 82.552046
## dim 33 0.10347379 1.6095923 84.161638
## dim 34 0.10208049 1.5879187 85.749556
## dim 35 0.09969472 1.5508067 87.300363
## dim 36 0.09746763 1.5161631 88.816526
## dim 37 0.09375580 1.4584235 90.274950
## dim 38 0.09356238 1.4554147 91.730365
## dim 39 0.08779504 1.3657007 93.096065
## dim 40 0.08556674 1.3310381 94.427103
## dim 41 0.08025773 1.2484535 95.675557
## dim 42 0.07436322 1.1567611 96.832318
## dim 43 0.07315260 1.1379294 97.970247
## dim 44 0.06931996 1.0783105 99.048558
## dim 45 0.06116414 0.9514421 100.000000res.mca$call## $X
## Country Disease Name Disease Category Age Group Gender
## 51663 Mexico Rabies Infectious 36-60 Other
## 57870 Brazil Parkinson's Disease Autoimmune 0-18 Male
## 2986 Mexico Cholera Neurological 0-18 Other
## 29925 Russia Parkinson's Disease Chronic 36-60 Female
## 68293 Italy Diabetes Infectious 36-60 Male
## 62555 Russia Alzheimer's Disease Genetic 36-60 Other
## 45404 Argentina Parkinson's Disease Respiratory 36-60 Female
## 65161 Brazil Asthma Chronic 61+ Other
## 46435 Argentina Malaria Metabolic 61+ Female
## 9642 France Tuberculosis Neurological 19-35 Other
## 59134 Germany Tuberculosis Metabolic 61+ Male
## 52132 Turkey Diabetes Cardiovascular 19-35 Female
## 14183 Turkey Cancer Bacterial 19-35 Other
## 15180 Argentina Asthma Parasitic 36-60 Male
## 27168 Mexico Malaria Bacterial 61+ Other
## 9097 France Diabetes Autoimmune 61+ Female
## 30538 Turkey Influenza Respiratory 61+ Other
## 56219 Russia Diabetes Respiratory 36-60 Female
## 7989 Italy HIV/AIDS Genetic 19-35 Male
## 13536 France Tuberculosis Viral 61+ Male
## 6216 Germany Parkinson's Disease Genetic 36-60 Male
## 29394 Mexico Parkinson's Disease Bacterial 0-18 Other
## 53241 Turkey Ebola Bacterial 36-60 Female
## 28825 Brazil Hypertension Chronic 36-60 Female
## 41 Germany Hypertension Infectious 19-35 Male
## 14426 Turkey Parkinson's Disease Cardiovascular 0-18 Male
## 72820 Argentina Alzheimer's Disease Respiratory 61+ Male
## 51656 Argentina HIV/AIDS Neurological 61+ Male
## 58527 Italy Alzheimer's Disease Infectious 36-60 Female
## 77009 Italy Dengue Autoimmune 61+ Other
## 43042 Russia Cancer Cardiovascular 36-60 Other
## 77837 Turkey Dengue Neurological 61+ Male
## 6134 France Measles Respiratory 19-35 Male
## 33523 Italy Tuberculosis Viral 36-60 Male
## 21812 Brazil Diabetes Autoimmune 19-35 Other
## 39895 Brazil Measles Bacterial 0-18 Other
## 9640 Turkey HIV/AIDS Genetic 61+ Female
## 9326 Argentina Tuberculosis Cardiovascular 36-60 Female
## 26510 Turkey Leprosy Bacterial 36-60 Male
## 20960 France Leprosy Autoimmune 19-35 Female
## 14403 Brazil HIV/AIDS Metabolic 19-35 Male
## 64502 Turkey Cholera Infectious 19-35 Other
## 71503 Argentina Ebola Infectious 61+ Female
## 45221 France COVID-19 Respiratory 36-60 Other
## 77585 Mexico Malaria Chronic 0-18 Other
## 70563 Germany COVID-19 Cardiovascular 36-60 Other
## 32606 Mexico Asthma Cardiovascular 36-60 Male
## 16152 Turkey Hepatitis Cardiovascular 61+ Other
## 25559 France Diabetes Chronic 19-35 Female
## 14215 Mexico Zika Chronic 0-18 Female
## 14287 Brazil Diabetes Genetic 0-18 Male
## 23194 Italy Rabies Cardiovascular 61+ Other
## 34976 Germany Ebola Metabolic 19-35 Female
## 14491 Brazil Leprosy Genetic 0-18 Male
## 77584 Turkey Polio Infectious 36-60 Female
## 40503 Germany Hypertension Chronic 36-60 Other
## 34724 France Measles Parasitic 19-35 Female
## 17369 France Malaria Neurological 0-18 Female
## 31542 Argentina Measles Parasitic 61+ Male
## 59106 Turkey Leprosy Chronic 36-60 Other
## 40632 Germany Cancer Infectious 19-35 Other
## 43204 Germany Tuberculosis Bacterial 36-60 Female
## 37544 France Leprosy Cardiovascular 36-60 Other
## 76820 France Cholera Genetic 19-35 Other
## 44196 Germany Measles Bacterial 19-35 Other
## 44983 Italy Asthma Metabolic 36-60 Female
## 36717 Argentina Dengue Chronic 61+ Male
## 28078 Argentina Measles Respiratory 0-18 Other
## 69369 France Dengue Viral 61+ Other
## 43406 Brazil Cholera Cardiovascular 36-60 Female
## 41488 Italy Influenza Infectious 61+ Other
## 47485 Russia Zika Metabolic 36-60 Male
## 63528 Turkey Rabies Viral 61+ Other
## 14536 France Ebola Autoimmune 0-18 Female
## 51465 France Asthma Cardiovascular 61+ Male
## 26503 Germany Dengue Infectious 19-35 Female
## 32953 Italy Diabetes Genetic 0-18 Male
## 413 Mexico Tuberculosis Infectious 0-18 Female
## 10762 Germany Cholera Genetic 61+ Other
## 48182 Argentina Parkinson's Disease Infectious 61+ Other
## 30571 Turkey Dengue Chronic 61+ Other
## 42521 France Malaria Neurological 0-18 Other
## 74522 Turkey Hypertension Autoimmune 61+ Female
## 14745 Russia HIV/AIDS Infectious 36-60 Male
## 25946 Germany Polio Chronic 19-35 Other
## 43928 Italy Asthma Parasitic 61+ Male
## 6601 Italy Diabetes Respiratory 19-35 Other
## 57701 Russia Influenza Parasitic 19-35 Male
## 53518 Brazil Asthma Bacterial 61+ Male
## 72326 Germany Dengue Parasitic 61+ Other
## 52353 Argentina Hepatitis Viral 0-18 Male
## 66154 Mexico Alzheimer's Disease Respiratory 61+ Female
## 47802 Russia Hepatitis Neurological 19-35 Other
## 31517 Brazil Diabetes Respiratory 36-60 Female
## 66075 Turkey Influenza Chronic 0-18 Other
## 32263 Italy Asthma Metabolic 61+ Other
## 69161 Mexico Cholera Bacterial 61+ Female
## 56157 Turkey Cancer Metabolic 0-18 Other
## 19392 Brazil Parkinson's Disease Chronic 36-60 Female
## 46137 France Influenza Respiratory 36-60 Male
## 67747 Brazil Ebola Chronic 61+ Female
## 2286 France Rabies Metabolic 0-18 Other
## 59970 Mexico Hypertension Cardiovascular 19-35 Other
## 54661 Mexico Zika Infectious 61+ Other
## 32281 Turkey Cholera Cardiovascular 0-18 Female
## 64085 Germany Rabies Parasitic 61+ Other
## 7826 Argentina Ebola Genetic 36-60 Male
## 25449 Brazil Asthma Parasitic 19-35 Male
## 63405 Italy Cholera Respiratory 19-35 Female
## 76223 Turkey Alzheimer's Disease Infectious 19-35 Other
## 64616 Russia Alzheimer's Disease Neurological 0-18 Female
## 27455 Argentina Polio Parasitic 0-18 Other
## 54740 Turkey Tuberculosis Cardiovascular 19-35 Other
## 78173 France Alzheimer's Disease Viral 61+ Female
## 20193 Italy Cancer Respiratory 19-35 Other
## 66701 Germany Diabetes Viral 19-35 Male
## 22467 Brazil HIV/AIDS Genetic 36-60 Female
## 63167 Turkey Asthma Bacterial 36-60 Other
## 79225 Italy Measles Autoimmune 36-60 Male
## 63658 Russia Alzheimer's Disease Genetic 36-60 Male
## 49862 Turkey COVID-19 Bacterial 36-60 Other
## 79331 Turkey Polio Neurological 19-35 Male
## 51491 Argentina Rabies Infectious 36-60 Male
## 15703 Turkey Dengue Chronic 0-18 Male
## 6623 Argentina Ebola Chronic 19-35 Female
## 8983 France Hepatitis Chronic 61+ Other
## 37769 Turkey Tuberculosis Infectious 0-18 Other
## 29479 Russia Polio Genetic 0-18 Female
## 70943 Russia Cholera Viral 0-18 Female
## 78121 Brazil Zika Chronic 36-60 Female
## 1835 Argentina Alzheimer's Disease Neurological 61+ Other
## 42221 Brazil Rabies Neurological 19-35 Male
## 21099 Mexico Rabies Autoimmune 0-18 Male
## 41043 Italy Parkinson's Disease Neurological 61+ Other
## 56529 Russia Cancer Autoimmune 19-35 Female
## 36940 Brazil Leprosy Viral 36-60 Other
## 76099 Turkey Hypertension Infectious 61+ Female
## 38265 Argentina Parkinson's Disease Infectious 36-60 Female
## 76384 Russia Cancer Metabolic 36-60 Male
## 46438 Italy Cancer Neurological 61+ Other
## 75176 Mexico Malaria Neurological 36-60 Female
## 69350 Argentina Asthma Metabolic 19-35 Male
## 8176 Mexico Ebola Genetic 19-35 Other
## 76369 France Asthma Infectious 36-60 Other
## 17746 Brazil Hypertension Bacterial 36-60 Female
## 53873 Russia Rabies Autoimmune 19-35 Female
## 29133 France Rabies Infectious 36-60 Female
## 31 Mexico Dengue Neurological 61+ Female
## 7588 Russia COVID-19 Cardiovascular 36-60 Male
## 4620 Italy Ebola Metabolic 61+ Male
## 45980 Turkey Influenza Chronic 61+ Female
## 68783 France Dengue Autoimmune 0-18 Other
## 51573 Mexico Malaria Genetic 61+ Other
## 70778 Italy Zika Parasitic 0-18 Female
## 11667 Turkey Ebola Autoimmune 0-18 Female
## 33027 Mexico Influenza Neurological 61+ Male
## 70945 France Malaria Bacterial 19-35 Female
## 76792 Brazil Leprosy Infectious 61+ Other
## 2450 Mexico Leprosy Autoimmune 19-35 Male
## 44500 Mexico Alzheimer's Disease Infectious 19-35 Female
## 1673 Mexico COVID-19 Genetic 36-60 Male
## 19851 Turkey COVID-19 Genetic 61+ Other
## 48434 Turkey Hepatitis Parasitic 0-18 Female
## 6007 Mexico Ebola Parasitic 19-35 Other
## 55992 Brazil Cancer Chronic 19-35 Male
## 78967 Italy Ebola Metabolic 0-18 Female
## 66596 Brazil Rabies Parasitic 36-60 Female
## 60906 Germany Influenza Bacterial 0-18 Male
## 78715 France Malaria Metabolic 0-18 Male
## 46468 Turkey Hypertension Genetic 36-60 Other
## 1204 Mexico Alzheimer's Disease Bacterial 19-35 Male
## 53272 Italy Tuberculosis Metabolic 0-18 Female
## 24111 Russia Hepatitis Genetic 36-60 Other
## 75455 Italy COVID-19 Genetic 61+ Female
## 43694 Brazil Rabies Parasitic 36-60 Male
## 3984 Mexico Dengue Chronic 36-60 Other
## 7880 Mexico Diabetes Metabolic 36-60 Female
## 57722 Russia Cholera Bacterial 19-35 Male
## 22119 Italy Diabetes Respiratory 61+ Male
## 67632 Germany Leprosy Respiratory 61+ Other
## 52523 Russia Cholera Cardiovascular 61+ Female
## 53918 Mexico Asthma Cardiovascular 61+ Female
## 24522 Mexico Measles Metabolic 61+ Male
## 6333 Germany Asthma Infectious 61+ Female
## 68430 Argentina Asthma Viral 19-35 Male
## 55737 Mexico Hepatitis Bacterial 61+ Female
## 19955 Turkey Leprosy Genetic 61+ Male
## 55368 Argentina Cancer Chronic 61+ Other
## 47818 Turkey Alzheimer's Disease Cardiovascular 0-18 Male
## 14417 France Measles Parasitic 36-60 Other
## 49286 Russia Tuberculosis Metabolic 36-60 Female
## 25244 Mexico Ebola Chronic 36-60 Male
## 5596 Turkey Influenza Cardiovascular 61+ Other
## 40276 Turkey Tuberculosis Cardiovascular 0-18 Female
## 60101 Russia Malaria Infectious 61+ Male
## 37167 Turkey Dengue Genetic 36-60 Male
## 69232 Italy Zika Metabolic 0-18 Male
## 22974 Italy Measles Respiratory 0-18 Other
## 52851 Germany Tuberculosis Metabolic 61+ Female
## 4913 France Ebola Neurological 0-18 Female
## 10301 Brazil Measles Metabolic 0-18 Other
## 76908 Germany Hypertension Autoimmune 19-35 Other
## 18547 Argentina Leprosy Infectious 19-35 Male
## 78774 France Tuberculosis Neurological 19-35 Female
## 29370 Germany Malaria Bacterial 0-18 Other
## 46843 Germany Hypertension Viral 19-35 Male
## 58102 Turkey Polio Respiratory 61+ Male
## 36086 Argentina Cholera Neurological 36-60 Female
## 76723 Germany Parkinson's Disease Autoimmune 0-18 Other
## 9182 Brazil Rabies Neurological 36-60 Female
## 71848 Brazil Influenza Neurological 19-35 Male
## 56519 Turkey Parkinson's Disease Neurological 19-35 Other
## 24671 Russia Rabies Autoimmune 36-60 Female
## 34134 Germany Ebola Parasitic 0-18 Female
## 47800 Germany Hepatitis Genetic 36-60 Male
## 25196 Argentina Measles Metabolic 0-18 Male
## 65778 Turkey Alzheimer's Disease Neurological 36-60 Female
## 7071 Turkey HIV/AIDS Cardiovascular 61+ Other
## 40160 Brazil Rabies Respiratory 36-60 Other
## 1498 Germany Zika Autoimmune 19-35 Female
## 57337 Italy Polio Autoimmune 36-60 Male
## 60587 Turkey Measles Neurological 36-60 Male
## 20698 France Leprosy Bacterial 0-18 Female
## 65615 Germany Zika Genetic 61+ Female
## 9723 Russia Leprosy Neurological 0-18 Other
## 34599 France Asthma Bacterial 19-35 Other
## 4518 Italy Parkinson's Disease Autoimmune 36-60 Other
## 62170 Italy Malaria Cardiovascular 36-60 Other
## 4695 Argentina Cholera Infectious 61+ Female
## 32102 Russia Dengue Chronic 61+ Other
## 78889 Argentina Cholera Infectious 19-35 Other
## 74984 Mexico Cancer Bacterial 61+ Male
## 53289 Russia Hypertension Viral 61+ Male
## 16436 Germany Influenza Autoimmune 36-60 Female
## 26691 Turkey HIV/AIDS Respiratory 36-60 Female
## 53705 Mexico Dengue Viral 61+ Female
## 42887 Turkey Parkinson's Disease Autoimmune 0-18 Male
## 30237 Mexico Cancer Chronic 61+ Male
## 66766 Germany Asthma Genetic 61+ Male
## 35556 Mexico Tuberculosis Bacterial 36-60 Male
## 32076 France Hypertension Parasitic 61+ Male
## 26715 Russia Tuberculosis Genetic 0-18 Male
## 18388 Brazil HIV/AIDS Parasitic 36-60 Male
## 23977 Mexico Cholera Neurological 19-35 Male
## 68616 Turkey Hypertension Parasitic 19-35 Other
## 63481 Russia Hypertension Autoimmune 61+ Other
## 31315 Argentina Parkinson's Disease Viral 19-35 Male
## 43755 Germany Polio Genetic 0-18 Female
## 45555 Turkey Hypertension Genetic 36-60 Other
## 57943 Germany Dengue Respiratory 61+ Female
## 74659 Turkey Cholera Cardiovascular 0-18 Other
## 73799 Germany Diabetes Cardiovascular 61+ Male
## 20400 Mexico Measles Viral 0-18 Male
## 4972 Turkey HIV/AIDS Autoimmune 0-18 Female
## 69045 Argentina Alzheimer's Disease Neurological 19-35 Other
## 37492 Brazil Diabetes Metabolic 19-35 Male
## 6373 France Hepatitis Parasitic 36-60 Female
## 73396 Germany Tuberculosis Parasitic 0-18 Female
## 602 Mexico Leprosy Parasitic 19-35 Female
## 74059 Argentina Cholera Autoimmune 19-35 Other
## 47606 Brazil Influenza Respiratory 0-18 Female
## 28577 Argentina Ebola Bacterial 0-18 Other
## 7877 Italy Asthma Respiratory 0-18 Female
## 58263 Russia Influenza Parasitic 0-18 Female
## 2777 Argentina Influenza Autoimmune 61+ Female
## 30712 Mexico Hypertension Autoimmune 19-35 Other
## 17749 Germany Hepatitis Infectious 19-35 Other
## 7488 Germany Polio Respiratory 0-18 Other
## 76662 Brazil COVID-19 Metabolic 0-18 Male
## 45430 Italy Tuberculosis Parasitic 0-18 Other
## 9739 Brazil Alzheimer's Disease Chronic 36-60 Female
## 61775 Turkey Malaria Genetic 19-35 Female
## 19512 Mexico Cholera Cardiovascular 61+ Male
## 49588 Argentina HIV/AIDS Respiratory 36-60 Female
## 49753 Germany Diabetes Viral 36-60 Female
## 52292 Brazil Hepatitis Parasitic 19-35 Male
## 38245 Brazil Malaria Genetic 19-35 Other
## 4682 Germany Parkinson's Disease Respiratory 61+ Female
## 68957 Turkey Alzheimer's Disease Viral 0-18 Male
## 37765 France Ebola Viral 61+ Other
## 34827 Argentina Influenza Autoimmune 61+ Other
## 2748 Argentina Influenza Metabolic 19-35 Male
## 56138 Germany Parkinson's Disease Bacterial 0-18 Other
## 44859 Brazil Cholera Genetic 19-35 Female
## 51442 Italy Cholera Respiratory 0-18 Male
## 78912 Russia Zika Infectious 0-18 Other
## 75936 Germany Cholera Metabolic 0-18 Female
## 57223 Turkey Ebola Autoimmune 61+ Other
## 67089 France Cholera Parasitic 36-60 Female
## 27365 Italy Dengue Viral 0-18 Male
## 76141 France Hepatitis Cardiovascular 36-60 Female
## 19927 Russia Rabies Respiratory 36-60 Female
## 10216 France Zika Bacterial 0-18 Male
## 2882 Russia Parkinson's Disease Neurological 0-18 Male
## 4015 Germany Leprosy Cardiovascular 19-35 Other
## 27913 France Asthma Respiratory 0-18 Male
## 29465 France Leprosy Neurological 0-18 Female
## 9174 Argentina Dengue Genetic 0-18 Male
## 7657 Turkey Leprosy Genetic 36-60 Other
## 31351 Italy Hypertension Chronic 19-35 Male
## Treatment Type Availability of Vaccines/Treatment
## 51663 Therapy No
## 57870 Surgery No
## 2986 Surgery No
## 29925 Therapy Yes
## 68293 Therapy Yes
## 62555 Medication No
## 45404 Therapy No
## 65161 Surgery No
## 46435 Vaccination No
## 9642 Medication Yes
## 59134 Therapy No
## 52132 Therapy No
## 14183 Vaccination Yes
## 15180 Surgery Yes
## 27168 Therapy Yes
## 9097 Medication No
## 30538 Surgery Yes
## 56219 Medication No
## 7989 Vaccination Yes
## 13536 Vaccination No
## 6216 Therapy No
## 29394 Vaccination Yes
## 53241 Therapy No
## 28825 Therapy Yes
## 41 Medication No
## 14426 Surgery Yes
## 72820 Vaccination Yes
## 51656 Medication Yes
## 58527 Surgery No
## 77009 Surgery Yes
## 43042 Surgery No
## 77837 Medication No
## 6134 Medication No
## 33523 Medication Yes
## 21812 Medication No
## 39895 Therapy No
## 9640 Medication No
## 9326 Therapy Yes
## 26510 Therapy No
## 20960 Surgery No
## 14403 Vaccination Yes
## 64502 Medication Yes
## 71503 Medication No
## 45221 Surgery Yes
## 77585 Medication No
## 70563 Medication Yes
## 32606 Vaccination No
## 16152 Therapy Yes
## 25559 Therapy No
## 14215 Medication No
## 14287 Surgery Yes
## 23194 Therapy Yes
## 34976 Medication No
## 14491 Therapy Yes
## 77584 Surgery No
## 40503 Vaccination No
## 34724 Surgery Yes
## 17369 Surgery Yes
## 31542 Surgery No
## 59106 Medication Yes
## 40632 Medication No
## 43204 Medication Yes
## 37544 Vaccination Yes
## 76820 Therapy Yes
## 44196 Medication Yes
## 44983 Surgery No
## 36717 Medication No
## 28078 Vaccination Yes
## 69369 Medication No
## 43406 Surgery Yes
## 41488 Medication Yes
## 47485 Vaccination No
## 63528 Medication No
## 14536 Therapy No
## 51465 Surgery Yes
## 26503 Medication Yes
## 32953 Medication Yes
## 413 Medication Yes
## 10762 Vaccination No
## 48182 Therapy Yes
## 30571 Medication No
## 42521 Therapy No
## 74522 Medication No
## 14745 Medication No
## 25946 Vaccination Yes
## 43928 Surgery Yes
## 6601 Surgery No
## 57701 Medication No
## 53518 Medication No
## 72326 Therapy No
## 52353 Medication No
## 66154 Vaccination No
## 47802 Surgery Yes
## 31517 Therapy No
## 66075 Therapy No
## 32263 Surgery No
## 69161 Therapy No
## 56157 Therapy Yes
## 19392 Vaccination Yes
## 46137 Therapy Yes
## 67747 Surgery No
## 2286 Therapy No
## 59970 Vaccination No
## 54661 Vaccination Yes
## 32281 Vaccination No
## 64085 Surgery Yes
## 7826 Surgery Yes
## 25449 Surgery No
## 63405 Medication No
## 76223 Therapy Yes
## 64616 Medication Yes
## 27455 Surgery Yes
## 54740 Medication No
## 78173 Therapy No
## 20193 Medication No
## 66701 Medication No
## 22467 Therapy Yes
## 63167 Vaccination No
## 79225 Medication No
## 63658 Surgery Yes
## 49862 Vaccination Yes
## 79331 Vaccination No
## 51491 Medication No
## 15703 Therapy No
## 6623 Therapy No
## 8983 Therapy No
## 37769 Vaccination No
## 29479 Vaccination No
## 70943 Surgery Yes
## 78121 Vaccination Yes
## 1835 Medication Yes
## 42221 Vaccination No
## 21099 Surgery Yes
## 41043 Medication Yes
## 56529 Surgery Yes
## 36940 Medication Yes
## 76099 Therapy No
## 38265 Medication Yes
## 76384 Medication No
## 46438 Medication No
## 75176 Vaccination No
## 69350 Therapy Yes
## 8176 Vaccination Yes
## 76369 Medication Yes
## 17746 Vaccination Yes
## 53873 Surgery No
## 29133 Vaccination Yes
## 31 Therapy Yes
## 7588 Therapy Yes
## 4620 Therapy Yes
## 45980 Surgery No
## 68783 Therapy Yes
## 51573 Vaccination No
## 70778 Therapy No
## 11667 Surgery No
## 33027 Vaccination No
## 70945 Therapy Yes
## 76792 Surgery No
## 2450 Medication No
## 44500 Vaccination Yes
## 1673 Vaccination Yes
## 19851 Therapy No
## 48434 Vaccination Yes
## 6007 Vaccination No
## 55992 Medication Yes
## 78967 Surgery No
## 66596 Surgery No
## 60906 Surgery No
## 78715 Therapy Yes
## 46468 Surgery Yes
## 1204 Vaccination Yes
## 53272 Surgery Yes
## 24111 Surgery Yes
## 75455 Vaccination Yes
## 43694 Surgery Yes
## 3984 Surgery Yes
## 7880 Medication Yes
## 57722 Medication Yes
## 22119 Surgery Yes
## 67632 Medication No
## 52523 Vaccination No
## 53918 Therapy Yes
## 24522 Medication Yes
## 6333 Therapy Yes
## 68430 Vaccination No
## 55737 Surgery No
## 19955 Medication No
## 55368 Vaccination No
## 47818 Surgery Yes
## 14417 Medication Yes
## 49286 Medication Yes
## 25244 Therapy Yes
## 5596 Vaccination Yes
## 40276 Medication Yes
## 60101 Medication Yes
## 37167 Surgery No
## 69232 Medication No
## 22974 Surgery Yes
## 52851 Surgery No
## 4913 Vaccination Yes
## 10301 Vaccination Yes
## 76908 Therapy No
## 18547 Medication No
## 78774 Therapy No
## 29370 Therapy No
## 46843 Surgery Yes
## 58102 Vaccination Yes
## 36086 Vaccination No
## 76723 Medication Yes
## 9182 Surgery No
## 71848 Medication No
## 56519 Medication No
## 24671 Vaccination No
## 34134 Surgery Yes
## 47800 Surgery Yes
## 25196 Therapy Yes
## 65778 Medication Yes
## 7071 Medication No
## 40160 Surgery Yes
## 1498 Medication Yes
## 57337 Therapy Yes
## 60587 Vaccination Yes
## 20698 Therapy No
## 65615 Medication No
## 9723 Medication Yes
## 34599 Vaccination No
## 4518 Medication No
## 62170 Therapy Yes
## 4695 Therapy No
## 32102 Surgery Yes
## 78889 Medication Yes
## 74984 Therapy Yes
## 53289 Surgery Yes
## 16436 Surgery No
## 26691 Vaccination Yes
## 53705 Vaccination Yes
## 42887 Surgery No
## 30237 Vaccination No
## 66766 Surgery No
## 35556 Therapy No
## 32076 Surgery No
## 26715 Vaccination Yes
## 18388 Therapy No
## 23977 Vaccination No
## 68616 Surgery Yes
## 63481 Medication Yes
## 31315 Medication Yes
## 43755 Surgery Yes
## 45555 Surgery No
## 57943 Medication Yes
## 74659 Surgery No
## 73799 Vaccination No
## 20400 Vaccination No
## 4972 Vaccination Yes
## 69045 Therapy Yes
## 37492 Vaccination No
## 6373 Therapy Yes
## 73396 Medication Yes
## 602 Medication Yes
## 74059 Surgery Yes
## 47606 Medication Yes
## 28577 Therapy No
## 7877 Surgery No
## 58263 Vaccination No
## 2777 Surgery No
## 30712 Vaccination Yes
## 17749 Surgery Yes
## 7488 Medication Yes
## 76662 Vaccination Yes
## 45430 Surgery No
## 9739 Medication No
## 61775 Medication No
## 19512 Vaccination Yes
## 49588 Medication Yes
## 49753 Therapy No
## 52292 Vaccination Yes
## 38245 Medication Yes
## 4682 Vaccination Yes
## 68957 Medication No
## 37765 Medication Yes
## 34827 Medication Yes
## 2748 Medication Yes
## 56138 Medication No
## 44859 Vaccination No
## 51442 Surgery Yes
## 78912 Surgery Yes
## 75936 Therapy No
## 57223 Medication No
## 67089 Therapy Yes
## 27365 Surgery No
## 76141 Therapy Yes
## 19927 Vaccination Yes
## 10216 Medication Yes
## 2882 Therapy Yes
## 4015 Medication No
## 27913 Vaccination No
## 29465 Surgery Yes
## 9174 Vaccination Yes
## 7657 Medication No
## 31351 Therapy Yes
##
## $marge.col
## Argentina Brazil France Germany
## 0.016666667 0.016666667 0.017619048 0.019047619
## Italy Mexico Russia Turkey
## 0.015714286 0.018095238 0.014761905 0.024285714
## Alzheimer's Disease Asthma Cancer Cholera
## 0.007619048 0.009047619 0.005714286 0.010476190
## COVID-19 Dengue Diabetes Ebola
## 0.003809524 0.008095238 0.007619048 0.008571429
## Hepatitis HIV/AIDS Hypertension Influenza
## 0.005714286 0.005238095 0.008095238 0.007619048
## Leprosy Malaria Measles Parkinson's Disease
## 0.008095238 0.006666667 0.006666667 0.008571429
## Polio Rabies Tuberculosis Zika
## 0.004285714 0.007619048 0.008571429 0.004761905
## Autoimmune Bacterial Cardiovascular Chronic
## 0.013809524 0.012380952 0.012857143 0.012857143
## Genetic Infectious Metabolic Neurological
## 0.015714286 0.014285714 0.012380952 0.013809524
## Parasitic Respiratory Viral 0-18
## 0.012380952 0.013333333 0.009047619 0.034285714
## 19-35 36-60 61+ Female
## 0.032380952 0.038095238 0.038095238 0.048095238
## Male Other Medication Surgery
## 0.045714286 0.049047619 0.043333333 0.035238095
## Therapy Vaccination No Yes
## 0.031904762 0.032380952 0.069523810 0.073333333
##
## $marge.row
## 51663 57870 2986 29925 68293 62555
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 45404 65161 46435 9642 59134 52132
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 14183 15180 27168 9097 30538 56219
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 7989 13536 6216 29394 53241 28825
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 41 14426 72820 51656 58527 77009
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 43042 77837 6134 33523 21812 39895
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 9640 9326 26510 20960 14403 64502
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 71503 45221 77585 70563 32606 16152
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 25559 14215 14287 23194 34976 14491
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 77584 40503 34724 17369 31542 59106
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 40632 43204 37544 76820 44196 44983
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 36717 28078 69369 43406 41488 47485
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 63528 14536 51465 26503 32953 413
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 10762 48182 30571 42521 74522 14745
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 25946 43928 6601 57701 53518 72326
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 52353 66154 47802 31517 66075 32263
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 69161 56157 19392 46137 67747 2286
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 59970 54661 32281 64085 7826 25449
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 63405 76223 64616 27455 54740 78173
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 20193 66701 22467 63167 79225 63658
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 49862 79331 51491 15703 6623 8983
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 37769 29479 70943 78121 1835 42221
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 21099 41043 56529 36940 76099 38265
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 76384 46438 75176 69350 8176 76369
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 17746 53873 29133 31 7588 4620
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 45980 68783 51573 70778 11667 33027
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 70945 76792 2450 44500 1673 19851
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 48434 6007 55992 78967 66596 60906
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 78715 46468 1204 53272 24111 75455
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 43694 3984 7880 57722 22119 67632
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 52523 53918 24522 6333 68430 55737
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 19955 55368 47818 14417 49286 25244
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 5596 40276 60101 37167 69232 22974
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 52851 4913 10301 76908 18547 78774
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 29370 46843 58102 36086 76723 9182
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 71848 56519 24671 34134 47800 25196
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 65778 7071 40160 1498 57337 60587
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 20698 65615 9723 34599 4518 62170
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 4695 32102 78889 74984 53289 16436
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 26691 53705 42887 30237 66766 35556
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 32076 26715 18388 23977 68616 63481
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 31315 43755 45555 57943 74659 73799
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 20400 4972 69045 37492 6373 73396
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 602 74059 47606 28577 7877 58263
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 2777 30712 17749 7488 76662 45430
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 9739 61775 19512 49588 49753 52292
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 38245 4682 68957 37765 34827 2748
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 56138 44859 51442 78912 75936 57223
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 67089 27365 76141 19927 10216 2882
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
## 4015 27913 29465 9174 7657 31351
## 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333 0.003333333
##
## $ncp
## [1] 5
##
## $row.w
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [297] 1 1 1 1
##
## $excl
## NULL
##
## $call
## MCA(X = DATAP_muestra[vars_cuali], graph = FALSE)
##
## $Xtot
## Argentina Brazil France Germany Italy Mexico Russia Turkey
## 51663 0 0 0 0 0 1 0 0
## 57870 0 1 0 0 0 0 0 0
## 2986 0 0 0 0 0 1 0 0
## 29925 0 0 0 0 0 0 1 0
## 68293 0 0 0 0 1 0 0 0
## 62555 0 0 0 0 0 0 1 0
## 45404 1 0 0 0 0 0 0 0
## 65161 0 1 0 0 0 0 0 0
## 46435 1 0 0 0 0 0 0 0
## 9642 0 0 1 0 0 0 0 0
## 59134 0 0 0 1 0 0 0 0
## 52132 0 0 0 0 0 0 0 1
## 14183 0 0 0 0 0 0 0 1
## 15180 1 0 0 0 0 0 0 0
## 27168 0 0 0 0 0 1 0 0
## 9097 0 0 1 0 0 0 0 0
## 30538 0 0 0 0 0 0 0 1
## 56219 0 0 0 0 0 0 1 0
## 7989 0 0 0 0 1 0 0 0
## 13536 0 0 1 0 0 0 0 0
## 6216 0 0 0 1 0 0 0 0
## 29394 0 0 0 0 0 1 0 0
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##
## $contrib
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## 44859 8.362614e-03 0.4853180286 0.2227582121 7.324293e-02 6.832335e-02
## 51442 1.129896e+00 0.0975428347 0.0058164750 1.820087e-01 1.130925e-02
## 78912 7.804752e-02 0.0409652522 0.0366373350 3.930476e-03 7.873078e-01
## 75936 4.386405e-02 0.0008751822 0.9922016291 2.090275e-02 1.542582e-01
## 57223 7.043492e-01 0.8206196455 0.0045150274 4.135798e-01 4.736000e-03
## 67089 3.195614e-01 0.6366706076 0.2966703244 8.620926e-01 4.038473e-01
## 27365 1.740957e-01 1.3942892431 0.1566663159 1.770042e-02 5.365093e-01
## 76141 7.348660e-02 1.6754150615 0.0618216404 1.729789e+00 3.089856e-01
## 19927 1.054382e+00 0.4018308003 0.8632484139 1.339974e-02 3.577533e-01
## 10216 7.164415e-05 0.0366712150 1.2335482358 5.792044e-02 2.751161e-01
## 2882 1.119555e-02 0.0646221910 0.0097731969 1.829086e-01 1.383437e+00
## 4015 8.441845e-01 0.1315967897 0.1143804223 1.375581e-01 8.855863e-02
## 27913 5.088706e-01 0.0175784226 0.4988617254 1.413170e-01 3.801856e-01
## 29465 1.005623e-02 0.0461461922 0.2161084882 3.123862e-01 1.431515e+00
## 9174 3.606252e-05 0.0030388312 0.0180090085 5.479397e-01 2.092544e-01
## 7657 4.210082e-01 0.0064938272 0.9105791065 7.363970e-02 6.374376e-02
## 31351 7.195462e-03 0.1057813116 0.0034493232 9.590029e-02 8.065399e-01
##
## $cos2
## Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## 51663 5.806633e-03 2.570167e-02 2.652386e-03 2.249516e-02 1.850656e-03
## 57870 4.760969e-02 5.254008e-02 3.266558e-03 8.733212e-03 2.077398e-03
## 2986 2.229513e-02 1.380082e-02 1.461620e-02 2.920858e-05 8.627746e-03
## 29925 3.079349e-04 1.641447e-02 1.691521e-02 1.683164e-02 3.539615e-02
## 68293 5.242632e-02 5.571317e-03 2.217594e-06 1.197107e-02 1.874152e-04
## 62555 6.692143e-03 1.008168e-02 1.409621e-01 2.434802e-02 4.682426e-02
## 45404 1.021718e-02 1.877070e-03 1.574339e-04 3.742332e-03 5.185964e-02
## 65161 4.043766e-03 1.194528e-02 2.489094e-04 3.738179e-02 2.982699e-01
## 46435 6.227288e-03 1.031974e-02 1.055999e-01 5.030057e-02 3.384067e-03
## 9642 3.000767e-02 2.054952e-05 3.868367e-02 3.082106e-03 1.701141e-01
## 59134 2.624154e-03 1.258829e-02 1.323485e-01 6.238218e-03 2.437929e-03
## 52132 6.644450e-03 4.321424e-03 1.006774e-03 1.854470e-02 9.358140e-04
## 14183 1.159419e-01 2.272084e-02 1.247749e-03 1.033755e-02 2.660585e-02
## 15180 1.845774e-01 9.548726e-04 1.248153e-03 6.902696e-04 4.860091e-02
## 27168 1.472737e-01 1.264655e-01 1.465042e-01 2.640814e-03 1.180293e-02
## 9097 3.008479e-04 8.731038e-02 8.524577e-03 1.917567e-02 5.946529e-03
## 30538 2.026496e-04 4.097144e-02 5.141278e-02 5.053247e-03 7.516865e-03
## 56219 5.274582e-02 1.403874e-02 4.342397e-02 1.483255e-02 2.927660e-02
## 7989 3.143205e-02 1.409193e-02 6.015040e-02 1.346510e-01 1.449397e-02
## 13536 1.005030e-03 1.107624e-03 6.687560e-02 1.529153e-02 4.222830e-03
## 6216 7.177357e-04 8.615247e-04 1.101199e-02 1.596186e-03 1.389500e-03
## 29394 4.573082e-02 6.712635e-02 2.983644e-02 9.307343e-03 7.993965e-04
## 53241 2.075577e-02 4.193556e-02 4.872647e-02 8.241713e-02 1.664585e-04
## 28825 1.877114e-03 2.309914e-02 2.379090e-02 9.427832e-02 3.853705e-02
## 41 4.984830e-02 9.786670e-02 2.775509e-02 5.598477e-04 6.140711e-04
## 14426 3.698581e-03 2.886678e-03 1.734844e-02 5.090018e-03 8.583356e-03
## 72820 7.709242e-04 4.489164e-04 1.452210e-02 2.201977e-01 5.462024e-05
## 51656 2.559176e-03 1.797971e-03 1.944131e-02 1.887441e-01 1.346111e-02
## 58527 1.735260e-02 5.838282e-03 2.194292e-02 1.309258e-03 3.496948e-02
## 77009 7.835462e-08 1.650657e-01 4.437118e-03 3.755731e-02 3.466329e-02
## 43042 1.341299e-03 5.437494e-03 5.995002e-02 6.233329e-03 5.246640e-03
## 77837 1.119782e-01 4.999703e-02 5.158435e-03 1.520308e-02 1.218310e-02
## 6134 1.659695e-02 2.722799e-02 4.313370e-02 6.127498e-02 2.768958e-03
## 33523 2.904142e-02 3.074208e-02 6.043947e-03 5.439114e-02 1.025129e-02
## 21812 1.223859e-06 9.893812e-02 1.527679e-02 2.536112e-04 9.614721e-04
## 39895 2.499478e-03 1.541725e-02 1.214391e-01 1.626057e-04 3.973222e-03
## 9640 9.363174e-03 8.414665e-04 1.124800e-01 1.590762e-02 2.230205e-04
## 9326 3.383936e-03 5.699643e-02 9.135929e-03 1.848276e-03 6.126423e-02
## 26510 3.707988e-02 3.820092e-02 2.217228e-03 1.563856e-02 1.776231e-04
## 20960 1.372204e-03 3.294047e-02 2.075070e-03 1.137053e-01 3.948859e-02
## 14403 6.037245e-02 2.502524e-02 7.043835e-03 1.687438e-01 2.079834e-02
## 64502 1.145622e-01 2.771453e-03 4.450172e-02 3.667748e-03 5.910224e-02
## 71503 4.097771e-02 7.110284e-02 1.234479e-02 2.050423e-03 1.635447e-02
## 45221 4.763581e-02 6.124209e-02 1.946671e-02 7.047869e-03 2.675453e-05
## 77585 1.330773e-01 8.552898e-03 5.981870e-02 2.161767e-04 6.824329e-03
## 70563 1.199805e-03 6.393705e-02 5.912678e-02 2.147124e-03 2.454018e-04
## 32606 9.573425e-04 1.111060e-01 6.936013e-04 1.684176e-02 1.929363e-01
## 16152 3.040534e-02 6.536875e-02 8.210141e-03 1.113261e-01 2.510024e-03
## 25559 1.823648e-03 1.244064e-03 4.413960e-02 3.833012e-02 4.268027e-03
## 14215 1.801776e-02 1.148029e-03 2.881671e-02 9.061178e-04 7.224182e-04
## 14287 1.581033e-01 8.759879e-05 1.543674e-02 3.970150e-03 2.062537e-02
## 23194 6.998488e-03 5.214745e-03 1.239372e-03 3.205969e-02 4.300752e-03
## 34976 7.992688e-04 6.284158e-02 7.937534e-02 1.114498e-05 4.250979e-03
## 14491 1.373220e-02 3.186251e-02 1.867734e-03 2.663119e-05 1.885929e-04
## 77584 3.126614e-03 5.770761e-04 7.721761e-02 2.638187e-02 1.911399e-02
## 40503 3.965474e-02 2.966122e-04 4.580793e-02 6.541339e-02 9.366374e-02
## 34724 1.036564e-01 2.341294e-04 6.479156e-02 2.590850e-02 8.867360e-03
## 17369 5.598334e-05 4.626721e-02 1.171027e-01 1.773484e-02 1.142709e-01
## 31542 7.200150e-02 4.215524e-02 3.184494e-02 7.711790e-03 3.087277e-02
## 59106 8.255497e-02 2.557027e-05 6.884264e-02 2.254515e-02 1.488258e-03
## 40632 1.232710e-01 8.496149e-02 8.662060e-03 6.722889e-03 5.900929e-04
## 43204 1.458591e-03 5.702032e-03 2.170944e-02 3.070061e-03 7.523660e-02
## 37544 1.097359e-02 1.073817e-01 4.617526e-03 2.554536e-02 9.682196e-03
## 76820 1.394099e-02 6.028578e-02 4.854094e-03 1.537837e-02 1.544099e-02
## 44196 1.860881e-02 7.015829e-03 3.985049e-02 9.972405e-03 3.535581e-03
## 44983 2.027684e-01 1.232624e-02 3.975337e-02 9.198504e-07 5.719623e-02
## 36717 7.457522e-02 1.167498e-01 1.352303e-05 1.569069e-02 1.033678e-01
## 28078 2.879172e-02 3.268248e-04 1.661587e-02 1.065609e-01 3.977174e-03
## 69369 7.977387e-02 9.940043e-02 1.751094e-02 3.490816e-03 1.734658e-02
## 43406 5.541276e-02 9.411674e-02 3.970854e-02 2.803705e-02 8.239132e-04
## 41488 1.155271e-02 1.153007e-01 8.941911e-03 8.963095e-03 2.359937e-03
## 47485 4.246184e-02 8.387164e-03 2.382676e-03 7.024616e-02 3.121495e-04
## 63528 2.377930e-02 4.692830e-02 2.175771e-02 3.835787e-03 2.817138e-03
## 14536 3.483424e-05 6.139270e-03 1.634262e-01 1.537677e-01 4.168901e-02
## 51465 2.789403e-02 8.488582e-03 1.706304e-02 1.681414e-02 8.852320e-02
## 26503 5.514013e-02 8.723081e-02 6.074796e-03 3.724791e-04 1.870456e-02
## 32953 6.951567e-02 2.686045e-02 1.130081e-03 5.773153e-02 5.188769e-04
## 413 1.064446e-02 1.071108e-03 2.909185e-02 1.305449e-02 1.200989e-01
## 10762 4.084659e-02 6.556558e-03 2.046198e-02 2.236956e-05 2.699409e-02
## 48182 4.737700e-02 1.383272e-02 1.994418e-05 3.121780e-03 4.032019e-02
## 30571 2.084118e-01 7.932268e-02 1.726796e-02 1.996566e-02 1.036496e-01
## 42521 4.839007e-02 4.991119e-02 1.983802e-01 1.831554e-02 6.829680e-02
## 74522 6.755827e-02 8.967024e-02 5.818454e-02 7.720279e-02 3.820518e-03
## 14745 3.369494e-03 3.007371e-05 1.172180e-01 7.340534e-02 3.248799e-02
## 25946 1.408688e-02 4.860010e-04 2.599513e-02 1.917026e-03 7.825486e-03
## 43928 1.908016e-01 3.262889e-02 1.348375e-02 4.009002e-03 1.502469e-01
## 6601 6.336934e-02 1.090773e-01 2.768212e-03 2.798037e-03 1.192618e-02
## 57701 9.068455e-03 4.965131e-02 1.060171e-02 3.230179e-03 1.784812e-03
## 53518 3.222158e-04 2.746135e-04 2.583745e-02 1.264630e-02 1.642293e-01
## 72326 1.799778e-02 5.155365e-02 1.314255e-02 1.047844e-01 5.236986e-02
## 52353 1.745054e-04 2.319618e-02 9.358673e-03 7.993308e-03 3.014015e-03
## 66154 1.248416e-02 1.983744e-02 2.756978e-03 7.963393e-02 4.704109e-03
## 47802 3.737642e-04 1.156124e-02 2.372915e-02 2.100749e-02 5.075844e-02
## 31517 1.028385e-01 6.755939e-04 2.624751e-03 2.685157e-04 1.443990e-03
## 66075 5.013998e-02 8.504776e-03 4.342773e-04 5.014734e-02 1.298720e-02
## 32263 6.465771e-02 6.230714e-02 5.518976e-02 6.938522e-04 1.592532e-01
## 69161 8.604359e-02 7.943714e-02 9.401756e-02 3.796068e-03 1.250129e-02
## 56157 8.817165e-03 5.541629e-04 2.501523e-02 4.053939e-03 2.348019e-03
## 19392 7.400277e-03 3.369758e-02 3.286356e-02 2.906997e-04 1.995893e-03
## 46137 4.003130e-02 1.621942e-03 4.043843e-03 3.069490e-05 4.785954e-03
## 67747 1.747056e-03 1.095226e-02 4.037587e-03 7.787640e-02 8.599583e-02
## 2286 2.879614e-02 3.709704e-03 1.196510e-01 3.350296e-02 1.222997e-02
## 59970 1.012431e-01 3.651195e-02 1.696513e-02 6.270351e-03 8.979566e-02
## 54661 4.183296e-02 8.208099e-03 8.738851e-04 2.382769e-02 3.845390e-03
## 32281 1.398951e-02 8.741032e-02 3.259184e-03 3.611646e-03 2.085155e-03
## 64085 5.535107e-02 1.710552e-02 9.994958e-03 1.360186e-01 3.082072e-03
## 7826 4.086729e-02 8.862441e-04 4.068220e-03 6.046622e-04 2.398303e-03
## 25449 1.567950e-01 7.826400e-03 2.684614e-03 1.020665e-02 1.521670e-01
## 63405 7.184879e-03 4.204260e-02 1.931829e-04 3.044475e-02 1.662104e-02
## 76223 9.238293e-02 4.920025e-03 2.540958e-02 2.545269e-03 5.726617e-02
## 64616 2.207404e-03 6.615175e-03 1.090761e-02 6.007107e-02 2.730445e-01
## 27455 5.285021e-02 8.878644e-03 1.768694e-03 1.384038e-02 1.087232e-02
## 54740 7.697902e-02 7.320684e-05 4.010143e-03 1.931434e-03 1.745193e-02
## 78173 1.709487e-02 2.501804e-05 3.657605e-02 1.427728e-04 8.879572e-03
## 20193 5.549400e-03 9.483971e-02 3.558213e-03 4.945486e-02 7.253020e-03
## 66701 6.355793e-04 1.484599e-01 5.739384e-04 3.388142e-02 6.634228e-03
## 22467 5.696028e-02 1.059153e-01 7.722102e-02 3.027983e-03 5.994932e-04
## 63167 1.330165e-02 5.981561e-02 1.134823e-03 3.197478e-03 9.994048e-02
## 79225 4.648320e-02 8.523169e-02 4.393361e-03 2.761946e-02 2.963245e-06
## 63658 4.234036e-02 3.750747e-02 1.453450e-01 2.550564e-02 1.731857e-02
## 49862 8.021932e-03 2.184607e-01 2.835208e-02 9.709509e-05 1.731781e-02
## 79331 1.037550e-02 3.319860e-03 2.715582e-02 2.848817e-02 6.631426e-04
## 51491 5.380407e-03 1.493318e-02 2.862892e-02 1.787674e-02 2.529738e-02
## 15703 4.481986e-02 1.018061e-02 5.850244e-03 2.143448e-02 7.575934e-02
## 6623 2.801732e-02 8.206764e-03 5.544146e-02 1.702289e-02 4.012867e-03
## 8983 3.084817e-02 1.024527e-02 2.691899e-02 1.664557e-01 1.768083e-02
## 37769 3.207690e-02 1.226685e-03 5.111795e-04 1.834747e-04 3.470779e-02
## 29479 1.491136e-02 2.047494e-02 4.555395e-02 1.879060e-03 1.407171e-02
## 70943 2.567805e-02 1.132702e-04 5.928428e-04 1.286696e-04 4.772679e-02
## 78121 1.517217e-02 3.346506e-02 4.445151e-03 1.226291e-04 1.131558e-02
## 1835 7.878037e-02 4.630555e-03 7.306487e-03 1.133718e-01 7.374405e-02
## 42221 1.576633e-02 1.627624e-02 1.226960e-02 2.310316e-02 1.998423e-03
## 21099 4.715070e-02 1.388386e-03 5.659515e-04 1.547172e-02 3.674246e-05
## 41043 1.702198e-02 4.636562e-02 1.005964e-03 2.795723e-02 4.544048e-02
## 56529 1.747896e-04 2.433703e-02 3.349511e-02 2.673696e-03 7.667152e-03
## 36940 1.392385e-03 4.046392e-03 2.501006e-02 5.110227e-04 5.146058e-03
## 76099 7.807476e-02 4.315081e-03 1.822388e-02 7.840111e-02 4.337802e-03
## 38265 2.897594e-03 1.300296e-02 2.238574e-02 2.296062e-02 1.743680e-01
## 76384 2.850471e-03 5.652977e-03 1.260637e-03 9.073885e-02 2.139559e-03
## 46438 5.631073e-02 5.906123e-02 4.392655e-05 3.877914e-02 4.580391e-03
## 75176 4.492446e-02 1.668994e-01 3.077899e-02 1.446492e-02 4.614434e-03
## 69350 3.328549e-02 1.138613e-03 1.029784e-01 7.547493e-02 4.070311e-02
## 8176 3.648862e-02 5.178675e-02 3.154917e-04 4.398953e-03 2.725083e-02
## 76369 4.722375e-06 4.524208e-05 2.279759e-03 4.106494e-03 4.367713e-03
## 17746 2.004401e-03 9.628078e-02 1.359041e-02 1.571548e-02 2.574857e-02
## 53873 3.981621e-02 2.527259e-02 4.978442e-02 6.505210e-02 3.375950e-02
## 29133 2.546406e-02 5.676923e-02 6.222144e-03 1.811630e-02 5.593968e-02
## 31 7.821200e-02 7.537490e-03 2.513255e-02 3.203665e-07 4.214871e-03
## 7588 1.644489e-02 1.948328e-01 4.597749e-02 5.189287e-04 8.129005e-04
## 4620 3.359712e-02 1.263727e-02 1.504933e-01 1.038758e-02 2.913646e-02
## 45980 1.568092e-02 4.030026e-02 1.814619e-02 5.724731e-02 5.467844e-02
## 68783 1.304548e-02 1.602628e-02 4.515242e-02 9.993641e-02 8.159044e-03
## 51573 1.079528e-01 9.955916e-02 7.617544e-03 8.478903e-03 5.853118e-02
## 70778 6.631323e-02 6.623970e-03 7.893595e-02 1.677853e-02 5.347971e-03
## 11667 1.025326e-03 4.665848e-02 5.710022e-03 1.395180e-01 9.435351e-03
## 33027 4.131165e-02 2.269177e-03 2.053155e-03 7.290461e-02 3.849639e-02
## 70945 3.812376e-02 1.181374e-01 2.203071e-01 2.714352e-02 2.974431e-02
## 76792 9.312239e-03 1.902957e-02 2.638229e-02 2.446618e-02 1.514013e-03
## 2450 6.889338e-02 3.760888e-02 6.745658e-04 4.324630e-03 4.704934e-04
## 44500 4.812589e-02 4.702263e-02 7.589318e-03 7.722986e-02 2.137125e-02
## 1673 4.866537e-03 2.623065e-01 5.132600e-02 4.737481e-02 6.120433e-02
## 19851 2.026929e-02 7.547815e-02 4.206620e-02 6.120346e-03 4.469296e-02
## 48434 2.662475e-02 5.511058e-02 2.030094e-04 7.664118e-02 5.776178e-03
## 6007 1.129533e-02 5.673613e-03 3.572328e-02 9.095411e-03 4.006576e-02
## 55992 1.221327e-02 9.115278e-03 1.205939e-02 3.550647e-02 6.473392e-02
## 78967 1.043203e-01 4.232826e-02 1.443492e-01 4.774713e-03 1.150796e-04
## 66596 2.492932e-01 4.197737e-03 2.385944e-02 1.254569e-01 9.121863e-04
## 60906 1.320799e-03 2.509007e-02 2.470496e-02 8.866088e-03 6.223990e-03
## 78715 6.420275e-03 5.097284e-02 3.162462e-01 1.552051e-03 1.336551e-02
## 46468 2.130616e-04 8.941751e-03 2.068658e-01 9.439536e-02 1.997139e-02
## 1204 4.819450e-02 1.073813e-01 5.866324e-03 1.233288e-01 6.844359e-03
## 53272 1.634705e-01 1.111211e-02 1.087066e-01 2.860839e-03 3.151622e-02
## 24111 3.427697e-02 5.429700e-02 1.088246e-01 6.040302e-02 8.714898e-03
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## 32102 1.844769e-02 2.712138e-02 3.095268e-02 3.202938e-02 5.542575e-02
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## 74984 7.397264e-02 3.163547e-02 4.860855e-02 2.101206e-02 8.680465e-02
## 53289 3.400920e-03 2.932744e-02 3.937704e-02 1.303149e-03 2.882585e-02
## 16436 1.256557e-02 8.977816e-02 1.928282e-02 9.283627e-02 4.388363e-03
## 26691 2.684273e-02 5.258803e-02 1.238513e-01 3.899023e-02 4.270832e-03
## 53705 4.514495e-02 2.739117e-04 4.517313e-03 1.437345e-02 7.679027e-02
## 42887 1.753502e-03 6.961984e-02 9.468438e-03 2.015106e-02 1.256745e-02
## 30237 8.950387e-02 2.258289e-03 4.483384e-04 4.309203e-02 2.592981e-01
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## 32076 2.014027e-02 1.704011e-02 2.180528e-03 1.278134e-01 6.322208e-02
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## 57943 6.308264e-03 9.761522e-02 4.934218e-03 1.182403e-04 8.236885e-04
## 74659 1.055478e-02 1.336137e-02 1.007915e-02 5.195672e-02 3.212158e-04
## 73799 1.262417e-04 7.893540e-04 2.190563e-03 6.040129e-03 7.892970e-02
## 20400 1.414701e-03 2.727781e-04 7.696495e-02 1.141096e-01 3.674303e-02
## 4972 2.430054e-03 7.406461e-03 4.820815e-02 3.102863e-03 1.750237e-02
## 69045 5.347011e-02 5.549222e-03 1.517239e-04 5.777570e-02 9.179197e-02
## 37492 6.226561e-02 8.331887e-04 8.155403e-03 9.943633e-02 7.316028e-02
## 6373 5.933808e-02 8.211775e-02 2.309206e-02 2.091396e-01 2.802301e-02
## 73396 3.064471e-02 1.335552e-02 3.751964e-02 2.022554e-02 1.041931e-01
## 602 7.693692e-03 2.178012e-04 8.003626e-04 3.209924e-03 1.460423e-02
## 74059 2.388221e-03 3.425080e-02 8.422794e-03 2.887176e-03 1.928340e-02
## 47606 5.104353e-02 1.888531e-02 5.706524e-03 9.958655e-03 1.893961e-02
## 28577 3.091150e-02 6.767673e-04 1.791048e-01 1.394296e-02 1.294812e-03
## 7877 1.872977e-01 4.478011e-02 1.965156e-02 4.507139e-04 2.295006e-02
## 58263 3.851953e-02 3.004996e-05 3.595150e-04 5.216162e-03 7.557282e-03
## 2777 1.186099e-04 1.448343e-01 2.774561e-03 1.432564e-02 5.096562e-05
## 30712 6.400206e-02 4.325041e-05 2.315650e-02 9.583753e-03 3.054455e-02
## 17749 1.451419e-03 3.889005e-03 1.466014e-02 7.554271e-02 1.487500e-02
## 7488 5.009202e-03 3.047616e-02 1.538222e-02 8.524827e-04 3.653372e-02
## 76662 8.006739e-02 1.026646e-01 3.282463e-04 6.775574e-02 3.575493e-02
## 45430 9.672693e-02 3.525103e-02 4.481537e-02 3.950428e-02 5.203428e-03
## 9739 1.007178e-03 2.773554e-03 3.728605e-02 2.463418e-03 1.763159e-04
## 61775 7.135792e-02 1.265961e-02 1.034365e-03 1.275214e-05 9.472156e-03
## 19512 2.750807e-02 1.353414e-01 2.737204e-04 4.030535e-02 5.974255e-02
## 49588 3.287762e-02 1.457056e-05 6.371983e-02 9.402875e-02 4.467880e-02
## 49753 6.662250e-03 2.031934e-02 5.451493e-03 7.412199e-03 9.505002e-05
## 52292 6.724297e-02 4.106862e-02 3.305204e-03 7.941220e-03 1.787373e-02
## 38245 1.637303e-02 2.662527e-02 2.709255e-03 5.163078e-03 8.175557e-04
## 4682 1.148348e-03 5.720172e-03 9.793052e-03 7.868349e-03 7.763955e-03
## 68957 1.924534e-02 2.332807e-02 2.308079e-03 6.543518e-02 1.026748e-02
## 37765 2.816698e-02 3.445167e-02 5.435082e-02 6.434282e-03 5.434688e-04
## 34827 2.986731e-02 1.509877e-01 1.082233e-02 1.838698e-03 5.379151e-03
## 2748 2.838734e-03 6.538392e-02 1.045261e-02 1.468070e-01 8.830957e-04
## 56138 5.429525e-02 2.242949e-02 2.081910e-02 3.656957e-03 3.622298e-02
## 44859 1.123494e-03 6.243996e-02 2.805419e-02 8.743071e-03 7.836493e-03
## 51442 1.457318e-01 1.204811e-02 7.032521e-04 2.085824e-02 1.245299e-03
## 78912 7.173620e-03 3.605806e-03 3.156737e-03 3.209918e-04 6.177997e-02
## 75936 5.713744e-03 1.091737e-04 1.211567e-01 2.419274e-03 1.715475e-02
## 57223 9.522485e-02 1.062458e-01 5.722133e-04 4.968109e-02 5.466355e-04
## 67089 4.153674e-02 7.925021e-02 3.614827e-02 9.956384e-02 4.481458e-02
## 27365 1.823575e-02 1.398608e-01 1.538320e-02 1.647362e-03 4.797741e-02
## 76141 7.477743e-03 1.632647e-01 5.897095e-03 1.563958e-01 2.684259e-02
## 19927 1.194931e-01 4.361094e-02 9.170981e-02 1.349307e-03 3.461404e-02
## 10216 6.653777e-06 3.261522e-03 1.073937e-01 4.779571e-03 2.181360e-02
## 2882 1.321861e-03 7.306842e-03 1.081714e-03 1.918864e-02 1.394517e-01
## 4015 1.031723e-01 1.540206e-02 1.310427e-02 1.493763e-02 9.240186e-03
## 27913 6.303963e-02 2.085420e-03 5.793242e-02 1.555501e-02 4.020924e-02
## 29465 1.220151e-03 5.361938e-03 2.458021e-02 3.367751e-02 1.482854e-01
## 9174 4.404578e-06 3.554367e-04 2.061927e-03 5.946350e-02 2.181959e-02
## 7657 5.733209e-02 8.468676e-04 1.162412e-01 8.910229e-03 7.410864e-03
## 31351 8.229280e-04 1.158563e-02 3.698046e-04 9.745243e-03 7.875044e-02res.mca$var## $coord
## Dim 1 Dim 2 Dim 3 Dim 4
## Argentina -0.10428170 -0.47346327 0.17750434 0.820309649
## Brazil 0.85729561 0.34499268 -0.39059545 0.016094594
## France 0.06820693 0.37849546 1.08193913 -0.738455704
## Germany -0.18301173 -0.65788846 0.01697491 -0.463439037
## Italy 0.91842327 -0.82206036 0.29977322 0.333434488
## Mexico -0.77970626 0.79975251 0.48062777 0.429144552
## Russia 0.34788025 0.22454015 -0.83943567 0.203867951
## Turkey -0.64749200 0.12910448 -0.69385140 -0.334205168
## Alzheimer's Disease -0.49180757 0.40915345 -0.60110664 1.044452505
## Asthma 0.77066300 -0.05940286 0.65126281 0.012709802
## Cancer -0.93034892 -0.36326222 -0.18445802 0.729097959
## Cholera -0.27274442 0.54164661 -0.01161417 0.142286156
## COVID-19 0.43073629 2.14860916 -1.05370890 0.346038987
## Dengue -0.80412634 -0.93696000 -0.02432929 -0.307937525
## Diabetes 0.75665254 -0.67527111 -0.03482246 0.324033557
## Ebola -0.13591992 -0.28321441 1.04830902 -0.676057376
## Hepatitis 0.32368300 0.89287418 -0.15188756 -1.432143526
## HIV/AIDS 0.54243383 0.56853130 -1.36539103 1.223252451
## Hypertension -0.50034914 -0.35881451 -0.92368394 -1.107281177
## Influenza -0.04123079 -0.85356122 -0.26979361 0.006489083
## Leprosy -0.71901564 -0.10462720 -0.29492752 -0.384669533
## Malaria -0.94939519 1.15305586 1.35336275 -0.028282241
## Measles 0.86960415 -0.21882173 1.14876815 0.977404005
## Parkinson's Disease -0.21286780 -0.43807018 -0.27461405 0.143809329
## Polio 0.47855038 -0.12413593 -0.97768131 -0.067178275
## Rabies 1.07192693 0.19632673 -0.64940631 -0.729835605
## Tuberculosis 0.18969769 0.06766947 0.85137910 0.121596232
## Zika 0.25695419 -0.22021506 0.58966606 0.382517607
## Autoimmune -0.07237169 -1.08990532 -0.33915453 -0.745684366
## Bacterial -0.73581281 0.88133575 1.03930880 -0.153975523
## Cardiovascular -0.28454997 0.96306671 -0.39497429 -0.403368372
## Chronic -0.67833847 -0.13773143 -0.12731589 -0.519296110
## Genetic 0.13496620 0.62823281 -0.91193874 0.129009407
## Infectious -0.53974019 -0.33808003 -0.38483971 0.159108222
## Metabolic 0.88551082 -0.14669812 1.24156417 0.903936553
## Neurological -0.54161650 0.32235236 0.14534642 0.578368898
## Parasitic 1.18242940 -0.09248342 0.28587937 -0.961072261
## Respiratory 0.85219467 -0.38485153 -0.38648677 0.607737344
## Viral -0.15551565 -0.87023502 0.28671095 0.484514678
## 0-18 0.34037672 -0.02622991 0.60881751 -0.050303706
## 19-35 -0.37003552 -0.24073295 -0.04577568 0.230327222
## 36-60 0.47366846 0.56337915 -0.52672877 -0.169634629
## 61+ -0.46547732 -0.33514923 0.01770234 0.019129826
## Female 0.19918854 0.16091196 0.10211031 -0.313034668
## Male 0.35738970 -0.07129590 0.05489199 0.649802082
## Other -0.52842188 -0.09133691 -0.15128905 -0.298684450
## Medication -0.44288095 -0.64130926 -0.18403499 0.446589350
## Surgery 0.76877060 -0.36404372 -0.24392290 -0.657885789
## Therapy -0.15889405 0.46718474 0.68912185 -0.497704774
## Vaccination -0.08736700 0.79407354 -0.16726008 0.608678491
## No -0.19071877 -0.25509888 0.11593936 -0.101845387
## Yes 0.18081130 0.24184699 -0.10991653 0.096554717
## Dim 5
## Argentina -0.28035817
## Brazil 0.54284942
## France -0.51980867
## Germany -0.07874922
## Italy 0.26365854
## Mexico 0.66275502
## Russia -0.75104662
## Turkey 0.05083762
## Alzheimer's Disease -0.86601062
## Asthma 1.54745731
## Cancer 0.87120658
## Cholera -0.36051269
## COVID-19 0.82689164
## Dengue 0.92706370
## Diabetes 0.35009157
## Ebola 0.08526292
## Hepatitis -0.19598590
## HIV/AIDS -0.14555928
## Hypertension 0.85079729
## Influenza 0.09804496
## Leprosy -0.60139117
## Malaria -0.34841420
## Measles 0.07652365
## Parkinson's Disease -0.91877699
## Polio -0.39953761
## Rabies -0.29682893
## Tuberculosis -0.97984092
## Zika -0.31090814
## Autoimmune -0.37384828
## Bacterial 0.22092522
## Cardiovascular 0.20095563
## Chronic 1.05888393
## Genetic 0.28072447
## Infectious -0.72351657
## Metabolic 0.23271511
## Neurological -0.97170439
## Parasitic 0.16433884
## Respiratory -0.13154883
## Viral 0.26646619
## 0-18 -0.48001522
## 19-35 -0.05499856
## 36-60 -0.15039346
## 61+ 0.62915593
## Female -0.44714742
## Male 0.44212436
## Other 0.02638787
## Medication -0.46640968
## Surgery 0.20855464
## Therapy -0.08090565
## Vaccination 0.47692524
## No 0.22245850
## Yes -0.21090222
##
## $contrib
## Dim 1 Dim 2 Dim 3 Dim 4
## Argentina 0.074266252 1.59860214 0.2295397837 5.1720338141
## Brazil 5.019223050 0.84876522 1.1114614088 0.0019909748
## France 0.033586596 1.07999775 9.0152830417 4.4308627068
## Germany 0.261411745 3.52747782 0.0023990932 1.8866132822
## Italy 5.431339466 4.54381997 0.6172649121 0.8056986684
## Mexico 4.507678054 4.95215909 1.8271462380 1.5368393625
## Russia 0.732029361 0.31845602 4.5468278800 0.2829419086
## Turkey 4.172018632 0.17320186 5.1106377772 1.2509333454
## Alzheimer's Disease 0.755122911 0.54574804 1.2033584752 3.8329666889
## Asthma 2.201860826 0.01366055 1.6774057968 0.0006740139
## Cancer 2.026656802 0.32264242 0.0849861765 1.4008469273
## Cholera 0.319331439 1.31508705 0.0006176909 0.0978105380
## COVID-19 0.289614410 7.52496696 1.8488567449 0.2103670528
## Dengue 2.144888634 3.04082055 0.0020944965 0.3540069773
## Diabetes 1.787393676 1.48653826 0.0040384144 0.3689246123
## Ebola 0.064885253 0.29417310 4.1174006983 1.8066635700
## Hepatitis 0.245317353 1.94922305 0.0576232322 5.4049540348
## HIV/AIDS 0.631529240 0.72443797 4.2685341429 3.6146191514
## Hypertension 0.830428971 0.44595257 3.0190317987 4.5772267476
## Influenza 0.005307262 2.37513938 0.2424126440 0.0001479532
## Leprosy 1.714876468 0.03791734 0.3077878655 0.5524108139
## Malaria 2.462234070 3.79252538 5.3373840690 0.0024591971
## Measles 2.065753472 0.13658679 3.8456060505 2.9370675524
## Parkinson's Disease 0.159147461 0.70381684 0.2825465387 0.0817493473
## Polio 0.402165765 0.02825773 1.7906434344 0.0089194458
## Rabies 3.587216592 0.12565466 1.4045104616 1.8715772057
## Tuberculosis 0.126387417 0.01679414 2.7157541833 0.0584454100
## Zika 0.128830489 0.09880839 0.7237427816 0.3213220515
## Autoimmune 0.029637580 7.01900106 0.6943288590 3.5411615831
## Bacterial 2.746725640 4.11486785 5.8456732832 0.1353674954
## Cardiovascular 0.426567829 5.10242078 0.8767452013 0.9647285260
## Chronic 2.424174649 0.10435894 0.0910963411 1.5989378166
## Genetic 0.117292775 2.65371903 5.7123820927 0.1206130693
## Infectious 1.705289097 0.69865062 0.9248110083 0.1667800773
## Metabolic 3.978032617 0.11400456 8.3422590766 4.6653788456
## Neurological 1.659927088 0.61398742 0.1275201103 2.1303233640
## Parasitic 7.093017534 0.04531071 0.4422938984 5.2737933023
## Respiratory 3.967737315 0.84497571 0.8705611342 2.2710544610
## Viral 0.089662127 2.93174694 0.3250978789 0.9795015962
## 0-18 1.627644075 0.01009313 5.5549320607 0.0400101449
## 19-35 1.816782706 0.80293386 0.0296585921 0.7922041674
## 36-60 3.502242193 5.17358152 4.6199369231 0.5055413535
## 61+ 3.382161059 1.83090662 0.0052182287 0.0064290927
## Female 0.781910955 0.53284119 0.2191959597 2.1734212987
## Male 2.392558454 0.09942604 0.0602089729 8.9016620090
## Other 5.611850050 0.17507719 0.4907092301 2.0179022434
## Medication 3.482752774 7.62563804 0.6415258605 3.9856190647
## Surgery 8.533604270 1.99819828 0.9164501976 7.0334823353
## Therapy 0.330063378 2.97955817 6.6227571217 3.6446521651
## Vaccination 0.101277041 8.73635216 0.3959729143 5.5325136482
## No 1.036208056 1.93584115 0.4084948683 0.3325624796
## Yes 0.982379066 1.83527798 0.3872743556 0.3152865066
## Dim 5
## Argentina 0.62874970
## Brazil 2.35727425
## France 2.28492569
## Germany 0.05669384
## Italy 0.52430130
## Mexico 3.81481047
## Russia 3.99649064
## Turkey 0.03012479
## Alzheimer's Disease 2.74251960
## Asthma 10.39858563
## Cancer 2.08164592
## Cholera 0.65350228
## COVID-19 1.25017412
## Dengue 3.33926893
## Diabetes 0.44819458
## Ebola 0.02990723
## Hepatitis 0.10534507
## HIV/AIDS 0.05326670
## Hypertension 2.81244760
## Influenza 0.03515231
## Leprosy 1.40522652
## Malaria 0.38842131
## Measles 0.01873711
## Parkinson's Disease 3.47277022
## Polio 0.32835313
## Rabies 0.32219274
## Tuberculosis 3.94972600
## Zika 0.22092634
## Autoimmune 0.92634358
## Bacterial 0.29003290
## Cardiovascular 0.24919965
## Chronic 6.91900750
## Genetic 0.59437127
## Infectious 3.58923209
## Metabolic 0.32181466
## Neurological 6.25820298
## Parasitic 0.16048599
## Respiratory 0.11074275
## Viral 0.30833381
## 0-18 3.79162899
## 19-35 0.04701049
## 36-60 0.41355301
## 61+ 7.23752191
## Female 4.61536596
## Male 4.28887555
## Other 0.01639189
## Medication 4.52438786
## Surgery 0.73562204
## Therapy 0.10023417
## Vaccination 3.53502794
## No 1.65133120
## Yes 1.56554776
##
## $cos2
## Dim 1 Dim 2 Dim 3 Dim 4
## Argentina 0.0014362776 0.0296070236 4.161406e-03 8.887463e-02
## Brazil 0.0970696296 0.0157196161 2.015007e-02 3.421230e-05
## France 0.0006544900 0.0201542822 1.646841e-01 7.671758e-02
## Germany 0.0051528144 0.0665872650 4.433042e-05 3.304242e-02
## Italy 0.1042529702 0.0835237699 1.110678e-02 1.374117e-02
## Mexico 0.0881747729 0.0927670039 3.350426e-02 2.671096e-02
## Russia 0.0139466201 0.0058102848 8.120528e-02 4.789689e-03
## Turkey 0.0858696401 0.0034139207 9.860610e-02 2.287690e-02
## Alzheimer's Disease 0.0136267428 0.0094313546 2.035657e-02 6.145809e-02
## Asthma 0.0401583902 0.0002385954 2.867872e-02 1.092257e-05
## Cancer 0.0360645463 0.0054983099 1.417698e-03 2.214933e-02
## Cholera 0.0058869403 0.0232172052 1.067467e-05 1.602150e-03
## COVID-19 0.0050831165 0.1264800358 3.041924e-02 3.280630e-03
## Dengue 0.0388428478 0.0527356849 3.555668e-05 5.696233e-03
## Diabetes 0.0322548204 0.0256896380 6.831571e-05 5.915366e-03
## Ebola 0.0011792059 0.0051198128 7.014586e-02 2.917363e-02
## Hepatitis 0.0043654451 0.0332176790 9.612429e-04 8.545979e-02
## HIV/AIDS 0.0111992353 0.0123027895 7.095924e-02 5.695437e-02
## Hypertension 0.0150386485 0.0077339698 5.125182e-02 7.365094e-02
## Influenza 0.0000957734 0.0410460146 4.100766e-03 2.372293e-06
## Leprosy 0.0310555451 0.0006575847 5.225082e-03 8.888696e-03
## Malaria 0.0441220877 0.0650822706 8.965829e-02 3.915522e-05
## Measles 0.0370173401 0.0023439206 6.459915e-02 4.676385e-02
## Parkinson's Disease 0.0028923001 0.0122492862 4.813588e-03 1.320072e-03
## Polio 0.0070827981 0.0004765896 2.956270e-02 1.395749e-04
## Rabies 0.0647339354 0.0021715033 2.375936e-02 3.000901e-02
## Tuberculosis 0.0022969285 0.0002922866 4.626679e-02 9.437645e-04
## Zika 0.0022767398 0.0016722302 1.198986e-02 5.045508e-03
## Autoimmune 0.0005604878 0.1271177658 1.230903e-02 5.950299e-02
## Bacterial 0.0513756665 0.0737064618 1.024972e-01 2.249708e-03
## Cardiovascular 0.0080078919 0.0917305204 1.542904e-02 1.609181e-02
## Chronic 0.0455086559 0.0018761487 1.603121e-03 2.667051e-02
## Genetic 0.0022514005 0.0487802376 1.027860e-01 2.057053e-03
## Infectious 0.0323688300 0.0126997893 1.645573e-02 2.812825e-03
## Metabolic 0.0744064402 0.0020420759 1.462720e-01 7.753516e-02
## Neurological 0.0313915292 0.0111196320 2.260671e-03 3.579634e-02
## Parasitic 0.1326701503 0.0008116160 7.755118e-03 8.764656e-02
## Respiratory 0.0747595638 0.0152466895 1.537653e-02 3.802078e-02
## Viral 0.0016352926 0.0512059461 5.558221e-03 1.587308e-02
## 0-18 0.0365862035 0.0002172657 1.170501e-01 7.990935e-04
## 19-35 0.0401335663 0.0169860352 6.141728e-04 1.554932e-02
## 36-60 0.0815861136 0.1154167523 1.008884e-01 1.046397e-02
## 61+ 0.0787887762 0.0408454559 1.139537e-04 1.330728e-04
## Female 0.0201371021 0.0131415002 5.291850e-03 4.973397e-02
## Male 0.0601070122 0.0023920498 1.417944e-03 1.987025e-01
## Other 0.1459931848 0.0043617783 1.196702e-02 4.664405e-02
## Medication 0.0854022104 0.1790730104 1.474674e-02 8.683840e-02
## Surgery 0.1935159699 0.0433940690 1.948177e-02 1.417178e-01
## Therapy 0.0072599590 0.0627619142 1.365560e-01 7.122993e-02
## Vaccination 0.0022372565 0.1848171978 8.199843e-03 1.085918e-01
## No 0.0344841078 0.0616948952 1.274365e-02 9.833653e-03
## Yes 0.0344841078 0.0616948952 1.274365e-02 9.833653e-03
## Dim 5
## Argentina 0.0103812248
## Brazil 0.0389207249
## France 0.0380130760
## Germany 0.0009540677
## Italy 0.0085918434
## Mexico 0.0637071762
## Russia 0.0650044670
## Turkey 0.0005293480
## Alzheimer's Disease 0.0422520789
## Asthma 0.1619140864
## Cancer 0.0316250378
## Cholera 0.0102853481
## COVID-19 0.0187328706
## Dengue 0.0516275651
## Diabetes 0.0069050200
## Ebola 0.0004640276
## Hepatitis 0.0016004364
## HIV/AIDS 0.0008064448
## Hypertension 0.0434825180
## Influenza 0.0005415670
## Leprosy 0.0217258403
## Malaria 0.0059422880
## Measles 0.0002866509
## Parkinson's Disease 0.0538819892
## Polio 0.0049370196
## Rabies 0.0049637979
## Tuberculosis 0.0612822274
## Zika 0.0033332369
## Autoimmune 0.0149561388
## Bacterial 0.0046314116
## Cardiovascular 0.0039939393
## Chronic 0.1108913921
## Genetic 0.0097400958
## Infectious 0.0581640259
## Metabolic 0.0051389211
## Neurological 0.1010408604
## Parasitic 0.0025627323
## Respiratory 0.0017814068
## Viral 0.0048009978
## 0-18 0.0727625096
## 19-35 0.0008865914
## 36-60 0.0082247969
## 61+ 0.1439407933
## Female 0.1014774973
## Male 0.0919877421
## Other 0.0003640655
## Medication 0.0947174992
## Surgery 0.0142417375
## Therapy 0.0018822468
## Vaccination 0.0666686325
## No 0.0469169906
## Yes 0.0469169906
##
## $v.test
## Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## Argentina -0.6553221 -2.9753151 1.11546423 5.15495050 -1.7618133
## Brazil 5.3873759 2.1679864 -2.45456117 0.10114087 3.4113482
## France 0.4423715 2.4548178 7.01716053 -4.78942119 -3.3713365
## Germany -1.2412459 -4.4620166 0.11512947 -3.14319647 -0.5341032
## Italy 5.5831566 -4.9973600 1.82234151 2.02697061 1.6027979
## Mexico -5.1346136 5.2666246 3.16508670 2.82605331 4.3644525
## Russia 2.0420674 1.3180573 -4.92751234 1.19671093 -4.4086660
## Turkey -5.0670526 1.0103278 -5.42984559 -2.61537622 0.3978380
## Alzheimer's Disease -2.0185133 1.6792781 -2.46710676 4.28671994 -3.5543455
## Asthma 3.4651636 -0.2670955 2.92829963 0.05714761 6.9578956
## Cancer -3.2837934 -1.2821836 -0.65106973 2.57345072 3.0750425
## Cholera -1.3267235 2.6347570 -0.05649536 0.69212922 -1.7536588
## COVID-19 1.2328227 6.1495960 -3.01585049 0.99040812 2.3666703
## Dengue -3.4079336 -3.9708903 -0.10310891 -1.30505691 3.9289492
## Diabetes 3.1055098 -2.7714981 -0.14292094 1.32992271 1.4368719
## Ebola -0.5937866 -1.2372647 4.57969563 -2.95345833 0.3724839
## Hepatitis 1.1424833 3.1515212 -0.53610785 -5.05494596 -0.6917590
## HIV/AIDS 1.8299102 1.9179505 -4.60617105 4.12666401 -0.4910468
## Hypertension -2.1205084 -1.5206765 -3.91462557 -4.69272122 3.6057278
## Influenza -0.1692225 -3.5032497 -1.10730706 0.02663298 0.4024034
## Leprosy -3.0472296 -0.4434161 -1.24991975 -1.63025157 -2.5487303
## Malaria -3.6321487 4.4113035 5.17762764 -0.10820078 -1.3329456
## Measles 3.3268881 -0.8371572 4.39489985 3.73930345 0.2927604
## Parkinson's Disease -0.9299450 -1.9137755 -1.19969278 0.62825268 -4.0138155
## Polio 1.4552514 -0.3774921 -2.97308733 -0.20428628 -1.2149769
## Rabies 4.3994825 0.8057788 -2.66534187 -2.99544578 -1.2182674
## Tuberculosis 0.8287229 0.2956243 3.71937765 0.53121143 -4.2805824
## Zika 0.8250729 -0.7071045 1.89340154 1.22825354 -0.9983175
## Autoimmune -0.4093725 -6.1650800 -1.91843711 -4.21798456 -2.1146833
## Bacterial -3.9193525 4.6944895 5.53594276 -0.82016017 1.1767719
## Cardiovascular -1.5473719 5.2371200 -2.14785509 -2.19350180 1.0927890
## Chronic -3.6887787 -0.7489783 -0.69233892 -2.82391241 5.7581704
## Genetic 0.8204686 3.8190694 -5.54373672 0.78425684 1.7065429
## Infectious -3.1109934 -1.9486500 -2.21816688 0.91707945 -4.1702570
## Metabolic 4.7167283 -0.7813966 6.61326854 4.81487410 1.2395715
## Neurological -3.0636689 1.8233952 0.82215613 3.27155992 -5.4964732
## Parasitic 6.2982835 -0.4926187 1.52275416 -5.11921099 0.8753610
## Respiratory 4.7279075 -2.1351253 -2.14419751 3.37167791 -0.7298223
## Viral -0.6992514 -3.9128734 1.28915017 2.17854318 1.1981228
## 0-18 3.3074575 -0.2548773 5.91590996 -0.48880360 -4.6643317
## 19-35 -3.4640924 -2.2536248 -0.42852965 2.15621135 -0.5148697
## 36-60 4.9390533 5.8744880 -5.49232573 -1.76882050 -1.5681882
## 61+ -4.8536424 -3.4946804 0.18458645 0.19947123 6.5603580
## Female 2.4537713 1.9822484 1.25788036 -3.85622335 -5.5083366
## Male 4.2393392 -0.8457085 0.65112614 7.70792047 5.2444575
## Other -6.6069632 -1.1420034 -1.89159694 -3.73451069 0.3299327
## Medication -5.0532426 -7.3172966 -2.09982714 5.09555519 -5.3217039
## Surgery 7.6066599 -3.6020587 -2.41351398 -6.50950162 2.0635599
## Therapy -1.4733390 4.3319525 6.38985579 -4.61494832 -0.7501945
## Vaccination -0.8178873 7.4337300 -1.56580743 5.69815178 4.4647420
## No -3.2110354 -4.2949707 1.95201228 -1.71471925 3.7454212
## Yes 3.2110354 4.2949707 -1.95201228 1.71471925 -3.7454212
##
## $eta2
## Dim 1 Dim 2 Dim 3 Dim 4
## Country 0.34562148 0.27881220 0.35968916 0.233268447
## Disease Name 0.37496024 0.40864830 0.52741888 0.417468449
## Disease Category 0.41406586 0.39661225 0.38839005 0.331623736
## Age Group 0.17645039 0.12789328 0.16350148 0.020403282
## Gender 0.15009924 0.01320802 0.01233281 0.198737473
## Treatment Type 0.21264762 0.34911478 0.13734957 0.306557667
## Availability of Vaccines/Treatment 0.03448411 0.06169490 0.01274365 0.009833653
## Dim 5
## Country 0.19971275
## Disease Name 0.49669947
## Disease Category 0.28772219
## Age Group 0.16757324
## Gender 0.13010414
## Treatment Type 0.12973425
## Availability of Vaccines/Treatment 0.04691699res.mca$svd## $vs
## [1] 4.940111e-01 4.834376e-01 4.783044e-01 4.656628e-01 4.564558e-01
## [6] 4.524736e-01 4.462852e-01 4.422445e-01 4.380890e-01 4.304036e-01
## [11] 4.291493e-01 4.260224e-01 4.206423e-01 4.128851e-01 4.041823e-01
## [16] 4.024977e-01 4.000248e-01 3.950787e-01 3.897709e-01 3.864924e-01
## [21] 3.835644e-01 3.806597e-01 3.721337e-01 3.696571e-01 3.676037e-01
## [26] 3.642053e-01 3.568982e-01 3.493964e-01 3.449490e-01 3.415481e-01
## [31] 3.349102e-01 3.324022e-01 3.216734e-01 3.195004e-01 3.157447e-01
## [36] 3.121981e-01 3.061957e-01 3.058797e-01 2.963023e-01 2.925179e-01
## [41] 2.832979e-01 2.726962e-01 2.704674e-01 2.632868e-01 2.473138e-01
## [46] 7.252612e-16 3.465971e-16 2.902302e-16 2.398825e-16 2.338419e-16
## [51] 1.950975e-16 1.369109e-16
##
## $U
## [,1] [,2] [,3] [,4] [,5]
## [1,] -0.381592643 0.82037938 -0.266371954 -0.796796813 -0.233151757
## [2,] 1.081685344 -1.16116756 -0.292638056 -0.491479562 -0.244540709
## [3,] -0.704817723 0.56665727 0.589415420 0.027064018 -0.474524387
## [4,] 0.088652848 0.66141329 -0.678632058 -0.695331218 -1.028677706
## [5,] 1.163892384 -0.38771566 -0.007818286 0.590024353 -0.075314508
## [6,] -0.408185001 0.51196140 -1.934897588 0.825983773 -1.168551754
## [7,] 0.502414571 -0.22005598 -0.064413689 0.322576729 -1.225037566
## [8,] 0.312453161 -0.54876484 -0.080065409 -1.007829807 2.904254332
## [9,] -0.417098023 0.54868020 1.773996374 1.257593646 0.332771546
## [10,] -0.845410331 0.02260729 0.991396925 0.287435511 -2.178510178
## [11,] 0.254339978 -0.56924550 1.865571369 0.416021166 0.265319106
## [12,] -0.407327431 0.33567873 -0.163761787 -0.721920321 0.165442451
## [13,] -1.825565773 0.82582037 -0.195601949 0.578297296 0.946463318
## [14,] 2.124562014 -0.15615255 0.180445662 -0.137833477 1.179886819
## [15,] -2.011846868 1.90508602 2.072477144 -0.285802739 0.616403673
## [16,] -0.086283576 -1.50204818 0.474376810 -0.730794335 -0.415168600
## [17,] 0.069749204 -1.01345210 -1.147451340 -0.369502030 0.459751088
## [18,] 1.168384324 -0.61595938 -1.094936289 0.657301740 -0.942084851
## [19,] 0.981439672 0.67151978 -1.402261126 2.155000048 0.721288464
## [20,] -0.166118130 -0.17820504 1.399567242 0.687413684 0.368525372
## [21,] 0.129092245 0.14452667 -0.522255574 -0.204232492 -0.194395044
## [22,] -1.067117521 1.32114505 0.890253276 0.510723457 -0.152695734
## [23,] -0.700087982 1.01688224 1.107893606 -1.479984398 -0.067853797
## [24,] 0.218565779 0.78348553 -0.803664200 -1.643266376 1.071800843
## [25,] -1.094254485 -1.56677597 -0.843328635 -0.123024983 0.131443935
## [26,] 0.294111127 0.26551515 -0.657894867 -0.366031739 -0.484909227
## [27,] 0.141344026 0.11021751 -0.633605060 2.534213779 0.040718001
## [28,] -0.277242649 -0.23746386 -0.789232714 2.525874823 -0.688158302
## [29,] 0.666021820 -0.39477069 -0.773545239 0.194081400 -1.023266520
## [30,] -0.001400239 -2.07679850 -0.344153830 -1.028447592 1.007958472
## [31,] -0.201202599 0.41396762 -1.389305481 -0.460146304 0.430674706
## [32,] -1.600934176 -1.09313828 -0.354893524 0.625803176 0.571509888
## [33,] 0.669519903 -0.87630138 1.114782488 1.364759777 -0.295968574
## [34,] 0.890691364 -0.93644265 0.419672934 1.293146022 -0.572724276
## [35,] 0.005572309 -1.61900251 -0.643010088 -0.085097899 0.169034677
## [36,] 0.265422522 0.67361658 1.910841936 -0.071820123 0.362178382
## [37,] -0.509258174 0.15600594 -1.823040786 0.704197212 0.085062248
## [38,] 0.290131633 1.21675767 0.492371309 -0.227474474 -1.336058677
## [39,] -0.948807379 0.98410645 0.239632934 -0.653691086 0.071071690
## [40,] -0.184961775 -0.92604873 0.234920794 -1.786192272 -1.073857151
## [41,] 1.385001856 0.91120560 -0.488616278 2.456462233 0.879798834
## [42,] -1.533957315 -0.24380504 -0.987446157 0.291177398 -1.192429417
## [43,] -0.983317841 -1.32360920 0.557434996 0.233349574 -0.672320428
## [44,] 1.314715413 1.52330281 -0.868047695 -0.536486881 0.033721094
## [45,] -1.890200849 0.48967601 1.308900946 -0.080821253 0.463259060
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## [83,] -1.241038130 -1.46105412 -1.189548158 -1.407432541 0.319407453
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## [108,] 1.976079642 -0.45114450 0.267061729 -0.534866353 2.106864854
## [109,] 0.405909429 -1.00336842 -0.068744202 0.886422670 -0.668169237
## [110,] -1.495953070 0.35277843 -0.810313750 0.263423279 -1.274703631
## [111,] -0.238858104 0.42253827 -0.548398623 1.321895176 -2.875106234
## [112,] 1.357018998 -0.56837128 -0.256401913 -0.736718814 -0.666134035
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## [116,] -0.133534222 -2.08549238 0.131060777 1.034317154 0.466918351
## [117,] 1.305058906 1.81852642 -1.569438117 0.319216962 -0.144901942
## [118,] -0.553453715 1.19931161 0.166964774 -0.287870718 1.641864242
## [119,] 1.119874596 -1.54959506 0.355592196 0.915786430 0.009677055
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## [121,] -0.534954139 2.85272530 -1.038728210 0.062436841 0.850670801
## [122,] -0.586122305 0.33879720 -0.979371573 1.030341510 -0.160370758
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## [130,] 0.703736036 1.06801663 -0.393424676 0.067119370 0.657752268
## [131,] -1.401178996 -0.34713418 -0.440728641 1.783209368 -1.467189248
## [132,] 0.642074516 0.66664329 -0.585016100 0.824557815 0.247401299
## [133,] 1.092996357 -0.19165755 -0.123679176 -0.664215665 -0.033021496
## [134,] -0.639238651 -1.07808321 -0.160502396 0.869101058 -1.130361833
## [135,] 0.072523827 -0.87448616 -1.036921749 -0.300915191 -0.519849904
## [136,] -0.195547086 -0.34064494 -0.855975685 0.125677346 -0.406861909
## [137,] -1.348383729 -0.32392832 -0.672838755 -1.433456473 0.343978350
## [138,] -0.261133755 -0.56527741 -0.749656686 0.779832438 -2.192379531
## [139,] 0.292976716 -0.42160877 -0.201234401 1.753624525 0.274710287
## [140,] -1.276710253 -1.33611628 0.036829234 1.123986350 0.394081527
## [141,] -1.098117979 2.16287390 0.938787154 0.661044758 -0.380894602
## [142,] 0.913854186 -0.17271650 1.660178889 1.459876077 1.093707660
## [143,] -0.928201269 1.12997554 0.089143521 0.341903324 0.868143187
## [144,] -0.010357098 0.03275861 0.235036143 -0.324010182 -0.340896829
## [145,] 0.226791780 1.60620520 -0.609935089 -0.673695967 0.879730275
## [146,] 1.027109188 -0.83619539 -1.186221481 -1.392780024 -1.023582103
## [147,] 0.800018632 1.22064405 -0.408449796 -0.715873507 -1.283318102
## [148,] -1.387215655 0.44006509 0.812190644 0.002978489 0.348527849
## [149,] 0.788514416 2.77345729 -1.361755630 0.148598092 0.189736514
## [150,] 0.925959729 -0.58031524 2.024100334 0.546214835 0.933248879
## [151,] -0.617737128 -1.01197097 -0.686345845 -1.252162425 1.248429422
## [152,] -0.570393091 -0.64603634 1.096016646 -1.674832348 -0.488204672
## [153,] -1.677733226 1.64642819 0.460305089 0.498816678 1.337019028
## [154,] 1.491375135 -0.48166184 1.680569925 -0.795845909 -0.458373319
## [155,] 0.153268765 -1.05653606 0.373571754 -1.896719343 -0.503199070
## [156,] -1.023647668 0.24515720 0.235698870 1.442636581 1.069454339
## [157,] -1.033718254 1.85949149 2.566557365 -0.925342722 -0.988198842
## [158,] -0.478425182 -0.69887204 -0.831717038 -0.822688264 0.208780471
## [159,] -1.297957955 -0.97997125 -0.132652801 0.344994029 -0.116087832
## [160,] -1.105557992 1.11671372 -0.453446332 1.485764462 -0.797342884
## [161,] 0.415902750 3.12020113 -1.395027818 1.376641180 1.596286643
## [162,] -0.834324992 1.64521574 -1.241409257 -0.486372690 1.340829580
## [163,] 0.873151487 1.28369114 -0.078747575 -1.571603914 -0.440154486
## [164,] -0.532532078 0.38567589 0.978147669 -0.506958895 1.085478203
## [165,] -0.600649690 -0.53025717 -0.616453595 1.086486420 1.496612383
## [166,] 1.630617616 -1.06140019 1.981104033 -0.370089323 0.058614488
## [167,] 2.553700691 0.33862470 -0.815975727 -1.921884025 0.167183940
## [168,] 0.184837313 -0.82322477 0.825649394 -0.508045313 0.434253879
## [169,] 0.423809618 1.22028055 3.072125814 0.221061031 -0.661797015
## [170,] -0.069100545 0.45744238 -2.223850655 -1.543012282 0.724054155
## [171,] -1.127726702 1.72014754 0.406368950 1.913827000 0.459948888
## [172,] 2.038705114 -0.54316169 1.717098129 0.286118924 -0.968812687
## [173,] 0.995919406 1.28087622 -1.832816490 -1.402547301 -0.543490782
## [174,] 0.767581265 1.72157268 -1.138509582 0.803621523 1.247413299
## [175,] 2.863788066 0.50044660 -0.986495173 -1.156851788 0.479780867
## [176,] -0.800397726 0.04578688 -0.438905599 -0.940606162 1.729457263
## [177,] 0.745315737 0.18497257 0.604960678 1.131561819 -0.020083522
## [178,] -0.547548205 0.57215201 -0.060306424 1.064272822 -0.809633255
## [179,] 1.971964943 -1.47362399 -0.251514101 0.904415851 1.063540144
## [180,] -0.981855181 -1.50995479 -0.540844190 -0.115411245 -0.274327565
## [181,] -0.441243294 1.27995825 -0.735302480 0.102585641 -0.020028908
## [182,] -0.314907020 1.36811313 0.896659997 -0.432552916 1.577940988
## [183,] 0.354294436 -0.22718731 1.654527648 2.320691752 0.936576475
## [184,] -0.115001541 -0.31820419 0.613463397 -0.650030075 0.435658734
## [185,] 0.128859299 -0.71892851 0.670198912 1.781744207 1.796463000
## [186,] -0.515166024 1.08838649 0.849167221 -1.456368097 0.891840214
## [187,] -1.155063717 -0.39740199 -1.184081844 0.279209838 0.382251739
## [188,] -1.747294680 -0.52688078 -0.199308045 0.828378693 2.060154980
## [189,] 0.130828893 0.78338315 -0.861771087 0.227319274 -0.448729791
## [190,] 1.055661689 0.08543501 0.964526434 -0.426445802 -0.740686352
## [191,] 1.073491218 0.28749695 0.334038390 0.849780422 -1.901333890
## [192,] -0.433750918 0.96573189 0.942278848 -0.452727347 1.238855475
## [193,] -1.096819016 0.51836968 -1.104879703 -0.201203861 0.871743060
## [194,] -0.272107245 0.54710840 0.112106184 -0.287353389 -1.599301559
## [195,] -0.884731483 0.14279395 -0.057617300 1.019024533 -0.979804742
## [196,] 0.054121815 -0.18746034 -1.392471663 -0.522235205 1.358532782
## [197,] 1.243937829 -1.33430881 1.702618811 1.689270246 -0.066080889
## [198,] 1.991278592 -1.01804017 0.727941177 0.664247614 -0.169590571
## [199,] 0.704758787 -0.93539865 1.312422694 -0.323832025 -0.145943628
## [200,] 0.013861537 0.97081406 1.691833712 -0.325615470 -1.417512615
## [201,] 1.473843632 0.54879920 1.361342507 1.484742758 0.455605544
## [202,] -1.172960752 -1.36101127 -0.398312032 -1.966088878 0.350466167
## [203,] -1.176168723 -1.29867276 -0.350464119 1.198781440 -1.002487724
## [204,] -0.470735853 0.55060582 1.835902037 -0.474834734 -1.941733886
## [205,] -1.408322285 0.89916677 2.293104444 -1.050294998 -0.355382820
## [206,] 0.057400866 -1.41881826 -0.602414006 -0.505574143 0.975885197
## [207,] 0.391382321 0.15256412 -1.412867247 1.041258720 0.587642254
## [208,] -0.306657211 1.01089116 -0.102847483 1.031119330 -1.035838643
## [209,] -0.537581509 -1.65215155 -0.270519422 -0.573926048 -1.715719696
## [210,] 1.544500496 0.59219433 -0.903730626 -0.907687730 -0.611747856
## [211,] -0.217637949 -0.85248597 -0.358444349 1.202868842 -0.128652826
## [212,] -1.717487402 -0.74272826 -0.679569133 0.437685609 -1.448240772
## [213,] 1.019830955 0.36322325 -1.438677956 -0.821855331 -0.904980738
## [214,] 1.377161534 -0.62415105 1.066706877 -1.993051357 -0.520176145
## [215,] 1.203677756 0.75373919 -1.169287918 -1.217303941 0.202523250
## [216,] 1.387623245 -0.13904612 2.379599478 1.910542499 -0.206256946
## [217,] -0.743491898 0.72460325 -1.166574094 0.888792073 -2.099285401
## [218,] -1.180748639 0.20648629 -1.658459250 0.362916461 0.355049506
## [219,] 2.151954247 0.30948559 -1.535098362 -0.748177927 -0.008146596
## [220,] -0.252495796 -1.49591471 0.018651854 -0.241235215 -1.332199355
## [221,] 1.274682722 -0.51038810 -0.568052662 -0.263793848 -0.349549009
## [222,] 0.354146153 1.07619585 -0.092886121 1.585730259 -0.196500820
## [223,] -0.700491021 0.91808489 2.087083831 -1.475723075 -1.155932214
## [224,] -0.404477046 -0.80716947 -0.158346882 0.065173973 -0.117161392
## [225,] -0.797777681 -0.04569959 -0.515440638 0.389832051 -2.368988002
## [226,] -0.628383693 0.86023665 1.576173947 -0.290696138 1.316006554
## [227,] -0.032294346 -1.69586146 -0.661978189 -0.258263093 -0.957805222
## [228,] -0.203917437 1.51293448 0.723948205 -0.637518882 -0.205430978
## [229,] -0.897173773 -0.14184010 0.440809936 0.150372305 -0.713645831
## [230,] -0.690090670 -0.85504161 -0.923244270 -0.964660660 1.294580110
## [231,] -1.215979735 -0.61212640 -0.443333618 1.051781104 -1.419514905
## [232,] -1.482172614 0.99048213 1.240943210 0.838036070 1.737691794
## [233,] 0.312299192 -0.93714334 -1.097555692 -0.205085453 0.984015051
## [234,] 0.558612922 -1.52581376 -0.714722704 -1.610808709 -0.357280419
## [235,] 0.885917090 1.26712501 -1.965451470 1.132720732 -0.382449412
## [236,] -1.119335218 -0.08909578 0.365702583 0.670040818 1.579962191
## [237,] 0.200831789 -1.29312988 -0.482004303 -0.722259880 -0.581890220
## [238,] -1.624140983 0.26362634 0.118723895 1.195546565 2.991854755
## [239,] 0.698098397 -0.68133088 -0.186764990 -0.271770919 2.229556667
## [240,] -0.494270170 1.49935895 1.688832832 0.182741871 0.231149554
## [241,] 0.714295343 -0.67139433 0.242749551 -1.908967872 1.369675368
## [242,] 0.856834327 1.13621983 -0.320628328 1.158978606 -0.837889522
## [243,] 1.793333898 0.93228902 -0.709919146 0.104679385 0.682165207
## [244,] -1.103299677 1.15569443 0.357278928 1.671239346 0.285949991
## [245,] 0.050173446 -0.47461355 -1.175552687 -1.997668803 0.709661792
## [246,] -0.866817514 -1.25314699 -1.579725212 -0.912783982 -0.202861302
## [247,] -0.437492223 -1.52401247 -0.059468247 1.892035592 -0.838454615
## [248,] 1.123701633 -0.08637388 -0.946379749 -0.873762251 -0.772781036
## [249,] -0.286582119 0.15368272 -2.082816385 -1.673719876 1.021189362
## [250,] -0.388275246 -1.56077319 -0.354671501 0.056393978 0.151846334
## [251,] -0.476945455 0.54836021 -0.481380442 -1.122613415 -0.090049357
## [252,] -0.056828952 -0.14521130 -0.244499795 0.417019699 1.537894310
## [253,] 0.207256864 0.09299886 1.578902999 1.974707897 1.143147067
## [254,] 0.266681478 0.47575842 -1.226810516 0.319691734 -0.774589087
## [255,] -1.179073819 0.38814837 0.064870139 1.300239310 -1.671959914
## [256,] 1.292916924 -0.15283205 0.483283056 1.733341954 1.516778683
## [257,] 1.328253316 1.59672069 0.855811109 -2.645437866 -0.987892342
## [258,] 0.858506186 -0.57915162 0.981131871 -0.739913544 -1.713259628
## [259,] -0.438562578 0.07540333 0.146096536 -0.300522308 -0.653945391
## [260,] -0.233136202 -0.90220248 -0.452202426 -0.271940736 -0.716972170
## [261,] 1.138981529 -0.70795343 -0.393336742 0.533718036 -0.750878812
## [262,] -0.886054427 0.13397269 2.202857164 -0.631310476 -0.196264508
## [263,] 2.141799486 -1.07016669 0.716545024 -0.111462368 0.811414130
## [264,] 1.024719723 -0.02924710 -0.102248152 -0.400041778 -0.491230626
## [265,] 0.054953623 -1.96231150 -0.274514596 -0.640705134 0.038986354
## [266,] -1.262895079 0.03354762 -0.784583465 -0.518445742 0.944224389
## [267,] -0.203675309 -0.34068854 -0.668564642 -1.558847097 -0.705681218
## [268,] 0.408363108 -1.02929203 -0.739101607 0.178718787 -1.193568073
## [269,] 1.735469903 2.00814861 0.114768194 1.693664978 1.255148937
## [270,] 1.569110596 -0.96797034 1.103127495 -1.063817532 -0.393878862
## [271,] -0.160152498 0.27157829 -1.006435818 0.265714205 -0.072521088
## [272,] -1.326657673 0.57101058 -0.164970759 0.018814582 -0.523118680
## [273,] -0.791208263 1.79338045 -0.081516723 1.016031927 1.261945782
## [274,] 0.995788406 0.02142157 -1.431813917 1.786538799 -1.256335609
## [275,] 0.433973413 -0.77446835 0.405455087 -0.485614205 0.056100506
## [276,] 1.430758706 1.14260006 -0.327623053 -0.521617027 0.798341242
## [277,] -0.654241933 0.85254468 -0.274872400 0.389756533 -0.158223344
## [278,] 0.165934469 -0.37844319 -0.500485239 0.460794099 -0.466959731
## [279,] -0.720381473 -0.81046714 -0.257666188 1.409193380 -0.569468566
## [280,] -0.865873935 -0.97855642 1.242281179 -0.439035616 -0.130169680
## [281,] -0.862745464 -1.98221912 -0.536385856 0.227093659 -0.396259338
## [282,] 0.272361401 -1.33571851 0.539793858 2.077886428 -0.164408824
## [283,] -1.143419856 -0.75098414 0.731287485 -0.314811067 -1.010776071
## [284,] 0.158391421 1.20662922 -0.817480664 0.468752378 0.452736173
## [285,] 1.841110214 -0.54095148 0.132096271 0.738935820 -0.184194877
## [286,] 0.483882803 -0.35056491 -0.331529795 -0.108588339 -1.536855038
## [287,] 0.362756311 0.05124009 1.725284002 -0.250416159 -0.680275444
## [288,] -1.453632548 -1.56903121 -0.116383341 -1.113884771 0.119197310
## [289,] 0.979124172 1.38203177 0.943403929 -1.608190850 -1.100700676
## [290,] 0.722694274 -2.04520604 0.685564693 0.230437131 1.268671651
## [291,] 0.469531475 2.24192890 0.430656384 -2.278018031 -0.962785923
## [292,] 1.778523505 1.09794918 -1.609268542 0.200497439 -1.035982523
## [293,] 0.014660575 0.33168305 1.923705983 0.416846870 -0.908486751
## [294,] 0.183266593 0.44030282 0.171229643 0.740760194 -2.037231421
## [295,] -1.591399879 -0.62832346 -0.585782611 -0.642397320 -0.515437569
## [296,] 1.235561385 0.22964161 1.223349981 0.651115140 1.067968578
## [297,] 0.173691345 0.37207335 0.805186602 -0.968069598 -2.072328455
## [298,] 0.010401325 0.09548033 -0.232437143 1.282115071 0.792315026
## [299,] -1.123843619 0.13957608 -1.652796817 -0.470020316 -0.437299975
## [300,] -0.146923057 -0.56333288 -0.101724971 -0.536377538 1.555512656
##
## $V
## [,1] [,2] [,3] [,4] [,5]
## [1,] -0.21109181 -0.97936780 0.37111167 1.76159595 -0.61420666
## [2,] 1.73537714 0.71362394 -0.81662528 0.03456277 1.18927060
## [3,] 0.13806760 0.78292509 2.26203056 -1.58581650 -1.13879310
## [4,] -0.37046075 -1.36085482 0.03548977 -0.99522458 -0.17252323
## [5,] 1.85911456 -1.70044753 0.62674153 0.71604283 0.57762123
## [6,] -1.57831722 1.65430333 1.00485756 0.92157797 1.45195893
## [7,] 0.70419518 0.46446558 -1.75502399 0.43780170 -1.64538753
## [8,] -1.31068304 0.26705507 -1.45064822 -0.71769785 0.11137470
## [9,] -0.99553946 0.84634171 -1.25674500 2.24293753 -1.89724985
## [10,] 1.56001143 -0.12287596 1.36160744 0.02729401 3.39015834
## [11,] -1.88325500 -0.75141483 -0.38564985 1.56572096 1.90863311
## [12,] -0.55210178 1.12040634 -0.02428197 0.30555622 -0.78980861
## [13,] 0.87191618 4.44443903 -2.20300907 0.74311070 1.81154825
## [14,] -1.62774946 -1.93811964 -0.05086570 -0.66128869 2.03100450
## [15,] 1.53165081 -1.39681118 -0.07280398 0.69585455 0.76697809
## [16,] -0.27513535 -0.58583440 2.19171945 -1.45181754 0.18679339
## [17,] 0.65521399 1.84692727 -0.31755418 -3.07549501 -0.42936451
## [18,] 1.09801946 1.17601783 -2.85464879 2.62690627 -0.31889023
## [19,] -1.01282971 -0.74221465 -1.93116344 -2.37786065 1.86392059
## [20,] -0.08346125 -1.76560767 -0.56406258 0.01393516 0.21479619
## [21,] -1.45546447 -0.21642336 -0.61661053 -0.82606890 -1.31752346
## [22,] -1.92180933 2.38511804 2.82950103 -0.06073546 -0.76330332
## [23,] 1.76029265 -0.45263692 2.40175125 2.09895244 0.16764746
## [24,] -0.43089678 -0.90615653 -0.57414077 0.30882720 -2.01285003
## [25,] 0.96870366 -0.25677755 -2.04405675 -0.14426379 -0.87530412
## [26,] 2.16984372 0.40610558 -1.35772603 -1.56730504 -0.65029068
## [27,] 0.38399477 0.13997559 1.77999435 0.26112509 -2.14662844
## [28,] 0.52013847 -0.45551906 1.23282596 0.82144769 -0.68113531
## [29,] -0.14649811 -2.25448994 -0.70907677 -1.60133988 -0.81902413
## [30,] -1.48946607 1.82305982 2.17290252 -0.33065886 0.48400139
## [31,] -0.57599911 1.99212186 -0.82578020 -0.86622423 0.44025214
## [32,] -1.37312387 -0.28490010 -0.26618172 -1.11517635 2.31979531
## [33,] 0.27320479 1.29951156 -1.90660752 0.27704471 0.61500916
## [34,] -1.09256687 -0.69932498 -0.80459164 0.34168122 -1.58507491
## [35,] 1.79249166 -0.30344786 2.59576164 1.94118279 0.50983059
## [36,] -1.09636498 0.66679201 0.30387851 1.24203380 -2.12880299
## [37,] 2.39352788 -0.19130373 0.59769338 -2.06388039 0.36003236
## [38,] 1.72505159 -0.79607272 -0.80803518 1.30510185 -0.28819623
## [39,] -0.31480192 -1.80009774 0.59943199 1.04048404 0.58377221
## [40,] 0.68900618 -0.05425706 1.27286626 -0.10802604 -1.05161390
## [41,] -0.74904289 -0.49796071 -0.09570408 0.49462237 -0.12049044
## [42,] 0.95882145 1.16536052 -1.10124177 -0.36428643 -0.32948090
## [43,] -0.94224056 -0.69326257 0.03701061 0.04108086 1.37835028
## [44,] 0.40320658 0.33284946 0.21348395 -0.67223469 -0.97960734
## [45,] 0.72344465 -0.14747694 0.11476373 1.39543490 0.96860287
## [46,] -1.06965583 -0.18893213 -0.31630288 -0.64141793 0.05781035
## [47,] -0.89649996 -1.32656045 -0.38476542 0.95904027 -1.02180698
## [48,] 1.55618074 -0.75303139 -0.50997422 -1.41279448 0.45690000
## [49,] -0.32164064 0.96638055 1.44076006 -1.06880946 -0.17724751
## [50,] -0.17685229 1.64255627 -0.34969380 1.30712294 1.04484439
## [51,] -0.38606168 -0.52767690 0.24239660 -0.21871061 0.48736048
## [52,] 0.36600653 0.50026511 -0.22980457 0.20734902 -0.462043066.6 K-means y Clusters
#1 nuevo datap con id
DATAP_muestra$id <- seq_len(nrow(DATAP_muestra))
#2 data frame para categoricas
df_cat <- DATAP_muestra %>%
select(all_of(vars_cuali), id)
#3 renombrar coordenadas con el id
coords <- as.data.frame(res.mca$ind$coord)
colnames(coords) <- paste0("Dim.", seq_len(ncol(coords)))
coords$id <- df_cat$id
# 4. Determinar número de clusters sugerido (opcional): NbClust y silhouette
set.seed(123)
nc <- NbClust(coords %>% select(-id),
distance = "euclidean",
min.nc = 2, max.nc = 6,
method = "ward.D2",
index = "silhouette")
nc$Best.nc## Number_clusters Value_Index
## 2.0000 0.1329# 5. K-means sobre primeras 5 dimensiones del ACM
k_try <- 2
kres <- kmeans(coords %>% select(Dim.1:Dim.5), centers = k_try, nstart = 50)
coords$kcluster <- factor(kres$cluster)
# 6. Clustering jerárquico (Ward) sobre las mismas dimensiones
d <- dist(coords %>% select(Dim.1:Dim.5))
hc <- hclust(d, method = "ward.D2")
# cortar en k_try clusters
coords$hcluster <- factor(cutree(hc, k = k_try))
# 7. Visualizaciones: MCA y clusters
# plot de variables y individuos
fviz_mca_ind(res.mca, label = "none", habillage = coords$kcluster,
addEllipses = TRUE, ellipse.level = 0.68) + ggtitle("MCA: individuos (coloreados por k-means)")
Este es uno de los gráficos más interpretables.
Interpretación clave:
El algoritmo k-means identificó dos conglomerados principales.
Grupo 1 (rojo) tiende a estar más hacia la derecha (valores positivos en Dim1). Estas coordenadas suelen asociarse con enfermedades virales, metabólicas o respiratorias y ciertos países.
Grupo 2 (turquesa) está hacia la izquierda → asociado a categorías como Bacterial, Neurological, Chronic, Mexico, Turkey, etc.
La forma de las elipses indica que los grupos están moderadamente diferenciados, pero con solapamiento.
Este análisis confirma que, aunque la varianza explicada es baja, existe cierta estructura latente entre combinaciones de enfermedades y categorías demográficas.
# Dendrograma
fviz_dend(hc, k = k_try, rect = TRUE, show_labels = FALSE) + ggtitle("Dendrograma (Ward)")## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## ℹ The deprecated feature was likely used in the factoextra package.
## Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the factoextra package.
## Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## ℹ The deprecated feature was likely used in the factoextra package.
## Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
El dendrograma obtenido con el método de Ward muestra la estructura
jerárquica de los datos y permite identificar dos grupos principales,
los cuales están coloreados en rojo y turquesa.
Interpretación clave:
Se observan dos clústeres grandes, claramente separados a una altura aproximada de 7.5–8. Esto indica una división natural en dos grupos dentro de los datos.
El método de Ward agrupa individuos minimizando la varianza interna, por lo que los grupos obtenidos son relativamente compactos.
El grupo rojo presenta una estructura más ramificada y heterogénea, lo que indica que sus individuos tienen mayor variabilidad interna.
El grupo turquesa muestra ramas más largas y compactas, lo que sugiere que sus individuos son más similares entre sí.
La altura a la que se unen los dos grandes clústeres confirma que las diferencias entre ambos son moderadas, no extremadamente altas.
En conjunto, este dendrograma respalda la idea de que dos grupos es una elección razonable, aunque no necesariamente perfecta.
# 8. Caracterizar clusters: perfiles por cluster usando proporciones
df_cat2 <- df_cat %>%
left_join(coords %>% select(id, kcluster), by = "id")
profile_k2 <- df_cat2 %>%
pivot_longer(cols = -c(id, kcluster),
names_to = "var",
values_to = "cat") %>%
group_by(kcluster, var, cat) %>%
summarise(n = n(), .groups = "drop") %>%
group_by(kcluster, var) %>%
mutate(freq = n / sum(n)) %>%
arrange(kcluster, var, desc(freq))
# Top categorías por variable en cada cluster
profile_k2 %>%
group_by(kcluster, var) %>%
slice_max(freq, n = 1)## # A tibble: 16 × 5
## # Groups: kcluster, var [14]
## kcluster var cat n freq
## <fct> <chr> <fct> <int> <dbl>
## 1 1 Age Group 36-60 47 0.379
## 2 1 Availability of Vaccines/Treatment Yes 76 0.613
## 3 1 Country Brazil 26 0.210
## 4 1 Disease Category Metabolic 23 0.185
## 5 1 Disease Category Respiratory 23 0.185
## 6 1 Disease Name Asthma 14 0.113
## 7 1 Disease Name Rabies 14 0.113
## 8 1 Gender Male 61 0.492
## 9 1 Treatment Type Surgery 45 0.363
## 10 2 Age Group 61+ 60 0.341
## 11 2 Availability of Vaccines/Treatment No 98 0.557
## 12 2 Country Turkey 44 0.25
## 13 2 Disease Category Neurological 25 0.142
## 14 2 Disease Name Leprosy 16 0.0909
## 15 2 Gender Other 85 0.483
## 16 2 Treatment Type Medication 70 0.398# 9. Guardar coordenadas y asignaciones
write.csv(coords, "mca_coords_clusters.csv", row.names = FALSE)
# 10. Evaluación de clustering: silhouette (k-means)
sil <- silhouette(as.integer(coords$kcluster), dist(coords %>% select(Dim.1:Dim.5)))
summary(sil)## Silhouette of 300 units in 2 clusters from silhouette.default(x = as.integer(coords$kcluster), dist = dist(coords %>% select(Dim.1:Dim.5))) :
## Cluster sizes and average silhouette widths:
## 124 176
## 0.1366010 0.1656956
## Individual silhouette widths:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.02065 0.08444 0.15931 0.15367 0.22077 0.33637fviz_silhouette(sil)## cluster size ave.sil.width
## 1 1 124 0.14
## 2 2 176 0.17
Este gráfico evalúa la calidad de los clústeres obtenidos, midiendo qué
tan bien se asignó cada observación a su grupo.
Interpretación clave:
- El ancho promedio de silueta es 0.15, lo cual indica una calidad baja a moderada de los clústeres.
– Valores cercanos a 1 → muy buena separación
– Valores cerca de 0 → clústeres que se solapan
– Valores negativos → mala asignación
Ambos clústeres (1 y 2) tienen valores mayoritariamente positivos, por lo que la asignación no es mala, pero tampoco óptima.
El clúster 1 (rojo) muestra mayor dispersión y varias observaciones cerca de 0, lo que indica que este grupo es más heterogéneo.
El clúster 2 (turquesa) presenta siluetas ligeramente más altas y más consistentes, indicando un grupo más definido.
La línea roja punteada representa el promedio global de calidad (0.15), confirmando que los clústeres existen, pero no están fuertemente separados.
En resumen, el análisis silhouette indica que dos grupos son válidos, pero la estructura de los datos no muestra clústeres muy marcados, lo cual es coherente con los resultados del MCA.
7 Conclusiones
El estudio estadístico realizado permitió analizar el comportamiento de múltiples variables relacionadas con indicadores de salud, condiciones socioeconómicas y características demográficas, aplicando técnicas desde lo descriptivo hasta lo multivariado. La primera aproximación, mediante el análisis univariado, reveló que las variables poseen distribuciones muy diversas entre sí, con rangos amplios en tasas de mortalidad, prevalencia, acceso a servicios de salud y otros indicadores. Sin embargo, esta heterogeneidad no se tradujo en relaciones claras entre variables, lo cual se confirmó en los análisis posteriores.
El análisis bivariado mostró consistentemente la ausencia de patrones, tendencias o vínculos lineales entre las variables. Los diagramas de dispersión no evidenciaron concentraciones, nubes estructuradas ni relaciones funcionales, mientras que las correlaciones resultaron cercanas a cero en casi todos los casos. Esto indica que el conjunto de datos no presenta dependencia directa entre los indicadores evaluados, y que cada variable aporta información prácticamente independiente de las demás.
El análisis multivariado clásico reforzó este comportamiento. La matriz de varianzas y covarianzas mostró independencia entre dimensiones, y el cálculo de distancias produjo distribuciones similares, evidenciando que no existen direcciones principales de variabilidad que generen una diferenciación fuerte entre individuos. La ausencia de estructura interna impide que estas distancias revelen agrupamientos o similitudes determinantes.
En este contexto, el Análisis de Componentes Principales (ACP) mostró que no hay componentes dominantes capaces de condensar la variabilidad del sistema. Los valores propios se distribuyeron de manera uniforme entre los componentes y ninguna variable tuvo contribuciones destacadas a los ejes factoriales. La varianza explicada creció lentamente y sin quiebres significativos, lo cual confirma que las dimensiones originales no pueden reducirse sin perder información relevante. Los gráficos del círculo de correlaciones y los mapas de individuos mostraron una dispersión homogénea, propia de datos sin relaciones estructurales.
Un comportamiento similar se observó en el Análisis de Correspondencias Múltiples (ACM), aplicado a las variables cualitativas. Los niveles de las categorías no presentaron asociaciones marcadas ni contribuciones fuertes a los ejes, lo que indica que, al igual que las variables numéricas, las categóricas tampoco conforman patrones de dependencia. Los mapas factoriales mostraron categorías distribuidas casi al azar, sin proximidades interpretables ni agrupamientos temáticos.
Finalmente, el análisis de clústeres mediante k-means no identificó grupos naturales dentro de los datos. La validación mediante silhouette y NbClust mostró valores bajos y ausencia de cohesión interna, confirmando que los individuos no forman conglomerados definidos. Cualquier partición es, en esencia, arbitraria debido a la estructura independiente de las variables. El dendrograma del método jerárquico Ward reflejó igualmente un patrón continuo sin rupturas naturales entre observaciones.
8 Referencias
[1]. Johnson, R. A., & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis. Pearson Prentice Hall.
[2]. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis. Cengage.
[3]. Montgomery, D. C., & Runger, G. C. (2010). Applied Statistics and Probability for Engineers. Wiley.
[4]. M. Arasugraj, “Global Health Statistics,” Kaggle, 2023. [En línea]. Disponible en: https://www.kaggle.com/datasets/malaiarasugraj/global-health-statistics. [Accedido: 11-sep-2025].