USArrests

Contexto

La base de datos USArrests contiene estadísticas en arrestos por cada 100,000 residentes por agresión, asesinato y violación en cada uno de los 50 estados de EE.UU en 1973.

Llamar librerías

library(cluster) #Agrupamiento
library(ggplot2) #Graficar
library(factoextra) #Visualizar Clusters
library(data.table) #Manejo de conjunto de datos grandes
library(readr)
library(tidyverse)
library(caret) 
library(lattice)
library(kernlab)
library(rpart)

Importar base de datos

df<- USArrests
datosUSA <- subset(df, select = -UrbanPop)
summary(datosUSA)

##      Murder          Assault           Rape      
##  Min.   : 0.800   Min.   : 45.0   Min.   : 7.30  
##  1st Qu.: 4.075   1st Qu.:109.0   1st Qu.:15.07  
##  Median : 7.250   Median :159.0   Median :20.10  
##  Mean   : 7.788   Mean   :170.8   Mean   :21.23  
##  3rd Qu.:11.250   3rd Qu.:249.0   3rd Qu.:26.18  
##  Max.   :17.400   Max.   :337.0   Max.   :46.00

#df<-USArrests
#datosUSA <- scale(df)

Generar los segmentos

grupos <- 3 #Se buscar el número óptimo de grupos o clusters
segmentos <- kmeans(datosUSA,grupos)

Asignar grupos a los datos

#asignacion <- cbind(datosUSA,cluster= segmentos$cluster)
asignacion <- data.frame(datosUSA, cluster = segmentos$cluster)

Graficar el cluster

fviz_cluster(segmentos, data = datosUSA)

Optimizar la cantidad de grupos

#La cantidad óptima de grupos corresponde al punto más alto de la gráfica 
set.seed(123)
optimizacion <- clusGap(datosUSA, FUN=kmeans, nstart=1, K.max=10)
plot(optimizacion, xlab= "Número de clusters k")

#El k OPTIMO ES EL COEFICIENTE DE SILUETA MÁXIMO
fviz_nbclust(df, kmeans, method = "wss") + ggtitle("Método del codo")

#EL k optimo es el coeficiente de silueta del punto de inflexión

Comparar segmentos

#promedio <- aggregate(asignacion, by=list(asignacion$cluster),FUN=mean)
#promedio 
promedio <- aggregate(. ~ cluster, data = asignacion, FUN = mean)
print(promedio)

##   cluster    Murder  Assault     Rape
## 1       1  4.270000  87.5500 14.39000
## 2       2  8.214286 173.2857 22.84286
## 3       3 11.812500 272.5625 28.37500

#table(asignacion$cluster)

Clasificación de los Clusters

# Crear una columna con etiquetas descriptivas para los clusters
asignacion$nivel_inseguridad <- factor(asignacion$cluster,
                                       levels = c(1, 2, 3),
                                       labels = c("Bajo", "Medio", "Alto"))

# Agregar los nombres de los estados a la tabla final
asignacion$Estado <- rownames(USArrests)

# Eliminar los nombres de los estados y reiniciar el índice de fila
rownames(asignacion) <- NULL

# Reordenar columnas para una mejor presentación
ClasEst <- asignacion[, c("Estado", "Assault", "Murder", "Rape", "nivel_inseguridad")]

# Mostrar el dataframe final
print(ClasEst)

##            Estado Assault Murder Rape nivel_inseguridad
## 1         Alabama     236   13.2 21.2              Alto
## 2          Alaska     263   10.0 44.5              Alto
## 3         Arizona     294    8.1 31.0              Alto
## 4        Arkansas     190    8.8 19.5             Medio
## 5      California     276    9.0 40.6              Alto
## 6        Colorado     204    7.9 38.7             Medio
## 7     Connecticut     110    3.3 11.1              Bajo
## 8        Delaware     238    5.9 15.8              Alto
## 9         Florida     335   15.4 31.9              Alto
## 10        Georgia     211   17.4 25.8             Medio
## 11         Hawaii      46    5.3 20.2              Bajo
## 12          Idaho     120    2.6 14.2              Bajo
## 13       Illinois     249   10.4 24.0              Alto
## 14        Indiana     113    7.2 21.0              Bajo
## 15           Iowa      56    2.2 11.3              Bajo
## 16         Kansas     115    6.0 18.0              Bajo
## 17       Kentucky     109    9.7 16.3              Bajo
## 18      Louisiana     249   15.4 22.2              Alto
## 19          Maine      83    2.1  7.8              Bajo
## 20       Maryland     300   11.3 27.8              Alto
## 21  Massachusetts     149    4.4 16.3             Medio
## 22       Michigan     255   12.1 35.1              Alto
## 23      Minnesota      72    2.7 14.9              Bajo
## 24    Mississippi     259   16.1 17.1              Alto
## 25       Missouri     178    9.0 28.2             Medio
## 26        Montana     109    6.0 16.4              Bajo
## 27       Nebraska     102    4.3 16.5              Bajo
## 28         Nevada     252   12.2 46.0              Alto
## 29  New Hampshire      57    2.1  9.5              Bajo
## 30     New Jersey     159    7.4 18.8             Medio
## 31     New Mexico     285   11.4 32.1              Alto
## 32       New York     254   11.1 26.1              Alto
## 33 North Carolina     337   13.0 16.1              Alto
## 34   North Dakota      45    0.8  7.3              Bajo
## 35           Ohio     120    7.3 21.4              Bajo
## 36       Oklahoma     151    6.6 20.0             Medio
## 37         Oregon     159    4.9 29.3             Medio
## 38   Pennsylvania     106    6.3 14.9              Bajo
## 39   Rhode Island     174    3.4  8.3             Medio
## 40 South Carolina     279   14.4 22.5              Alto
## 41   South Dakota      86    3.8 12.8              Bajo
## 42      Tennessee     188   13.2 26.9             Medio
## 43          Texas     201   12.7 25.5             Medio
## 44           Utah     120    3.2 22.9              Bajo
## 45        Vermont      48    2.2 11.2              Bajo
## 46       Virginia     156    8.5 20.7             Medio
## 47     Washington     145    4.0 26.2             Medio
## 48  West Virginia      81    5.7  9.3              Bajo
## 49      Wisconsin      53    2.6 10.8              Bajo
## 50        Wyoming     161    6.8 15.6             Medio

view(ClasEst)

ClasEst$nivel_inseguridad <- as.factor(ClasEst$nivel_inseguridad)

Entrenamiento del modelo

set.seed(321)
renglones_entrenamiento <- createDataPartition(ClasEst$nivel_inseguridad, p=0.8, list=FALSE)
entrenamiento <-ClasEst[renglones_entrenamiento, ]
prueba <- ClasEst[-renglones_entrenamiento, ]

Modelo de ML para Redes Neuronales

modelo5 <- train(nivel_inseguridad ~ ., data = entrenamiento,
                 method = "nnet", #Cambiar por modelo
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method = "cv", number=10),
                 trace = FALSE
                 )

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoArkansas, EstadoFlorida,
## EstadoNebraska, EstadoPennsylvania, EstadoWyoming
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoArkansas, EstadoFlorida,
## EstadoNebraska, EstadoPennsylvania, EstadoWyoming
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoArkansas, EstadoFlorida,
## EstadoNebraska, EstadoPennsylvania, EstadoWyoming
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoArkansas, EstadoFlorida,
## EstadoNebraska, EstadoPennsylvania, EstadoWyoming
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoArkansas, EstadoFlorida,
## EstadoNebraska, EstadoPennsylvania, EstadoWyoming
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoArkansas, EstadoFlorida,
## EstadoNebraska, EstadoPennsylvania, EstadoWyoming
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoArkansas, EstadoFlorida,
## EstadoNebraska, EstadoPennsylvania, EstadoWyoming
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoArkansas, EstadoFlorida,
## EstadoNebraska, EstadoPennsylvania, EstadoWyoming
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoArkansas, EstadoFlorida,
## EstadoNebraska, EstadoPennsylvania, EstadoWyoming

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoArizona, EstadoIdaho,
## EstadoSouth Dakota, EstadoVirginia
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoArizona, EstadoIdaho,
## EstadoSouth Dakota, EstadoVirginia
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoArizona, EstadoIdaho,
## EstadoSouth Dakota, EstadoVirginia
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoArizona, EstadoIdaho,
## EstadoSouth Dakota, EstadoVirginia
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoArizona, EstadoIdaho,
## EstadoSouth Dakota, EstadoVirginia
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoArizona, EstadoIdaho,
## EstadoSouth Dakota, EstadoVirginia
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoArizona, EstadoIdaho,
## EstadoSouth Dakota, EstadoVirginia
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoArizona, EstadoIdaho,
## EstadoSouth Dakota, EstadoVirginia
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoArizona, EstadoIdaho,
## EstadoSouth Dakota, EstadoVirginia

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoMichigan, EstadoMinnesota,
## EstadoOregon, EstadoWisconsin
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoMichigan, EstadoMinnesota,
## EstadoOregon, EstadoWisconsin
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoMichigan, EstadoMinnesota,
## EstadoOregon, EstadoWisconsin
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoMichigan, EstadoMinnesota,
## EstadoOregon, EstadoWisconsin
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoMichigan, EstadoMinnesota,
## EstadoOregon, EstadoWisconsin
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoMichigan, EstadoMinnesota,
## EstadoOregon, EstadoWisconsin
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoMichigan, EstadoMinnesota,
## EstadoOregon, EstadoWisconsin
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoMichigan, EstadoMinnesota,
## EstadoOregon, EstadoWisconsin
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoMichigan, EstadoMinnesota,
## EstadoOregon, EstadoWisconsin

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoLouisiana, EstadoMissouri,
## EstadoNew Hampshire, EstadoNorth Carolina
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoLouisiana, EstadoMissouri,
## EstadoNew Hampshire, EstadoNorth Carolina
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoLouisiana, EstadoMissouri,
## EstadoNew Hampshire, EstadoNorth Carolina
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoLouisiana, EstadoMissouri,
## EstadoNew Hampshire, EstadoNorth Carolina
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoLouisiana, EstadoMissouri,
## EstadoNew Hampshire, EstadoNorth Carolina
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoLouisiana, EstadoMissouri,
## EstadoNew Hampshire, EstadoNorth Carolina
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoLouisiana, EstadoMissouri,
## EstadoNew Hampshire, EstadoNorth Carolina
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoLouisiana, EstadoMissouri,
## EstadoNew Hampshire, EstadoNorth Carolina
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoLouisiana, EstadoMissouri,
## EstadoNew Hampshire, EstadoNorth Carolina

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoMaine, EstadoNew Jersey,
## EstadoSouth Carolina, EstadoWest Virginia
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoMaine, EstadoNew Jersey,
## EstadoSouth Carolina, EstadoWest Virginia
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoMaine, EstadoNew Jersey,
## EstadoSouth Carolina, EstadoWest Virginia
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoMaine, EstadoNew Jersey,
## EstadoSouth Carolina, EstadoWest Virginia
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoMaine, EstadoNew Jersey,
## EstadoSouth Carolina, EstadoWest Virginia
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoMaine, EstadoNew Jersey,
## EstadoSouth Carolina, EstadoWest Virginia
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoMaine, EstadoNew Jersey,
## EstadoSouth Carolina, EstadoWest Virginia
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoMaine, EstadoNew Jersey,
## EstadoSouth Carolina, EstadoWest Virginia
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoMaine, EstadoNew Jersey,
## EstadoSouth Carolina, EstadoWest Virginia

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoDelaware, EstadoIndiana,
## EstadoMaryland, EstadoMassachusetts
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoDelaware, EstadoIndiana,
## EstadoMaryland, EstadoMassachusetts
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoDelaware, EstadoIndiana,
## EstadoMaryland, EstadoMassachusetts
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoDelaware, EstadoIndiana,
## EstadoMaryland, EstadoMassachusetts
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoDelaware, EstadoIndiana,
## EstadoMaryland, EstadoMassachusetts
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoDelaware, EstadoIndiana,
## EstadoMaryland, EstadoMassachusetts
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoDelaware, EstadoIndiana,
## EstadoMaryland, EstadoMassachusetts
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoDelaware, EstadoIndiana,
## EstadoMaryland, EstadoMassachusetts
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoDelaware, EstadoIndiana,
## EstadoMaryland, EstadoMassachusetts

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoIllinois, EstadoKansas,
## EstadoTexas, EstadoUtah
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoIllinois, EstadoKansas,
## EstadoTexas, EstadoUtah
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoIllinois, EstadoKansas,
## EstadoTexas, EstadoUtah
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoIllinois, EstadoKansas,
## EstadoTexas, EstadoUtah
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoIllinois, EstadoKansas,
## EstadoTexas, EstadoUtah
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoIllinois, EstadoKansas,
## EstadoTexas, EstadoUtah
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoIllinois, EstadoKansas,
## EstadoTexas, EstadoUtah
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoIllinois, EstadoKansas,
## EstadoTexas, EstadoUtah
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoIllinois, EstadoKansas,
## EstadoTexas, EstadoUtah

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoGeorgia, EstadoNevada,
## EstadoNorth Dakota
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoGeorgia, EstadoNevada,
## EstadoNorth Dakota
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoGeorgia, EstadoNevada,
## EstadoNorth Dakota
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoGeorgia, EstadoNevada,
## EstadoNorth Dakota
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoGeorgia, EstadoNevada,
## EstadoNorth Dakota
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoGeorgia, EstadoNevada,
## EstadoNorth Dakota
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoGeorgia, EstadoNevada,
## EstadoNorth Dakota
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoGeorgia, EstadoNevada,
## EstadoNorth Dakota
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoGeorgia, EstadoNevada,
## EstadoNorth Dakota

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoHawaii, EstadoNew York,
## EstadoOhio, EstadoOklahoma
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoHawaii, EstadoNew York,
## EstadoOhio, EstadoOklahoma
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoHawaii, EstadoNew York,
## EstadoOhio, EstadoOklahoma
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoHawaii, EstadoNew York,
## EstadoOhio, EstadoOklahoma
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoHawaii, EstadoNew York,
## EstadoOhio, EstadoOklahoma
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoHawaii, EstadoNew York,
## EstadoOhio, EstadoOklahoma
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoHawaii, EstadoNew York,
## EstadoOhio, EstadoOklahoma
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoHawaii, EstadoNew York,
## EstadoOhio, EstadoOklahoma
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoHawaii, EstadoNew York,
## EstadoOhio, EstadoOklahoma

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoColorado, EstadoMississippi,
## EstadoTennessee, EstadoVermont
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoColorado, EstadoMississippi,
## EstadoTennessee, EstadoVermont
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoColorado, EstadoMississippi,
## EstadoTennessee, EstadoVermont
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoColorado, EstadoMississippi,
## EstadoTennessee, EstadoVermont
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoColorado, EstadoMississippi,
## EstadoTennessee, EstadoVermont
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoColorado, EstadoMississippi,
## EstadoTennessee, EstadoVermont
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoColorado, EstadoMississippi,
## EstadoTennessee, EstadoVermont
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoColorado, EstadoMississippi,
## EstadoTennessee, EstadoVermont
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: EstadoColorado, EstadoMississippi,
## EstadoTennessee, EstadoVermont