Preprocesamiento de datos

Carga de librerias

library(rpart)

## Warning: package 'rpart' was built under R version 3.5.3

library(rpart.plot)

## Warning: package 'rpart.plot' was built under R version 3.5.3

library(C50)

## Warning: package 'C50' was built under R version 3.5.3

library(nomclust)

## Warning: package 'nomclust' was built under R version 3.5.3

library(readxl)

## Warning: package 'readxl' was built under R version 3.5.3

library(ggpubr)

## Warning: package 'ggpubr' was built under R version 3.5.3

## Loading required package: ggplot2

## Warning: package 'ggplot2' was built under R version 3.5.3

## Loading required package: magrittr

## Warning: package 'magrittr' was built under R version 3.5.2

library(ggplot2)
library(knitr)

## Warning: package 'knitr' was built under R version 3.5.3

library(e1071)

## Warning: package 'e1071' was built under R version 3.5.3

library(naivebayes)

## Warning: package 'naivebayes' was built under R version 3.5.3

library(caret)

## Warning: package 'caret' was built under R version 3.5.3

## Loading required package: lattice

1) Lectura de datos

data<- read_excel("hidro con falla.xlsx")
colnames(data)

##  [1] "TMRCLA"  "TMA1LA"  "TMA2LA"  "TACCLA"  "VEACLA"  "VERCLA"  "TMRCLOA"
##  [8] "TACCLOA" "VER"     "TTA"     "PAA"     "TAaT"    "TAaC"

hist(data$PAA,col = c("red","blue"))

data<- data[data$PAA!=0, ]
hist(data$PAA,col = c("red","blue"))

knitr::kable(head(data), caption = "Datos Cargados")

a=1

2) Parametros de variables

p1<- c(80,85)
p2<- c(80,85)
p3<- c(80,85)
p4<- c(55,60)
p5<- c(2.5,3.5)
p6<- c(2.5,3.5)
p7<- c(80,85)
p8<- c(60,65)
p9<- c(2.5,3.5)
p10<- c(55,60)
p11<- c(127,130)
p12<- c(50,54)
p13<- c(42,48)
parametro=data.frame( rbind(p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11,p12,p13))
row.names(parametro)=c("TMRCLA","TMA1LA","TMA2LA","TACCLA","VEACLA","VERCLA","TMRCLOA","TACCLOA","VER","TTA","PAA","TAaT","TAaC")
colnames(parametro)=c("Alarm","Trip")
knitr::kable(parametro, caption = "Parametros de configuracion")

a=2

3) Transformacion de datos continuos a discretos

data$TMRCLA=ifelse(data$TMRCLA>=parametro["TMRCLA","Alarm"],ifelse(data$TMRCLA>=parametro["TMRCLA","Trip"],"Trip","Alarm"),"Ok")
data$TMA1LA=ifelse(data$TMA1LA>=80,ifelse(data$TMA1LA>=85,"Trip","Alarm"),"Ok")
data$TMA2LA=ifelse(data$TMA2LA>=80,ifelse(data$TMA2LA>=85,"Trip","Alarm"),"Ok")
data$TACCLA=ifelse(data$TACCLA>=55,ifelse(data$TACCLA>=60,"Trip","Alarm"),"Ok")
data$VEACLA=ifelse(data$VEACLA>=2.5,ifelse(data$VEACLA>=3.5,"Trip","Alarm"),"Ok")
data$VERCLA=ifelse(data$VERCLA>=2.5,ifelse(data$VERCLA>=3.5,"Trip","Alarm"),"Ok")
data$TMRCLOA=ifelse(data$TMRCLOA>=80,ifelse(data$TMRCLOA>=85,"Trip","Alarm"),"Ok")
data$TACCLOA=ifelse(data$TACCLOA>=60,ifelse(data$TACCLOA>=65,"Trip","Alarm"),"Ok")
data$VER=ifelse(data$VER>=2.5,ifelse(data$VER>=3.5,"Trip","Alarm"),"Ok")
data$TTA=ifelse(data$TTA>=55,ifelse(data$TTA>=60,"Trip","Alarm"),"Ok")
data$PAA=ifelse(data$PAA<=130,ifelse(data$PAA<=127,"Low Trip","Low Alarm"),ifelse(data$PAA>=150,ifelse(data$PAA>=157,"High Trip","Alarm High"),"Ok"))
data$TAaT=ifelse(data$TAaT>=50,ifelse(data$TAaT>=54,"Trip","Alarm"),"Ok")
data$TAaC=ifelse(data$TAaC>=42,ifelse(data$TAaC>=48,"Trip","Alarm"),"Ok")
knitr::kable(head(data), caption = "Datos discretizados")

a=3

4) evaluacion de eventos registrados con fallas y sin fallas

data$evento=ifelse(data$TMRCLA=="Trip"|data$TMA1LA=="Trip"| data$TMA2LA=="Trip"| data$TACCLA=="Trip"| data$VEACLA=="Trip"| data$VERCLA=="Trip"| data$TMRCLOA=="Trip" | data$TACCLOA=="Trip"| data$VER=="Trip"| data$TTA=="Trip"| data$PAA=="Low Trip"|data$PAA=="High Trip"| data$TAaT=="Trip"| data$TAaC=="Trip", "EN FALLA","SIN FALLA")
Trips=data[data$evento=="EN FALLA",]
Trips$evento<=NULL

## logical(0)

knitr::kable(head(data), caption = "Datos discretizados")

knitr::kable(head(Trips[,1:14]), caption = "Trips del sistema")

barplot(prop.table(table(Trips$PAA)), col = c("blue","green","orange","red"))

write.csv(Trips,"Trips.cvs")
write.csv(data,"Datos discretos.cvs")
a<-1

5) Normalizacion de los Estados

  Trips<- Trips
  Tabla<- Trips
Tabla$TMRCLAok<- ifelse(Trips$TMRCLA == "Ok", 1, 0)
Tabla$TMRCLAAlarm<- ifelse(Trips$TMRCLA == "Alarm", 1, 0)
Tabla$TMRCLATrip<- ifelse(Trips$TMRCLA == "Trip", 1, 0)

Tabla$TMA1LAok<- ifelse(Trips$TMA1LA == "Ok", 1, 0)
Tabla$TMA1LAAlarm<- ifelse(Trips$TMA1LA == "Alarm", 1, 0)
Tabla$TMA1LATrip<- ifelse(Trips$TMA1LA == "Trip", 1, 0)

Tabla$TMA2LAok<- ifelse(Trips$TMA2LA == "Ok", 1, 0)
Tabla$TMA2LAAlarm<- ifelse(Trips$TMA2LA == "Alarm", 1, 0)
Tabla$TMA2LATrip<- ifelse(Trips$TMA2LA == "Trip", 1, 0)

Tabla$TACCLAok<- ifelse(Trips$TACCLA == "Ok", 1, 0)
Tabla$TACCLAAlarm<- ifelse(Trips$TACCLA == "Alarm", 1, 0)
Tabla$TACCLATrip<- ifelse(Trips$TACCLA == "Trip", 1, 0)

Tabla$VEACLAok <- ifelse(Trips$VEACLA == "Ok", 1, 0)
Tabla$VEACLAAlarm<- ifelse(Trips$VEACLA == "Alarm", 1, 0)
Tabla$VEACLATrip<- ifelse(Trips$VEACLA == "Trip", 1, 0)

Tabla$VERCLAok <- ifelse(Trips$VERCLA == "Ok", 1, 0)
Tabla$VERCLAAlarm<- ifelse(Trips$VERCLA == "Alarm", 1, 0)
Tabla$VERCLATrip<- ifelse(Trips$VERCLA == "Trip", 1, 0)

Tabla$TMRCLOAok <- ifelse(Trips$TMRCLOA == "Ok", 1, 0)
Tabla$TMRCLOAAlarm<- ifelse(Trips$TMRCLOA == "Alarm", 1, 0)
Tabla$TMRCLOATrip<- ifelse(Trips$TMRCLOA == "Trip", 1, 0)

Tabla$TACCLOAok <- ifelse(Trips$TACCLOA == "Ok", 1, 0)
Tabla$TACCLOAAlarm<- ifelse(Trips$TACCLOA == "Alarm", 1, 0)
Tabla$TACCLOATrip<- ifelse(Trips$TACCLOA == "Trip", 1, 0)

Tabla$VERok <- ifelse(Trips$VER == "Ok", 1, 0)
Tabla$VERAlarm<- ifelse(Trips$VER == "Alarm", 1, 0)
Tabla$VERTrip<- ifelse(Trips$VER == "Trip", 1, 0)

Tabla$TTAok <- ifelse(Trips$TTA == "Ok", 1, 0)
Tabla$TTAAlarm<- ifelse(Trips$TTA == "Alarm", 1, 0)
Tabla$TTATrip<- ifelse(Trips$TTA == "Trip", 1, 0)

Tabla$PAAok <- ifelse(Trips$PAA == "Ok", 1, 0)
Tabla$PAAAlarmB<- ifelse(Trips$PAA == "Low Alarm", 1, 0)
Tabla$PAAAlarmA<- ifelse(Trips$PAA == "High Alarm", 1, 0)
Tabla$PAATripB<- ifelse(Trips$PAA == "Low Trip", 1, 0)
Tabla$PAATripA<- ifelse(Trips$PAA == "High Trip", 1, 0)

Tabla$TAaTok <- ifelse(Trips$TAaT == "Ok", 1, 0)
Tabla$TAaTAlarm<- ifelse(Trips$TAaT == "Alarm", 1, 0)
Tabla$TAaTTrip<- ifelse(Trips$TAaT == "Trip", 1, 0)

Tabla$TAaCok <- ifelse(Trips$TAaC == "Ok", 1, 0)
Tabla$TAaCAlarm<- ifelse(Trips$TAaC == "Alarm", 1, 0)
Tabla$TAaCTrip<- ifelse(Trips$TAaC == "Trip", 1, 0)

Datos_para_cluster<- Tabla[, -c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)]

kable( head(Datos_para_cluster[,c(1,2,3,4,5,6,7,8)]))

a<-1

5) Determinar Numero optimo de Cluster

library(ggplot2)
sumbt<- kmeans(Datos_para_cluster, centers = 9, iter.max = 50)$betweenss
sumbt2<- kmeans(Datos_para_cluster, centers = 9, iter.max = 50)$tot.withinss
for(i in 1:15) sumbt[i]<- kmeans(Datos_para_cluster, centers = i, nstart = 50)$betweenss
for(i in 1:15) sumbt2[i]<- kmeans(Datos_para_cluster, centers = i, nstart = 50)$tot.withinss

plot(1:15, sumbt, type="o", col="blue", lwd=1, main="Optimal Number of Cluster", xlab="Number of cluster", ylab="Distance",las=1, col.axis="black")
lines(1:15, sumbt2 ,type="o", col="green", lwd=1) 
legend("bottomleft",col=c("blue","green"),legend =c("Betweenss cluster","Withinss cluster"), lwd=2, bty = "n",  inset = 0.6)
points(1:15, sumbt, pch = 21, bg = "white")
points(1:15, sumbt2, pch = 21, bg = "white")
abline(v = 9, col="red", lwd=2, lty=2)

a<-1

6) Cluster Jerarquico

library(ggdendro)

## Warning: package 'ggdendro' was built under R version 3.5.3

library(scatterplot3d)

## Warning: package 'scatterplot3d' was built under R version 3.5.2

library(dendextend)

## Warning: package 'dendextend' was built under R version 3.5.3

## 
## ---------------------
## Welcome to dendextend version 1.12.0
## Type citation('dendextend') for how to cite the package.
## 
## Type browseVignettes(package = 'dendextend') for the package vignette.
## The github page is: https://github.com/talgalili/dendextend/
## 
## Suggestions and bug-reports can be submitted at: https://github.com/talgalili/dendextend/issues
## Or contact: <tal.galili@gmail.com>
## 
##  To suppress this message use:  suppressPackageStartupMessages(library(dendextend))
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## 
## Attaching package: 'dendextend'

## The following object is masked from 'package:ggdendro':
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##     theme_dendro

## The following object is masked from 'package:ggpubr':
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library(factoextra)

## Warning: package 'factoextra' was built under R version 3.5.3

## Welcome! Related Books: `Practical Guide To Cluster Analysis in R` at https://goo.gl/13EFCZ

library(grDevices)


Trips_1<-Trips[,c(1:13)]
scalada<- scale(Datos_para_cluster)
distancia_scalada<- dist(scalada, method = 'euclidean')
ch_scalada<- hclust(distancia_scalada, method = 'ward.D')
nk<-9


h12<- hcut(distancia_scalada, k=nk, stand = TRUE, hc_func ="diana", hc_method = "ward.D")
h13<- hcut(distancia_scalada, k=nk, stand = TRUE, hc_func ="diana", hc_method = "single")
h14<- hcut(distancia_scalada, k=nk, stand = TRUE, hc_func ="diana", hc_method = "complete")
h15<- hcut(distancia_scalada, k=nk, stand = TRUE, hc_func ="diana", hc_method = "average")
h21<- hcut(distancia_scalada, k=nk, stand = TRUE, hc_func ="agnes", hc_method = "ward.D2")
h22<- hcut(distancia_scalada, k=nk, stand = TRUE, hc_func ="agnes", hc_method = "ward.D")
h23<- hcut(distancia_scalada, k=nk, stand = TRUE, hc_func ="agnes", hc_method = "single")
h24<- hcut(distancia_scalada, k=nk, stand = TRUE, hc_func ="agnes", hc_method = "complete")
h25<- hcut(distancia_scalada, k=nk, stand = TRUE, hc_func ="agnes", hc_method = "average")
h31<- hcut(distancia_scalada, k=nk, stand = TRUE, hc_func ="hclust", hc_method = "ward.D2")
h32<- hcut(distancia_scalada, k=nk, stand = TRUE, hc_func ="hclust", hc_method = "ward.D")
h33<- hcut(distancia_scalada, k=nk, stand = TRUE, hc_func ="hclust", hc_method = "single")
h34<- hcut(distancia_scalada, k=nk, stand = TRUE, hc_func ="hclust", hc_method = "complete")
h35<- hcut(distancia_scalada, k=nk, stand = TRUE, hc_func ="hclust", hc_method = "average")
a<-1

h<-h24
#Cluster en 2 dimensiones
plot(fviz_cluster(h, geom = 'point', show.clust.cent = FALSE, main = "distance function euclidean, grouping method Complete",pointsize = 1, xlab = "Dimension 1", ylab = "Dimension 2"))

#Cluster Jerarquico
plot(fviz_dend(h, rect = TRUE, k = 9, show_labels = FALSE, xlab ="Clusters", lwd = 0.1, main = "distance function euclidean, grouping method Complete"))

a<-1

7) Crear Cluster

set.seed(80)

distancia_scalada<- dist(scalada, method = 'euclidean')
cluster<- hcut(distancia_scalada, k=9, stand = TRUE, hc_func ="agnes", hc_method = "complete")
Trips$cluster <- cluster$cluster
a<-1

8) Organizar Datos despues del cluster y asignar etiquetas

cluster1<- Trips[ cluster$cluster==1,]
cluster2<- Trips[ cluster$cluster==2,]
cluster3<- Trips[ cluster$cluster==3,]
cluster4<- Trips[ cluster$cluster==4,]
cluster5<- Trips[ cluster$cluster==5,]
cluster6<- Trips[ cluster$cluster==6,]
cluster7<- Trips[ cluster$cluster==7,]
cluster8<- Trips[ cluster$cluster==8,]
cluster9<- Trips[ cluster$cluster==9,]
cluster10<- Trips[ cluster$cluster==10,]
datos_listos<- Trips[-c(14)]
datos_listos$Caso<- ifelse(cluster$cluster==1, "Caso 1",
                    ifelse(cluster$cluster==2, "Caso 2",
                    ifelse(cluster$cluster==3, "Caso 3",
                    ifelse(cluster$cluster==4, "Caso 4",
                    ifelse(cluster$cluster==5, "Caso 5",
                    ifelse(cluster$cluster==6, "Caso 6",
                    ifelse(cluster$cluster==7, "Caso 7",
                    ifelse(cluster$cluster==8, "Caso 8",
                    ifelse(cluster$cluster==9, "Caso 9",
                    ifelse(cluster$cluster==10, "Caso 10",NaN))))))))))
kable(  datos_listos[c(1:5),])

a<-1

##Quitar datos duplicados

cluster1<-unique(cluster1)
cluster2<-unique(cluster2)
cluster3<-unique(cluster3)
cluster4<-unique(cluster4)
cluster5<-unique(cluster5)
cluster6<-unique(cluster6)
cluster7<-unique(cluster7)
cluster8<-unique(cluster8)
cluster9<-unique(cluster9)

9) Preparar Set de entrenamiento y test para la red Bayesiana

# Se crea una particion aleatoria del 30%  para test y 70% para entrenamiento a partir  de los datos del cluster 1
n=nrow(cluster1)
seleccion<-sample(n, floor(0.3*n))
Test_C1<- cluster1[seleccion,-14]
Test_C1$Caso<- "Caso 1"
Train_C1<- cluster1[-c(seleccion),-14]
Train_C1$Caso<- "Caso 1"

# Se crea una particion aleatoria del 30%  para test y 70% para entrenamiento a partir  de los datos del cluster 2
n=nrow(cluster2)
seleccion<-sample(n, floor(0.3*n))
Test_C2<- cluster2[seleccion,-14]
Test_C2$Caso<- "Caso 2"
Train_C2<- cluster2[-c(seleccion),-14]
Train_C2$Caso<- "Caso 2"

# Se crea una particion aleatoria del 30%  para test y 70% para entrenamiento a partir  de los datos del cluster 3
n=nrow(cluster3)
seleccion<-sample(n, floor(0.3*n))
Test_C3<- cluster3[seleccion,-14]
Test_C3$Caso<- "Caso 3"
Train_C3<- cluster3[-c(seleccion),-14]
Train_C3$Caso<- "Caso 3"

# Se crea una particion aleatoria del 30%  para test y 70% para entrenamiento a partir  de los datos del cluster 4
n=nrow(cluster4)
seleccion<-sample(n, floor(0.3*n))
Test_C4<- cluster4[seleccion,-14]
Test_C4$Caso<- "Caso 4"
Train_C4<- cluster4[-c(seleccion),-14]
Train_C4$Caso<- "Caso 4"

# Se crea una particion aleatoria del 30%  para test y 70% para entrenamiento a partir  de los datos del cluster 5
n=nrow(cluster5)
seleccion<-sample(n, floor(0.3*n))
Test_C5<- cluster5[seleccion,-14]
Test_C5$Caso<- "Caso 5"
Train_C5<- cluster5[-c(seleccion),-14]
Train_C5$Caso<- "Caso 5"

# Se crea una particion aleatoria del 30%  para test y 70% para entrenamiento a partir  de los datos del cluster 6
n=nrow(cluster6)
seleccion<-sample(n, floor(0.3*n))
Test_C6<- cluster6[seleccion,-14]
Test_C6$Caso<- "Caso 6"
Train_C6<- cluster6[-c(seleccion),-14]
Train_C6$Caso<- "Caso 6"

# Se crea una particion aleatoria del 30%  para test y 70% para entrenamiento a partir  de los datos del cluster 7
n=nrow(cluster7)
seleccion<-sample(n, floor(0.3*n))
Test_C7<- cluster7[seleccion,-14]
Test_C7$Caso<- "Caso 7"
Train_C7<- cluster7[-c(seleccion),-14]
Train_C7$Caso<- "Caso 7"

# Se crea una particion aleatoria del 30%  para test y 70% para entrenamiento a partir  de los datos del cluster 8
n=nrow(cluster8)
seleccion<-sample(n, floor(0.3*n))
Test_C8<- cluster8[seleccion,-14]
Test_C8$Caso<- "Caso 8"
Train_C8<- cluster8[-c(seleccion),-14]
Train_C8$Caso<- "Caso 8"

# Se crea una particion aleatoria del 30%  para test y 70% para entrenamiento a partir  de los datos del cluster 9
n=nrow(cluster9)
seleccion<-sample(n, floor(0.3*n))
Test_C9<- cluster9[seleccion,-14]
Test_C9$Caso<- "Caso 9"
Train_C9<- cluster9[-c(seleccion),-14]
Train_C9$Caso<- "Caso 9"

#se agregan las particiones creadas al dataframe Train y Test
Test<- rbind(Test_C1,Test_C2)
Test<- rbind(Test,Test_C3)
Test<- rbind(Test,Test_C4)
Test<- rbind(Test,Test_C5)
Test<- rbind(Test,Test_C6)
Test<- rbind(Test,Test_C7)
Test<- rbind(Test,Test_C8)
Test<- rbind(Test,Test_C9)
Test<- unique(Test)
Train<- rbind(Train_C1,Train_C2)
Train<- rbind(Train,Train_C3)
Train<- rbind(Train,Train_C4)
Train<- rbind(Train,Train_C5)
Train<- rbind(Train,Train_C6)
Train<- rbind(Train,Train_C7)
Train<- rbind(Train,Train_C8)
Train<- rbind(Train,Train_C9)
a<-1

10) Crear modelo de Naive Bayes

modelo <- naive_bayes(Caso ~ ., data = Train)
kable( modelo$prior)

kable(modelo$tables$TMRCLA)

kable(modelo$tables$TMA1LA)

kable(modelo$tables$TMA2LA)

kable(modelo$tables$TACCLA)

kable(modelo$tables$VEACLA)

kable(modelo$tables$VERCLA)

kable(modelo$tables$TMRCLOA)

kable(modelo$tables$TACCLOA)

kable(modelo$tables$VER)

kable(modelo$tables$TTA)

kable(modelo$tables$PAA)

kable(modelo$tables$TAaT)

kable(modelo$tables$TAaC)

a<-4

11) Prueba completa del set de test

como el sistema de identificacion de fallas funciono correctamente con una observacion del set de Test se procede a evaluar la totalidad de dicho set

set.seed(80)
pred <- predict(modelo, Test, threshold = 100)
pred

##  [1] Caso 1 Caso 1 Caso 1 Caso 2 Caso 2 Caso 2 Caso 2 Caso 3 Caso 3 Caso 3
## [11] Caso 3 Caso 4 Caso 4 Caso 4 Caso 1 Caso 5 Caso 5 Caso 6 Caso 6 Caso 6
## [21] Caso 7 Caso 7 Caso 8 Caso 8 Caso 8 Caso 9 Caso 9 Caso 1 Caso 9
## 9 Levels: Caso 1 Caso 2 Caso 3 Caso 4 Caso 5 Caso 6 Caso 7 ... Caso 9

tab <- table(Test$Caso, pred, dnn = c("Actual", "Predicha"))
kable(tab)

a<-confusionMatrix(tab)
a

## Confusion Matrix and Statistics
## 
##         Predicha
## Actual   Caso 1 Caso 2 Caso 3 Caso 4 Caso 5 Caso 6 Caso 7 Caso 8 Caso 9
##   Caso 1      3      0      0      0      0      0      0      0      0
##   Caso 2      0      4      0      0      0      0      0      0      0
##   Caso 3      0      0      4      0      0      0      0      0      0
##   Caso 4      0      0      0      3      0      0      0      0      0
##   Caso 5      1      0      0      0      2      0      0      0      0
##   Caso 6      0      0      0      0      0      3      0      0      0
##   Caso 7      0      0      0      0      0      0      2      0      0
##   Caso 8      0      0      0      0      0      0      0      3      0
##   Caso 9      1      0      0      0      0      0      0      0      3
## 
## Overall Statistics
##                                           
##                Accuracy : 0.931           
##                  95% CI : (0.7723, 0.9915)
##     No Information Rate : 0.1724          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.9221          
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: Caso 1 Class: Caso 2 Class: Caso 3
## Sensitivity                 0.6000        1.0000        1.0000
## Specificity                 1.0000        1.0000        1.0000
## Pos Pred Value              1.0000        1.0000        1.0000
## Neg Pred Value              0.9231        1.0000        1.0000
## Prevalence                  0.1724        0.1379        0.1379
## Detection Rate              0.1034        0.1379        0.1379
## Detection Prevalence        0.1034        0.1379        0.1379
## Balanced Accuracy           0.8000        1.0000        1.0000
##                      Class: Caso 4 Class: Caso 5 Class: Caso 6
## Sensitivity                 1.0000       1.00000        1.0000
## Specificity                 1.0000       0.96296        1.0000
## Pos Pred Value              1.0000       0.66667        1.0000
## Neg Pred Value              1.0000       1.00000        1.0000
## Prevalence                  0.1034       0.06897        0.1034
## Detection Rate              0.1034       0.06897        0.1034
## Detection Prevalence        0.1034       0.10345        0.1034
## Balanced Accuracy           1.0000       0.98148        1.0000
##                      Class: Caso 7 Class: Caso 8 Class: Caso 9
## Sensitivity                1.00000        1.0000        1.0000
## Specificity                1.00000        1.0000        0.9615
## Pos Pred Value             1.00000        1.0000        0.7500
## Neg Pred Value             1.00000        1.0000        1.0000
## Prevalence                 0.06897        0.1034        0.1034
## Detection Rate             0.06897        0.1034        0.1034
## Detection Prevalence       0.06897        0.1034        0.1379
## Balanced Accuracy          1.00000        1.0000        0.9808

#a$byClass