Boxplot outlier removal causing problems with NbClust - r

Question

I have a dataset from which I had to remove outliers. I used the boxplot method to remove my outliers however, I feel this method has changed the structure of my data from a table like structure to just a list. I am trying to use NbClust to get a prediction on the amount of clusters I should use. I also applied z-score scaling before attempting to use NbClust. I am really new to R and I am not sure how to change it back and/or if this is the reason the error is occurring with NbClust
The data also showed as "846 obs. of 18 variables" before outlier removal
to "List of 18" after outlier removal (Shown in the Global Environment panel)
Error: Error in t(jeu) %*% jeu :
requires numeric/complex matrix/vector arguments
I think the correct thing is to change it into a data frame but I am not too sure how to do correctly do this.
Data before outlier removal using boxplot method:
After outliers removed using boxplot method:
Reproduceable example
library(reshape2)
library(NbClust)
vehData <-
structure(
list(
Samples = 1:6,
Comp = c(95L, 91L, 104L, 93L, 85L,
107L),
Circ = c(48L, 41L, 50L, 41L, 44L, 57L),
D.Circ = c(83L,
84L, 106L, 82L, 70L, 106L),
Rad.Ra = c(178L, 141L, 209L, 159L,
205L, 172L),
Pr.Axis.Ra = c(72L, 57L, 66L, 63L, 103L, 50L),
Max.L.Ra = c(10L,
9L, 10L, 9L, 52L, 6L),
Scat.Ra = c(162L, 149L, 207L, 144L, 149L,
255L),
Elong = c(42L, 45L, 32L, 46L, 45L, 26L),
Pr.Axis.Rect = c(20L,
19L, 23L, 19L, 19L, 28L),
Max.L.Rect = c(159L, 143L, 158L, 143L,
144L, 169L),
Sc.Var.Maxis = c(176L, 170L, 223L, 160L, 241L, 280L),
Sc.Var.maxis = c(379L, 330L, 635L, 309L, 325L, 957L),
Ra.Gyr = c(184L,
158L, 220L, 127L, 188L, 264L),
Skew.Maxis = c(70L, 72L, 73L,
63L, 127L, 85L),
Skew.maxis = c(6L, 9L, 14L, 6L, 9L, 5L),
Kurt.maxis = c(16L,
14L, 9L, 10L, 11L, 9L),
Kurt.Maxis = c(187L, 189L, 188L, 199L,
180L, 181L),
Holl.Ra = c(197L, 199L, 196L, 207L, 183L, 183L),
Class = c("van", "van", "saab", "van", "bus", "bus")
),
row.names = c(NA,
6L), class = "data.frame")
#Remove outliers
removeOutliers <- function(data) {
OutVals <- boxplot(data)$out
remOutliers <- sapply(data, function(x) x[!x %in% OutVals])
return (remOutliers)
}
# Scale data -> same as scale() function
z_score <- function(x){
return ((x - mean(x))/sd(x))
}
vehDataRemove1 <- vehData[, -1]
vehDataRemove2 <- vehDataRemove1[,-19]
vehData <- vehDataRemove2
vehClass <- vehData$Class
#Begin removing outliers
removeOutliers1 <- removeOutliers(vehData)
removeOutliers2 <- removeOutliers(removeOutliers1)
removeOutliers3 <- removeOutliers(removeOutliers2)
removeOutliers4 <- removeOutliers(removeOutliers3)
cleanVehicleData <- removeOutliers4
cl_vehDataScale <- lapply(cleanVehicleData, z_score)
set.seed(26)
clusterNo <- NbClust(cl_vehDataScale, distance="euclidean", min.nc=2, max.nc=10,
method="kmeans", index="all")