Unsupervised clustering to detect rough structures and outliers.

Handle and operate on Nx3 matrix, where N is the number of samples and data are collected on 3 variables.

rubikclust(X, min.space = 0.4, rotation = TRUE)

Arguments

X

A data matrix for which rows represent samples and the 3 columns represent features. Missingness is not allowed.

min.space

A value to specify a minimum space between 2 consecutive projected values. Default = 0.4.

rotation

To specify if rotation is enabled or not. Default = TRUE.

Value

The returned value is a vector of numbers representing cluster memberships.

Details

The function rubikClust is able to take up to 3 variables (N x 3 matrix). In case, a matrix contains more than 3 columns, only the first three columns are used; the other columns are ignored.

Examples

#Load simulated dataset
data(example_SNP)

PCs <- cal.pc.linear(simsnp$snp, no.pc = 3)

#Run rubikclust with the default parameters
groups <- rubikclust(PCs$PC)
#Check clustering results
print(groups)
#>   [1] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
#>  [38] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
#>  [75] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 2 1 3

#Check cluster's distribution
table(groups)
#> groups
#>   1   2   3   4 
#>   1   1   1 100 

#Check the plot, highlight the points according to the clustering result
mylabels <- paste0("group", as.factor(groups))
plot3views( PCs$PC, labels = mylabels)


#Run rubikclust with min.space = 0.02
groups <- rubikclust(PCs$PC, min.space = 0.02)
#Check clustering results
print(groups)
#>   [1] 18 18 18 18 18 18 18 18  4 15 18 18 18 18 18 18 18 18 18 18 18 18 16 20 20
#>  [26] 17 18 20 18 18 18 18 18 18 18 20 18 20 18 18 18 18 18 19 18 20 20 18 18 18
#>  [51] 11  9  9  9 11  9 14  9  9  9  9  9  9  9  9 11  9  9 10  9  9 11  9  9  9
#>  [76] 11 11  9 11  9  9  7  6  9 11  9  8  9 11  9 13  9 12 11  9  5 11  9  9 11
#> [101]  2  3  1

#Check cluster's distribution
table(groups)
#> groups
#>  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 
#>  1  1  1  1  1  1  1  1 30  1 12  1  1  1  1  1  1 38  1  7 

#Check the plot, highlight the points according to the clustering result
mylabels <- paste0("group", as.factor(groups))
plot3views( PCs$PC, labels = mylabels)