Description Usage Arguments Value Examples
This function computes a set of basis vectors suitable for outlier detection.
1 
xx 
The input data in a dataframe, matrix or tibble format. 
frac 
The cutoff quantile for 
norm 
The normalization technique. Default is MedianIQR, which normalizes each column of meidan 
k 
Parameter 
A list with the following components:

The basis vectors suitable for outlier detection. 

The dobin coordinates of the data 

The The associated 

The pairs in 

Columns in 
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  # A bimodal distribution in six dimensions, with 5 outliers in the middle.
set.seed(1)
x2 < rnorm(405)
x3 < rnorm(405)
x4 < rnorm(405)
x5 < rnorm(405)
x6 < rnorm(405)
x1_1 < rnorm(mean = 5, 400)
mu2 < 0
x1_2 < rnorm(5, mean=mu2, sd=0.2)
x1 < c(x1_1, x1_2)
X1 < cbind(x1,x2,x3,x4,x5,x6)
X2 < cbind(1*x1_1,x2[1:400],x3[1:400],x4[1:400],x5[1:400],x6[1:400])
X < rbind(X1, X2)
labs < c(rep(0,400), rep(1,5), rep(0,400))
out < dobin(X)
plot(out$coords[ , 1:2], col=as.factor(labs), pch=20)

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