The argument “edf.trans” of the function “clover_plot” provides an option to apply, in each quadrant and each marginal separately, the transformation based on the cumulative distribution function (the default value is FALSE). This can make it easier to distinguish points with very similar coordinates in the clover plot. After the transformation the classifiers and boundary curves are not plotted. Note that usually the depth values contain many zeros, resulting in all the transformed values being away from zero. This may happen also for the illumination for certain values of “alphaX” and/or “alphaY”.
First we generate data from bivariate normal distributions with different means and the same variance matrices and compute the necessary quantities:
library(mvtnorm)
n1 <- 100
n2 <- 75
set.seed(2020)
X <- rmvnorm(n1, mean=c(0,0), sigma=diag(2)) # Observations of class 1
Y <- rmvnorm(n2, mean=c(2,2), sigma=diag(2)) # Observations of class 2
res <- clover_calc(X, Y)The clover plot after the transformations:
clover_plot(res, edf.trans=TRUE)## Misclassification rate of the QDA classifier: 0.0857
## Non-classification rate of the QDA classifier: 0
##
## Misclassification rate of the DD1 classifier: 0.0914
## Non-classification rate of the DD1 classifier: 0.0686The clover plot without the transformations:
clover_plot(res)## Misclassification rate of the QDA classifier: 0.0857
## Non-classification rate of the QDA classifier: 0
##
## Misclassification rate of the DD1 classifier: 0.0914
## Non-classification rate of the DD1 classifier: 0.0686