plot.rsc_cv: Plot method for rsc_cv objects

Description

Plot the cross-validation estimates of the Frobenius loss.

Usage

# S3 method for rsc_cv
plot(x, …)

Arguments

Output from rsc_cv, that is an S3 object of class "rsc_cv".

…

additional arguments passed to plot.default.

Value

Plot the Frobenius loss estimated via cross-validation (y-axis) vs threshold values (x-axis). The dotted blue line represents the average expected normalized Frobenius loss, while the vertical segments around the average are 1-standard-error error bars (see Details in rsc_cv.

The vertical dashed red line identifies the minimum of the average loss, that is the optimal threshold flagged as "minimum". The vertical dashed green line identifies the optimal selection flagged as "minimum1se" in the output of rsc_cv (see Details in rsc_cv).

References

Serra, A., Coretto, P., Fratello, M., and Tagliaferri, R. (2018). Robust and sparsecorrelation matrix estimation for the analysis of high-dimensional genomics data. Bioinformatics, 34(4), 625-634. doi:10.1093/bioinformatics/btx642

Examples

Run this code

# NOT RUN {
## simulate a random sample from a multivariate Cauchy distribution
## note: example in high-dimension are obtained increasing p
set.seed(1)
n   <- 100  # sample size
p   <- 10   # dimension
dat <- matrix(rt(n*p, df = 1), nrow = n, ncol = p)
colnames(dat) <- paste0("Var", 1:p)

   
## perform 10-fold cross-validation repeated R=10 times
## note: for multi-core machines experiment with 'ncores'
set.seed(2)
a <- rsc_cv(x = dat, R = 10, K = 10, ncores = 1)
a

## plot the cross-validation estimates
plot(a)
   
## pass additional parameters to graphics::plot
plot(a , cex = 2)
# }