plot.rsc_cv {RSC} | R Documentation |
Plot method for rsc_cv objects
Description
Plot the cross-validation estimates of the Frobenius loss.
Usage
## S3 method for class 'rsc_cv'
plot(x, ...)
Arguments
x |
Output from |
... |
additional arguments passed to |
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
See Also
Examples
## 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)