1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
|
\name{plot.rsc_cv}
\alias{plot.rsc_cv}
\title{
Plot method for rsc_cv objects
}
\description{
Plot the cross-validation estimates of the Frobenius loss.
}
\usage{
\method{plot}{rsc_cv}(x, \dots)
}
\arguments{
\item{x}{
Output from \code{\link{rsc_cv}}, that is an S3 object of class \code{"rsc_cv"}.
}
\item{\dots}{
additional arguments passed to \code{\link[graphics]{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 \emph{1-standard-error} error bars
(see \emph{Details} in \code{\link{rsc_cv}}.
The vertical dashed red line identifies the minimum of the average
loss, that is the optimal threshold flagged as \code{"minimum"}. The
vertical dashed green line identifies the optimal selection flagged
as \code{"minimum1se"} in the output of \code{\link{rsc_cv}} (see
\emph{Details} in \code{\link{rsc_cv}}).
}
\section{References}{
Serra, A., Coretto, P., Fratello, M., and Tagliaferri, R. (2018).
Robust and sparsecorrelation matrix estimation for the analysis of
high-dimensional genomics data. \emph{Bioinformatics}, 34(4),
625-634. doi:10.1093/bioinformatics/btx642
}
\seealso{
\code{\link{rsc_cv}}
}
\examples{
\donttest{
## 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)
a
## plot the cross-validation estimates
plot(a)
## pass additional parameters to graphics::plot
plot(a , cex = 2)
}
}
|
