version 1.0

1 files changed, 84 insertions, 0 deletions

diff --git a/man/plot.cv_rsc.Rd b/man/plot.cv_rsc.Rd
new file mode 100755
index 0000000..0d7b2c2
--- /dev/null
+++ b/man/plot.cv_rsc.Rd
@@ -0,0 +1,84 @@
+\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)
+}
+}