plot.rpca

object containing the <code>sdev</code> component, such as that returned
by <code>rpca</code>

str c('var', 'ratio', 'cum', 'eigenvals'), optional 

type

arguments passed to or from other methods, see <code><a rd-options="" href="/link/plot?package=rsvd&version=0.3" data-mini-rdoc="rsvd::plot">plot</a></code>.

.................


Creates a screeplpot. By default the explained variance is plotted
agaings the number of the principal component.
Alternatively the eigenvalues, explained variance ratio or the cumulative
explained variance ratio can be plotted.


Randomized singular value decomposition (rsvd) is a very fast probabilistic
algorithm to compute an approximated low-rank singular value decomposition of large
data sets with high accuracy. SVD plays a central role in data analysis and scientific computing.
SVD is also widely used for computing principal component analysis (PCA), a linear dimensionality reduction technique.
Randomized PCA (rpca) is using the approximated singular value decomposition to compute the most significant principal components. In addition several plot functions are provided.

plot.rpca: Screeplot

Description

Usage

Arguments

See Also

Examples