ggbiplot

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

rpcaObj

array_like 
an array with two values indicating which two PCs should be plotted,
by default the first two PCs are used, e.g., $c(1,2)$. 

optional, factor 
factor indicating groups

groups

ellipse

alpha.ellipse

loadings

labels

arguments passed to or from other methods, see <code><a rd-options="ggplot2" href="/link/ggplot?package=rsvd&version=0.6&to=ggplot2" data-mini-rdoc="ggplot2::ggplot">ggplot</a></code>.


Biplot for <code>rPCA</code> using ggplot2


Randomized singular value decomposition (rsvd) is a very fast probabilistic algorithm that can be used to compute the near optimal low-rank singular value decomposition of massive data sets with high accuracy. SVD plays a central role in data analysis and scientific computing. SVD is also widely used for computing (randomized) principal component analysis (PCA), a linear dimensionality reduction technique. Randomized PCA (rpca) uses the approximated singular value decomposition to compute the most significant principal components. This package also includes a function to compute (randomized) robust principal component analysis (RPCA). In addition several plot functions are provided.

N Benjamin Erichson

rsvd

Randomized Singular Value Decomposition

ggbiplot function

arguments passed to or from other methods, see <code><a rd-options='ggplot2' href='ggplot'>ggplot</a></code>.

ggbiplot: Biplot for `rPCA` using ggplot2

Description

Usage

Arguments

See Also

Examples