ggbiplot: Biplot for 'rpca' using 'ggplot'.

In rsvd: Randomized Singular Value Decomposition

Description

Creates a pretty biplot which is showing the individual factor map overlayed by the variables factor map, i.e, plotting both the principal component scores and directions.

Usage

ggbiplot(
  rpcaObj,
  pcs = c(1, 2),
  loadings = TRUE,
  groups = NULL,
  alpha = 0.6,
  ellipse = TRUE,
  alpha.ellipse = 0.2,
  var_labels = TRUE,
  var_labels.names = NULL,
  ind_labels = TRUE,
  ind_labels.names = NULL
)

Arguments

rpcaObj	Object returned by the rpca function.
pcs	Array_like. An array with two values indicating the two PCs which should be used for plotting. By default the first two PCs are used, e.g., c(1,2).
loadings	Bool (TRUE, FALSE), optional. If TRUE, the eigenvectors are unit scaled by the square root of the eigenvalues W = W * diag(sqrt(eigvals)).
groups	Factor, optional. Factor indicating groups.
alpha	Scalar, optional. Alpha transparency for scatter plot.
ellipse	Bool (TRUE, FALSE), optional. Draw a 1sd data ellipse for each group, if TRUE.
alpha.ellipse	Scalar, optional. Alpha transparency for ellipse.
var_labels	Bool (TRUE, FALSE), optional. Plot variable names, if TRUE.
var_labels.names	Array_like, optional. User specific labels for the individuals.
ind_labels	Bool (TRUE, FALSE), optional. Plot data point names, if TRUE.
ind_labels.names	Array_like, optional. User specific labels for data points.

Author(s)

N. Benjamin Erichson, [e-mail]

See Also

rpca, ggplot

Examples

#See ?rpca

Example output

rsvd documentation built on April 16, 2021, 9:06 a.m.