ggBiplot: PCA biplots with ggplot2

In abnormally-distributed/cvreg: Cross Validation and Robust Estimation Utilities

Description

PCA biplots with ggplot2

Usage

ggBiplot(
  fit,
  choices = c(1, 2),
  groups = NULL,
  pc.biplot = T,
  coord_equal = F,
  ellipse = F,
  ellipse.prob = 0.9,
  kde = F,
  circle = F,
  circle.prob = 0.9,
  var.axes = T,
  var.names = T,
  varname.size = 4.125,
  varname.adjust = 1,
  varname.abbrev = T,
  labels = NULL,
  labels.size = 3,
  scale = 1,
  obs.scale = 1 - scale,
  var.scale = scale,
  alpha = 0.75,
  lwd = 1.25,
  klwd = 1.5,
  ...
)

Arguments

fit	object types supported from this package: "PrincipalComp", "sdr", "gpls", "gpcr", "ldarob", "facanal". "Pca" (rrcov), "PcaRobust" (rrcov), "mvPCA" (MNM), "ics" (ICS), "factanal" (stats), "fa" (psych), "lda" (MASS)
choices	a vector of length 2 indicating which components to plot. defaults to the first two, ie c(1, 2).
groups	optional factor variable indicating the groups that the observations belong to. if provided the points will be colored according to groups
pc.biplot	for compatibility with biplot.princomp()
coord_equal	should the coordinate system be set to maintain a symmetric shaped plot? defaults to FALSE.
ellipse	draw a gaussian data ellipse for each group?
ellipse.prob	size of the ellipse in gaussian probability
kde	if TRUE, plots the kernel density estimate contours instead of points. useful for dense data. defaults to FALSE.
circle	draw a correlation circle? (only applies when prcomp was called with scale = TRUE and when var.scale = 1)
var.axes	draw arrows for the variables?
var.names	should labels be drawn for the variable axes?
varname.size	size of the text for variable names
varname.adjust	adjustment factor the placement of the variable names, >= 1 means farther from the arrow
varname.abbrev	whether or not to abbreviate the variable names
labels	optional vector of labels for the observations
labels.size	size of the text used for the labels
scale	covariance biplot (scale = 1), form biplot (scale = 0). when scale = 1, the inner product between the variables approximates the covariance and the distance between the points approximates the Mahalanobis distance.
obs.scale	scale factor to apply to observations
var.scale	scale factor to apply to variables
alpha	alpha transparency value for the points (0 = transparent, 1 = opaque)
lwd	the size of the variable axes. defaults to 1.25.
klwd	the thickness of the kde contours if kde=TRUE. defaults to 1.5.

Value

a ggplot2 plot

Examples

  data(wine)
  wine.pca <- PrincipalComp(wine, ncomp = 3)
  print(ggBiplot(wine.pca, groups = wine.class, ellipse = TRUE))

abnormally-distributed/cvreg documentation built on May 3, 2020, 3:45 p.m.