mcaplot: Simple and enhanced plot of MCA solutions

Description

This function is intended as an alternative to plot.mjca for plotting multiple correspondence analysis solutions. It provides more flexibility for labeling factor levels and connecting them with lines. It does not support some features of plot.mjca (centroids, supplementary points, arrows, etc.)

Usage

mcaplot(obj, 
  map = "symmetric", 
  dim = 1:2, 
  col = c("blue", "red", "brown", "black", "green3", "purple"), 
  pch = 15:20, 
  cex = 1.2, 
  pos = 3, 
  lines = TRUE, 
  lwd = 2, 
  legend = FALSE, 
  legend.pos = "topright", 
  xlab = "_auto_", 
  ylab = "_auto_", 
  rev.axes  = c(FALSE, FALSE),
  ...)

Value

Returns the coordinates of the category points invisibly

Arguments

obj: An "mjca" object
map: Character string specifying the map type, i.e., the scaling applied to coordinates for different types of MCA representations. Allowed options include: "symmetric" (default), "rowprincipal", "colprincipal", "symbiplot", "rowgab", "colgab", "rowgreen", "colgreen". See mjca for details.
dim: Dimensions to plot, an integer vector of length 2
col: Vector of colors, one for each factor in the MCA
pch: Vector of point symbols for the category levels, one for each factor
cex: Character size for points and level labels
pos: Position of level labels relative to the category points; either a single number or a vector of length equal to the number of category points.
lines: A logical or an integer vector indicating which factors are to be joined with lines using multilines
lwd: Line width(s) for the lines
legend: Logical; draw a legend for the factor names?
legend.pos: Position of the legend in the plot, as in legend
xlab,ylab: Labels for horizontal and vertical axes. The default, "_auto_" means that the function auto-generates a label of the form "Dimension X (xx.x %)"
rev.axes: A logical vector of length 2, where TRUE reverses the direction of the corresponding axis
...: Arguments passed down to plot

Author

Michael Friendly

Examples

Run this code

require(ca)
data(Titanic)
titanic.mca <- mjca(Titanic)
mcaplot(titanic.mca, legend=TRUE, legend.pos="topleft")

data(HairEyeColor)
haireye.mca <- mjca(HairEyeColor)
mcaplot(haireye.mca, legend=TRUE, cex.lab=1.3)