plot.i_mca {idm} | R Documentation |
Plotting 2D maps in Multiple Correspondence Analysis
Description
Graphical display of Multiple Correspondence Analysis results in two dimensions
Usage
## S3 method for class 'i_mca'
plot(x, dims = c(1,2), what = c(TRUE,TRUE),
contrib = "none", dataname = NULL, labels = NULL, animation = TRUE,
frames = 10, zoom = TRUE, movie_format = "gif", binary = FALSE,...)
Arguments
x |
Multiple correspondence analysis object returned by |
dims |
Numerical vector of length 2 indicating the dimensions to plot on horizontal and vertical axes respectively; default is first dimension horizontal and second dimension vertical |
what |
Vector of two logicals specifying the contents of the plot(s). First entry indicates if the rows (observations) are displayed in principal coordinates and the second entry if the variable categories are displayed in principal coordinates ( |
contrib |
Vector of two character strings specifying if attribute contributions should be represented by different label size. Available options are |
dataname |
String prefix used for custom naming of output files; default is the name of the output object |
labels |
String vector of variable labels |
animation |
Logical indicating whether animated GIF or PDF files are created and saved to the hard drive or a static plot is created ( |
frames |
Number of animation frames shown per iteration ( |
zoom |
Logical indicating whether axis limits change during the animation creating a zooming effect; applicable only when |
binary |
Logical indicating whether the categories associated with attribute presence are displayed on the plot; applicable only when the data are 0/1 |
movie_format |
Specifies if the animated plot is saved in the working directory either in |
... |
Details
The function plot.i_mca
makes a two-dimensional map of the object created by i_mca
with respect to two selected dimensions. In this map both the row and column points are scaled to have inertias (weighted variances) equal to the principal inertia (eigenvalue or squared singular value) along the principal axes, that is both rows and columns are in pricipal coordinates.
References
Greenacre, M.J. (1993) Correspondence Analysis in Practice. London: Academic Press.
Greenacre, M.J. (1993) Biplots in Correspondence Analysis, Journal of Applied Statistics, 20, 251-269.
ImageMagick: http://www.imagemagick.org; GraphicsMagick: http://www.graphicsmagick.org
See Also
Examples
data("women", package = "idm")
res_iMCAl = i_mca(data1 = women[1:50, 1:4], data2 = women[51:300, 1:4],
method = "live", nchunk = 4)
#static plot, final solution
plot(res_iMCAl, contrib = "ctr", animation = FALSE)
#\donttest is used here because the code calls the saveLatex function of the animation package
#which requires ImageMagick or GraphicsMagick and
#Adobe Acrobat Reader to be installed in your system
#See help(im.convert) for details on the configuration of ImageMagick or GraphicsMagick.
#Creates animated plots in PDF for objects and variables
plot(res_iMCAl, contrib = "ctr", animation = TRUE, frames = 10, movie_format = 'pdf')