MCAvariants (version 2.6.1)

plot.MCAvariants: Main plot function for classical and ordered multiple correspondence analysis

Description

This function allows the analyst to produce the suitable graphical displays with respect to the classical and ordered multiple correspondence analysis. The main plot function called from the main function MCAvariants. It produces classical graphical displays for catype = "mca" and catype = "omca".

Usage

# S3 method for MCAvariants
plot(x, catype = "mca", firstaxis = 1, lastaxis = 2, thirdaxis = 3, cex = 0.8, 
cex.lab = 0.8, prop = 1, plot3d = FALSE, plotind= FALSE, M=2,...)

Arguments

x

Represents the set of the output parameters of the main function MCAvariants of the R object class mcacorporateris.

catype

The input parameter specifying what variant of correspondence analysis is requested.

firstaxis

The dimension reflected along the horizontal axis.

lastaxis

The dimension reflected along the vertical axis.

thirdaxis

The third axis number when plot3d = TRUE. By default, thirdaxis = 3.

cex

The parameter that specifies the size of character labels of points in graphical displays. By default, it is equal to 1.

cex.lab

The parameter cex.lab that specifies the size of character labels of axes in graphical displays. By default, cex.lab = 0.8.

prop

The scaling parameter for specifying the limits of the plotting area. By default, it is equal to 1.

plot3d

The logical parameter specifies whether a 3D plot is to be included in the output or not. By default, plot3d = FALSE.

plotind

The logical parameter specifies whether a plot of individuals is to be included in the output or not. By default, plotind = FALSE.

M

The number of axes M considered when portraying the elliptical confidence regions.
By default, it is equal to M = 2.

...

Further arguments passed to or from other methods.

Author

Rosaria Lombardo and Eric J Beh

Details

It produces classical graphical displays. Further when catype is equal to "omca", the individual clusters are portrayed.

References

Lombardo R and Meulman JJ (2010) Journal of Classification, 27, 191-210.
Beh EJ Lombardo R (2014) Correspondence Analysis, Theory, Practice and New Strategies. Wiley

Examples

data(satisfaction)
res1=MCAvariants(satisfaction, catype = "mca", np=5)
plot(res1)
res2=MCAvariants(satisfaction, catype = "omca", np = 5, vordered=c(TRUE,TRUE,TRUE,TRUE,TRUE))
plot(res2)