plot.MCAvariants function

Main plot function for classical and ordered multiple correspondence analysis

Main plot function for classical and ordered multiple correspondence analysis

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".

## S3 method for class '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.

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

Author(s)

Rosaria Lombardo and Eric J Beh

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)
MCAvariants packageRead PDF manual