clustrd (version 1.0.2)

plot.clusmca: Plotting function for clusmca() output.

Description

Plotting function that creates a ggplot2 based map of the object scores and a scatter plot of both the attribute scores and the centroids.

Usage

"plot"(x, dims = c(1,2), disp = TRUE, cludesc = FALSE, what = c(TRUE,TRUE),
attlabs = NULL, binary = FALSE, ...)

Arguments

x

Object returned by clusmca()

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

disp

A logical value indicating whether the plots are shown in the R window or saved as PDF files in the working directory (default = TRUE)

what

Vector of two logical values specifying the contents of the plots. First entry indicates whether a scatterplot of the objects is displayed in principal coordinates. Second entry indicates whether a scatterplot of the attribute categories is displayed in principal coordinates. The default is c(TRUE, TRUE) and the resultant plot is a biplot of both objects and attribute categories with gamma-based scaling (see van de Velden et al. (2016))

cludesc

A logical value indicating whether a series of barplots is produced showing the largest (in absolute value) standardized residuals per attribute for each cluster (default = FALSE)

attlabs

Vector of attribute labels; if not provided, default labeling is applied

binary

Vector of attribute labels; if not provided, default labeling is applied

...

Further arguments to be transferred to clusmca()

References

Hwang, H., Dillon, W. R. and Takane, Y. (2006). An extension of multiple correspondence analysis for identifying heterogenous subgroups of respondents, Psychometrika, 71, 161-171.

Iodice D' Enza, A. and Palumbo, F. (2013). Iterative factor clustering of binary data. Computational Statistics, 28(2), 789-807.

Van de Velden M., Iodice D' Enza, A. and Palumbo, F. (2016). Cluster correspondence analysis.Psychometrika (in press) DOI: 10.1007/s11336-016-9514-0

See Also

plot.cluspca

Examples

data("hsq")
outclusMCA = clusmca(hsq[,1:8], 3, 2, method = "iFCB")
plot(outclusMCA, cludesc = TRUE)