plot.mccca {mccca}

plot mccca object.

Description

plot mccca object.

Usage

## S3 method for class 'mccca'
plot(
  x,
  main = "MCCCA result",
  catelabel = NULL,
  classlabel = NULL,
  classlabel.legend = NULL,
  xlim = NULL,
  ylim = NULL,
  sort.clssize = TRUE,
  break.size = NULL,
  output.coord = FALSE,
  connect.cord = TRUE,
  include.variname = TRUE,
  scale.gamma = TRUE,
  scatter.level = 2,
  plot.setting = list(alp.point = 0.3, alp.seg = 0.8, txtsize = 3, txtsize.legend = 10),
  ...
)

Arguments

x

An object of class mccca, a list of MCCCA outputs.

main

A character giving the title of biplot.

catelabel

A characteristic vector of length Q giving labels for all categories to be displayed on the biplot (Q=sum(q.vec)). If NULL, rownames(B) are used.

classlabel

A characteristic vector of length C (C:the number of class) giving labels for all classes to be displayed on the biplot. If NULL, labels specified in create.MCCCAdata are used.

classlabel.legend

A characteristic vector of length C giving labels for all classes to be used on the legend (this can be longer). If NULL, classlabel is used.

xlim

A numeric vector of length 2 giving the range of plot on the x (horizontal) axis. If NULL, the range is automatically determined.

ylim

A numeric vector of length 2 for the y (vertical) axis (same role as xlim).

sort.clssize

If TRUE, the class-specific cluster numbers are sorted in the order of cluster size. The default is TRUE.

break.size

An integer vector that adjusts the size of bubble displayed on the legend.

output.coord

If TRUE, the output will be Cocls.mat and Cocate.mat. See value.

connect.cord

If TRUE, lines are drawn between original (estimated by MCCCA) coordinates and coordinates moved to avoid overlap.

include.variname

If TRUE, variable name is included in category labels in the biplot (ex.a point of category "male" in "v1"(the name of 1st variable) is displayed as "v1:male" on the biplot).

scale.gamma

If TRUE, quantifications are scaled such that the average squared deviation from the origin of the row and column points is the same (See section 2.3 in the paper).

scatter.level

A numeric value that adjusts the scatter of points in the biplot. The higher the value, the more scattered the points are. The default is 2.

plot.setting

A list of biplot settings. See details.

...

Additional arguments passed to print.

Details

Parameters in plot.setting are as follows:

-alp.point:A numeric value from 0 to 1 which adjusts the transparency of the bubble point. The default is 0.3.

-alp.seg:A numeric value from 0 to 1 which adjusts the transparency of the segments between texts and points. The default is 0.8.

-txtsize:A numeric value which adjusts the textsize on the biplot. The default is 3.

-txtsize.legend:A numeric value which adjusts the textsize of the legend on the biplot. The default is 10.

Value

If output.coord is TRUE, returns a list with the following elements.

Cocls.mat

A (Kx4) coordinate matrix of clusters, where the last two columns are the coordinates estimated by MCCCA, and the first two columns are the coordinates moved from the estimated coordinates to prevent overlap.

Cocate.mat

A (Kx4) coordinate matrix of categories (each column plays the same role as Cocls.mat)

References

Takagishi & Michel van de Velden (2022): Visualizing Class Specific Heterogeneous Tendencies in Categorical Data, Journal of Computational and Graphical Statistics, DOI: 10.1080/10618600.2022.2035737

See Also

MCCCA

Examples

#setting
N <- 100 ; J <- 5 ; Ktrue <- 2 ; q.vec <- rep(5,J) ; noise.prop <- 0.2
extcate.vec=c(2,3)#the number of categories for each external variable

#generate categorical variable data
catedata.list <- generate.onedata(N=N,J=J,Ktrue=Ktrue,q.vec=q.vec,noise.prop = noise.prop)
data.cate=catedata.list$data.mat
clstr0.vec=catedata.list$clstr0.vec
#generate external variable data
data.ext=generate.ext(N,extcate.vec=extcate.vec)

#create mccca.list to be applied to MCCCA function
mccca.data=create.MCCCAdata(data.cate,ext.mat=data.ext,clstr0.vec =clstr0.vec)

#specify the number of cluster for each of C classes
C=length(mccca.data$data.list)
K.vec=rep(2,C)
#apply MCCCA
mccca.res=MCCCA(mccca.data,K.vec=K.vec)

#plot MCCCA result
plot(mccca.res)