CAinterprTools (version 1.1.0)

caPercept: Perceptual map-like Correspondence Analysis scatterplot

Description

This command allows to plot a variant of the traditional Correspondence Analysis scatterplots that allows facilitating the interpretation of the results. It aims at producing what in marketing research is called perceptual map, a visual representation of the CA results that seeks to avoid the problem of interpreting inter-spatial distance. It represents only one type of points (say, column points), and "gives names to the axes" corresponding to the major row category contributors to the two selected dimensions.

Usage

caPercept(
  data,
  x = 1,
  y = 2,
  focus = "row",
  dim.corr = x,
  guide = FALSE,
  size.labls = 3
)

Arguments

data

Contingency table, in dataframe format.

x

First dimensions to be plotted.

y

Second dimensions to be plotted.

focus

Takes "row" (default) if the interest is in assessing the contribution of the rows to the definition of the dimensions, "col" if the interest is on the columns.

dim.corr

Dimension for which the points' correlation (column points if focus is set to "row", row points if focus is set to "col") will be computed and used as input value for the size of the points. The default value is the smaller of the two input dimensions (i.e., x).

guide

TRUE or FALSE (default) if the user does or doesn't want the points being given a color code indicating with which of the two selected dimension they have a higher relative correlation.

size.labls

Adjust the size of the characters used in the labels that give names to the axes.

See Also

caPlot

Examples

# NOT RUN {
data(brand_coffee)

caPercept(brand_coffee,1,2,focus="col",dim.corr=1, guide=FALSE)

#In the returned plot, axes are given names according to the major contributing column categories 
# (i.e., coffee brands in this datset), while the points correspond to the row categories 
#(i.e., attributes). Points' size is proportional to the correlation of points with the 1st 
#dimension. If 'guide' is set to TRUE, the returned plot is similar to the preceding one, 
# but the points are given colour according to whether they are more correlated 
# (in relative terms) to the first or to the second of the selected dimensions. 
# In this example, points flagged with "->Dim 1" are more correlated to the 1st dimension, 
# while those flagged with "->Dim 2" have a higher correlation with the 2nd dimension.

# }