View source: R/ggadd_interaction.R
ggadd_interaction | R Documentation |
Adds the interaction between two categorical supplementary variables to a MCA cloud of variables, using the ggplot2 framework.
ggadd_interaction(p, resmca, v1, v2, sel1=1:nlevels(v1), sel2=1:nlevels(v2), axes=c(1,2), col=NULL, textsize=5, legend='right')
p |
ggplot object with the cloud of variables |
resmca |
object of class |
v1 |
Factor. The first categorical supplementary variable. |
v2 |
Factor. The second categorical supplementary variable. |
sel1 |
Numeric vector of indexes of the categories of the first supplementary variable to be used in interaction. By default, every categories are used. |
sel2 |
Numeric vector of indexes of the categories of the second supplementary variable to be used in interaction. By default, every categories are used. |
axes |
numeric vector of length 2, specifying the components (axes) to plot. Default is c(1,2). |
col |
Colors for the labels of the categories. Can be the name of a palette from the RcolorBrewer package, 'bw' for a black and white palette (uses |
textsize |
Size of the labels of categories. Default is 5. |
legend |
the position of legends ("none", "left", "right", "bottom", "top", or two-element numeric vector). Default is right. |
a ggplot object
Nicolas Robette
Le Roux B. and Rouanet H., Multiple Correspondence Analysis, SAGE, Series: Quantitative Applications in the Social Sciences, Volume 163, CA:Thousand Oaks (2010).
Le Roux B. and Rouanet H., Geometric Data Analysis: From Correspondence Analysis to Stuctured Data Analysis, Kluwer Academic Publishers, Dordrecht (June 2004).
ggcloud_variables
, ggadd_supvar
, ggcloud_indiv
, ggadd_corr
, ggadd_ellipses
, ggadd_density
## Performs a specific MCA on 'Taste' example data set ## ignoring every 'NA' (i.e. 'not available') categories, ## draws the cloud of categories ## and adds the interaction between Gender and Age. data(Taste) getindexcat(Taste[,1:11]) mca <- speMCA(Taste[,1:11],excl=c(3,6,9,12,15,18,21,24,27,30,33)) p <- ggcloud_variables(mca, col='lightgrey', shapes=FALSE) ggadd_interaction(p, mca, Taste$Gender, Taste$Age)
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