plot.stMCA | R Documentation |
Plots a 'standardized' Multiple Correspondence Analysis (resulting from stMCA
function),
i.e. the clouds of individuals or categories.
## S3 method for class 'stMCA' plot(x, type = "v", axes = 1:2, points = "all", threshold = 2.58, groups=NULL, col = "dodgerblue4", app = 0, ...)
x |
object of class |
type |
character string: 'v' to plot the categories (default), 'i' to plot individuals' points, 'inames' to plot individuals' names |
axes |
numeric vector of length 2, specifying the components (axes) to plot (c(1,2) is default) |
points |
character string. If 'all' all points are plotted (default); if 'besth' only those who are the most correlated to horizontal axis are plotted; if 'bestv' only those who are the most correlated to vertical axis are plotted; if 'best' only those who are the most coorelated to horizontal or vertical axis are plotted. |
threshold |
numeric value. V-test minimal value for the selection of plotted categories. |
groups |
only if x$call$input.mca = 'multiMCA', i.e. if the MCA standardized to x object was a |
col |
color for the points of the individuals or for the labels of the categories (default is 'dodgerblue4') |
app |
numerical value. If 0 (default), only the labels of the categories are plotted and their size is constant; if 1, only the labels are plotted and their size is proportional to the weights of the categories; if 2, points (triangles) and labels are plotted, and points size is proportional to the weight of the categories. |
... |
further arguments passed to or from other methods, such as cex, cex.main, ... |
A category is considered to be one of the most correlated to a given axis if its test-value is higher then 2.58 (which corresponds to a 0.05 threshold).
Nicolas Robette
Robette, Bry and Roueff, 2014, "Un dialogue de sourds dans le theatre statistique? Analyse geometrique des donnees et effets de structure", forthcoming
stMCA
, textvarsup
, conc.ellipse
## Performs a standardized MCA on 'Music' example data set ## ignoring every 'NA' (i.e. 'not available') categories ## and controlling for age, ## and then draws the cloud of categories. data(Music) mca <- speMCA(Music[,1:5],excl=c(3,6,9,12,15)) stmca <- stMCA(mca,control=list(Music$Age)) plot(stmca) plot(stmca,axes=c(2,3),points='best',col='darkred',app=1)
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