Plots Multiple Factor Analysis data, resulting from
object of class
character string: 'v' to plot the categories (default), 'i' to plot individuals' points, 'inames' to plot individuals' names
numeric vector of length 2, specifying the components (axes) to plot (c(1,2) is default)
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.
numeric value. V-test minimal value for the selection of plotted categories.
numeric vector specifying the groups of categories to plot. By default, every groups of categories will be plotted
a color for the points of the individuals or a vector of colors for the labels of the groups of categories (by default, rainbow palette is used)
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).
Escofier, B. and Pages, J. (1994) "Multiple Factor Analysis (AFMULT package)". Computational Statistics and Data Analysis, 18, 121-140.
# specific MCA on music variables of Taste example data set ## another one on movie variables of 'Taste' example data set, ## and then a Multiple Factor Analysis and plots the results. data(Taste) # specific MCA on music variables of Taste example data set mca1 <- speMCA(Taste[,1:5], excl = c(3,6,9,12,15)) # specific MCA on movie variables of Taste example data set mca2 <- speMCA(Taste[,6:11], excl = c(3,6,9,12,15,18)) # Multiple Factor Analysis mfa <- multiMCA(list(mca1,mca2)) # plot plot.multiMCA(mfa, col = c("darkred", "darkblue")) # plot of the second set of variables (movie) plot.multiMCA(mfa, groups = 2, app = 1)