Ellipses for bootstrap validation of MCA, through the computation of the coordinates of supplementary variables for bootstrap replications of the data.
ggbootvalid_supvars(resmca, vars = NULL, axes = c(1,2), K = 30, ellipse = "norm", level = 0.95, col = NULL, active = FALSE, legend = "right")
object of class
A data frame of categorical supplementary variables. All these variables should be factors.
numeric vector of length 2, specifying the components (axes) to plot. Default is c(1,2).
integer. Number of bootstrap replications (default is 30).
character string. The type of ellipse. The default "norm" assumes a multivariate normal distribution, "t" assumes a multivariate t-distribution, and "euclid" draws a circle with the radius equal to level, representing the euclidean distance from the center.
numerical value. The level at which to draw an ellipse, or, if
ellipse="euclid", the radius of the circle to be drawn.
Character string. Color name for the ellipses and labels of the categories. If NULL (default), the default
ggplot2 palette is used, with one color per variable.
logical. If TRUE, the labels of active variables are added to the plot in lightgray. Default is FALSE.
the position of legends ("none", "left", "right", "bottom", "top", or two-element numeric vector). Default is right.
The bootstrap technique is used here as an internal (and non-parametric) validation procedure of the results of a multiple correspondence analysis. For supplementary variables, only partial bootstrap is possible. The partial bootstrap does not compute new MCAs: it projects bootstrap replications of the initial data as supplementary elements of the MCA. See references for more details.
The default parameters for ellipses assume a multivariate normal distribution drawn at level 0.95.
col argument is NULL, ellipses and labels are colored according to the variables, using the default
ggplot2 palette. The palette can be customized using any
scale_color_* function, such as
Lebart L. (2006). "Validation Techniques in Multiple Correspondence Analysis". In M. Greenacre et J. Blasius (eds), Multiple Correspondence Analysis and related techniques, Chapman and Hall/CRC, p.179-196.
Lebart L. (2007). "Which bootstrap for principal axes methods?". In P. Brito et al. (eds), Selected Contributions in Data Analysis and Classification, Springer, p.581-588.
# specific MCA of Taste example data set data(Taste) junk <- c("FrenchPop.NA", "Rap.NA", "Rock.NA", "Jazz.NA", "Classical.NA", "Comedy.NA", "Crime.NA", "Animation.NA", "SciFi.NA", "Love.NA", "Musical.NA") mca <- speMCA(Taste[,1:11], excl = junk) # bootstrap validation ellipses # for three supplementary variables sup <- Taste[,c("Gender", "Age", "Educ")] ggbootvalid_supvars(mca, sup)