plot.MIFAMD: Plot the graphs for the Multiple Imputation in FAMD

In missMDA: Handling Missing Values with Multivariate Data Analysis

Plot the graphs for the Multiple Imputation in FAMD

Description

From the multiple imputed datasets, the function plots graphs for the individuals, categories and dimensions for the Factor Analysis of Mixed Data (FAMD)

Usage

## S3 method for class 'MIFAMD'
plot(x, choice = "all", axes = c(1, 2), new.plot = TRUE, 
   main = NULL, level.conf = 0.95, graph.type = c("ggplot","classic"), ...)

Arguments

x	an object of class MIFAMD
choice	the graph(s) to plot. By default "all" the graphs are plotted. "ind.proc" the procrustean representation of the individuals, "dim" the representation of the dimensions of the FAMD, "ind.supp" the projection of the individuals as supplementary individuals, "mod.supp" the projection of the categories, "var" the projection of the quantitative variables as supplementary variables
axes	a length 2 vector specifying the components to plot
new.plot	boolean, if TRUE, a new graphical device is created
main	string corresponding to the title of the graph you draw (by default NULL and a title is chosen)
level.conf	confidence level used to construct the ellipses. By default, 0.95
graph.type	a character that gives the type of graph used: "ggplot" or "classic"
...	further arguments passed to or from other methods

Details

Plots the multiple imputed datasets obtained by the function MIFAMD. The idea is to represent the multiple imputed dataset on a reference configuration (the map obtained from the FAMD on the incomplete dataset). Different ways are available to take into account and visualize the supplement variability due to missing values.

Value

Four graphs can be drawn:

ind.supp	The individuals of the imputed datasets are projected as supplementary individuals onto the reference FAMD map; then confidence ellipses are drawn
mod.supp	The individuals of the imputed datasets are projected as supplementary individuals onto the reference FAMD map, but only categories are plotted; then confidence ellipses are drawn
ind.proc	A FAMD is performed on each imputed dataset and each configuration of scores is rotated onto the reference FAMD map with procrustes rotation; then confidence ellipses are drawn
var	The quantitative variables of the imputed datasets are projected as supplementary variables onto the reference FAMD map
dim	The dimensions of each imputed dataset are projected as supplementary variables onto the dimensions of the reference FAMD dimensions

Author(s)

Francois Husson [e-mail]

References

Audigier, V., Husson, F. & Josse, J. (2015). A principal components method to impute mixed data. Advances in Data Analysis and Classification, 10(1), 5-26. <doi:10.1007/s11634-014-0195-1>

See Also

MIFAMD,imputeFAMD

Examples

## Not run: 
data(ozone)

## First the number of components has to be chosen 
##   (for the reconstruction step)
## nb <- estim_ncpFAMD(ozone) ## Time-consuming, nb = 2


## Multiple Imputation
res.mi<-MIFAMD(ozone,ncp = 2,nboot=50)

## Plot the graphs
plot(res.mi)

## End(Not run)

missMDA documentation built on Feb. 12, 2026, 5:07 p.m.