missMDA (version 1.14)

plot.MIMCA: Plot the graphs for the Multiple Imputation in MCA

Description

From the multiple imputed datasets, the function plots graphs for the individuals, categories and dimensions for the Multiple Correspondance Analysis (MCA)

Usage

# S3 method for MIMCA
plot(x, choice = "all", axes = c(1, 2), new.plot = TRUE, 
   main = NULL, level.conf = 0.95, …)

Arguments

x

an object of class MIMCA

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 MCA, "ind.supp" the projection of the individuals as supplementary individuals, "mod.supp" the projection of the categories

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

…

further arguments passed to or from other methods

Value

Four graphs can be drawn:

ind.supp

The individuals of the imputed datasets are projected as supplementary individuals onto the reference MCA map; then confidence ellipses are drawn

mod.supp

The individuals of the imputed datasets are projected as supplementary individuals onto the reference MCA map, but only categories are plotted; then confidence ellipses are drawn

ind.proc

A PCA is performed on each imputed dataset and each configuration of scores is rotated onto the reference MCA map with procrustes rotation; then confidence ellipses are drawn

dim

The dimensions of each imputed dataset are projected as supplementary variables onto the dimensions of the reference MCA dimensions

Details

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

References

Audigier, V., Husson, F., Josse, J. (2016). MIMCA: Multiple imputation for categorical variables with multiple correspondence analysis

See Also

MIMCA,imputeMCA

Examples

# NOT RUN {
data(TitanicNA)

## First the number of components has to be chosen 
##   (for the reconstruction step)
## nb <- estim_ncpMCA(TitanicNA) ## Time-consuming, nb = 5

## Multiple Imputation
res.mi <- MIMCA(TitanicNA, ncp=5, verbose=TRUE)

## Plot the graphs
plot(res.mi)
# }