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

In missMDA: Handling Missing Values with Multivariate Data Analysis

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 class '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

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.

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

Author(s)

Audigier Vincent [e-mail], Francois Husson [e-mail] and Julie Josse [e-mail]

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)

## End(Not run)

missMDA documentation built on Nov. 17, 2023, 5:07 p.m.