factoextra (version 2.2.0)

get_mca: Extract the results for individuals/variables - MCA

Description

Extract all the results (coordinates, squared cosine and contributions) for the active individuals/variable categories from Multiple Correspondence Analysis (MCA) outputs.

• get_mca(): Extract the results for variables and individuals

• get_mca_ind(): Extract the results for individuals only

• get_mca_var(): Extract the results for variables only

For FactoMineR MCA results, get_mca() and get_mca_var() also support element = "quanti.sup" for quantitative supplementary variables and report a clean package-level error when that result is absent.

Read more: Multiple Correspondence Analysis (MCA) in R: Compute, Visualize & Interpret.

Usage

get_mca(res.mca, element = c("var", "ind", "mca.cor", "quanti.sup"))
get_mca_var(res.mca, element = c("var", "mca.cor", "quanti.sup"))
get_mca_ind(res.mca)

Value

a list of matrices containing the results for the active individuals/variable categories, including:

coord

coordinates for the individuals/variable categories

cos2

cos2 for the individuals/variable categories

contrib

contributions of the individuals/variable categories

inertia

inertia of the individuals/variable categories

Arguments

res.mca

an object of class MCA [FactoMineR], acm [ade4], expoOutput/epMCA [ExPosition].

element

the element to subset from the output. Possible values are "var" for variables, "ind" for individuals, "mca.cor" for correlation between variables and principal dimensions, and "quanti.sup" for quantitative supplementary variables in FactoMineR MCA results.

Author

Alboukadel Kassambara

References

https://www.datanovia.com/learn/

See Also

fviz_mca. Online tutorial: Multiple Correspondence Analysis (MCA) in R: Compute, Visualize & Interpret.

Examples

# \donttest{
# Multiple Correspondence Analysis
# ++++++++++++++++++++++++++++++
# Install and load FactoMineR to compute MCA
# install.packages("FactoMineR")
library("FactoMineR")
data(poison)
res.mca <- MCA(poison, quanti.sup = 1:2, graph = FALSE)
 
 # Extract the results for variable categories
 var <- get_mca_var(res.mca)
 print(var)
 head(var$coord) # coordinates of variables
 head(var$cos2) # cos2 of variables
 head(var$contrib) # contributions of variables
 
 # Extract the results for individuals
 ind <- get_mca_ind(res.mca)
 print(ind)
 head(ind$coord) # coordinates of individuals
 head(ind$cos2) # cos2 of individuals
 head(ind$contrib) # contributions of individuals
 
 # You can also use the function get_mca()
 get_mca(res.mca, "ind") # Results for individuals
 get_mca(res.mca, "var") # Results for variable categories
 quanti.sup <- get_mca(res.mca, "quanti.sup")
 head(quanti.sup$coord) # coordinates of quantitative supplementary variables
 # }