get_mca: Extract the results for individuals/variables in Multiple Correspondence Analysis


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

These functions are included in factoextra package.

Install and load factoextra

The package devtools is required for the installation as factoextra is hosted on github.

# install.packages("devtools")
library("devtools")
install_github("kassambara/factoextra")

Load factoextra :

library("factoextra")

Usage

# Extract the results for variables and individuals
get_mca(res.mca, element = c("var", "ind"))
# Extract the results for individuals only
get_mca_var(res.mca)
# Extract the results for variables only
get_mca_ind(res.mca)

Arguments

Argument Description
res.mca an object of class MCA [FactoMineR], acm [ade4].
element the element to subset from the output. Allowed values are “var” (for active variables) or “ind” (for active individuals).

Value

A list of matrices containing all the results for the active individuals/variables including:

  • coord: coordinates for the individuals/variables
  • cos2: cos2 for the individuals/variables
  • contrib: contributions of the individuals/variables

Examples

Multiple Correspondence Analysis

A Multiple Correspondence Analysis (MCA) is performed using the function MCA() [in FactoMineR] and poison data [in FactoMineR]:

# Multiple Correspondence Analysis
# ++++++++++++++++++++++++++++++
# Install and load FactoMineR to compute MCA
# install.packages("FactoMineR")
library("FactoMineR")
data(poison)
poison.active <- poison[1:55, 5:15]
head(poison.active[, 1:6])
    Nausea Vomiting Abdominals   Fever   Diarrhae   Potato
1 Nausea_y  Vomit_n     Abdo_y Fever_y Diarrhea_y Potato_y
2 Nausea_n  Vomit_n     Abdo_n Fever_n Diarrhea_n Potato_y
3 Nausea_n  Vomit_y     Abdo_y Fever_y Diarrhea_y Potato_y
4 Nausea_n  Vomit_n     Abdo_n Fever_n Diarrhea_n Potato_y
5 Nausea_n  Vomit_y     Abdo_y Fever_y Diarrhea_y Potato_y
6 Nausea_n  Vomit_n     Abdo_y Fever_y Diarrhea_y Potato_y
res.mca <- MCA(poison.active, graph=FALSE)

Extract the results for variables

# Extract the results for variable categories
var <- get_mca_var(res.mca)
print(var)
Multiple Correspondence Analysis Results for variables
 ===================================================
  Name       Description                  
1 "$coord"   "Coordinates for categories" 
2 "$cos2"    "Cos2 for categories"        
3 "$contrib" "contributions of categories"
head(var$coord) # coordinates of variables
              Dim 1       Dim 2        Dim 3       Dim 4       Dim 5
Nausea_n  0.2673909  0.12139029 -0.265583253  0.03376130  0.07370500
Nausea_y -0.9581506 -0.43498187  0.951673323 -0.12097801 -0.26410958
Vomit_n   0.4790279 -0.40919465  0.084492799  0.27361142  0.05245250
Vomit_y  -0.7185419  0.61379197 -0.126739198 -0.41041713 -0.07867876
Abdo_n    1.3180221 -0.03574501 -0.005094243 -0.15360951 -0.06986987
Abdo_y   -0.6411999  0.01738946  0.002478280  0.07472895  0.03399075
head(var$cos2) # cos2 of variables
             Dim 1        Dim 2        Dim 3       Dim 4       Dim 5
Nausea_n 0.2562007 0.0528025759 2.527485e-01 0.004084375 0.019466197
Nausea_y 0.2562007 0.0528025759 2.527485e-01 0.004084375 0.019466197
Vomit_n  0.3442016 0.2511603912 1.070855e-02 0.112294813 0.004126898
Vomit_y  0.3442016 0.2511603912 1.070855e-02 0.112294813 0.004126898
Abdo_n   0.8451157 0.0006215864 1.262496e-05 0.011479077 0.002374929
Abdo_y   0.8451157 0.0006215864 1.262496e-05 0.011479077 0.002374929
head(var$contrib) # contributions of variables
             Dim 1       Dim 2        Dim 3      Dim 4      Dim 5
Nausea_n  1.515869  0.81100008 4.670018e+00 0.08449397 0.48977906
Nausea_y  5.431862  2.90608363 1.673423e+01 0.30277007 1.75504164
Vomit_n   3.733667  7.07226253 3.627455e-01 4.25893721 0.19036376
Vomit_y   5.600500 10.60839380 5.441183e-01 6.38840581 0.28554563
Abdo_n   15.417637  0.02943661 7.192511e-04 0.73219636 0.18424268
Abdo_y    7.500472  0.01432051 3.499060e-04 0.35620363 0.08963157

Extract the results for individuals

# Extract the results for individuals
ind <- get_mca_ind(res.mca)
print(ind)
Multiple Correspondence Analysis Results for individuals
 ===================================================
  Name       Description                       
1 "$coord"   "Coordinates for the individuals" 
2 "$cos2"    "Cos2 for the individuals"        
3 "$contrib" "contributions of the individuals"
head(ind$coord) # coordinates of individuals
       Dim 1       Dim 2       Dim 3       Dim 4       Dim 5
1 -0.4525811 -0.26415072  0.17151614  0.01369348 -0.11696806
2  0.8361700 -0.03193457 -0.07208249 -0.08550351  0.51978710
3 -0.4481892  0.13538726 -0.22484048 -0.14170168 -0.05004753
4  0.8803694 -0.08536230 -0.02052044 -0.07275873 -0.22935022
5 -0.4481892  0.13538726 -0.22484048 -0.14170168 -0.05004753
6 -0.3594324 -0.43604390 -1.20932223  1.72464616  0.04348157
head(ind$cos2) # cos2 of individuals
       Dim 1        Dim 2        Dim 3        Dim 4        Dim 5
1 0.34652591 0.1180447167 0.0497683175 0.0003172275 0.0231460846
2 0.55589562 0.0008108236 0.0041310808 0.0058126211 0.2148103098
3 0.54813888 0.0500176790 0.1379484860 0.0547920948 0.0068349171
4 0.74773962 0.0070299584 0.0004062504 0.0051072923 0.0507479873
5 0.54813888 0.0500176790 0.1379484860 0.0547920948 0.0068349171
6 0.02485357 0.0365775483 0.2813443706 0.5722083217 0.0003637178
head(ind$contrib) # contributions of individuals
     Dim 1      Dim 2        Dim 3        Dim 4      Dim 5
1 1.110927 0.98238297  0.498254685  0.003555817 0.31554778
2 3.792117 0.01435818  0.088003703  0.138637089 6.23134138
3 1.089470 0.25806722  0.856229950  0.380768961 0.05776914
4 4.203611 0.10259105  0.007132055  0.100387990 1.21319013
5 1.089470 0.25806722  0.856229950  0.380768961 0.05776914
6 0.700692 2.67693398 24.769968729 56.404214518 0.04360547

get_mca: Extract the results for both individuals and variables

# You can also use the function get_mca()
get_mca(res.mca, "ind") # Results for individuals
Multiple Correspondence Analysis Results for individuals
 ===================================================
  Name       Description                       
1 "$coord"   "Coordinates for the individuals" 
2 "$cos2"    "Cos2 for the individuals"        
3 "$contrib" "contributions of the individuals"
get_mca(res.mca, "var") # Results for variable categories
Multiple Correspondence Analysis Results for variables
 ===================================================
  Name       Description                  
1 "$coord"   "Coordinates for categories" 
2 "$cos2"    "Cos2 for categories"        
3 "$contrib" "contributions of categories"

Infos

This analysis has been performed using R software (ver. 3.1.2) and factoextra (ver. 1.0.2)


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