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
an object of class MCA [FactoMineR], acm [ade4], expoOutput/epMCA [ExPosition].
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, "quanti.sup" for quantitative supplementary variables.
a list of matrices containing the results for the active individuals/variable categories including :
coordinates for the individuals/variable categories
cos2 for the individuals/variable categories
contributions of the individuals/variable categories
inertia of the individuals/variable categories
# \donttest{
# 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 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
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
# 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"
# }