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

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)

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, "quanti.sup" for quantitative supplementary variables.

Value

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

References

http://www.sthda.com/english/

Examples

# 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"