get_mfa: Extract the results for individuals/variables/group/partial...

In factoextra: Extract and Visualize the Results of Multivariate Data Analyses

Description

Extract all the results (coordinates, squared cosine and contributions) for the active individuals/quantitative variables/qualitative variable categories/groups/partial axes from Multiple Factor Analysis (MFA) outputs.

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

• get_mfa_ind(): Extract the results for individuals only

• get_mfa_var(): Extract the results for variables (quantitatives, qualitatives and groups)

• get_mfa_partial_axes(): Extract the results for partial axes only

Usage

get_mfa(
  res.mfa,
  element = c("ind", "quanti.var", "quali.var", "group", "partial.axes")
)

get_mfa_ind(res.mfa)

get_mfa_var(res.mfa, element = c("quanti.var", "quali.var", "group"))

get_mfa_partial_axes(res.mfa)

Arguments

res.mfa	an object of class MFA [FactoMineR].
element	the element to subset from the output. Possible values are "ind", "quanti.var", "quali.var", "group" or "partial.axes".

Value

a list of matrices containing the results for the active individuals/quantitative variable categories/qualitative variable categories/groups/partial axes including :

coord	coordinates for the individuals/quantitative variable categories/qualitative variable categories/groups/partial axes
cos2	cos2 for the individuals/quantitative variable categories/qualitative variable categories/groups/partial axes
contrib	contributions of the individuals/quantitative variable categories/qualitative variable categories/groups/partial axes
inertia	inertia of the individuals/quantitative variable categories/qualitative variable categories/groups/partial axes

Author(s)

Alboukadel Kassambara [e-mail]

Fabian Mundt [e-mail]

Examples

# Multiple Factor Analysis
# ++++++++++++++++++++++++
# Install and load FactoMineR to compute MFA
# install.packages("FactoMineR")
library("FactoMineR")
data(poison)
res.mfa <- MFA(poison, group=c(2,2,5,6), type=c("s","n","n","n"),
name.group=c("desc","desc2","symptom","eat"), num.group.sup=1:2,
graph = FALSE)
 
 # Extract the results for qualitative variable categories
 var <- get_mfa_var(res.mfa, "quali.var")
 print(var)
 head(var$coord) # coordinates of qualitative variables
 head(var$cos2) # cos2 of qualitative variables
 head(var$contrib) # contributions of qualitative variables
 
 # Extract the results for individuals
 ind <- get_mfa_ind(res.mfa)
 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_mfa()
 get_mfa(res.mfa, "ind") # Results for individuals
 get_mfa(res.mfa, "quali.var") # Results for qualitative variable categories
 

Example output

Loading required package: ggplot2
Welcome! Related Books: `Practical Guide To Cluster Analysis in R` at https://goo.gl/13EFCZ
Multiple Factor Analysis results for qualitative variable categories 
 ===================================================
  Name       Description                      
1 "$coord"   "Coordinates"                    
2 "$cos2"    "Cos2, quality of representation"
3 "$contrib" "Contributions"                  
              Dim.1        Dim.2       Dim.3       Dim.4       Dim.5
Nausea_n  0.2995559 -0.008263233 -0.15087999 -0.05120534  0.02684045
Nausea_y -1.0734086  0.029609918  0.54065330  0.18348582 -0.09617827
Vomit_n   0.4923055 -0.335833158  0.06315216  0.20249017  0.03344304
Vomit_y  -0.7384582  0.503749737 -0.09472824 -0.30373526 -0.05016455
Abdo_n    1.4594717 -0.253368918 -0.02659215 -0.10539596 -0.03153966
Abdo_y   -0.7100132  0.123260555  0.01293672  0.05127371  0.01534362
             Dim.1        Dim.2       Dim.3       Dim.4        Dim.5
Nausea_n 0.5675315 0.0004318514 0.143978489 0.016583068 0.0045563154
Nausea_y 0.5675315 0.0004318514 0.143978489 0.016583068 0.0045563154
Vomit_n  0.5335539 0.2482881886 0.008779814 0.090264467 0.0024621829
Vomit_y  0.5335539 0.2482881886 0.008779814 0.090264467 0.0024621829
Abdo_n   0.9163124 0.0276158818 0.000304200 0.004778594 0.0004279236
Abdo_y   0.9163124 0.0276158818 0.000304200 0.004778594 0.0004279236
             Dim.1       Dim.2       Dim.3     Dim.4      Dim.5
Nausea_n  1.041575 0.002357361 1.112155571 0.1542638 0.05473887
Nausea_y  3.732310 0.008447211 3.985224130 0.5527787 0.19614761
Vomit_n   2.158985 2.988262782 0.149528552 1.8513432 0.06521886
Vomit_y   3.238478 4.482394173 0.224292828 2.7770149 0.09782829
Abdo_n   10.349759 0.927762633 0.014461496 0.2735807 0.03163984
Abdo_y    5.035018 0.451343984 0.007035322 0.1330933 0.01539236
Multiple Factor Analysis results for individuals 
 ===================================================
  Name                      Description                      
1 "$coord"                  "Coordinates"                    
2 "$cos2"                   "Cos2, quality of representation"
3 "$contrib"                "Contributions"                  
4 "$coord.partiel"          "Partial coordinates"            
5 "$within.inertia"         "Within inertia"                 
6 "$within.partial.inertia" "Within partial inertia"         
       Dim.1       Dim.2       Dim.3       Dim.4      Dim.5
1 -0.8980293 -0.19968268  0.10527090 -0.06375957 -0.2468778
2  1.6550439 -0.41095346 -0.16606815 -0.49335618  1.4414215
3 -0.8673037  0.09906989 -0.27126101 -0.42418672 -0.2250902
4  1.7839172 -0.56856945 -0.04179541 -0.09829156 -0.6203236
5 -0.8673037  0.09906989 -0.27126101 -0.42418672 -0.2250902
6 -1.1229099 -1.07440938 -4.26919514  3.89647532  0.9498171
       Dim.1       Dim.2        Dim.3       Dim.4      Dim.5
1 0.35650962 0.017626699 0.0048989935 0.001797136 0.02694352
2 0.39631064 0.024434418 0.0039901519 0.035215824 0.30060672
3 0.50651695 0.006608988 0.0495480243 0.121161803 0.03411653
4 0.60082570 0.061033205 0.0003298044 0.001824029 0.07265003
5 0.50651695 0.006608988 0.0495480243 0.121161803 0.03411653
6 0.03403476 0.031158209 0.4919544784 0.409804540 0.02435078
      Dim.1      Dim.2        Dim.3       Dim.4     Dim.5
1 1.0075082 0.08591005  0.028403241  0.01143425 0.1948150
2 3.4220518 0.36387188  0.070684466  0.68460183 6.6411041
3 0.9397448 0.02114689  0.188593192  0.50609384 0.1619465
4 3.9757303 0.69651464  0.004477221  0.02717374 1.2299713
5 0.9397448 0.02114689  0.188593192  0.50609384 0.1619465
6 1.5752786 2.48715469 46.713592970 42.70323449 2.8836218
Multiple Factor Analysis results for individuals 
 ===================================================
  Name                      Description                      
1 "$coord"                  "Coordinates"                    
2 "$cos2"                   "Cos2, quality of representation"
3 "$contrib"                "Contributions"                  
4 "$coord.partiel"          "Partial coordinates"            
5 "$within.inertia"         "Within inertia"                 
6 "$within.partial.inertia" "Within partial inertia"         
Multiple Factor Analysis results for qualitative variable categories 
 ===================================================
  Name       Description                      
1 "$coord"   "Coordinates"                    
2 "$cos2"    "Cos2, quality of representation"
3 "$contrib" "Contributions"                  

factoextra documentation built on April 2, 2020, 1:09 a.m.