install.packages(c(“FactoMineR”, “factoextra”))

library("FactoMineR")
library("factoextra")
## Loading required package: ggplot2
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
library("ade4")
## 
## Attaching package: 'ade4'
## The following object is masked from 'package:FactoMineR':
## 
##     reconst
library("ExPosition")
## Loading required package: prettyGraphs
library(forecast)
## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo
library(h2o)
## 
## ----------------------------------------------------------------------
## 
## Your next step is to start H2O:
##     > h2o.init()
## 
## For H2O package documentation, ask for help:
##     > ??h2o
## 
## After starting H2O, you can use the Web UI at http://localhost:54321
## For more information visit https://docs.h2o.ai
## 
## ----------------------------------------------------------------------
## 
## Attaching package: 'h2o'
## The following objects are masked from 'package:stats':
## 
##     cor, sd, var
## The following objects are masked from 'package:base':
## 
##     &&, %*%, %in%, ||, apply, as.factor, as.numeric, colnames,
##     colnames<-, ifelse, is.character, is.factor, is.numeric, log,
##     log10, log1p, log2, round, signif, trunc
library(TSstudio)
library(tidyr)
library(Quandl)
## Loading required package: xts
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
library(ggplot2)
library(maps)
library(ggmap)
## ℹ Google's Terms of Service: <https://mapsplatform.google.com>
## ℹ Please cite ggmap if you use it! Use `citation("ggmap")` for details.
library(sp)
library(stats)
library(graphics)
library(rnaturalearth)
## Support for Spatial objects (`sp`) will be deprecated in {rnaturalearth} and will be removed in a future release of the package. Please use `sf` objects with {rnaturalearth}. For example: `ne_download(returnclass = 'sf')`
library(listviewer)
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggmap':
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##     wind
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##     layout
library(dplyr)
## 
## ######################### Warning from 'xts' package ##########################
## #                                                                             #
## # The dplyr lag() function breaks how base R's lag() function is supposed to  #
## # work, which breaks lag(my_xts). Calls to lag(my_xts) that you type or       #
## # source() into this session won't work correctly.                            #
## #                                                                             #
## # Use stats::lag() to make sure you're not using dplyr::lag(), or you can add #
## # conflictRules('dplyr', exclude = 'lag') to your .Rprofile to stop           #
## # dplyr from breaking base R's lag() function.                                #
## #                                                                             #
## # Code in packages is not affected. It's protected by R's namespace mechanism #
## # Set `options(xts.warn_dplyr_breaks_lag = FALSE)` to suppress this warning.  #
## #                                                                             #
## ###############################################################################
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:xts':
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##     first, last
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(carat)
library(sf)
## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1; sf_use_s2() is TRUE

#SECCIÓN 5

data(poison)
head(poison[, 1:7], 3)
##   Age Time   Sick Sex   Nausea Vomiting Abdominals
## 1   9   22 Sick_y   F Nausea_y  Vomit_n     Abdo_y
## 2   5    0 Sick_n   F Nausea_n  Vomit_n     Abdo_n
## 3   6   16 Sick_y   F Nausea_n  Vomit_y     Abdo_y
poison.active <- poison[1:55, 5:15]
head(poison.active[, 1:6],3)
##     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
summary(poison.active)[, 1:4]
##       Nausea      Vomiting   Abdominals     Fever   
##  Nausea_n:43   Vomit_n:33   Abdo_n:18   Fever_n:20  
##  Nausea_y:12   Vomit_y:22   Abdo_y:37   Fever_y:35
for (i in 1:4) {
plot(poison.active[,i], main=colnames(poison.active)[i],
ylab = "Count", col= "steelblue", las = 2)
}

MCA(poison.active, ncp = 5, graph = TRUE)
## Warning: ggrepel: 10 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

## **Results of the Multiple Correspondence Analysis (MCA)**
## The analysis was performed on 55 individuals, described by 11 variables
## *The results are available in the following objects:
## 
##    name              description                       
## 1  "$eig"            "eigenvalues"                     
## 2  "$var"            "results for the variables"       
## 3  "$var$coord"      "coord. of the categories"        
## 4  "$var$cos2"       "cos2 for the categories"         
## 5  "$var$contrib"    "contributions of the categories" 
## 6  "$var$v.test"     "v-test for the categories"       
## 7  "$var$eta2"       "coord. of variables"             
## 8  "$ind"            "results for the individuals"     
## 9  "$ind$coord"      "coord. for the individuals"      
## 10 "$ind$cos2"       "cos2 for the individuals"        
## 11 "$ind$contrib"    "contributions of the individuals"
## 12 "$call"           "intermediate results"            
## 13 "$call$marge.col" "weights of columns"              
## 14 "$call$marge.li"  "weights of rows"
res.mca <- MCA(poison.active, graph = FALSE)
print (res.mca)
## **Results of the Multiple Correspondence Analysis (MCA)**
## The analysis was performed on 55 individuals, described by 11 variables
## *The results are available in the following objects:
## 
##    name              description                       
## 1  "$eig"            "eigenvalues"                     
## 2  "$var"            "results for the variables"       
## 3  "$var$coord"      "coord. of the categories"        
## 4  "$var$cos2"       "cos2 for the categories"         
## 5  "$var$contrib"    "contributions of the categories" 
## 6  "$var$v.test"     "v-test for the categories"       
## 7  "$var$eta2"       "coord. of variables"             
## 8  "$ind"            "results for the individuals"     
## 9  "$ind$coord"      "coord. for the individuals"      
## 10 "$ind$cos2"       "cos2 for the individuals"        
## 11 "$ind$contrib"    "contributions of the individuals"
## 12 "$call"           "intermediate results"            
## 13 "$call$marge.col" "weights of columns"              
## 14 "$call$marge.li"  "weights of rows"
eig.val <- get_eigenvalue(res.mca)
# head(eig.val)
fviz_screeplot(res.mca, addlabels = TRUE, ylim = c(0,45))

fviz_mca_biplot(res.mca,
                repel = TRUE, #Avoid text overlapping (slow if many point
                ggtheme = theme_minimal())

var <- get_mca_var(res.mca)
var
## Multiple Correspondence Analysis Results for variables
##  ===================================================
##   Name       Description                  
## 1 "$coord"   "Coordinates for categories" 
## 2 "$cos2"    "Cos2 for categories"        
## 3 "$contrib" "contributions of categories"
# Coordinates
head(var$coord)
##               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
# Cos2: quality on the factore map
head(var$cos2)
##              Dim 1        Dim 2        Dim 3       Dim 4       Dim 5
## Nausea_n 0.2562007 0.0528025759 2.527485e-01 0.004084375 0.019466197
## Nausea_y 0.2562007 0.0528025759 2.527485e-01 0.004084375 0.019466197
## Vomit_n  0.3442016 0.2511603912 1.070855e-02 0.112294813 0.004126898
## Vomit_y  0.3442016 0.2511603912 1.070855e-02 0.112294813 0.004126898
## Abdo_n   0.8451157 0.0006215864 1.262496e-05 0.011479077 0.002374929
## Abdo_y   0.8451157 0.0006215864 1.262496e-05 0.011479077 0.002374929
#Contributions to the principal components
head(var$contrib)
##              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
fviz_mca_var(res.mca, choice = "mca.cor",
             repel = TRUE, #Avoid text overlapping (slow)
             ggtheme = theme_minimal())

head(round(var$coord, 2), 4)
##          Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## Nausea_n  0.27  0.12 -0.27  0.03  0.07
## Nausea_y -0.96 -0.43  0.95 -0.12 -0.26
## Vomit_n   0.48 -0.41  0.08  0.27  0.05
## Vomit_y  -0.72  0.61 -0.13 -0.41 -0.08
fviz_mca_var(res.mca,
             repel = TRUE, #Avoid text overlapping (slow)
             ggtheme = theme_minimal())

fviz_mca_var(res.mca, col.var="black", shape.var =15,
             repel = TRUE)

head(var$cos2, 4)
##              Dim 1      Dim 2      Dim 3       Dim 4       Dim 5
## Nausea_n 0.2562007 0.05280258 0.25274850 0.004084375 0.019466197
## Nausea_y 0.2562007 0.05280258 0.25274850 0.004084375 0.019466197
## Vomit_n  0.3442016 0.25116039 0.01070855 0.112294813 0.004126898
## Vomit_y  0.3442016 0.25116039 0.01070855 0.112294813 0.004126898
#Color by cos2 values: quality on the factor map
fviz_mca_var(res.mca, col.var = "cos2",
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE, # Avoid text overlapping
             ggtheme = theme_minimal())

# Change the transparency by cos2 values

fviz_mca_var(res.mca, alpha.var="cos2",
             repel = TRUE,
             ggtheme = theme_minimal())

library("corrplot")
## corrplot 0.92 loaded
corrplot(var$cos2, is.corr=FALSE)

# Cos2 of variable categories on Dim.1 and Dim.2
fviz_cos2(res.mca, choice = "var", axes = 1:2)

head(round(var$contrib,2), 4)
##          Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## Nausea_n  1.52  0.81  4.67  0.08  0.49
## Nausea_y  5.43  2.91 16.73  0.30  1.76
## Vomit_n   3.73  7.07  0.36  4.26  0.19
## Vomit_y   5.60 10.61  0.54  6.39  0.29
# Contributions of rows to dimension 1
fviz_contrib(res.mca, choice = "var", axes = 1, top = 15)

# Contributions of rows to dimension 2
fviz_contrib(res.mca, choice = "var", axes = 2, top = 15)

# Total contribution to dimension 1 and 2
fviz_contrib(res.mca, choice = "var", axes = 1:2, top = 15)

fviz_mca_var(res.mca, col.var = "contrib",
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE, # avoid text overlapping (slow)
             ggtheme = theme_minimal()
             )

# Change the transparency by contrib values
fviz_mca_var(res.mca, alpha.var="contrib",
             repel = TRUE,
             ggtheme = theme_minimal())

ind <- get_mca_ind(res.mca)
ind
## Multiple Correspondence Analysis Results for individuals
##  ===================================================
##   Name       Description                       
## 1 "$coord"   "Coordinates for the individuals" 
## 2 "$cos2"    "Cos2 for the individuals"        
## 3 "$contrib" "contributions of the individuals"
# Coordinates of colum points
head(ind$coord)
##        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
# Quality of representation
head(ind$cos2)
##        Dim 1        Dim 2        Dim 3        Dim 4        Dim 5
## 1 0.34652591 0.1180447167 0.0497683175 0.0003172275 0.0231460846
## 2 0.55589562 0.0008108236 0.0041310808 0.0058126211 0.2148103098
## 3 0.54813888 0.0500176790 0.1379484860 0.0547920948 0.0068349171
## 4 0.74773962 0.0070299584 0.0004062504 0.0051072923 0.0507479873
## 5 0.54813888 0.0500176790 0.1379484860 0.0547920948 0.0068349171
## 6 0.02485357 0.0365775483 0.2813443706 0.5722083217 0.0003637178
# Contributions
head(ind$contrib)
##      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
fviz_mca_ind(res.mca, col.ind = "cos2",
             gradients.col = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE, # Avoid text overlapping (slow if many points
             ggtheme = theme_minimal())

# Cos2 of individuals
fviz_cos2(res.mca, choice = "ind", axes = 1:2, top = 20)

# Contribution of individuals to the dimensions
fviz_contrib(res.mca, choice= "ind", axes = 1:2, top = 20)

fviz_mca_ind(res.mca,
             label = "none", # hide individual labels
             habillage = "Vomiting", # color by groups
             pallete = c("#00AFBB", "#E7B800"),
             addEllipses = TRUE, ellipse.type = "confidence",
             ggtheme = theme_minimal())

# habillage = index of the colum to be used as grouping variable
fviz_mca_ind(res.mca, habillage = 2, addEllipses = TRUE)

# habillage = external grouping variable
fviz_mca_ind(res.mca, habillage = poison$Vomiting, addEllipses = TRUE)

fviz_ellipses(res.mca, c("Vomiting", "Fever"),
              geom = "point")
## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## ℹ Please use `gather()` instead.
## ℹ The deprecated feature was likely used in the factoextra package.
##   Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

fviz_ellipses(res.mca, 1:4, geom = "point")

res.desc <- dimdesc(res.mca, axes = c(1,2))

# Description of dimension 1
res.desc[[1]]
## 
## Link between the variable and the categorical variable (1-way anova)
## =============================================
##                   R2      p.value
## Abdominals 0.8451157 4.055640e-23
## Diarrhae   0.7994680 3.910776e-20
## Fever      0.7846788 2.600566e-19
## Mayo       0.3829749 4.756234e-07
## Vomiting   0.3442016 2.510738e-06
## Nausea     0.2562007 8.062777e-05
## Cheese     0.1944181 7.534834e-04
## 
## Link between variable and the categories of the categorical variables
## ================================================================
##                       Estimate      p.value
## Abdominals=Abdo_n    0.5671866 4.055640e-23
## Diarrhae=Diarrhea_n  0.5380920 3.910776e-20
## Fever=Fever_n        0.5330918 2.600566e-19
## Mayo=Mayo_n          0.4644981 4.756234e-07
## Vomiting=Vomit_n     0.3466915 2.510738e-06
## Nausea=Nausea_n      0.3547892 8.062777e-05
## Cheese=Cheese_n      0.3830043 7.534834e-04
## Cheese=Cheese_y     -0.3830043 7.534834e-04
## Nausea=Nausea_y     -0.3547892 8.062777e-05
## Vomiting=Vomit_y    -0.3466915 2.510738e-06
## Mayo=Mayo_y         -0.4644981 4.756234e-07
## Fever=Fever_y       -0.5330918 2.600566e-19
## Diarrhae=Diarrhea_y -0.5380920 3.910776e-20
## Abdominals=Abdo_y   -0.5671866 4.055640e-23
# Description of dimension 2
res.desc[[2]]
## 
## Link between the variable and the categorical variable (1-way anova)
## =============================================
##                  R2      p.value
## Courgette 0.4464145 2.500166e-08
## Potato    0.3957543 2.690662e-07
## Vomiting  0.2511604 9.728027e-05
## Icecream  0.1409011 4.743927e-03
## 
## Link between variable and the categories of the categorical variables
## ================================================================
##                       Estimate      p.value
## Courgette=Courg_n    0.4176013 2.500166e-08
## Potato=Potato_y      0.4977523 2.690662e-07
## Vomiting=Vomit_y     0.1838104 9.728027e-05
## Icecream=Icecream_n  0.2597197 4.743927e-03
## Icecream=Icecream_y -0.2597197 4.743927e-03
## Vomiting=Vomit_n    -0.1838104 9.728027e-05
## Potato=Potato_n     -0.4977523 2.690662e-07
## Courgette=Courg_y   -0.4176013 2.500166e-08
MCA(poison.active, ind.sup = NULL, quanti.sup = NULL, quali.sup=NULL,
    graph = TRUE, axes = c(1,2))
## Warning: ggrepel: 10 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

## **Results of the Multiple Correspondence Analysis (MCA)**
## The analysis was performed on 55 individuals, described by 11 variables
## *The results are available in the following objects:
## 
##    name              description                       
## 1  "$eig"            "eigenvalues"                     
## 2  "$var"            "results for the variables"       
## 3  "$var$coord"      "coord. of the categories"        
## 4  "$var$cos2"       "cos2 for the categories"         
## 5  "$var$contrib"    "contributions of the categories" 
## 6  "$var$v.test"     "v-test for the categories"       
## 7  "$var$eta2"       "coord. of variables"             
## 8  "$ind"            "results for the individuals"     
## 9  "$ind$coord"      "coord. for the individuals"      
## 10 "$ind$cos2"       "cos2 for the individuals"        
## 11 "$ind$contrib"    "contributions of the individuals"
## 12 "$call"           "intermediate results"            
## 13 "$call$marge.col" "weights of columns"              
## 14 "$call$marge.li"  "weights of rows"
res.mca <- MCA(poison, ind.sup =53:55,
               quanti.sup = 1:2, quali.sup = 3:4, graph=FALSE)

# Supplementary qualitative variable categories
res.mca$quali.sup
## $coord
##              Dim 1         Dim 2       Dim 3        Dim 4       Dim 5
## Sick_n  1.41809140  0.0020394048  0.13199139 -0.016036841 -0.08354663
## Sick_y -0.63026284 -0.0009064021 -0.05866284  0.007127485  0.03713184
## F      -0.03108147  0.1123143957  0.05033124 -0.055927173 -0.06832928
## M       0.03356798 -0.1212995474 -0.05435774  0.060401347  0.07379562
## 
## $cos2
##              Dim 1        Dim 2       Dim 3        Dim 4       Dim 5
## Sick_n 0.893770319 1.848521e-06 0.007742990 0.0001143023 0.003102240
## Sick_y 0.893770319 1.848521e-06 0.007742990 0.0001143023 0.003102240
## F      0.001043342 1.362369e-02 0.002735892 0.0033780765 0.005042401
## M      0.001043342 1.362369e-02 0.002735892 0.0033780765 0.005042401
## 
## $v.test
##             Dim 1        Dim 2      Dim 3       Dim 4      Dim 5
## Sick_n  6.7514655  0.009709509  0.6284047 -0.07635063 -0.3977615
## Sick_y -6.7514655 -0.009709509 -0.6284047  0.07635063  0.3977615
## F      -0.2306739  0.833551410  0.3735378 -0.41506855 -0.5071119
## M       0.2306739 -0.833551410 -0.3735378  0.41506855  0.5071119
## 
## $eta2
##            Dim 1        Dim 2       Dim 3        Dim 4       Dim 5
## Sick 0.893770319 1.848521e-06 0.007742990 0.0001143023 0.003102240
## Sex  0.001043342 1.362369e-02 0.002735892 0.0033780765 0.005042401
# Supplementary quantitative variables
res.mca$quanti
## $coord
##             Dim 1       Dim 2       Dim 3       Dim 4       Dim 5
## Age   0.003934896 -0.00741340 -0.26494536  0.20015501  0.02928483
## Time -0.838158507 -0.08330586 -0.08718851 -0.08421599 -0.02316931
# Supplementary individuals
res.mca$ind.sup
## $coord
##         Dim 1     Dim 2      Dim 3      Dim 4      Dim 5
## 53  1.0835684 0.5172478  0.5794063  0.5390903  0.4553650
## 54 -0.1249473 0.1417271 -0.1765234 -0.1526587 -0.2779565
## 55 -0.4315948 0.1270468 -0.2071580 -0.1186804 -0.1891760
## 
## $cos2
##         Dim 1      Dim 2      Dim 3      Dim 4      Dim 5
## 53 0.36304957 0.08272764 0.10380536 0.08986204 0.06411692
## 54 0.03157652 0.04062716 0.06302535 0.04713607 0.15626590
## 55 0.50232519 0.04352713 0.11572730 0.03798314 0.09650827
#Biplot of individuals and variable categories
fviz_mca_biplot(res.mca, repel = TRUE,
                ggtheme = theme_minimal())

fviz_mca_var(res.mca, choice = "mca.cor",
             repel = TRUE)

fviz_mca_var(res.mca, repel = TRUE,
             ggtheme = theme_minimal())

fviz_mca_var(res.mca, choice = "quanti.sup",
             ggtheme = theme_minimal())

fviz_mca_ind(res.mca,
             label = "ind.sup", #Show the label of ind.sup only
             ggtheme = theme_minimal())

# Visualize variable categories with cos2 >= 0.4
fviz_mca_var(res.mca, select.var = list(cos2 = 0.4))

# Top 10 active variables with the highest cos2
fviz_mca_var(res.mca, select.var = list(cos2 = 10))

# Select by names
name <- list(name = c("Fever_n", "Abdo_y", "Diarrhea_n", "Fever_Y", "Vomit_Y", "Vomit_n"))

fviz_mca_var(res.mca, select.var = name)

# top 5 contributing individuals and variable categories
fviz_mca_biplot(res.mca, select.ind = list(contrib = 5),
                select.var = list(contrib = 5),
                ggtheme = theme_minimal())

# Scree plot
scree.plot <- fviz_eig(res.mca)

# Biplot of row and colum variables
biplot.mca <- fviz_mca_biplot(res.mca)

library(ggpubr)
## 
## Attaching package: 'ggpubr'
## The following object is masked from 'package:forecast':
## 
##     gghistogram
ggexport(plotlist = list(scree.plot, biplot.mca),
         filename = "MCA.pdf")
## file saved to MCA.pdf
# Export into a TXT file
write.infile(res.mca, "mca.txt", sep = "\t")

# Export into a CSV file
write.infile(res.mca, "mca.csv", sep = ";")