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Visualize Multiple Correspondence Analysis

Source: R/fviz_mca.R
fviz_mca.Rd

Multiple Correspondence Analysis (MCA) is an extension of simple CA to analyse a data table containing more than two categorical variables. fviz_mca() provides ggplot2-based elegant visualization of MCA outputs from the R functions: MCA [in FactoMineR], acm [in ade4], and expoOutput/epMCA [in ExPosition]. Read more: Multiple Correspondence Analysis (MCA) in R: Compute, Visualize & Interpret.

  • fviz_mca_ind(): Graph of individuals

  • fviz_mca_var(): Graph of variables

  • fviz_mca_biplot(): Biplot of individuals and variables

  • fviz_mca(): An alias of fviz_mca_biplot()

Usage

fviz_mca_ind(
  X,
  axes = c(1, 2),
  geom = c("point", "text"),
  geom.ind = geom,
  repel = FALSE,
  habillage = "none",
  palette = NULL,
  addEllipses = FALSE,
  col.ind = "blue",
  col.ind.sup = "darkblue",
  alpha.ind = 1,
  shape.ind = 19,
  map = "symmetric",
  select.ind = list(name = NULL, cos2 = NULL, contrib = NULL),
  quanti.sup = FALSE,
  col.quanti.sup = "#D55E00",
  ...
)

fviz_mca_var(
  X,
  choice = c("var.cat", "mca.cor", "var", "quanti.sup"),
  axes = c(1, 2),
  geom = c("point", "text"),
  geom.var = geom,
  repel = FALSE,
  col.var = "red",
  alpha.var = 1,
  shape.var = 17,
  col.quanti.sup = "blue",
  col.quali.sup = "darkgreen",
  map = "symmetric",
  select.var = list(name = NULL, cos2 = NULL, contrib = NULL),
  ...
)

fviz_mca_biplot(
  X,
  axes = c(1, 2),
  geom = c("point", "text"),
  geom.ind = geom,
  geom.var = geom,
  repel = FALSE,
  label = "all",
  invisible = "none",
  habillage = "none",
  addEllipses = FALSE,
  palette = NULL,
  arrows = c(FALSE, FALSE),
  map = "symmetric",
  title = "MCA - Biplot",
  quanti.sup = FALSE,
  col.quanti.sup = "#D55E00",
  ...
)

fviz_mca(X, ...)

Arguments

X

an object of class MCA [FactoMineR], acm [ade4] and expoOutput/epMCA [ExPosition].

axes

a numeric vector of length 2 specifying the dimensions to be plotted.

geom

a text specifying the geometry to be used for the graph. Allowed values are the combination of c("point", "arrow", "text"). Use "point" (to show only points); "text" to show only labels; c("point", "text") or c("arrow", "text") to show arrows and texts. Using c("arrow", "text") is sensible only for the graph of variables.

geom.ind, geom.var

as geom but for individuals and variables, respectively. Default is geom.ind = c("point", "text), geom.var = c("point", "text").

repel

logical; whether to use ggrepel to avoid overplotting text labels. The old jitter argument is kept for backward compatibility and is converted to repel = TRUE with a deprecation warning.

habillage

an optional factor variable for coloring the observations by groups. Default value is "none". If X is an MCA object from FactoMineR package, habillage can also specify the index of the factor variable in the data.

palette

the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty". Can be also a numeric vector of length(groups); in this case a basic color palette is created using the function palette.

addEllipses

logical value. If TRUE, draws ellipses around the individuals when habillage != "none".

col.ind, col.var

color for individuals and variables, respectively. Can be a continuous variable or a factor variable. Possible values also include "cos2", "contrib", "coord", "x", and "y". In this case, the colors for individuals/variables are automatically controlled by their qualities ("cos2"), contributions ("contrib"), coordinates (x^2 + y^2 , "coord"), x values("x") or y values("y"). To use automatic coloring (by cos2, contrib, ....), make sure that habillage ="none".

col.ind.sup

color for supplementary individuals

alpha.ind, alpha.var

controls the transparency of individual and variable colors, respectively. The value can vary from 0 (total transparency) to 1 (no transparency). Default value is 1. Possible values also include "cos2", "contrib", "coord", "x", and "y". In this case, the transparency for individual/variable colors are automatically controlled by their qualities ("cos2"), contributions ("contrib"), coordinates (x^2 + y^2 , "coord"), x values("x") or y values("y"). To use this, make sure that habillage ="none".

shape.ind, shape.var

point shapes of individuals and variables.

map

character string specifying the map type. Allowed options include: "symmetric", "rowprincipal", "colprincipal", "symbiplot", "rowgab", "colgab", "rowgreen" and "colgreen". See details

select.ind, select.var

a selection of individuals/variables to be drawn. Allowed values are NULL or a list containing the arguments name, cos2 or contrib:

  • name is a character vector containing individuals/variables to be drawn

  • cos2 if cos2 is in [0, 1], ex: 0.6, then individuals/variables with a cos2 > 0.6 are drawn. if cos2 > 1, ex: 5, then the top 5 individuals/variables with the highest cos2 are drawn.

  • contrib if contrib > 1, ex: 5, then the top 5 individuals/variables with the highest contrib are drawn

  • union: logical. When several of name/cos2/contrib are given, FALSE (default) combines them with AND (each condition further narrows the selection); TRUE combines them with OR (an element is kept if it matches any condition), e.g. named items plus the top-cos2 ones.

quanti.sup

logical. If TRUE, the supplementary quantitative variables of a FactoMineR MCA are overlaid on the individuals / biplot map as correlation arrows (see the Details section). Default FALSE leaves the map unchanged. Used by fviz_mca_ind() and fviz_mca_biplot().

col.quanti.sup, col.quali.sup

a color for the quantitative/qualitative supplementary variables.

...

Additional arguments.

  • in fviz_mca_ind() and fviz_mca_var() (including choice = "mca.cor"): Additional arguments are passed to the functions fviz() and ggpubr::ggpar().

  • in fviz_mca_biplot() and fviz_mca(): Additional arguments are passed to fviz_mca_ind() and fviz_mca_var().

choice

the graph to plot. Allowed values include: i) "var" and "mca.cor" for plotting the correlation between variables and principal dimensions; ii) "var.cat" for variable categories and iii) "quanti.sup" for the supplementary quantitative variables.

label

a text specifying the elements to be labelled. Default value is "all". Allowed values are "all", "none", or a combination of c("ind", "ind.sup","var", "quali.sup", "quanti.sup"). "ind" can be used to label only active individuals. "ind.sup" is for supplementary individuals. "var" is for active variable categories. "quali.sup" is for supplementary qualitative variable categories. "quanti.sup" is for quantitative supplementary variables.

invisible

a text specifying the elements to be hidden on the plot. Default value is "none". Allowed values are "all", "none", or a combination of c("ind", "ind.sup","var", "quali.sup", "quanti.sup").

arrows

Vector of two logicals specifying if the plot should contain points (FALSE, default) or arrows (TRUE). First value sets the rows and the second value sets the columns.

title

the title of the graph

Value

a ggplot

Details

The default plot of MCA is a "symmetric" plot in which both rows and columns are in principal coordinates. In this situation, it's not possible to interpret the distance between row points and column points. To overcome this problem, the simplest way is to make an asymmetric plot. The argument "map" can be used to change the plot type. For more explanation, read the details section of fviz_ca documentation.

quanti.sup = TRUE overlays the supplementary quantitative variables on the individuals / biplot map. Each such variable is drawn as an arrow from the origin in the direction of its correlations with the shown dimensions (X$quanti.sup$coord), so the arrow points toward the region of the cloud where the variable takes larger values. Each arrow's length is proportional to the variable's absolute correlation with the shown dimensions (a correlation of 1 reaches about 80% of the individual-cloud extent), so a weak covariate draws a short arrow. The overlay labels are repelled by default (needs the ggrepel package). Arrow lengths are relative to the cloud, so compare directions and relative lengths rather than absolute sizes.

See also

get_mca, fviz_pca, fviz_ca, fviz_mfa, fviz_hmfa. Online tutorial: Multiple Correspondence Analysis (MCA) in R: Compute, Visualize & Interpret.

Author

Alboukadel Kassambara alboukadel.kassambara@gmail.com

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)
#>     Nausea Vomiting Abdominals   Fever   Diarrhae   Potato   Fish   Mayo
#> 1 Nausea_y  Vomit_n     Abdo_y Fever_y Diarrhea_y Potato_y Fish_y Mayo_y
#> 2 Nausea_n  Vomit_n     Abdo_n Fever_n Diarrhea_n Potato_y Fish_y Mayo_y
#> 3 Nausea_n  Vomit_y     Abdo_y Fever_y Diarrhea_y Potato_y Fish_y Mayo_y
#> 4 Nausea_n  Vomit_n     Abdo_n Fever_n Diarrhea_n Potato_y Fish_y Mayo_n
#> 5 Nausea_n  Vomit_y     Abdo_y Fever_y Diarrhea_y Potato_y Fish_y Mayo_y
#> 6 Nausea_n  Vomit_n     Abdo_y Fever_y Diarrhea_y Potato_y Fish_n Mayo_y
#>   Courgette   Cheese   Icecream
#> 1   Courg_y Cheese_y Icecream_y
#> 2   Courg_y Cheese_n Icecream_y
#> 3   Courg_y Cheese_y Icecream_y
#> 4   Courg_y Cheese_y Icecream_y
#> 5   Courg_y Cheese_y Icecream_y
#> 6   Courg_y Cheese_y Icecream_y
res.mca <- MCA(poison.active, graph=FALSE)

# Graph of individuals
# +++++++++++++++++++++

# Default Plot
# Color of individuals: col.ind = "steelblue"
fviz_mca_ind(res.mca, col.ind = "steelblue")


# 1. Control automatically the color of individuals 
   # using the "cos2" or the contributions "contrib"
   # cos2 = the quality of the individuals on the factor map
# 2. To keep only point or text use geom = "point" or geom = "text".
# 3. Change themes: https://www.datanovia.com/learn/data-visualization/ggplot2/themes

fviz_mca_ind(res.mca, col.ind = "cos2", repel = TRUE)


if (FALSE) { # \dontrun{     
# You can also control the transparency 
# of the color by the cos2
fviz_mca_ind(res.mca, alpha.ind="cos2") 
} # }
     
# Color individuals by groups, add concentration ellipses
# Remove labels: label = "none".
grp <- as.factor(poison.active[, "Vomiting"])
p <- fviz_mca_ind(res.mca, label="none", habillage=grp,
       addEllipses=TRUE, ellipse.level=0.95)
print(p)


# Overlay supplementary quantitative variables as correlation arrows.
# Fit the MCA with quanti.sup, then set quanti.sup = TRUE when plotting.
res.mca2 <- MCA(poison, quanti.sup = 1:2, quali.sup = 3:4, graph = FALSE)
fviz_mca_ind(res.mca2, label = "none", habillage = poison$Vomiting,
             addEllipses = TRUE, quanti.sup = TRUE)



# Change group colors using RColorBrewer color palettes
# Read more: https://www.datanovia.com/learn/data-visualization/ggplot2/colors
p + scale_color_brewer(palette="Dark2") +
    scale_fill_brewer(palette="Dark2") 

     
# Change group colors manually
# Read more: https://www.datanovia.com/learn/data-visualization/ggplot2/colors
p + scale_color_manual(values=c("#999999", "#E69F00"))+
 scale_fill_manual(values=c("#999999", "#E69F00"))

             
             
# Select and visualize some individuals (ind) with select.ind argument.
 # - ind with cos2 >= 0.4: select.ind = list(cos2 = 0.4)
 # - Top 20 ind according to the cos2: select.ind = list(cos2 = 20)
 # - Top 20 contributing individuals: select.ind = list(contrib = 20)
 # - Select ind by names: select.ind = list(name = c("44", "38", "53",  "39") )
 
# Example: Select the top 40 according to the cos2
fviz_mca_ind(res.mca, select.ind = list(cos2 = 20))


 
# Graph of variable categories
# ++++++++++++++++++++++++++++
# Default plot: use repel = TRUE to avoid overplotting
fviz_mca_var(res.mca, col.var = "#FC4E07")


# Control variable colors using their contributions
# use repel = TRUE to avoid overplotting
fviz_mca_var(res.mca, col.var = "contrib",
  gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"))

        
    
# Biplot
# ++++++++++++++++++++++++++
grp <- as.factor(poison.active[, "Vomiting"])
fviz_mca_biplot(res.mca, repel = TRUE, col.var = "#E7B800",
 habillage = grp, addEllipses = TRUE, ellipse.level = 0.95)

 
 if (FALSE) { # \dontrun{
# Keep only the labels for variable categories: 
fviz_mca_biplot(res.mca, label ="var")

# Keep only labels for individuals
fviz_mca_biplot(res.mca, label ="ind")

# Hide variable categories
fviz_mca_biplot(res.mca, invisible ="var")

# Hide individuals
fviz_mca_biplot(res.mca, invisible ="ind")

# Control automatically the color of individuals using the cos2
fviz_mca_biplot(res.mca, label ="var", col.ind="cos2")
       
# Change the color by groups, add ellipses
fviz_mca_biplot(res.mca, label="var", col.var ="blue",
   habillage=grp, addEllipses=TRUE, ellipse.level=0.95)
               
# Select the top 30 contributing individuals
# And the top 10 variables
fviz_mca_biplot(res.mca,  
               select.ind = list(contrib = 30),
               select.var = list(contrib = 10)) 
} # }