fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining


Description

Draw the graph of individuals/variables from the output of Multiple Correspondence Analysis (MCA).

The following functions, from factoextra package are use:

  • fviz_mca_ind(): Graph of individuals
  • fviz_mca_var(): Graph of variable categories
  • fviz_mca_biplot() (or fviz_mca()): Biplot of individuals and variable categories

Install and load factoextra

The package devtools is required for the installation as factoextra is hosted on github.

# install.packages("devtools")
library("devtools")
install_github("kassambara/factoextra")

Load factoextra :

library("factoextra")

Usage

# Graph of individuals
fviz_mca_ind(X, axes = c(1, 2), 
  geom = c("point", "text"), label = "all", invisible = "none",
  labelsize = 4, pointsize = 2, habillage = "none",
  addEllipses = FALSE, ellipse.level = 0.95, col.ind = "blue",
  col.ind.sup = "darkblue", alpha.ind = 1, shape.ind = 19,
  select.ind = list(name = NULL, cos2 = NULL, contrib = NULL),
  map = "symmetric", 
  jitter = list(what = "label", width = NULL, height = NULL), ...)
# Graph of variables
fviz_mca_var(X, axes = c(1, 2),
  geom = c("point", "text"), label = "all",
  invisible = "none", labelsize = 4, pointsize = 2, col.var = "red",
  alpha.var = 1, shape.var = 17, col.quanti.sup = "blue",
  col.quali.sup = "darkgreen", col.circle = "grey70",
  select.var = list(name = NULL, cos2 = NULL, contrib = NULL),
  map = "symmetric", 
  jitter = list(what = "label", width = NULL, height = NULL))
# Biplot of individuals and variables
fviz_mca_biplot(X, axes = c(1, 2), geom = c("point", "text"),
  label = "all", invisible = "none", labelsize = 4, pointsize = 2,
  habillage = "none", addEllipses = FALSE, ellipse.level = 0.95,
  col.ind = "blue", col.ind.sup = "darkblue", alpha.ind = 1,
  col.var = "red", alpha.var = 1, col.quanti.sup = "blue",
  col.quali.sup = "darkgreen", shape.ind = 19, shape.var = 17,
  select.var = list(name = NULL, cos2 = NULL, contrib = NULL),
  select.ind = list(name = NULL, cos2 = NULL, contrib = NULL),
  map = "symmetric", arrows = c(FALSE, FALSE), 
  jitter = list(what = "label", width = NULL, height = NULL), ...)
# An alias of fviz_mca_biplot()
fviz_mca(X, ...)

Arguments

Argument Description
X an object of class MCA [FactoMineR]; mca [ade4].
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 both types.
label a text specifying the elements to be labelled. Default value is “all”. Allowed values are “none” or the 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 the combination of c(“ind”, “ind.sup”,“var”, “quali.sup”, “quanti.sup”).
labelsize font size for the labels.
pointsize the size of points.
habillage an optional factor variable for coloring the observations by groups. Default value is “none”. If X is a MCA object from FactoMineR package, habillage can also specify the supplementary qualitative variable (by its index or name) to be used for coloring individuals by groups (see ?MCA in FactoMineR).
addEllipses logical value. If TRUE, draws ellipses around the individuals when habillage != “none”.
ellipse.level the size of the concentration ellipse in normal probability.
col.ind,col.var colors for individuals and variables, respectively. Possible values include also : “cos2”, “contrib”, “coord”, “x” or “y”. In this case, the colors for individuals/variables are automatically controlled by their qualities of representation (“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 variate from 0 (total transparency) to 1 (no transparency). Default value is 1. Possible values include also : “cos2”, “contrib”, “coord”, “x” or “y”. In this case, the transparency for the individual/variable colors are automatically controlled by their qualities (“cos2”), contributions (“contrib”), coordinates (x2+y2 , “coord”), x values(“x”) or y values(“y”). To use this, make sure that habillage =“none”.
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 cos2 are drawn
map character string specifying the map type. Allowed options include: "symmetric", "rowprincipal", "colprincipal", "symbiplot", "rowgab", "colgab", "rowgreen" and "colgreen". See details
jitter a parameter used to jitter the points in order to reduce overplotting. It’s a list containing the objects what, width and height (Ex.; jitter = list(what, width, height)). what: the element to be jittered. Possible values are “point” or “p”; “label” or “l”; “both” or “b”. width: degree of jitter in x direction (ex: 0.2). height: degree of jitter in y direction (ex: 0.2).
col.quanti.sup, col.quali.sup a color for the quantitative/qualitative supplementary variables.
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.
Arguments to be passed to the function fviz_mca_biplot().

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. This means that, the column profiles must be presented in row space or vice-versa. The allowed options for the argument map are:

  • “rowprincipal” or “colprincipal”: asymmetric plots with either rows in principal coordinates and columns in standard coordinates, or vice versa. These plots preserve row metric or column metric respectively.

  • “symbiplot”: Both rows and columns are scaled to have variances equal to the singular values (square roots of eigenvalues), which gives a symmetric biplot but does not preserve row or column metrics.

  • “rowgab” or “colgab”: Asymmetric maps, proposed by Gabriel & Odoroff (1990), with rows (respectively, columns) in principal coordinates and columns (respectively, rows) in standard coordinates multiplied by the mass of the corresponding point.

  • “rowgreen” or “colgreen”: The so-called contribution biplots showing visually the most contributing points (Greenacre 2006b). These are similar to “rowgab” and “colgab” except that the points in standard coordinates are multiplied by the square root of the corresponding masses, giving reconstructions of the standardized residuals.

Value

A ggplot2 plot

Examples

Multiple Correspondence Analysis

A Multiple Correspondence Analysis (MCA) is performed using the function MCA() [in FactoMineR] and poison data [in FactoMineR]:

# 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)

fviz_mca_ind(): Graph of individuals

# Default plot
fviz_mca_ind(res.mca)

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Change title and axis labels
fviz_mca_ind(res.mca) +
 labs(title = "MCA", x = "Dim.1", y ="Dim.2" )

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Change axis limits by specifying the min and max
fviz_mca_ind(res.mca) +
   xlim(-0.8, 1.5) + ylim (-1.5, 1.5)

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Use text only
fviz_mca_ind(res.mca, geom = "text")

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Use points only
fviz_mca_ind(res.mca, geom="point")

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Change the size of points
fviz_mca_ind(res.mca, geom="point", pointsize = 4)

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Change point color and theme
fviz_mca_ind(res.mca, col.ind = "blue")+
   theme_minimal()

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Reduce overplotting
fviz_mca_ind(res.mca, 
             jitter = list(width = 0.2, height = 0.2))

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Control automatically the color of individuals
# using the cos2 or the contributions
# cos2 = the quality of the individuals on the factor map
fviz_mca_ind(res.mca, col.ind="cos2")

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Gradient color
fviz_mca_ind(res.mca, col.ind="cos2") +
      scale_color_gradient2(low="white", mid="blue",
      high="red", midpoint=0.4)

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Change the theme and use only points
fviz_mca_ind(res.mca, col.ind="cos2", geom = "point") +
      scale_color_gradient2(low="white", mid="blue",
      high="red", midpoint=0.4)+ theme_minimal()

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Color by the contributions
fviz_mca_ind(res.mca, col.ind="contrib") +
      scale_color_gradient2(low="white", mid="blue",
      high="red", midpoint=1.5)

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Control the transparency of the color by the
# contributions
fviz_mca_ind(res.mca, alpha.ind="contrib") +
     theme_minimal()

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Color individuals by groups
grp <- as.factor(poison.active[, "Vomiting"])
fviz_mca_ind(res.mca, label="none", habillage=grp)

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Add ellipses
p <- fviz_mca_ind(res.mca, label="none", habillage=grp,
             addEllipses=TRUE, ellipse.level=0.95)
print(p)

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Change group colors using RColorBrewer color palettes
p + scale_color_brewer(palette="Dark2") +
   theme_minimal()

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

p + scale_color_brewer(palette="Paired") +
     theme_minimal()

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

p + scale_color_brewer(palette="Set1") +
     theme_minimal()

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Change color manually
p + scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"))

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Select and visualize individuals with cos2 >= 0.4
fviz_mca_ind(res.mca, select.ind = list(cos2 = 0.4))

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Select the top 20 according to the cos2
fviz_mca_ind(res.mca, select.ind = list(cos2 = 20))

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Select the top 20 contributing individuals
fviz_mca_ind(res.mca, select.ind = list(contrib = 20))

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Select by names
fviz_mca_ind(res.mca,
select.ind = list(name = c("44", "38", "53",  "39")))

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

fviz_mca_var(): Graph of variable categories

# Default plot
fviz_mca_var(res.mca)

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Change color and theme
fviz_mca_var(res.mca, col.var="steelblue")+
 theme_minimal()

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Control variable colors using their contributions
fviz_mca_var(res.mca, col.var = "contrib")+
 scale_color_gradient2(low = "white", mid = "blue",
           high = "red", midpoint = 2) +
 theme_minimal()

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Control the transparency of variables using their contributions
fviz_mca_var(res.mca, alpha.var = "contrib") +
   theme_minimal()

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Select and visualize categories with cos2 >= 0.4
fviz_mca_var(res.mca, select.var = list(cos2 = 0.4))

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Select the top 10 contributing variable categories
fviz_mca_var(res.mca, select.var = list(contrib = 10))

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Select by names
fviz_mca_var(res.mca,
 select.var= list(name = c("Courg_n", "Fever_y", "Fever_n")))

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

fviz_mca_biplot(): Biplot of individuals of variable categories

fviz_mca_biplot(res.mca)

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Keep only the labels for variable categories
fviz_mca_biplot(res.mca, label ="var")

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

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

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

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

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

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

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# Control automatically the color of individuals using the cos2
fviz_mca_biplot(res.mca, label ="var", col.ind="cos2") +
       theme_minimal()

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# 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) +
   theme_minimal()

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

# 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))

fviz_mca: Quick Multiple Correspondence Analysis data visualization - R software and data mining

Infos

This analysis has been performed using R software (ver. 3.2.1) and factoextra (ver. 1.0.3)


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