Description

Graph of column/row variables from the output of Correspondence Analysis (CA).

The following functions, from factoextra package are use:

• fviz_ca_row(): Graph of row variables
• fviz_ca_col(): Graph of column variables
• fviz_ca_biplot(): Biplot of row and column variables
• fviz_ca(): An alias of fviz_ca_biplot()

These functions are included in factoextra package.

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 row variables
fviz_ca_row(X, axes = c(1, 2), shape.row = 19,
  geom = c("point", "text"), label = "all", 
  invisible = "none", labelsize = 4, pointsize = 2,
  col.row = "blue", col.row.sup = "darkblue", alpha.row = 1,
  select.row = list(name = NULL, cos2 = NULL, contrib = NULL),
  map = "symmetric",
  jitter = list(what = "label", width = NULL, height = NULL), ...)

# Graph of column variables
fviz_ca_col(X, axes = c(1, 2), shape.col = 17,
  geom = c("point", "text"), label = "all",
  invisible = "none", labelsize = 4, pointsize = 2,
  col.col = "red", col.col.sup = "darkred", alpha.col = 1,
  select.col = list(name = NULL, cos2 = NULL, contrib = NULL),
  map = "symmetric",
 jitter = list(what = "label", width = NULL, height = NULL), ...)

# Biplot of row and column  variables
fviz_ca_biplot(X, axes = c(1, 2), shape.row = 19, shape.col = 17,
  geom = c("point", "text"), label = "all", invisible = "none",
  labelsize = 4, pointsize = 2, col.col = "red",
  col.col.sup = "darkred", alpha.col = 1, col.row = "blue",
  col.row.sup = "darkblue", alpha.row = 1,
  select.col = list(name = NULL, cos2 = NULL, contrib = NULL),
  select.row = 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_ca_biplot()
fviz_ca(X, ...)

Arguments

<td>**jitter**</td><td>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).</td></tr>

<td>**alpha.col,alpha.row**</td><td>controls the transparency of colors. The value can variate from 0 (total transparency) to 1 (no transparency). Default value is 1. Allowed values include also : "cos2", "contrib", "coord", "x" or "y" as for the arguments col.col and col.row..</td></tr>

Details

The default plot of CA 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

# Install and load FactoMineR to compute CA
# install.packages("FactoMineR")
library("FactoMineR")
data(housetasks)
head(housetasks)

           Wife Alternating Husband Jointly
Laundry     156          14       2       4
Main_meal   124          20       5       4
Dinner       77          11       7      13
Breakfeast   82          36      15       7
Tidying      53          11       1      57
Dishes       32          24       4      53

res.ca <- CA(housetasks, graph=FALSE)

# Default plot
fviz_ca_row(res.ca)

# Change title and axis labels
fviz_ca_row(res.ca) +
 labs(title = "CA", x = "Dim.1", y ="Dim.2" )

# Change axis limits by specifying the min and max
fviz_ca_row(res.ca) +
   xlim(-1.3, 1.7) + ylim (-1.5, 1)

# Use text only
fviz_ca_row(res.ca, geom = "text")

# Use points only
fviz_ca_row(res.ca, geom="point")

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

# Change point color and theme
fviz_ca_row(res.ca, col.row = "violet")+
   theme_minimal()

# Control automatically the color of row points
# using the cos2 or the contributions
# cos2 = the quality of the rows on the factor map
fviz_ca_row(res.ca, col.row="cos2")

# Gradient color
fviz_ca_row(res.ca, col.row="cos2") +
      scale_color_gradient2(low="white", mid="blue",
      high="red", midpoint=0.5)

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

# Color by the contributions
fviz_ca_row(res.ca, col.row="contrib") +
      scale_color_gradient2(low="white", mid="blue",
      high="red", midpoint=10)

# Control the transparency of the color by the
# contributions
fviz_ca_row(res.ca, alpha.row="contrib") +
     theme_minimal()

# Select and visualize rows with cos2 > 0.5
fviz_ca_row(res.ca, select.row = list(cos2 = 0.5))

# Select the top 7 according to the cos2
fviz_ca_row(res.ca, select.row = list(cos2 = 7))

# Select the top 7 contributing rows
fviz_ca_row(res.ca, select.row = list(contrib = 7))

# Select by names
fviz_ca_row(res.ca,
select.row = list(name = c("Breakfeast", "Repairs", "Holidays")))

# Default plot
fviz_ca_col(res.ca)

# Change color and theme
fviz_ca_col(res.ca, col.col="steelblue")+
 theme_minimal()

# Control colors using their contributions
fviz_ca_col(res.ca, col.col = "contrib")+
 scale_color_gradient2(low = "white", mid = "blue",
           high = "red", midpoint = 25) +
 theme_minimal()

# Control the transparency of variables using their contributions
fviz_ca_col(res.ca, alpha.col = "contrib") +
   theme_minimal()

# Select and visualize columns with cos2 >= 0.4
fviz_ca_col(res.ca, select.col = list(cos2 = 0.4))

# Select the top 3 contributing columns
fviz_ca_col(res.ca, select.col = list(contrib = 3))

# Select by names
fviz_ca_col(res.ca,
 select.col= list(name = c("Wife", "Husband", "Jointly")))

# Symetric Biplot of rows and columns
fviz_ca_biplot(res.ca)

# Asymetric biplot, use arrows for columns
fviz_ca_biplot(res.ca, map ="rowprincipal",
 arrow = c(FALSE, TRUE))

# Keep only the labels for row points
fviz_ca_biplot(res.ca, label ="row")

# Keep only labels for column points
fviz_ca_biplot(res.ca, label ="col")

# Hide row points
fviz_ca_biplot(res.ca, invisible ="row")

# Hide column points
fviz_ca_biplot(res.ca, invisible ="col")

# Control automatically the color of rows using the cos2
fviz_ca_biplot(res.ca, col.row="cos2") +
       theme_minimal()

# Select the top 7 contributing rows
# And the top 3 columns
fviz_ca_biplot(res.ca,
               select.row = list(contrib = 7),
               select.col = list(contrib = 3))

Infos

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