fviz_contrib - Quick visualization of row/column contributions - R software and data mining


Description

This function can be used to visualize the contributions of rows/columns from the results of Principal Component Analysis (PCA), Correspondence Analysis (CA) and Multiple Correspondence Analysis (MCA) functions.

The function fviz_contrib() [in factoextra package] is used.

Install and load factoextra

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

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

Load factoextra :

library("factoextra")

Usage

fviz_contrib(X, choice = c("row", "col", "var", "ind"), 
             axes = 1, fill = "steelblue", color = "steelblue",
             sort.val = c("desc", "asc", "none"), top = Inf)

Arguments

Argument Description
X an object of class PCA, CA and MCA [FactoMineR]; prcomp and princomp [stats]; dudi, pca, coa and acm [ade4]; ca [ca package].
choice allowed values are “row” and “col” for CA; “var” and “ind” for PCA or MCA.
axes a numeric vector specifying the dimension(s) of interest.
fill a fill color for the bar plot.
color an outline color for the bar plot.
sort.val a string specifying whether the value should be sorted. Allowed values are “none” (no sorting), “asc” (for ascending) or “desc” (for descending).
top a numeric value specifying the number of top elements to be shown.
not used.

Details

The function fviz_contrib() creates a barplot of row/column contributions. A reference dashed line is also shown on the barplot. This reference line corresponds to the expected value if the contribution where uniform.

For a given dimension, any row/column with a contribution above the reference line could be considered as important in contributing to the dimension.

Value

A ggplot2 plot

Examples

Principal component analysis

A principal component analysis (PCA) is performed using the built-in R function prcomp() and the decathlon2 [in factoextra] data

data(decathlon2)
decathlon2.active <- decathlon2[1:23, 1:10]
res.pca <- prcomp(decathlon2.active,  scale = TRUE)
# variable contributions on axis 1
fviz_contrib(res.pca, choice="var", axes = 1 )

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

# sorting
fviz_contrib(res.pca, choice="var", axes = 1,
           sort.val ="asc")

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

# select the top 7 contributing variables
fviz_contrib(res.pca, choice="var", axes = 1, top = 7 )

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

# Change theme and color
fviz_contrib(res.pca, choice="var", axes = 1,
         fill = "lightgray", color = "black") +
         theme_minimal() +
         theme(axis.text.x = element_text(angle=45))

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

# Variable contributions on axis 2
fviz_contrib(res.pca, choice="var", axes = 2)

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

# Variable contributions on axes 1 + 2
fviz_contrib(res.pca, choice="var", axes = 1:2)

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

# Contributions of individuals on axis 1
fviz_contrib(res.pca, choice="ind", axes = 1)

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

Correspondence Analysis

The function CA() in FactoMineR package is used:

# Install and load FactoMineR to compute CA
# install.packages("FactoMineR")
library("FactoMineR")
data("housetasks")
res.ca <- CA(housetasks, graph = FALSE)
# Visualize row contributions on axes 1
fviz_contrib(res.ca, choice ="row", axes = 1)

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

# Visualize row contributions on axes 1 + 2
fviz_contrib(res.ca, choice ="row", axes = 1:2)

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

# Visualize column contributions on axes 1
fviz_contrib(res.ca, choice ="col", axes = 1)

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

Multiple Correspondence Analysis

The function MCA() in FactoMineR package is used:

library(FactoMineR)
data(poison)
res.mca <- MCA(poison, quanti.sup = 1:2,
              quali.sup = 3:4, graph=FALSE)
# Visualize individual contributions on axes 1
fviz_contrib(res.mca, choice ="ind", axes = 1)

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

# Select the top 20
fviz_contrib(res.mca, choice ="ind", axes = 1, top = 20)

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

# Visualize variable categorie contributions on axes 1
fviz_contrib(res.mca, choice ="var", axes = 1)

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

Infos

This analysis has been performed using R software (ver. 3.1.2) and factoextra (ver. 1.0.2)


Enjoyed this article? I’d be very grateful if you’d help it spread by emailing it to a friend, or sharing it on Twitter, Facebook or Linked In.

Show me some love with the like buttons below... Thank you and please don't forget to share and comment below!!
Avez vous aimé cet article? Je vous serais très reconnaissant si vous aidiez à sa diffusion en l'envoyant par courriel à un ami ou en le partageant sur Twitter, Facebook ou Linked In.

Montrez-moi un peu d'amour avec les like ci-dessous ... Merci et n'oubliez pas, s'il vous plaît, de partager et de commenter ci-dessous!






This page has been seen 4692 times