fviz_cos2: Quick visualization of the quality of representation of rows/columns - R software and data mining
Description
This function can be used to visualize the quality of representation (cos2) of rows/columns from the results of Principal Component Analysis (PCA), Correspondence Analysis (CA) and Multiple Correspondence Analysis (MCA) functions.
The function fviz_cos2() [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_cos2(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. |
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 cos2 on axis 1
fviz_cos2(res.pca, choice="var", axes = 1 )
# sorting
fviz_cos2(res.pca, choice="var", axes = 1,
sort.val ="asc")
# select the top 7 contributing variables
fviz_cos2(res.pca, choice="var", axes = 1, top = 7 )
# Change theme and color
fviz_cos2(res.pca, choice="var", axes = 1,
fill = "lightgray", color = "black") +
theme_minimal() +
theme(axis.text.x = element_text(angle=45))
# Variable cos2 on axis 2
fviz_cos2(res.pca, choice="var", axes = 2)
# Variable cos2 on axes 1 + 2
fviz_cos2(res.pca, choice="var", axes = 1:2)
# cos2 of individuals on axis 1
fviz_cos2(res.pca, choice="ind", axes = 1)
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 cos2 on axes 1
fviz_cos2(res.ca, choice ="row", axes = 1)
# Visualize row cos2 on axes 1 + 2
fviz_cos2(res.ca, choice ="row", axes = 1:2)
# Visualize column cos2 on axes 1
fviz_cos2(res.ca, choice ="col", axes = 1)
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 cos2 on axes 1
fviz_cos2(res.mca, choice ="ind", axes = 1)
# Select the top 20
fviz_cos2(res.mca, choice ="ind", axes = 1, top = 20)
# Visualize variable categories cos2 on axes 1
fviz_cos2(res.mca, choice ="var", axes = 1)
Infos
This analysis has been performed using R software (ver. 3.1.2) and factoextra (ver. 1.0.2)
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