factoextra (version 1.0.3)

fviz_contrib: Visualize the contributions of row/column elements

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), Multiple Correspondence Analysis (MCA) and Multiple Factor Analysis (MFA) functions.

Usage

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

Arguments

X

an object of class PCA, CA, MCA and MFA [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.

sortcontrib

see the argument sort.val

...

not used

Value

a ggplot2 plot

Functions

• fviz_pca_contrib: deprecated function. Use fviz_contrib()

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.

References

http://www.sthda.com

Examples

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

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

# 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))

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

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

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

# Multiple Correspondence Analysis
# +++++++++++++++++++++++++++++++++
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)
# Select the top 20
fviz_contrib(res.mca, choice ="ind", axes = 1, top = 20)
# Visualize variable categorie contributions on axes 1
fviz_contrib(res.mca, choice ="var", axes = 1)

# Multiple Factor Analysis
# ++++++++++++++++++++++++
library(FactoMineR)
data(poison)
res.mfa <- MFA(poison, group=c(2,2,5,6), type=c("s","n","n","n"),
               name.group=c("desc","desc2","symptom","eat"),
               num.group.sup=1:2, graph=FALSE)

# Visualize individual contributions on axes 1
fviz_contrib(res.mfa, choice ="ind", axes = 1)
# Select the top 20
fviz_contrib(res.mfa, choice ="ind", axes = 1, top = 20)
# Visualize catecorical variable categorie contributions on axes 1
fviz_contrib(res.mfa, choice ="quali.var", axes = 1)