factoextra (version 1.0.3)

eigenvalue: Extract and visualize the eigenvalues/variances of dimensions

Description

Eigenvalues correspond to the amount of the variation explained by each principal component (PC). Read more: Principal Component Analysis

• get_eig(): Extract the eigenvalues/variances of the principal dimensions
• fviz_eig(): Plot the eigenvalues/variances against the number of dimensions
• get_eigenvalue(): an alias of get_eig()
• fviz_screeplot(): an alias of fviz_eig()

These functions support the results of Principal Component Analysis (PCA), Correspondence Analysis (CA), Multiple Correspondence Analysis (MCA), Multiple Factor Analysis (MFA) and Hierarchical Multiple Factor Analysis (HMFA) functions.

Usage

get_eig(X)
get_eigenvalue(X)
fviz_eig(X, choice = c("variance", "eigenvalue"), geom = c("bar", "line"), barfill = "steelblue", barcolor = "steelblue", linecolor = "black", ncp = 10, addlabels = FALSE, hjust = 0, ...)
fviz_screeplot(...)

Arguments

X

an object of class PCA, CA, MCA, MFA and HMFA [FactoMineR]; prcomp and princomp [stats]; dudi, pca, coa and acm [ade4]; ca and mjca [ca package].

choice

a text specifying the data to be plotted. Allowed values are "variance" or "eigenvalue".

geom

a text specifying the geometry to be used for the graph. Allowed values are "bar" for barplot, "line" for lineplot or c("bar", "line") to use both types.

barfill

fill color for bar plot.

barcolor

outline color for bar plot.

linecolor

color for line plot (when geom contains "line").

ncp

a numeric value specifying the number of dimensions to be shown.

addlabels

logical value. If TRUE, labels are added at the top of bars or points showing the information retained by each dimension.

hjust

horizontal adjustment of the labels.

...

optional arguments to be passed to the functions geom_bar(), geom_line(), geom_text() or fviz_eig().

Value

• get_eig() (or get_eigenvalue()): returns a data.frame containing 3 columns: the eigenvalues, the percentage of variance and the cumulative percentage of variance retained by each dimension.
• fviz_eig() (or fviz_screeplot()): returns a ggplot2

References

http://www.sthda.com

See Also

fviz_pca, fviz_ca, fviz_mca, fviz_mfa, fviz_hmfa

Examples

# Principal Component Analysis
# ++++++++++++++++++++++++++
data(iris)
res.pca <- prcomp(iris[, -5],  scale = TRUE)

# Extract eigenvalues/variances
get_eig(res.pca)

# Default plot
fviz_eig(res.pca)

# Customize the plot
   # - Add labels
   # - Change line color, bar fill and color. 
   # - Change axis limits and themes
   
p <- fviz_eig(res.pca, addlabels = TRUE, hjust = -0.3,
           linecolor = "#FC4E07", 
           barfill="white", barcolor ="darkblue")+ 
     ylim(0, 85)+ # y axis limits
     theme_minimal() # themes: http://www.sthda.com/english/wiki/ggplot2-themes
print(p)

# Change plot title and axis labels
p + labs(title = "Variances - PCA",
        x = "Principal Components", y = "% of variances")

  
# Scree plot - Eigenvalues
fviz_eig(res.pca, choice = "eigenvalue", addlabels=TRUE)

# Use only bar  or line plot: geom = "bar" or geom = "line"
fviz_eig(res.pca, geom="line")
 
        
# Correspondence Analysis
# +++++++++++++++++++++++++++++++++
library(FactoMineR)
data(housetasks)
res.ca <- CA(housetasks, graph = FALSE)
get_eig(res.ca)
fviz_eig(res.ca, linecolor = "#FC4E07",
   barcolor = "#00AFBB", barfill = "#00AFBB")+
   theme_minimal()

# Multiple Correspondence Analysis
# +++++++++++++++++++++++++++++++++
library(FactoMineR)
data(poison)
res.mca <- MCA(poison, quanti.sup = 1:2, 
              quali.sup = 3:4, graph=FALSE)
get_eig(res.mca)
fviz_eig(res.mca, linecolor = "#FC4E07",
   barcolor = "#2E9FDF", barfill = "#2E9FDF")+
   theme_minimal()
 
 
# Multiple Factor Analysis
# +++++++++++++++++++++++++++++++++
library(FactoMineR)
data(wine)
res.mfa <- MFA(wine, group=c(2,5,3,10,9,2), type=c("n",rep("s",5)),
               ncp=5, name.group=c("orig","olf","vis","olfag","gust","ens"),
               num.group.sup=c(1,6), graph=FALSE)
get_eig(res.mfa)
fviz_eig(res.mfa, linecolor = "#FC4E07",
   barcolor = "#E7B800", barfill = "#E7B800")+
   theme_minimal()