factoextra (version 1.0.3)

facto_summarize: Subset and summarize the output of factor analyses

Description

Subset and summarize the results of Principal Component Analysis (PCA), Correspondence Analysis (CA), Multiple Correspondence Analysis (MCA) and Multiple Factor Analysis (MFA) functions from several packages.

Usage

facto_summarize(X, element, node.level = 1, group.names, result = c("coord", "cos2", "contrib"), axes = 1:2, select = NULL)

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].

element

the element to subset from the output. Possible values are "row" or "col" for CA; "var" or "ind" for PCA and MCA; 'quanti.var', 'quali.var' or 'ind' for MFA

node.level

a single number indicating the HMFA node level.

group.names

a vector containing the name of the groups (by default, NULL and the group are named group.1, group.2 and so on).

result

the result to be extracted for the element. Possible values are the combination of c("cos2", "contrib", "coord")

axes

a numeric vector specifying the axes of interest. Default values are 1:2 for axes 1 and 2.

select

a selection of variables. Allowed values are NULL or a list containing the arguments name, cos2 or contrib. Default is list(name = NULL, cos2 = NULL, contrib = NULL):

• name: is a character vector containing variable names to be selected
• cos2: if cos2 is in [0, 1], ex: 0.6, then variables with a cos2 > 0.6 are selected. if cos2 > 1, ex: 5, then the top 5 variables with the highest cos2 are selected
• contrib: if contrib > 1, ex: 5, then the top 5 variables with the highest cos2 are selected.

Value

A data frame containing the (total) coord, cos2 and the contribution for the axes.

Details

If length(axes) > 1, then the columns contrib and cos2 correspond to the total contributions and total cos2 of the axes. In this case, the column coord is calculated as x^2 + y^2 + ...+; x, y, ... are the coordinates of the points on the specified axes.

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)

# Summarize variables on axes 1:2
facto_summarize(res.pca, "var", axes = 1:2)[,-1]
# Select the top 5 contributing variables
facto_summarize(res.pca, "var", axes = 1:2,
           select = list(contrib = 5))[,-1]
# Select variables with cos2 >= 0.6
facto_summarize(res.pca, "var", axes = 1:2,
           select = list(cos2 = 0.6))[,-1]
# Select by names
facto_summarize(res.pca, "var", axes = 1:2,
     select = list(name = c("X100m", "Discus", "Javeline")))[,-1]
           
# Summarize individuals on axes 1:2
facto_summarize(res.pca, "ind", axes = 1:2)[,-1]

# Correspondence Analysis
# ++++++++++++++++++++++++++
# Install and load FactoMineR to compute CA
# install.packages("FactoMineR")
library("FactoMineR")
data("housetasks")
res.ca <- CA(housetasks, graph = FALSE)
# Summarize row variables on axes 1:2
facto_summarize(res.ca, "row", axes = 1:2)[,-1]
# Summarize column variables on axes 1:2
facto_summarize(res.ca, "col", axes = 1:2)[,-1]

# Multiple Correspondence Analysis
# +++++++++++++++++++++++++++++++++
library(FactoMineR)
data(poison)
res.mca <- MCA(poison, quanti.sup = 1:2, 
              quali.sup = 3:4, graph=FALSE)
# Summarize variables on axes 1:2
res <- facto_summarize(res.mca, "var", axes = 1:2)
head(res)
# Summarize individuals on axes 1:2
res <- facto_summarize(res.mca, "ind", axes = 1:2)
head(res)

# 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)
# Summarize categorcial variables on axes 1:2
res <- facto_summarize(res.mfa, "quali.var", axes = 1:2)
head(res)
# Summarize individuals on axes 1:2
res <- facto_summarize(res.mfa, "ind", axes = 1:2)
head(res)