linear_FA: Exploratory Factor Analysis

In Rdimtools: Dimension Reduction and Estimation Methods

Exploratory Factor Analysis

Description

do.fa is an optimization-based implementation of a popular technique for Exploratory Data Analysis. It is closely related to principal component analysis.

Usage

do.fa(X, ndim = 2, ...)

Arguments

X	an (n\times p) matrix whose rows are observations and columns represent independent variables.
ndim	an integer-valued number of loading variables, or target dimension.
...	extra parameters including maxiter maximum number of iterations (default: 10). tolerance stopping criterion in a Frobenius norm (default: 1e-8).

Value

a named Rdimtools S3 object containing

Y

an (n\times ndim) matrix whose rows are embedded observations.

projection

a (p\times ndim) whose columns are basis for projection.

loadings

a (p\times ndim) matrix whose rows are extracted loading factors.

noise

a length-p vector of estimated noise.

algorithm

name of the algorithm.

Author(s)

Kisung You

References

\insertRef

spearman_general_1904Rdimtools

Examples

## use iris data
data(iris)
set.seed(100)
subid = sample(1:150,50)
X     = as.matrix(iris[subid,1:4])
lab   = as.factor(iris[subid,5])

## compare with PCA and MDS
out1 <- do.fa(X, ndim=2)
out2 <- do.mds(X, ndim=2)
out3 <- do.pca(X, ndim=2)

## visualize three different projections
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=lab, main="Factor Analysis")
plot(out2$Y, pch=19, col=lab, main="MDS")
plot(out3$Y, pch=19, col=lab, main="PCA")
par(opar)

Rdimtools documentation built on Dec. 28, 2022, 1:44 a.m.