THERE ARE THREE WAYS TO PERFORM PCA IN R

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There are two ways to perform PCA in R: princomp() and prcomp()

There are three ways to perform PCA in R: princomp() , prcomp() and pca() in labdsv library . Essentially, they compute the same values (technically, princomp() and labdsv package computes an eigen analysis and prcomp() computes a singular value decomposition.).


The prcomp() function is a numerically stable routine that returns a “prcomp object” that contains the square-root of the eigenvalues (“sdev”), the eigenvectors (“rotation”), and the scores. And so the preferred method is prcomp().


The princomp() function is slightly less stable, but has more features. It returns a “princomp object” that contains the square-root of the eigenvalues (“sdev”), the eigenvectors (“loadings”), the means for each variable (“center”) and the scores (“scores”), as well as some other things. Typing summary(princomp) or summary(prcomp) will return the percent of variation explained.


--By SungHyun Kang


## princomp()


p1 <- princomp(USArrests, cor = TRUE) ## using correlation matrix

## p1 <- princomp(USArrests) ## using covariance matrix


summary(p1)

loadings(p1)

plot(p1)

biplot(p1)

p1$scores

screeplot(p1) ## identical with plot()

screeplot(p1, npcs=4, type="lines")


## Formula interface

princomp(~ ., data = USArrests, cor = TRUE) ## identical with princomp(USArrests, cor = TRUE)

p2 <- princomp(~ Murder + Assault + UrbanPop, data = USArrests, cor = TRUE)

p2$scores



## prcomp()


## USArrests data vary by orders of magnitude, so scaling is appropriate

p3 <- prcomp(USArrests, scale = TRUE) ## using correlation matrix

## p3 <- prcomp(USArrests) ## using covariance matrix


print(p3)

summary(p3)

plot(p3) ## Scree plot

biplot(p3)


## Formula interface

p4 <- prcomp(~ Murder + Assault + UrbanPop, data = USArrests, scale = TRUE)





## pca() in “labdsv” package


library(labdsv) ## You first have to load the LabDSV library.

p5 <- pca(USArrests, dim=4, cor = TRUE) ## using correlation matrix

## p5 <-pca(USArrests, dim=4) ## using covariance matrix


summary(p5)

varplot.pca(p5) ## scree plot and cumulative variances plot

loadings.pca(p5)

plot(p5)




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Tags: perform pca, three, perform, there