pca: Principal Components Analysis

Description

Principal components analysis is a eigenanalysis of a correlation or covariance matrix used to project a high-dimensional system to fewer dimensions.

Usage

pca(mat, cor = FALSE, dim = min(nrow(mat),ncol(mat)))
# S3 method for pca
summary(object, dim = length(object$sdev), ...)
# S3 method for pca
scores(x, labels = NULL, dim = length(x$sdev))
# S3 method for pca
loadings(x, dim = length(x$sdev), digits = 3, cutoff = 0.1)
varplot.pca(x, dim=length(x$sdev))

Value

an object of class "pca", a list with components:

scores: a matrix of the coordinates of the samples in the reduced space
loadings: a matrix of the contributions of the variables to the axes of the reduced space.
sdev: a vector of standard deviations for each dimension

Arguments

mat: a matrix or data.frame of interest, samples as rows, attributes as columns
cor: logical: whether to use a correlation matrix (if TRUE), or covariance matrix (if FALSE)
dim: the number of dimensions to return
object: an object of class ‘pca’
x: an object of class ‘dsvord’ and type='pca'
labels: an (optional) vector of labels to identify points
digits: number of digits to report
cutoff: threshold to suppress printing small values
...: arguments to pass to function summary

Author

David W. Roberts droberts@montana.edu http://ecology.msu.montana.edu/droberts/droberts.html

Details

PCA is a common multivariate technique. The version here is simply a wrapper for the prcomp function to make its use and plotting consistent with the other LabDSV functions.

References

http://ecology.msu.montana.edu/labdsv/R/labs/lab7/lab7.html

Examples

Run this code

data(bryceveg) # returns a vegetation data.frame
data(brycesite)
x <- pca(bryceveg,dim=10)  # returns the first 10 eigenvectors 
                           # and loadings
plot(x)
surf(x,brycesite$elev)
points(x,brycesite$depth=='deep')

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