labdsv (version 1.8-0)

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)

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 ‘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

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

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

See Also

princomp, prcomp, pco, nmds, fso, cca

Examples

# NOT RUN {
    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')
# }