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 class 'pca':
summary(object, dim = length(object$sdev), ...)
## S3 method for class 'pca':
scores(x, labels = NULL, dim = length(x$sdev))
## S3 method for class 'pca':
loadings(x, dim = length(x$sdev), digits = 3, cutoff = 0.1)

Arguments

mat

a matrix or dataframe 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

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:
scoresa matrix of the coordinates of the samples in the reduced space
loadingsa matrix of the contributions of the variables to the axes of the reduced space.
sdeva 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

Examples

Run this code

data(bryceveg) # returns a vegetation dataframe
    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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