princomp {mva}

R Documentation

Principal Components Analysis

Description

princomp performs a principal components analysis on the given data matrix and returns the results as an object of class princomp.

Usage

## S3 method for class 'formula'
princomp(x, data = NULL, subset, na.action, ...)
## Default S3 method:
princomp(x, cor = FALSE, scores = TRUE, covmat = NULL,
         subset = rep(TRUE, nrow(as.matrix(x))), ...)

Arguments

x	a formula or matrix (or data frame) which provides the data for the principal components analysis.
data	an optional data frame containing the variables in the formula x. By default the variables are taken from environment(x).
subset	an optional vector used to select rows (observations) of the data matrix x.
na.action	a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options, and is na.fail if that is unset. The “factory-fresh” default is na.omit.
cor	a logical value indicating whether the calculation should use the correlation matrix or the covariance matrix.
scores	a logical value indicating whether the score on each principal component should be calculated.
covmat	a covariance matrix, or a covariance list as returned by cov.wt, cov.mve or cov.mcd. If supplied, this is used rather than the covariance matrix of x.
...	arguments passed to or from other methods. If x is a formula one might specify cor or scores.

Details

princomp is a generic function with "formula" and "default" methods.

The calculation is done using eigen on the correlation or covariance matrix, as determined by cor. This is done for compatibility with the S-PLUS result. A preferred method of calculation is to use svd on x, as is done in prcomp.

Note that the default calculation uses divisor N for the covariance matrix.

The print method for the these objects prints the results in a nice format and the plot method produces a scree plot (screeplot). There is also a biplot method.

If x is a formula then the standard NA-handling is applied to the scores (if requested): see napredict.

Value

princomp returns a list with class "princomp" containing the following components:

sdev	the standard deviations of the principal components.
loadings	the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors). This is of class "loadings": see loadings for its print method.
center	the means that were subtracted.
scale	the scalings applied to each variable.
n.obs	the number of observations.
scores	if scores = TRUE, the scores of the supplied data on the principal components.
call	the matched call.
na.action	If relevant.

References

Mardia, K. V., J. T. Kent and J. M. Bibby (1979). Multivariate Analysis, London: Academic Press.

Venables, W. N. and B. D. Ripley (1997, 9). Modern Applied Statistics with S-PLUS, Springer-Verlag.

See Also

summary.princomp, screeplot, biplot.princomp, prcomp, cor, cov, eigen.

Examples

## The variances of the variables in the
## USArrests data vary by orders of magnitude, so scaling is appropriate
data(USArrests)
(pc.cr <- princomp(USArrests))  # inappropriate
princomp(USArrests, cor = TRUE) # =^= prcomp(USArrests, scale=TRUE)
## Similar, but different:
## The standard deviations differ by a factor of sqrt(49/50)

summary(pc.cr <- princomp(USArrests, cor = TRUE))
loadings(pc.cr)  ## note that blank entries are small but not zero
plot(pc.cr) # shows a screeplot.
biplot(pc.cr)

## Formula interface
princomp(~ ., data = USArrests, cor = TRUE)
# NA-handling
USArrests[1, 2] <- NA
pc.cr <- princomp(~ ., data = USArrests, na.action=na.exclude, cor = TRUE)
pc.cr$scores