stats (version 3.5.2)

princomp: Principal Components Analysis

Description

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

Usage

princomp(x, …)
# S3 method for formula
princomp(formula, data = NULL, subset, na.action, …)
# S3 method for default
princomp(x, cor = FALSE, scores = TRUE, covmat = NULL,
         subset = rep_len(TRUE, nrow(as.matrix(x))), fix_sign = TRUE, …)
# S3 method for princomp
predict(object, newdata, …)

Arguments

formula

a formula with no response variable, referring only to numeric variables.

data

an optional data frame (or similar: see model.frame) containing the variables in the formula formula. By default the variables are taken from environment(formula).

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.

x

a numeric matrix or data frame which provides the data for the principal components analysis.

cor

a logical value indicating whether the calculation should use the correlation matrix or the covariance matrix. (The correlation matrix can only be used if there are no constant variables.)

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 (and cov.mve or cov.mcd from package MASS). If supplied, this is used rather than the covariance matrix of x.

fix_sign

Should the signs of the loadings and scores be chosen so that the first element of each loading is non-negative?

…

arguments passed to or from other methods. If x is a formula one might specify cor or scores.

object

Object of class inheriting from "princomp".

newdata

An optional data frame or matrix in which to look for variables with which to predict. If omitted, the scores are used. If the original fit used a formula or a data frame or a matrix with column names, newdata must contain columns with the same names. Otherwise it must contain the same number of columns, to be used in the same order.

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. These are non-null only if x was supplied, and if covmat was also supplied if it was a covariance list. For the formula method, napredict() is applied to handle the treatment of values omitted by the na.action.

call

the matched call.

na.action

If relevant.

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 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.

princomp only handles so-called R-mode PCA, that is feature extraction of variables. If a data matrix is supplied (possibly via a formula) it is required that there are at least as many units as variables. For Q-mode PCA use prcomp.

References

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

Venables, W. N. and B. D. Ripley (2002). Modern Applied Statistics with S, Springer-Verlag.

See Also

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

Examples

# NOT RUN {
require(graphics)

## The variances of the variables in the
## USArrests data vary by orders of magnitude, so scaling is appropriate
(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
## The signs of the columns of the loadings are arbitrary
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(~ Murder + Assault + UrbanPop,
                  data = USArrests, na.action = na.exclude, cor = TRUE)
# }
# NOT RUN {
pc.cr$scores[1:5, ]
# }
# NOT RUN {
## (Simple) Robust PCA:
## Classical:
(pc.cl  <- princomp(stackloss))
# }
# NOT RUN {
## Robust:
(pc.rob <- princomp(stackloss, covmat = MASS::cov.rob(stackloss)))
# }

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