| princomp {stats} | R Documentation |
princomp performs a principal components analysis on the given
numeric data matrix and returns the results as an object of class
princomp.
princomp(x, ...)
## S3 method for class 'formula':
princomp(formula, data = NULL, subset, na.action, ...)
## Default S3 method:
princomp(x, cor = FALSE, scores = TRUE, covmat = NULL,
subset = rep(TRUE, nrow(as.matrix(x))), ...)
## S3 method for class 'princomp':
predict(object, newdata, ...)
formula |
a formula with no response variable. |
data |
an optional data 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. |
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. |
... |
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.
|
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.
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.
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. |
The signs of the columns of the loadings and scores are arbitrary, and so may differ between different programs for PCA, and even between different builds of R.
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.
summary.princomp, screeplot,
biplot.princomp,
prcomp, cor, cov,
eigen.
## 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
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)
pc.cr$scores