prcomp {stats}  R Documentation 
Performs a principal components analysis on the given data matrix
and returns the results as an object of class prcomp
.
prcomp(x, ...) ## S3 method for class 'formula': prcomp(formula, data = NULL, subset, na.action, ...) ## Default S3 method: prcomp(x, retx = TRUE, center = TRUE, scale. = FALSE, tol = NULL, ...) ## S3 method for class 'prcomp': 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 NA s. The default is set by
the na.action setting of options , and is
na.fail if that is unset. The “factoryfresh”
default is na.omit . 
... 
arguments passed to or from other methods. If x is
a formula one might specify scale. or tol . 
x 
a numeric or complex matrix (or data frame) which provides the data for the principal components analysis. 
retx 
a logical value indicating whether the rotated variables should be returned. 
center 
a logical value indicating whether the variables
should be shifted to be zero centered. Alternately, a vector of
length equal the number of columns of x can be supplied.
The value is passed to scale . 
scale. 
a logical value indicating whether the variables should
be scaled to have unit variance before the analysis takes
place. The default is FALSE for consistency with S, but
in general scaling is advisable. Alternatively, a vector of length
equal the number of columns of x can be supplied. The
value is passed to scale . 
tol 
a value indicating the magnitude below which components
should be omitted. (Components are omitted if their
standard deviations are less than or equal to tol times the
standard deviation of the first component.)
With the default null setting, no components
are omitted. Other settings for tol could be tol = 0 or
tol = sqrt(.Machine$double.eps) , which would omit
essentially constant components. 
object 
Object of class inheriting from "prcomp" 
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.

The calculation is done by a singular value decomposition of the
(centered and possibly scaled) data matrix, not by using
eigen
on the covariance matrix. This
is generally the preferred method for numerical accuracy. The
print
method for the these objects prints the results in a nice
format and the plot
method produces a scree plot.
prcomp
returns a list with class "prcomp"
containing the following components:
sdev 
the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix). 
rotation 
the matrix of variable loadings (i.e., a matrix
whose columns contain the eigenvectors). The function
princomp returns this in the element loadings . 
x 
if retx is true the value of the rotated data (the
centred (and scaled if requested) data multiplied by the
rotation matrix) is returned. 
center, scale 
the centering and scaling used, or FALSE . 
The signs of the columns of the rotation matrix are arbitrary, and so may differ between different programs for PCA, and even between different builds of R.
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
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, SpringerVerlag.
biplot.prcomp
,
princomp
, cor
, cov
,
svd
, eigen
.
## the variances of the variables in the ## USArrests data vary by orders of magnitude, so scaling is appropriate prcomp(USArrests) # inappropriate prcomp(USArrests, scale = TRUE) prcomp(~ Murder + Assault + Rape, data = USArrests, scale = TRUE) plot(prcomp(USArrests)) summary(prcomp(USArrests, scale = TRUE)) biplot(prcomp(USArrests, scale = TRUE))