princomp {mva} | R Documentation |
princomp
performs a principal components analysis on the given
data matrix and returns the results as an object of class
princomp
.
princomp(x, data = NULL, subset, na.action, ...) princomp(x, cor = FALSE, scores = TRUE, covmat = NULL, subset = rep(TRUE, nrow(as.matrix(x))), ...)
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 NA s. 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 . |
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
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. |
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.
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 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