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
.
loadings
extracts the loadings.
screeplot
plots the variances against the number of the
principal component. This is also the plot
method.
princomp(x, cor = FALSE, scores = TRUE, covmat = NULL, subset = rep(TRUE, nrow(as.matrix(x)))) loadings(x) screeplot(x, npcs = min(10, length(x$sdev)), type = c("barplot", "lines"), main = deparse(substitute(x)), ...) plot(x, ...) print(x, ...) summary(object) predict(object, ...)
x |
a 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 , cov.mve or cov.mcd .
If supplied, this is used rather than the covariance matrix of
x . |
subset |
a vector used to select rows (observations) of the
data matrix x . |
x, object |
an object of class "princomp" , as
from princomp() . |
npcs |
the number of principal components to be plotted. |
type |
the type of plot. |
... |
graphics parameters. |
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.
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). |
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. |
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.
## 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) plot(pc.cr) # does a screeplot. biplot(pc.cr)