| 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)