sla.princomp {RScaLAPACK} | R Documentation |
The function sla.prcomp
performs a principal components analysis on the give data matrix (using sla.eigen
) and returns the results as a object of class princomp
.
sla.princomp(x, ...) ## S3 method for class 'formula' sla.princomp(formula, data = NULL, subset, na.action, NPROWS=0, NPCOLS=0, MB=16, ...) ## Default S3 method: sla.princomp(x, cor = FALSE, scores = TRUE, covmat = NULL, subset = rep(TRUE, nrow(as.matrix(x))), NPROWS=0, NPCOLS=0, MB=16, ...)
formula |
a formula with no response variable. |
data |
an optional data frame containing the variables in the
formula |
x |
a matrix or data frame which provides the data for the principal components analysis. |
subset |
an optional vector used to select rows (observations) of the
data matrix |
na.action |
a function which indicates what should happen
when the data contain |
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
|
... |
arguments passed to or from other methods. If |
NPROWS |
Number of Process Rows in the Process Grid. |
NPCOLS |
Number of Process Cols in the Process Grid. |
MB |
Block Size. |
If the number of processor rows and columns are both zero, one processor is used. If the number of processor rows is nonzero and the number of processor columns is zero, then the processor rows is taken to be a number of processors, and a grid is made accordingly.
sla.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
|
center |
the means that were subtracted. |
scale |
the scalings applied to each variable. |
n.obs |
the number of observations. |
scores |
if |
call |
the matched call. |
na.action |
If relevant. |
Nagiza Samatova (samatovan@ornl.gov), Guruprasad Kora (koragh@ornl.gov), Srikanth Yoginath (yoginathsb@ornl.gov), David Bauer (bauerda@ornl.gov)
summary.princomp
, screeplot
,
biplot.princomp
,
prcomp
, cor
, cov
,
eigen
.
http://www.google.com/search?q=principal+components+analysis
http://www.netlib.org/scalapack/
prcomp
for the normal PCA function.
data(USArrests) sla.princomp(USArrests, cor=TRUE, NPROWS=2, NPCOLS=2, MB=2)