Principal Components Analysis
Description
Performs a principal components analysis and returns the results as an object of class PcaClassic (aka constructor).
Usage
1 2 3 4 5 6 
Arguments
formula 
a formula with no response variable, referring only to numeric variables. 
data 
an optional data frame (or similar: see

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 
... 
arguments passed to or from other methods. 
x 
a numeric matrix (or data frame) which provides the data for the principal components analysis. 
k 
number of principal components to compute. If 
kmax 
maximal number of principal components to compute.
Default is 
scale 
a value indicating whether and how the variables should be scaled
to have unit variance (only possible if there are no constant
variables). If 
signflip 
a logical value indicating wheather to try to solve
the sign indeterminancy of the loadings  ad hoc approach setting
the maximum element in a singular vector to be positive. Default is

crit.pca.distances 
criterion to use for computing the cutoff values for the orthogonal and score distances. Default is 0.975. 
trace 
whether to print intermediate results. Default is 
Value
An S4 object of class PcaClassicclass
which is a subclass of the
virtual class Pcaclass
.
Note
This function can be seen as a wrapper arround prcomp() from stats
which
returns the results of the PCA in a class compatible with the object model for robust PCA.
Author(s)
Valentin Todorov valentin.todorov@chello.at
References
Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. URL http://www.jstatsoft.org/v32/i03/.
See Also
Pcaclass
, PcaClassicclass
,