Performs a principal components analysis and returns the results as an object of class PcaClassic (aka constructor).
PcaClassic(x, ...)
# S3 method for default
PcaClassic(x, k = ncol(x), kmax = ncol(x),
scale=FALSE, signflip=TRUE, crit.pca.distances = 0.975, trace=FALSE, ...)
# S3 method for formula
PcaClassic(formula, data = NULL, subset, na.action, ...)An S4 object of class PcaClassic-class which is a subclass of the
virtual class Pca-class.
a formula with no response variable, referring only to numeric variables.
an optional data frame (or similar: see
model.frame) containing the variables in the
formula formula.
an optional vector used to select rows (observations) of the
data matrix x.
a function which indicates what should happen
when the data contain NAs. The default is set by
the na.action setting of options, and is
na.fail if that is unset. The default is na.omit.
arguments passed to or from other methods.
a numeric matrix (or data frame) which provides the data for the principal components analysis.
number of principal components to compute. If k is missing,
or k = 0, the algorithm itself will determine the number of
components by finding such k that \(l_k/l_1 >= 10.E-3\) and
\(\Sigma_{j=1}^k l_j/\Sigma_{j=1}^r l_j >= 0.8\).
It is preferable to investigate the scree plot in order to choose the number
of components and then run again. Default is k=ncol(x).
maximal number of principal components to compute.
Default is kmax=10. If k is provided, kmax
does not need to be specified, unless k is larger than 10.
a value indicating whether and how the variables should be scaled
to have unit variance (only possible if there are no constant
variables). If scale=FALSE (default) or scale=NULL no scaling is
performed (a vector of 1s is returned in the scale slot). If scale=TRUE
the data are scaled to have unit variance. Alternatively it can be a function
like sd or Qn or a vector of length equal the number of columns
of x. The value is passed to the underlying function and the result
returned is stored in the scale slot. Default is scale=FALSE.
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
signflip = TRUE
criterion to use for computing the cutoff values for the orthogonal and score distances. Default is 0.975.
whether to print intermediate results. Default is trace = FALSE
Valentin Todorov valentin.todorov@chello.at
Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1--47. tools:::Rd_expr_doi("10.18637/jss.v032.i03").
Pca-class, PcaClassic-class,