PcaGrid {rrcov} | R Documentation |
Computes an approximation of the PP-estimators for PCA using the grid search algorithm in the plane.
PcaGrid(x, ...) ## Default S3 method: PcaGrid(x, k = 0, kmax = ncol(x), na.action = na.fail, trace=FALSE, ...) ## S3 method for class 'formula': PcaGrid(formula, data = NULL, subset, na.action, ...)
formula |
a formula with no response variable, referring only to numeric variables. |
data |
an optional data frame (or similar: see
model.frame ) containing the variables in the
formula formula . |
subset |
an optional vector used to select rows (observations) of the
data matrix x . |
na.action |
a function which indicates what should happen
when the data contain NA s. 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. |
x |
a numeric matrix (or data frame) which provides the data for the principal components analysis. |
k |
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
Σ_{j=1}^k l_j/Σ_{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=0 . |
kmax |
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. |
trace |
whether to print intermediate results. Default is trace = FALSE |
PcaGrid
, serving as a constructor for objects of class PcaGrid-class
is a generic function with "formula" and "default" methods. For details see PCAgrid
and the relevant references.
An S4 object of class PcaGrid-class
which is a subclass of the
virtual class PcaRobust-class
.
Valentin Todorov valentin.todorov@chello.at
C. Croux, P. Filzmoser, M. Oliveira, (2007). Algorithms for Projection-Pursuit Robust Principal Component Analysis, Chemometrics and Intelligent Laboratory Systems, 87, 225.
# multivariate data with outliers library(mvtnorm) x <- rbind(rmvnorm(200, rep(0, 6), diag(c(5, rep(1,5)))), rmvnorm( 15, c(0, rep(20, 5)), diag(rep(1, 6)))) # Here we calculate the principal components with PCAgrid pc <- PcaGrid(x, 6) # we could draw a biplot too: biplot(pc) # we could use another objective function, and # maybe only calculate the first three principal components: pc <- PcaGrid(x, 3, method="qn") biplot(pc) # now we want to compare the results with the non-robust principal components pc <- PcaClassic(x) # again, a biplot for comparision: biplot(pc)