| PcaProj {rrcov} | R Documentation |
A fast and simple algorithm for approximating the PP-estimators for PCA: Croux and Ruiz-Gazen (2005)
PcaProj(x, ...)
## Default S3 method:
PcaProj(x, k = 0, kmax = ncol(x), na.action = na.fail, trace=FALSE, ...)
## S3 method for class 'formula':
PcaProj(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 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. |
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 |
PcaProj, serving as a constructor for objects of class PcaProj-class
is a generic function with "formula" and "default" methods. For details see PCAproj and the relevant references.
An S4 object of class PcaProj-class which is a subclass of the
virtual class PcaRobust-class.
Valentin Todorov valentin.todorov@chello.at
C. Croux, A. Ruiz-Gazen (2005). High breakdown estimators for principal components: The projection-pursuit approach revisited, Journal of Multivariate Analysis, 95, 206–226.
# 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 <- PcaProj(x, 6)
# we could draw a biplot too:
biplot(pc)
# we could use another calculation method and another objective function, and
# maybe only calculate the first three principal components:
pc <- PcaProj(x, 3, method="qn", CalcMethod="sphere")
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)