PcaCov {rrcov} | R Documentation |
Robust PCA are obtained by replacing the classical covariance matrix
by a robust covariance estimator. This can be one of the available
in rrcov
estimators, i.e. MCD, OGK, M or S estimator.
PcaCov(x, ...) ## Default S3 method: PcaCov(x, k = 0, kmax = ncol(x), corr=FALSE, cov.control=CovControlMcd(), na.action = na.fail, trace=FALSE, ...) ## S3 method for class 'formula': PcaCov(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. |
corr |
a logical value indicating whether the calculation should use
the correlation matrix or the covariance matrix (the correlation matrix
can only be used if there are no constant variables). Default is corr=FALSE . |
cov.control |
specifies which covariance estimator to use by providing
a CovControl-class object.
The default is CovControlMcd-class which will indirectly call CovMcd |
trace |
whether to print intermediate results. Default is trace = FALSE |
PcaCov
, serving as a constructor for objects of class PcaCov-class
is a generic function with "formula" and "default" methods. For details see the relevant references.
An S4 object of class PcaCov-class
which is a subclass of the
virtual class PcaRobust-class
.
Valentin Todorov valentin.todorov@chello.at
## PCA of the Hawkins Bradu Kass's Artificial Data ## using all 4 variables data(hbk) pca <- PcaCov(hbk) pca ## Compare with the classical PCA prcomp(hbk) ## or PcaClassic(hbk) ## If you want to print the scores too, use print(pca, print.x=TRUE) ## Using the formula interface PcaCov(~., data=hbk) ## To plot the results: plot(pca) # distance plot pca2 <- PcaCov(hbk, k=2) plot(pca2) # PCA diagnostic plot (or outlier map) ## Use the standard plots available for for prcomp and princomp screeplot(pca) biplot(pca)