rrcov: PcaCov – R documentation

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PcaCov

Robust PCA based on a robust covariance matrix

Description

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.

Usage

PcaCov(x, ...)
## Default S3 method:
PcaCov(x, k = ncol(x), kmax = ncol(x), cov.control=CovControlMcd(), 
    scale = FALSE, signflip = TRUE, crit.pca.distances = 0.975, trace=FALSE, ...)
## S3 method for class 'formula'
PcaCov(formula, data = NULL, subset, na.action, ...)

Arguments

`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=ncol(x)`.
`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.
`cov.control`	specifies which covariance estimator to use by providing a `CovControl-class` object. The default is `CovControlMcd-class` which will indirectly call `CovMcd`. If `cov.control=NULL` is specified, the classical estimates will be used by calling `CovClassic`

`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 `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 by the estimator used to compute the covariance matrix (`MCD` by default). 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`.
`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 `signflip = FALSE`
`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 `trace = FALSE`

Details

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.

Value

An S4 object of class PcaCov-class which is a subclass of the virtual class PcaRobust-class.

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/.

Examples

## 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)

rrcov

Scalable Robust Estimators with High Breakdown Point

v1.5-5

GPL (>= 2)

Authors

Valentin Todorov [aut, cre] (<https://orcid.org/0000-0003-4215-0245>)

Initial release

2020-07-31