The class `PcaRobust`

searves as a base class for deriving all other
classes representing the results of the robust Principal Component Analisys methods

A virtual Class: No objects may be created from it.

`call`

:Object of class

`"language"`

`center`

:Object of class

`"vector"`

the center of the data`loadings`

:Object of class

`"matrix"`

the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors)`eigenvalues`

:Object of class

`"vector"`

the eigenvalues`scores`

:Object of class

`"matrix"`

the scores - the value of the projected on the space of the principal components data (the centred (and scaled if requested) data multiplied by the`loadings`

matrix) is returned. Hence,`cov(scores)`

is the diagonal matrix`diag(eigenvalues)`

`k`

:Object of class

`"numeric"`

number of (choosen) principal components`sd`

:Object of class

`"Uvector"`

Score distances within the robust PCA subspace`od`

:Object of class

`"Uvector"`

Orthogonal distances to the robust PCA subspace`cutoff.sd`

:Object of class

`"numeric"`

Cutoff value for the score distances`cutoff.od`

:Object of class

`"numeric"`

Cutoff values for the orthogonal distances`flag`

:Object of class

`"Uvector"`

The observations whose score distance is larger than cutoff.sd or whose orthogonal distance is larger than cutoff.od can be considered as outliers and receive a flag equal to zero. The regular observations receive a flag 1`n.obs`

:Object of class

`"numeric"`

the number of observations

Class `"Pca"`

, directly.

No methods defined with class "PcaRobust" in the signature.

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.
URL http://www.jstatsoft.org/v32/i03/.

1 | ```
showClass("PcaRobust")
``` |

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