The Spherical Principal Components procedure was proposed by Locantore et al., (1999) as a functional data analysis method. The idea is to perform classical PCA on the the data, \ projected onto a unit sphere. The estimates of the eigenvectors are consistent and the procedure is extremly fast. The simulations of Maronna (2005) show that this method has very good performance.

Objects can be created by calls of the form `new("PcaLocantore", ...)`

but the
usual way of creating `PcaLocantore`

objects is a call to the function
`PcaLocantore`

which serves as a constructor.

`delta`

:Accuracy parameter

`quan`

:Object of class

`"numeric"`

The quantile h used throughout the algorithm`call`

,`center`

,`scale`

,`loadings`

,`eigenvalues`

,`scores`

,`k`

,`sd`

,`od`

,`cutoff.sd`

,`cutoff.od`

,`flag`

,`n.obs`

:-
from the

`"Pca"`

class.

Class `"PcaRobust"`

, directly.
Class `"Pca"`

, by class "PcaRobust", distance 2.

- getQuan
`signature(obj = "PcaLocantore")`

: ...

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/.

`PcaRobust-class`

, `Pca-class`

, `PcaClassic`

, `PcaClassic-class`

1 | ```
showClass("PcaLocantore")
``` |

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