Compute score and orthogonal distances for Principal Components (objects of class 'Pca')
Compute score and orthogonal distances for an object (derived from)Pca-class.
pca.distances(obj, data, r, crit=0.975)
obj |
an object of class (derived from) |
data |
The data matrix for which the |
r |
rank of data |
crit |
Criterion to use for computing the cutoff values. |
This function calculates the score and orthogonal distances and the appropriate cutoff values for identifying outlying observations. The computed values are used to create a vector a of flags, one for each observation, identifying the outliers.
An S4 object of class derived from the virtual class Pca-class -
the same object passed to the function, but with the score and orthogonal
distances as well as their cutoff values and the corresponding flags appended to it.
Valentin Todorov valentin.todorov@chello.at
M. Hubert, P. J. Rousseeuw, K. Vanden Branden (2005), ROBPCA: a new approach to robust principal components analysis, Technometrics, 47, 64–79.
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/.
## PCA of the Hawkins Bradu Kass's Artificial Data ## using all 4 variables data(hbk) pca <- PcaHubert(hbk) pca.distances(pca, hbk, rankMM(hbk))
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