Description Usage Arguments Value See Also Examples
Define a two dimensional feature space using the first two principal components generated from
the fetures matrix returned by extract_tsfeatures
1 | get_pc_space(features, robust = TRUE, kpc = 2)
|
features |
Feature matrix returned by |
robust |
If TRUE, a robust PCA will be used on the feature matrix. |
kpc |
Desired number of components to return. |
It returns a list with class 'pcattributes' containing the following components:
pcnorm |
The scores of the firt kpc pricipal components |
center, scale |
The centering and scaling used |
rotation |
the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors).
The function |
PCAproj
, prcomp
, find_odd_streams
,
extract_tsfeatures
, set_outlier_threshold
, gg_featurespace
1 2 | features <- extract_tsfeatures(anomalous_stream[1:100, 1:100])
pc <- get_pc_space(features)
|
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