Augment data with results from a robust principal component analysis

Augment the data with information from an rrcov::Pca* object (such as the output of rrcov::PcaHubert() or rrcov::PcaClassic()). The returned tibble contains the principal component scores (.fittedPC1, .fittedPC2, ...), the score distance (.sd) and the orthogonal distance (.od) of each observation. The score distance measures how far an observation lies from the centre within the projection subspace, while the orthogonal distance measures how far it lies from the subspace. If data is supplied, its columns are returned alongside these results.

Usage

# S3 method for class 'Pca'
augment(x, data = NULL, ...)

Arguments

x

An rrcov::Pca* object.

data

The original data matrix or data frame used to compute the PCA. If supplied, its columns are bound to the left of the returned tibble.

...

Unused.

Value

A tibble::tibble() with one row per observation.

Author

Rob J Hyndman

Examples

Y <- oldfaithful[, c("duration", "waiting")]
pca <- rrcov::PcaHubert(as.matrix(Y), k = 1)
broom::augment(pca, data = Y)
#> # A tibble: 2,097 × 5
#>    duration waiting .fittedPC1    .sd   .od
#>       <dbl>   <dbl>      <dbl>  <dbl> <dbl>
#>  1      196    5940      -98.6 0.224  49.9 
#>  2      240    5820       20.9 0.0474  4.42
#>  3      121    3900     1942.  4.41   99.0 
#>  4      240    5280      561.  1.27    2.45
#>  5      210    5580      261.  0.593  31.4 
#>  6      240    5760       80.9 0.183   3.65
#>  7      205    5160      681.  1.55   31.0 
#>  8      214    5400      441.  1.00   25.1 
#>  9      240    6060     -219.  0.497   7.47
#> 10      242    6060     -219.  0.497   5.47
#> # ℹ 2,087 more rows