PCA via SVD for dimension reduction (R parity)

Wraps stats::prcomp.

Usage

morie_pca_dimension_reduction(x, n_components = NULL, seed = 0L)

Arguments

x

Numeric matrix.

n_components

Number of components (default min(n, p)).

seed

Unused for the SVD path; kept for API parity.

Value

Named list: estimate, components, explained_variance, explained_variance_ratio, singular_values, scores, n_components, n, method.

Examples

set.seed(1)
morie_pca_dimension_reduction(x = rnorm(50))
#> $estimate
#> [1] 1
#> 
#> $components
#>     [,1]
#> PC1    1
#> 
#> $explained_variance
#> [1] 0.6912159
#> 
#> $explained_variance_ratio
#> [1] 1
#> 
#> $singular_values
#> [1] 5.819758
#> 
#> $scores
#>               PC1
#>  [1,] -0.72690209
#>  [2,]  0.08319504
#>  [3,] -0.93607689
#>  [4,]  1.49483252
#>  [5,]  0.22905949
#>  [6,] -0.92091666
#>  [7,]  0.38698077
#>  [8,]  0.63787643
#>  [9,]  0.47533307
#> [10,] -0.40583667
#> [11,]  1.41133289
#> [12,]  0.28939496
#> [13,] -0.72168886
#> [14,] -2.31514817
#> [15,]  1.02448264
#> [16,] -0.14538189
#> [17,] -0.11663854
#> [18,]  0.84338793
#> [19,]  0.72077292
#> [20,]  0.49345304
#> [21,]  0.81852909
#> [22,]  0.68168802
#> [23,] -0.02588330
#> [24,] -2.08979998
#> [25,]  0.51937747
#> [26,] -0.15657702
#> [27,] -0.25624379
#> [28,] -1.57120066
#> [29,] -0.57859834
#> [30,]  0.31749328
#> [31,]  1.25823127
#> [32,] -0.20323601
#> [33,]  0.28722333
#> [34,] -0.15425332
#> [35,] -1.47750784
#> [36,] -0.51544284
#> [37,] -0.49473823
#> [38,] -0.15976168
#> [39,]  0.99957709
#> [40,]  0.66272747
#> [41,] -0.26497188
#> [42,] -0.35380996
#> [43,]  0.59651510
#> [44,]  0.45621492
#> [45,] -0.78920397
#> [46,] -0.80794344
#> [47,]  0.26413368
#> [48,]  0.66808464
#> [49,] -0.21279449
#> [50,]  0.78065945
#> 
#> $n_components
#> [1] 1
#> 
#> $n
#> [1] 50
#> 
#> $method
#> [1] "PCA via SVD"
#>