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

morie_pca_dimension_reduction(x = rnorm(50))
#> $estimate
#> [1] 1
#> 
#> $components
#>     [,1]
#> PC1    1
#> 
#> $explained_variance
#> [1] 0.923268
#> 
#> $explained_variance_ratio
#> [1] 1
#> 
#> $singular_values
#> [1] 6.726078
#> 
#> $scores
#>               PC1
#>  [1,] -1.27236010
#>  [2,] -0.56161786
#>  [3,]  0.63034144
#>  [4,]  1.12220376
#>  [5,]  0.71994398
#>  [6,] -0.35130167
#>  [7,]  0.07210556
#>  [8,] -0.69224084
#>  [9,] -0.70486254
#> [10,]  0.89700612
#> [11,] -1.00323696
#> [12,]  1.96764959
#> [13,] -0.07796397
#> [14,]  0.22689445
#> [15,] -0.72617208
#> [16,] -0.56203307
#> [17,] -1.30466051
#> [18,] -0.17056976
#> [19,]  0.43133803
#> [20,]  0.33665997
#> [21,] -0.76918086
#> [22,] -0.77626635
#> [23,] -0.48984309
#> [24,]  1.50841629
#> [25,] -1.12494800
#> [26,] -0.16669597
#> [27,]  1.91471745
#> [28,] -0.08861926
#> [29,] -1.34748508
#> [30,] -0.65241381
#> [31,]  0.49781560
#> [32,] -0.36324725
#> [33,] -0.54952074
#> [34,] -0.33156161
#> [35,]  0.10285227
#> [36,]  1.61086439
#> [37,] -0.07620949
#> [38,]  1.09315512
#> [39,]  0.64310974
#> [40,] -0.10128427
#> [41,] -1.52054638
#> [42,] -0.50876169
#> [43,] -0.47751483
#> [44,]  0.05951006
#> [45,]  1.31255430
#> [46,]  2.30543460
#> [47,]  1.55993668
#> [48,] -0.12079534
#> [49,] -1.74417177
#> [50,] -0.37642424
#> 
#> $n_components
#> [1] 1
#> 
#> $n
#> [1] 50
#> 
#> $method
#> [1] "PCA via SVD"
#>