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] 0.9228227
#> 
#> $explained_variance_ratio
#> [1] 1
#> 
#> $singular_values
#> [1] 6.724456
#> 
#> $scores
#>                PC1
#>  [1,]  0.815596586
#>  [2,] -0.665492385
#>  [3,] -0.289534355
#>  [4,]  1.338490069
#>  [5,] -0.716081669
#>  [6,]  0.570412214
#>  [7,] -0.202507857
#>  [8,] -0.599036508
#>  [9,]  1.301709167
#> [10,] -0.333524006
#> [11,] -0.321445706
#> [12,]  0.587428257
#> [13,]  0.806232289
#> [14,] -1.032324532
#> [15,]  1.037923046
#> [16,] -0.744815260
#> [17,] -0.995393525
#> [18,] -2.442086901
#> [19,] -0.254441021
#> [20,]  0.208013807
#> [21,] -0.816420204
#> [22,]  1.133205342
#> [23,]  0.511201979
#> [24,] -0.009047149
#> [25,]  1.698753271
#> [26,] -1.611380195
#> [27,]  0.176690249
#> [28,]  0.736756748
#> [29,] -1.104292854
#> [30,] -0.365804341
#> [31,] -0.164806260
#> [32,] -0.553955456
#> [33,] -0.039255114
#> [34,] -0.263621923
#> [35,] -0.940213676
#> [36,]  0.652594136
#> [37,] -0.685457149
#> [38,] -0.735318445
#> [39,]  0.372385109
#> [40,]  1.586320867
#> [41,] -1.870176510
#> [42,]  0.089062799
#> [43,]  0.288398882
#> [44,] -0.079724722
#> [45,]  2.451323172
#> [46,]  1.148177495
#> [47,] -0.643487902
#> [48,] -0.734177645
#> [49,]  0.804898470
#> [50,]  0.898249318
#> 
#> $n_components
#> [1] 1
#> 
#> $n
#> [1] 50
#> 
#> $method
#> [1] "PCA via SVD"
#>