Get left singular vectors in a tibble
Usage
get_svd_u(fa, factors = 1:fa$rank)
get_svd_v(fa, factors = 1:fa$rank)
get_varimax_z(fa, factors = 1:fa$rank)
get_varimax_y(fa, factors = 1:fa$rank)Arguments
- fa
A
vsp_fa()object.- factors
The specific columns to index into. The most reliable option here is to index with an integer vector of column indices, but you could also use a character vector if columns have been named. By default returns all factors/singular vectors.
Value
A tibble::tibble() with one row for each node, and one column
containing each of the requested factor or singular vector, plus
an additional id column.
Functions
get_svd_v(): Get right singular vectors in a tibbleget_varimax_z(): Get varimax Y factors in a tibbleget_varimax_y(): Get varimax Z factors in a tibble
Examples
data(enron, package = "igraphdata")
fa <- vsp(enron, rank = 30)
#> This graph was created by an old(er) igraph version.
#> ℹ Call `igraph::upgrade_graph()` on it to use with the current igraph version.
#> For now we convert it on the fly...
fa
#> Vintage Sparse PCA Factor Analysis
#>
#> Rows (n): 184
#> Cols (d): 184
#> Factors (rank): 30
#> Lambda[rank]: 0.2077
#> Components
#>
#> Z: 184 x 30 [matrix]
#> B: 30 x 30 [matrix]
#> Y: 184 x 30 [matrix]
#> u: 184 x 30 [matrix]
#> d: 30 [numeric]
#> v: 184 x 30 [matrix]
#>
get_svd_u(fa)
#> # A tibble: 184 × 31
#> id u01 u02 u03 u04 u05 u06 u07 u08
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 row001 0.0000162 2.80e-4 5.22e-4 4.71e-4 6.45e-4 1.33e-3 3.33e-3 -3.81e-2
#> 2 row002 0.000588 2.23e-3 6.97e-4 1.17e-2 7.25e-3 2.31e-3 7.09e-3 3.41e-3
#> 3 row003 0.0000942 6.95e-4 4.32e-4 4.50e-3 5.87e-3 1.57e-2 2.30e-4 2.53e-3
#> 4 row004 0.000105 6.05e-4 -2.63e-5 3.79e-3 2.12e-3 -1.04e-3 -1.42e-4 4.74e-4
#> 5 row005 0.00252 1.06e-2 1.99e-2 1.61e-2 9.37e-3 8.45e-3 5.80e-2 1.82e-2
#> 6 row006 0.00299 7.59e-3 1.45e-3 8.55e-3 5.97e-3 4.60e-3 5.44e-2 1.29e-2
#> 7 row007 0.00146 1.80e-2 6.87e-3 2.43e-2 8.48e-3 1.53e-4 4.58e-2 -2.76e-2
#> 8 row008 0.00193 3.97e-3 -2.91e-4 2.06e-3 1.08e-3 1.38e-3 1.12e-2 2.21e-3
#> 9 row009 0.00150 4.77e-3 -1.82e-4 3.61e-2 -7.74e-2 1.80e-2 1.06e-4 -1.90e-4
#> 10 row010 0.000329 4.10e-3 7.46e-3 1.05e-2 1.22e-2 2.37e-2 7.10e-2 -7.62e-1
#> # ℹ 174 more rows
#> # ℹ 22 more variables: u09 <dbl>, u10 <dbl>, u11 <dbl>, u12 <dbl>, u13 <dbl>,
#> # u14 <dbl>, u15 <dbl>, u16 <dbl>, u17 <dbl>, u18 <dbl>, u19 <dbl>,
#> # u20 <dbl>, u21 <dbl>, u22 <dbl>, u23 <dbl>, u24 <dbl>, u25 <dbl>,
#> # u26 <dbl>, u27 <dbl>, u28 <dbl>, u29 <dbl>, u30 <dbl>
get_svd_v(fa)
#> # A tibble: 184 × 31
#> id v01 v02 v03 v04 v05 v06 v07 v08
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 col001 0.000291 0.00122 0.00107 0.00402 2.65e-3 0.00242 1.49e-2 -5.75e-2
#> 2 col002 0.00209 0.00856 0.00679 0.0269 1.65e-2 0.00562 7.04e-2 1.19e-2
#> 3 col003 0.000888 0.00378 0.00238 0.0246 2.60e-2 0.0585 1.57e-2 1.11e-2
#> 4 col004 0.000317 0.00131 0.000631 0.00867 7.56e-3 0.0122 7.10e-3 4.11e-3
#> 5 col005 0.00564 0.0179 0.0376 0.0275 1.57e-2 0.0109 1.21e-1 2.64e-2
#> 6 col006 0.00222 0.00745 0.00147 0.0105 3.76e-3 0.00511 6.15e-2 1.65e-2
#> 7 col007 0.00383 0.0437 0.0129 0.122 5.38e-2 -0.0283 1.25e-1 -4.77e-2
#> 8 col008 0.00189 0.00150 0.000384 0.00392 9.75e-4 0.00143 1.33e-2 3.26e-3
#> 9 col009 0.000393 0.00153 -0.000196 0.0158 -3.37e-2 0.00766 -4.64e-4 3.77e-4
#> 10 col010 0.000450 0.00818 0.0147 0.00770 1.15e-2 0.0248 4.34e-2 -4.81e-1
#> # ℹ 174 more rows
#> # ℹ 22 more variables: v09 <dbl>, v10 <dbl>, v11 <dbl>, v12 <dbl>, v13 <dbl>,
#> # v14 <dbl>, v15 <dbl>, v16 <dbl>, v17 <dbl>, v18 <dbl>, v19 <dbl>,
#> # v20 <dbl>, v21 <dbl>, v22 <dbl>, v23 <dbl>, v24 <dbl>, v25 <dbl>,
#> # v26 <dbl>, v27 <dbl>, v28 <dbl>, v29 <dbl>, v30 <dbl>
get_varimax_z(fa)
#> # A tibble: 184 × 31
#> id z01 z02 z03 z04 z05 z06 z07 z08
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 row0… 2.42e-4 -0.00245 -2.99e-2 3.37e-4 9.96e-5 -0.0114 -0.00849 0.502
#> 2 row0… -2.52e-3 0.00135 6.70e-4 -1.63e-1 -1.47e-2 0.0471 0.190 0.00181
#> 3 row0… 2.98e-4 -0.100 1.17e-4 -3.62e-3 -2.06e-2 0.187 -0.158 0.00303
#> 4 row0… -7.75e-5 -0.0183 1.17e-4 5.42e-2 -5.58e-3 0.00165 -0.0367 -0.00106
#> 5 row0… -2.31e-3 0.00150 2.57e-1 -1.42e-2 -4.38e-2 0.00629 1.18 -0.0179
#> 6 row0… -3.46e-2 -0.0527 -2.61e-2 -1.26e-2 -1.83e-2 0.0282 0.408 -0.0286
#> 7 row0… -1.08e-3 -0.327 -6.01e-1 -6.98e-2 -9.85e-2 -0.0709 0.509 0.0511
#> 8 row0… 1.58e-2 -0.0518 -1.34e-2 -1.03e-2 -4.12e-3 -0.0139 0.225 -0.0244
#> 9 row0… 2.22e-3 0.0752 3.30e-2 -6.50e-4 -5.00e-1 -0.0278 -0.0740 -0.00556
#> 10 row0… 7.13e-4 -0.0119 1.95e-2 -5.06e-3 -7.08e-3 0.00341 -0.00369 13.4
#> # ℹ 174 more rows
#> # ℹ 22 more variables: z09 <dbl>, z10 <dbl>, z11 <dbl>, z12 <dbl>, z13 <dbl>,
#> # z14 <dbl>, z15 <dbl>, z16 <dbl>, z17 <dbl>, z18 <dbl>, z19 <dbl>,
#> # z20 <dbl>, z21 <dbl>, z22 <dbl>, z23 <dbl>, z24 <dbl>, z25 <dbl>,
#> # z26 <dbl>, z27 <dbl>, z28 <dbl>, z29 <dbl>, z30 <dbl>
get_varimax_y(fa)
#> # A tibble: 184 × 31
#> id y01 y02 y03 y04 y05 y06 y07 y08
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 col001 -0.00455 -1.72e-2 8.06e-3 -3.08e-3 -0.0100 0.0155 0.317 1.16
#> 2 col002 -0.0282 -1.30e-1 1.46e-1 6.56e-2 -0.0243 -0.0199 2.15 -0.00828
#> 3 col003 -0.0274 -4.84e-1 -4.23e-2 -8.76e-2 -0.149 0.788 -0.319 0.00535
#> 4 col004 -0.0119 -1.87e-1 -1.93e-2 -6.55e-2 -0.0527 0.146 -0.227 -0.0128
#> 5 col005 -0.0168 5.16e-2 1.33e-1 1.29e-1 -0.0170 -0.0563 2.32 -0.0376
#> 6 col006 -0.0663 -1.07e-2 3.98e-2 2.43e-2 -0.0454 0.00226 1.12 -0.0182
#> 7 col007 -0.0124 5.94e-1 1.90e-1 3.10e-2 0.00379 -0.0280 0.807 0.0115
#> 8 col008 0.0142 5.29e-2 -1.00e-2 -8.83e-3 -0.0823 -0.0227 -0.0102 -0.00142
#> 9 col009 -0.00243 1.73e-3 5.17e-4 1.16e-3 -0.210 -0.00822 -0.0244 -0.00329
#> 10 col010 0.00404 5.07e-4 -1.94e-1 -9.31e-4 0.0119 -0.278 -0.181 5.51
#> # ℹ 174 more rows
#> # ℹ 22 more variables: y09 <dbl>, y10 <dbl>, y11 <dbl>, y12 <dbl>, y13 <dbl>,
#> # y14 <dbl>, y15 <dbl>, y16 <dbl>, y17 <dbl>, y18 <dbl>, y19 <dbl>,
#> # y20 <dbl>, y21 <dbl>, y22 <dbl>, y23 <dbl>, y24 <dbl>, y25 <dbl>,
#> # y26 <dbl>, y27 <dbl>, y28 <dbl>, y29 <dbl>, y30 <dbl>