Get most important hubs for each Z factor
Usage
get_z_hubs(fa, hubs_per_factor = 10, factors = 1:fa$rank)
get_y_hubs(fa, hubs_per_factor = 10, factors = 1:fa$rank)Arguments
- fa
A
vsp_fa()object.- hubs_per_factor
The number of important nodes to get per latent factor. Defaults to
10.- 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() where each row corresponds to a single
hub, and three columns:
id: Node id of hub nodefactor: Which factor that node is a hub for. Nodes can be hubs of multiple factors.loading: The actual value of the hubs factor loading for that factor.
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_z_hubs(fa)
#> # A tibble: 300 × 3
#> # Groups: factor [30]
#> id factor loading
#> <chr> <chr> <dbl>
#> 1 row179 z01 13.6
#> 2 row181 z01 0.159
#> 3 row110 z01 0.103
#> 4 row073 z01 0.0611
#> 5 row097 z01 0.0463
#> 6 row075 z01 -0.0416
#> 7 row106 z01 -0.0407
#> 8 row058 z01 0.0405
#> 9 row164 z01 0.0398
#> 10 row006 z01 -0.0346
#> # ℹ 290 more rows
get_y_hubs(fa)
#> # A tibble: 300 × 3
#> # Groups: factor [30]
#> id factor loading
#> <chr> <chr> <dbl>
#> 1 col179 y01 13.6
#> 2 col110 y01 0.217
#> 3 col052 y01 0.217
#> 4 col148 y01 0.198
#> 5 col099 y01 0.156
#> 6 col068 y01 0.142
#> 7 col181 y01 0.113
#> 8 col114 y01 -0.0971
#> 9 col057 y01 0.0803
#> 10 col054 y01 0.0786
#> # ℹ 290 more rows