Convert an lsa Fit to a tna Network — lsa_to

Convert an lsa fit to a tna-class network object usable by the tna package's centrality, pruning, community, and bootstrap routines.

Usage

lsa_to_tna(x, ...)

# S3 method for class 'lsa'
lsa_to_tna(x, weights = c("prob", "count", "adj_res", "lift"), ...)

# S3 method for class 'lsa_group'
lsa_to_tna(x, weights = c("prob", "count", "adj_res", "lift"), ...)

Arguments

x: An lsa fit from lsa(), or an lsa_group from lsa(..., group = ).
...: Method-specific arguments.
weights: Character. Which matrix to expose as the tna edge weights. One of "prob" (row-normalised probabilities, default), "count" (raw observed counts), "adj_res" (adjusted residuals, over-representation network), or "lift" (observed / expected association strength).

Value

For an lsa fit, a tna object with weights, inits, labels, and, when available, data slots. For an lsa_group, a group_tna object.

Details

This function is deliberately named lsa_to_tna(), not as_tna(), to avoid overlapping export names with sibling packages.

Examples

fit <- lsa(engagement)
net <- lsa_to_tna(fit, weights = "prob")
tna::centralities(net)
#> # A tibble: 3 × 10
#>   state    OutStrength InStrength ClosenessIn ClosenessOut Closeness Betweenness
#> * <fct>          <dbl>      <dbl>       <dbl>        <dbl>     <dbl>       <dbl>
#> 1 Active         0.302      0.324      0.0811       0.0779     0.100           0
#> 2 Average        0.390      0.664      0.160        0.0973     0.160           2
#> 3 Disenga…       0.517      0.221      0.0691       0.101      0.114           0
#> # ℹ 3 more variables: BetweennessRSP <dbl>, Diffusion <dbl>, Clustering <dbl>