Compact overview of a codiversification scan: the run settings, how many nodes are significant per method, and a short preview of the strongest nodes (Newick and tree-distance columns are hidden).
Usage
# S3 method for class 'codiv'
print(x, alpha = 0.05, n = 6, ...)Arguments
- x
A
codivobject fromcodiv().- alpha
Significance threshold for the summary counts; default 0.05.
- n
Number of preview rows to show; default 6.
- ...
Ignored.
Examples
# \donttest{
sim <- simulate_codiv_data(n_hosts = 10, n_clades = 3, seed = 1)
res <- codiv(sim$host_tree, sim$symbiont_tree, sim$links,
methods = "hommola", permutations = 99)
#> Creating unique node labels
#> Of the 59 internal nodes in the symbiont tree,
#> 49 (83%) have span > 0 and <= 10% of max (0 dropped for zero span)
#> 5 (8%) have 7-500 symbiont tips (0 dropped as too large)
#> 5 (8%) have >= 3 hosts
#> Scanning 5 nodes for codiversification.
#> Scanning 5 nodes across 3 cores ...
res # calls print.codiv
#> <codiv> codiversification scan
#> Nodes scanned: 5
#> Methods: hommola
#> Permutations: 99
#> Nodes with p < 0.05: hommola 0/5
#>
#> Top nodes:
#> Node_ID N_Symbionts N_Hosts Hommola_r Hommola_pvalue
#> Node_49 12 4 0.5514694 0.18
#> Node_36 9 3 -0.4922685 0.67
#> Node_15 12 6 -0.2673787 0.72
#> Node_17 10 5 -0.2492913 0.80
#> Node_18 8 4 -0.1790181 0.73
print(res, n = 10) # show more preview rows
#> <codiv> codiversification scan
#> Nodes scanned: 5
#> Methods: hommola
#> Permutations: 99
#> Nodes with p < 0.05: hommola 0/5
#>
#> Top nodes:
#> Node_ID N_Symbionts N_Hosts Hommola_r Hommola_pvalue
#> Node_49 12 4 0.5514694 0.18
#> Node_36 9 3 -0.4922685 0.67
#> Node_15 12 6 -0.2673787 0.72
#> Node_17 10 5 -0.2492913 0.80
#> Node_18 8 4 -0.1790181 0.73
# }