bioregion (version 1.3.0)

bind_pairwise: Combine and enrich bioregion (dis)similarity object(s)

Description

Combine two bioregion.pairwise objects and/or compute new pairwise metrics based on the columns of the object(s).

Usage

bind_pairwise(primary_metrics, secondary_metrics, new_metrics = NULL)

Value

A new bioregion.pairwise object containing the combined and/or enriched data. It includes all original metrics from the inputs, as well as any newly computed metrics.

Arguments

primary_metrics

A bioregion.pairwise object. This is the main set of pairwise metrics that will be used as a base for the combination.

secondary_metrics

A second bioregion.pairwise object to be combined with primary_metrics. It must have the same sites identifiers and pairwise structure. Can be set to NULL if new_metrics is specified.

new_metrics

A character vector or a single character string specifying custom formula(s) based on the column names of primary_metrics and secondary_metrics (see Details).

Author

Maxime Lenormand ()
Boris Leroy ()
Pierre Denelle ()

Details

When both primary_metrics and secondary_metrics are provided and if the pairwise structure is identical the function combine the two objects. If new_metrics is provided, each formula is evaluated based on the column names of primary_metrics (and secondary_metrics if provided).

See Also

For more details illustrated with a practical example, see the vignette: https://biorgeo.github.io/bioregion/articles/a3_pairwise_metrics.html.

Associated functions: dissimilarity similarity as_bioregion_pairwise

Examples

comat <- matrix(sample(0:1000, size = 50, replace = TRUE,
prob = 1 / 1:1001), 5, 10)
rownames(comat) <- paste0("s", 1:5)
colnames(comat) <- paste0("sp", 1:10)

sim <- bind_pairwise(primary_metrics = similarity(comat, 
                                                               metric = "abc"),
                                  secondary_metrics = similarity(comat, 
                                                                 metric = "Simpson"),
                                  new_metrics = "1 - (b + c) / (a + b + c)")