nichetools (version 0.3.2)

extract_similarities: extract similarities

Description

Extract niche similarities from objects created by {SIBER}.

Usage

extract_similarities(data, type = c("bay", "ml"), community_df = NULL)

Arguments

data

a list of results from either maxLikOverlap() or bayesianOverlap().

type

a character that is either "bay" or "ml" which indicates whether the community metrics to be extracted are from a Bayesian analysis or a maximum-likelihood.

community_df

a four column data frame. One of the columns has to be named community and the data in the column will be numeric as a character string(e.g., "1", "2", "3"). This is the order of the community names and will be used to join the actual community names to the correct data. These are the same class and values required by the function, createSiberObject() from SIBER. The second column will be the names of the groups that are needed to supply required by the function, createSiberObject() from SIBER. The third and fourth columns contains the actual names of the communities and groups the user is working with (e.g., "region", "common_name").

Examples

library(purrr)
library(SIBER)

# ---- create community names data frame ----
# uncomment to use
# str(demo.siber.data.2)

demo.siber.data.2$group_name <- as.factor(demo.siber.data.2$group)

demo.siber.data.2$group <- as.numeric(demo.siber.data.2$group_name) |>
as.character()

demo.siber.data.2$community_name <- as.factor(demo.siber.data.2$community)

demo.siber.data.2$community <- as.numeric(demo.siber.data.2$community_name) |>
as.character()

cg_name <- demo.siber.data.2 |>
dplyr::distinct(community, group, community_name, group_name)

# ---- create comparsions ----
cg_names_within_c <- create_comparisons(cg_name,
                                        comparison = "within")

demo.siber.data.2 <- demo.siber.data.2[,1:4]

siber_example <- createSiberObject(demo.siber.data.2)

ml_within_overlap <- cg_names_within_c |>
map(~ maxLikOverlap(.x$cg_1, .x$cg_2, siber_example,
p.interval = NULL, n = 100), .progress = TRUE)

ml_95_within_com <- extract_similarities(ml_within_overlap, type = "ml",
community_df = cg_name)