nichetools (version 0.3.2)

extract_layman: extract Layman metrics

Description

Extract Bayesian estimates for the following six layman metrics, \(\delta^{13}\)C range, \(\delta^{15}\)N range, total area (TA), distance to centroid (CD), distance to the nearest neighbour (NND), and the standard deviation of the distance to the nearest neighbour (SDNND) from data objects created by SIBER. To learn more about the following metrics please review Layman et al. (2008).

Usage

extract_layman(
  data,
  type = NULL,
  community_df = NULL,
  data_format = NULL,
  isotope_x = NULL,
  isotope_y = NULL,
  element_x = NULL,
  element_y = NULL
)

Value

A tibble containing four rows when data_format is set to its default which is long. These four rows are the following, community, the_name_of_the_communities, metric and post_est.

Arguments

data

a list created by the function bayesianLayman() from the package SIBER.

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 two column data frame. One of the columns has to be named community and the data in the column will be numerics 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 contains the actual names of the communities that the user is working with (e.g., "region").

data_format

a character string that decides whether the returned object is in long or wide format. Default is "long", with the alternative supplied being "wide".

isotope_x

a numeric that will be used in the labeling processes for the range of the x. Default is 13 (e.g., \(\delta\)^13 C). This will create a column called labels, that will only be created when data_format is set to long.

isotope_y

a numeric that will be used in the labeling processes for the range of the y isotope. Default is 15 (e.g., \(\delta\)^15 N). #' This will create a column called labels, that will only be created when data_format is set to long.

element_x

a character that will be used in the labeling process for the range of the x isotope. Default is C (e.g., \(\delta\)^13 C). This will create a column called labels, that will only be created when data_format is set to long.

element_y

a character that will be used in the labeling process for the range of the y isotope. Default is N (e.g., \(\delta\)^13 N). #' This will create a column called labels, that will only be created when data_format is set to long.

See Also

SIBER::bayesianLayman() and SIBER::createSiberObject()

Examples

library(SIBER)

# ---- bring in SIBER demo data ----
# uncomenet to use
# str(demo.siber.data)

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

demo.siber.data.2$community <- as.numeric(demo.siber.data.2$community_names) |>
as.character()
c_names <- demo.siber.data.2 |>
dplyr::distinct(community, community_names)


demo.siber.data_2 <- demo.siber.data.2[,1:4]
# ---- create the siber object ----
siber.example <- createSiberObject(demo.siber.data_2)

# ---- view Bayesian estimates of mu and sigma produced by SIBER ---
# uncomment to use
# str(post_sam_siber)

# ---- extract posterior estimates of mu -----

mu_post <- extractPosteriorMeans(siber.example, post_sam_siber)

# ---- Bayesian estimates of layman metrics using SIBER ----

layman_b <- bayesianLayman(mu.post = mu_post)

# ---- use nichetools to extract Bayesian estimates of Layman metrics ----

layman_be <- extract_layman(layman_b, community_df = c_names)

layman_be