metacommunity: Metacommunity

Description

Functions to generate a metacommunity object.

Usage

metacommunity(partition, similarity)
# S4 method for data.frame,missing
metacommunity(partition)
# S4 method for numeric,missing
metacommunity(partition)
# S4 method for matrix,missing
metacommunity(partition)
# S4 method for data.frame,matrix
metacommunity(partition, similarity)
# S4 method for numeric,matrix
metacommunity(partition, similarity)
# S4 method for matrix,matrix
metacommunity(partition, similarity)
# S4 method for missing,similarity
metacommunity(partition, similarity)
# S4 method for numeric,similarity
metacommunity(partition, similarity)
# S4 method for data.frame,similarity
metacommunity(partition, similarity)
# S4 method for matrix,similarity
metacommunity(partition, similarity)
# S4 method for ANY,phylo
metacommunity(partition, similarity)

Arguments

partition

two-dimensional matrix of mode numeric with rows as types, columns as subcommunities, and elements containing the relative abundances of types in subcommunities. For phylogenetic diversity, see Details

similarity

(optional) object of class similarity

Value

metacommunity() returns an object of class metacommunity (see Fields).

Fields

type_abundance: two-dimensional matrix of mode numeric with rows as types (species), columns as subcommunities, and each element containing the relative abundance of types in each subcommunity relative to the metacommunity as a whole. In the phylogenetic case, this corresponds to the proportional abundance of historical species, which is calculated from the proportional abundance of terminal taxa

similarity

two-dimensional matrix of mode numeric with rows as types, columns as types, and elements containing pairwise similarities between types

similarity_components

list containing the components necessary to calculate similarity. This list is empty when precompute_dist = TRUE when calculating distance. When a pairwise distance matrix is too large and precompute_dist = FALSE, this list contains all the information required to calculate pairwise distance between types

similarity_parameters

list containing parameters associated with converting pairwise distances to similarities (the dist2sim() arguments)

ordinariness

two-dimensional matrix of mode numeric with rows as types, columns as subcommunities, and elements containing the ordinariness of types within subcommunities

subcommunity_weights

vector of mode numeric containing subcommunity weights

type_weights

two-dimensional matrix of mode numeric, with rows as types, columns as subcommunities, and elements containing weights of types within a subcommunity

dat_ID

object of class character denoting the type of diversity being calculated. This can be "naive", "genetic", "taxonomic", and so on

raw_abundance

[Phylogenetic] two-dimensional matrix of mode numeric with rows as types, columns as subcommunities, and elements containing the relative abundance of present day species

raw_structure

[Phylogenetic] two-dimensional matrix of mode numeric with rows as historical species, columns as present day species, and elements containing historical species lengths within lineages

parameters

[Phylogenetic] data.frame containing parameters associated with each historic species in the phylogeny

Examples

Run this code

# NOT RUN {
# Naive-type
partition <- cbind(a = c(1,1,1,0,0), b = c(0,1,0,1,1))
row.names(partition) <- paste0("sp", 1:5)
partition <- partition / sum(partition)
meta <- metacommunity(partition)

# }