rdiversity (version 1.2.1)

inddiv: Calculate individual-level diversity

Description

Generic function for calculating individual-level diversity.

Usage

inddiv(data, qs)
# S4 method for powermean
inddiv(data, qs)
# S4 method for relativeentropy
inddiv(data, qs)
# S4 method for metacommunity
inddiv(data, qs)

Arguments

data

matrix of mode numeric; containing diversity components.

qs

vector of mode numeric; parameter of conservatism.

Value

Returns a standard output of class tibble, with columns:

• measure: raw or normalised, alpha, beta, rho, or gamma

• q: parameter of conservatism

• type_level: "subcommunity"

• type_name: label attributed to type

• partition_level: level of diversity, i.e. subcommunity

• partition_name: label attributed to partition

• diversity: calculated subcommunity diversity

Details

data may be input as three different classes:

• power_mean: calculates raw and normalised subcomunity alpha, rho or gamma diversity by taking the powermean of diversity components

• relativeentropy: calculates raw or normalised subcommunity beta diversity by taking the relative entropy of diversity components

• metacommunity: calculates all subcommunity measures of diversity

References

Reeve, R., T. Leinster, C. Cobbold, J. Thompson, N. Brummitt, S. Mitchell, and L. Matthews. 2016. How to partition diversity. arXiv 1404.6520v3:1<U+2013>9.

Examples

# NOT RUN {
# Define metacommunity
pop <- data.frame(a = c(1,3), b = c(1,1))
row.names(pop) <- paste0("sp", 1:2)
pop <- pop/sum(pop)
meta <- metacommunity(pop)

# Calculate subcommunity gamma diversity (takes the power mean)
g <- raw_gamma(meta)
inddiv(g, 0:2)

# Calculate subcommunity beta diversity (takes the relative entropy)
b <- raw_beta(meta)
inddiv(b, 0:2)

# Calculate all measures of individual diversity
inddiv(meta, 0:2)

# }