inddiv: Calculate individual-level diversity

In rdiversity: Measurement and Partitioning of Similarity-Sensitive Biodiversity

Calculate individual-level diversity

Description

Generic function for calculating individual-level diversity.

Usage

inddiv(data, qs)

## S4 method for signature 'powermean'
inddiv(data, qs)

## S4 method for signature 'relativeentropy'
inddiv(data, qs)

## S4 method for signature 'metacommunity'
inddiv(data, qs)

Arguments

data	matrix of mode numeric; containing diversity components
qs	vector of mode numeric containing q values

Details

data may be input as three different classes:

• power_mean: calculates raw and normalised subcommunity 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

Value

inddiv() returns a standard output of class rdiv

References

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

See Also

subdiv for subcommunity-level diversity and metadiv for metacommunity-level diversity.

Examples

# Define metacommunity
pop <- cbind.data.frame(A = c(1,1), B = c(2,0), C = c(3,1))
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)

rdiversity documentation built on May 6, 2022, 9:06 a.m.