Package 'rdiversity'

Description

rdiversity is an R package based around a framework for measuring and partitioning biodiversity using similarity-sensitive diversity measures. It provides functionality for measuring alpha, beta and gamma diversity of metacommunities (e.g. ecosystems) and their constituent subcommunities, where similarity may be defined as taxonomic, phenotypic, genetic, phylogenetic, functional, and so on. It uses the diversity measures described in the arXiv paper, 'How to partition diversity'.

Details

• For more information go to our GitHub page; https://github.com/boydorr/rdiversity

• Please raise an issue if you find any problems; https://github.com/boydorr/rdiversity/issues

• This package is cross-validated against our Julia package; https://github.com/EcoJulia/Diversity.jl

Author(s)

Sonia Mitchell
Richard Reeve <[email protected]> (maintainer)

References

Reeve, R., T. Leinster, C. Cobbold, J. Thompson, N. Brummitt, S. Mitchell, and L. Matthews. 2016. How to partition diversity. (https://arxiv.org/abs/1404.6520)

Description

Converts an integer (Base10) to a binary (Base2) number. It also converts a logical vector to a binary (Base2) number (see examples).

Usage

as.binary(x, signed=FALSE, littleEndian=FALSE, size=2, n=0, logic=FALSE)

Arguments

Details

The binary number is represented by a logical vector. The bit order usually follows the same endianess as the byte order. No floating-point support. If logic is set to TRUE an integer vector is intepreted as a logical vector (>0 becomes TRUE and 0 becomes FALSE)

• Little Endian (LSB) —> (MSB)

• Big Endian (MSB) <— (LSB)

Auto switch to signed if num < 0.

Value

a vector of class binary.

See Also

is.binary and binary

Examples

as.binary(0xAF)
as.binary(42)
as.binary(42, littleEndian=TRUE)
as.binary(c(0xAF, 0xBF, 0xFF))
as.binary(c(2,4,8,16,32), signed=TRUE, size=1)
as.binary(-1, signed=TRUE, size=1)
as.binary(1:7, n=3)
as.binary(sample(2^8,3),n=8)
as.binary(c(1,1,0), signed=TRUE, logic=TRUE)
as.binary(c(TRUE,TRUE,FALSE), logic=TRUE)

Description

Adds two binary numbers. (x + y)

Usage

binAdd(x, y)

Arguments

Details

Little-Endian and unsigned is not supported at the moment. No floating point supported. if x or y is signed the return value will also be signed.

Value

The sum of x and y. Returns a binary vector.

See Also

base::as.logical , base::is.logical, base::raw

Examples

five <- as.binary(5); ten <- as.binary(10);
as.numeric(rdiversity:::binAdd(ten, five))
rdiversity:::binAdd(as.binary(c(0,1), logic=TRUE), as.binary(c(1,0), logic=TRUE))

Description

Create objects of type binary.

Usage

binary(n, signed=FALSE, littleEndian=FALSE)

Arguments

Details

The binary number is represented by a logical vector. The bit order usually follows the same endianess as the byte order. How to read:

• Little Endian (LSB) —> (MSB)

• Big Endian (MSB) <— (LSB)

The Big Endian endianess stores its MSB at the lowest adress. The Little Endian endianess stores its MSB at the highest adress.

e.g. b <-binary(8).

• "Little Endian" : MSB at b[1] and LSB at b[8].

• "Big Endian" : LSB at b[1] and MSB at b[8].

No floating-point support.

Value

a vector of class binary of length n. By default filled with zeros(0).

See Also

as.binary and is.binary.

Examples

b <- rdiversity:::binary(8)
summary(b)
b <- rdiversity:::binary(16, signed=TRUE)
summary(b)
b <- rdiversity:::binary(32, littleEndian=TRUE)
summary(b)

Description

Binary sequence.

Usage

binSeq(x, ...)

Arguments

Value

a sequence list of binary digits.

See Also

binary

Examples

rdiversity:::binSeq(0:4)

Description

Used to increase readabilaty

Usage

byte()

Value

The size of one byte (8)

See Also

fillUpToByte

Description

A simple helper function that returns the minimum number of byte needed to hold the amount of n bit.

Usage

bytesNeeded(n)

Arguments

Value

The number of minimum byte needed to hold n bit.

See Also

fillUpToByte or byte

Examples

ten <- as.binary(10)
rdiversity:::bytesNeeded(length(ten))

Description

Function to cut the phylogeny to a specified depth from the tip with the greatest distance from the root.

Usage

chainsaw(partition, ps, depth)

Arguments

Value

chainsaw() returns an object of class metacommunity

Description

Converts distance objects into similarity objects.

Usage

dist2sim(dist, transform, k = 1, normalise = TRUE, max_d)

Arguments

Details

Distances can be transformed either *linearly* or *exponentially*. That is 1 - k * dist for non-negative values, or exp(-k * dist), respectively. If normalise is true, then dist = dist/max_d.

Value

dist2sim(x) returns an object of class similarity.

Description

Container for class distance.

Usage

distance(distance, dat_id)

## S4 method for signature 'matrix,character'
distance(distance, dat_id)

## S4 method for signature 'matrix,missing'
distance(distance, dat_id)

Arguments

Value

distance() returns an object of class distance.

Description

Container for class distance.

Usage

## S4 method for signature 'distance'
show(object)

Arguments

Fields

distance

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

dat_id

object of class character describing the class of distance / similarity being used, e.g. "naive", "taxonomic", and so on

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

Description

Fills up the binary number with zeros(0) or ones(1), to the size n in bit.

Usage

fillUpToBit(x, n, value=FALSE)

Arguments

Details

No floating point supported.

Value

binary number. A binary vector with the desired size.

See Also

fillUpToByte.

Examples

rdiversity:::fillUpToBit(as.binary(c(1,1), logic=TRUE), n=4)
rdiversity:::fillUpToBit(as.binary(c(1,0,1), logic=TRUE), n=4, value=FALSE)

Description

Fills up the binary number with zeros(0) or ones(1), to the size in Byte.

Usage

fillUpToByte(x, size=0, value=FALSE)

Arguments

Details

No floating point supported.

Value

binary number. A binary vector with the desired size.

See Also

fillUpToBit.

Examples

rdiversity:::fillUpToByte(as.binary(c(1,1), logic=TRUE), size=2)
rdiversity:::fillUpToByte(as.binary(c(1,0,1), logic=TRUE), size=2, value=FALSE)

Description

Converts a vcfR object to a matrix of pairwise genetic distances.

Usage

gen2dist(vcf, biallelic = FALSE)

Arguments

Value

gen2dist(x) returns an object of class distance containing a matrix of pairwise genetic distances.

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

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)

Description

test for object "binary".

Usage

is.binary(x)

Arguments

Value

TRUE or FALSE.

See Also

as.binary and binary

Description

Helper function load Attributes

Usage

loadAttributes(x, l)

Arguments

Description

Calculates similarity-sensitive metacommunity gamma diversity (the metacommunity similarity-sensitive diversity). This measure may be calculated for a series of orders, represented as a vector of qs.

Usage

meta_gamma(meta, qs)

Arguments

Value

meta_gamma returns a standard output of class rdiv

References

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

Examples

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 metacommunity gamma diversity
meta_gamma(meta, 0:2)

Description

Functions to generate a metacommunity object.

Usage

metacommunity(partition, similarity)

## S4 method for signature 'data.frame,missing'
metacommunity(partition)

## S4 method for signature 'numeric,missing'
metacommunity(partition)

## S4 method for signature 'matrix,missing'
metacommunity(partition)

## S4 method for signature 'missing,similarity'
metacommunity(partition, similarity)

## S4 method for signature 'numeric,similarity'
metacommunity(partition, similarity)

## S4 method for signature 'data.frame,similarity'
metacommunity(partition, similarity)

## S4 method for signature 'matrix,similarity'
metacommunity(partition, similarity)

Arguments

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

See Also

metacommunity-class

Examples

# 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)

Description

Container for class metacommunity.

Usage

## S4 method for signature 'metacommunity'
show(object)

Arguments

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 the pairwise similarity of 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 describing the class of distance / similarity being used, e.g. "naive", "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

Description

Generic function for calculating metacommunity-level diversity.

Usage

metadiv(data, qs)

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

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

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

Arguments

Details

data may be input as one of three different classes:

• powermean: raw or normalised metacommunity alpha, rho or gamma diversity components; will calculate metacommunity-level raw or normalised metacommunity alpha, rho or gamma diversity

• relativeentropy: raw or normalised metacommunity beta diversity components; will calculate metacommunity-level raw or normalised metacommunity beta diversity

• metacommunity: will calculate all metacommunity measures of diversity

Value

metadiv() 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

inddiv for type-level diversity and subdiv for subcommunity-level diversity.

Examples

# Define metacommunity
pop <- data.frame(a = c(1,3), b = c(1,1))
pop <- pop / sum(pop)
meta <- metacommunity(pop)

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

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

# Calculate all measures of metacommunity diversity
metadiv(meta, 0:2)

Description

Negates the binary number x. Negation x -> -x or -x -> x

Usage

negate(x)

Arguments

Details

An »unsigned« number will be returned as »signed« regardless of whether the value is negative. No floating point supported.

Value

The negated number of x. Returns a binary vector with signed=TRUE

See Also

switchEndianess or fillUpToByte.

Examples

summary(rdiversity:::negate(as.binary(5, signed=TRUE)))
summary(rdiversity:::negate(as.binary(-5, signed=TRUE)))
summary(rdiversity:::negate(as.binary(5, signed=FALSE)))

Description

Calculates the low-level diversity component necessary for calculating normalised alpha diversity.

Usage

norm_alpha(meta)

Arguments

Details

Values generated from norm_alpha() may be input into subdiv() and metadiv() to calculate normalised subcommunity and metacommunity alpha diversity.

Value

norm_alpha returns an object of class powermean

References

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

Examples

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 normalised alpha component
a <- norm_alpha(meta)
subdiv(a, 1)
metadiv(a, 1)

Description

Calculates the low-level diversity component necessary for calculating normalised beta diversity.

Usage

norm_beta(meta)

Arguments

Details

Values generated from norm_beta() may be input into subdiv() and metadiv() to calculate normalised subcommunity and metacommunity beta diversity.

Value

norm_beta returns an object of class relativeentropy

References

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

Examples

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 normalised beta component
b <- norm_beta(meta)
subdiv(b, 1)
metadiv(b, 1)

Description

Calculates similarity-sensitive normalised metacommunity alpha diversity (the average similarity-sensitive diversity of subcommunities). This measure may be calculated for a series of orders, represented as a vector of qs.

Usage

norm_meta_alpha(meta, qs)

Arguments

Value

norm_meta_alpha returns a standard output of class rdiv

References

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

Examples

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 normalised metacommunity alpha diversity
norm_meta_alpha(meta, 0:2)

Description

Calculates similarity-sensitive normalised metacommunity beta diversity (the effective number of distinct subcommunities. This measure may be calculated for a series of orders, represented as a vector of qs.

Usage

norm_meta_beta(meta, qs)

Arguments

Value

norm_meta_beta returns a standard output of class rdiv

References

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

Examples

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 normalised metacommunity beta diversity
norm_meta_beta(meta, 0:2)

Description

Calculates similarity-sensitive normalised metacommunity rho diversity (the average representativeness of subcommunities. This measure may be calculated for a series of orders, represented as a vector of qs.

Usage

norm_meta_rho(meta, qs)

Arguments

Value

norm_meta_rho returns a standard output of class rdiv

References

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

Examples

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 normalised metacommunity rho diversity
norm_meta_rho(meta, 0:2)

Description

Calculates the low-level diversity component necessary for calculating normalised rho diversity.

Usage

norm_rho(meta)

Arguments

Details

Values generated from norm_rho() may be input into subdiv() and metadiv() to calculate normalised subcommunity and metacommunity rho diversity.

Value

norm_rho returns an object of class powermean

References

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

Examples

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 normalised rho component
r <- norm_rho(meta)
subdiv(r, 1)
metadiv(r, 1)

Description

Calculates similarity-sensitive normalised subcommunity alpha diversity (the diversity of subcommunity j in isolation. This measure may be calculated for a series of orders, represented as a vector of qs.

Usage

norm_sub_alpha(meta, qs)

Arguments

Value

norm_sub_alpha returns a standard output of class rdiv

References

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

Examples

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 normalised subcommunity alpha diversity
norm_sub_alpha(meta, 0:2)

Description

Calculates similarity-sensitive normalised subcommunity beta diversity (an estimate of the effective number of distinct subcommunities). This measure may be calculated for a series of orders, represented as a vector of qs.

Usage

norm_sub_beta(meta, qs)

Arguments

Value

norm_sub_beta returns a standard output of class rdiv

References

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

Examples

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 normalised subcommunity beta diversity
norm_sub_beta(meta, 0:2)

Description

Calculates similarity-sensitive normalised subcommunity rho diversity (the representativeness of subcommunity j). This measure may be calculated for a series of orders, represented as a vector of qs.

Usage

norm_sub_rho(meta, qs)

Arguments

Value

norm_sub_rho returns a standard output of class rdiv

References

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

Examples

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 normalised subcommunity rho diversity
norm_sub_rho(meta, 0:2)

Description

Group generic Ops operators

Usage

## S3 method for class 'binary'
Ops(e1, e2)

Arguments

Description

Calculates the relative abundance of historical species.

Usage

phy_abundance(partition, structure_matrix)

Arguments

Description

Converts an object into class phylo into class phy_struct.

Usage

phy_struct(tree, partition)

Arguments

Value

phy_struct() returns a list containing:

Description

Packages all inputs into an object of class similarity.

Usage

phy2branch(tree, partition, depth = 1)

Arguments

Value

phy2branch() returns an object of class similarity.

Description

Converts any phylo object to a matrix of pairwise tip-to-tip distances.

Usage

phy2dist(tree, precompute_dist = TRUE)

Arguments

Value

phy2sim(x) returns an object of class distance containing a matrix of pairwise tip-to-tip distances.

Description

power_mean() calculates the power mean of a set of values.

Usage

power_mean(values, order = 1, weights = rep(1, length(values)))

Arguments

Details

Calculates the order-th power mean of a single set of non-negative values, weighted by weights; by default, weights are equal and order is 1, so this is just the arithmetic mean. Equal weights and a order of 0 gives the geometric mean, and an order of -1 gives the harmonic mean.

Value

Weighted power mean

Examples

values <- sample(1:50, 5)
power_mean(values)

Description

Container for class powermean.

Fields

results

data.frame containing rdiversity output

measure

object of class character naming the diversity measure being calculated

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

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 describing the class of distance / similarity being used, e.g. "naive", "taxonomic", and so on

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)

Description

This method prints the binary number.

Usage

## S3 method for class 'binary'
print(x, ...)

Arguments

Value

Output in ones and zeros (binary vector).

See Also

summary.binary provides some additional information.

Description

Calculates the low-level diversity component necessary for calculating alpha diversity.

Usage

raw_alpha(meta)

Arguments

Details

Values generated from raw_alpha() may be input into subdiv() and metadiv() to calculate raw subcommunity and metacommunity alpha diversity.

Value

raw_alpha returns an object of class powermean

References

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

Examples

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 raw alpha component
a <- raw_alpha(meta)
subdiv(a, 1)
metadiv(a, 1)

Description

Calculates the low-level diversity component necessary for calculating raw beta diversity.

Usage

raw_beta(meta)

Arguments

Details

Values generated from raw_beta() may be input into subdiv() and metadiv() to calculate raw subcommunity and metacommunity beta diversity.

Value

raw_beta returns an object of class relativeentropy

References

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

Examples

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 raw beta component
b <- raw_beta(meta)
subdiv(b, 1)
metadiv(b, 1)

Description

Calculates the low-level diversity component necessary for calculating gamma diversity.

Usage

raw_gamma(meta)

Arguments

Details

Values generated from raw_gamma() may be input into subdiv() and metadiv() to calculate subcommunity and metacommunity gamma diversity.

Value

raw_gamma returns an object of class powermean

References

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

Examples

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 gamma component
g <- raw_gamma(meta)
subdiv(g, 1)
metadiv(g, 1)

Description

Calculates similarity-sensitive raw metacommunity alpha diversity (the naive-community metacommunity diversity). This measure may be calculated for a series of orders, represented as a vector of qs.

Usage

raw_meta_alpha(meta, qs)

Arguments

Value

raw_meta_alpha returns a standard output of class rdiv

References

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

Examples

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 raw metacommunity alpha diversity
raw_meta_alpha(meta, 0:2)

Description

Calculates similarity-sensitive raw metacommunity beta diversity (the average distinctiveness of subcommunities). This measure may be calculated for a series of orders, represented as a vector of qs.

Usage

raw_meta_beta(meta, qs)

Arguments

Value

raw_meta_beta returns a standard output of class rdiv

References

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

Examples

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 raw metacommunity beta diversity
raw_meta_beta(meta, 0:2)

Description

Calculates similarity-sensitive raw metacommunity rho diversity (the average redundancy of subcommunities. This measure may be calculated for a series of orders, represented as a vector of qs.

Usage

raw_meta_rho(meta, qs)

Arguments

Value

raw_meta_rho returns a standard output of class rdiv

References

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

Examples

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 metacommunity rho diversity
raw_meta_rho(meta, 0:2)

Description

Calculates the low-level diversity component necessary for calculating raw rho diversity.

Usage

raw_rho(meta)

Arguments

Details

Values generated from raw_rho() may be input into subdiv() and metadiv() to calculate raw subcommunity and metacommunity rho diversity.

Value

raw_rho returns an object of class powermean

References

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

Examples

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 raw rho component
r <- raw_rho(meta)
subdiv(r, 1)
metadiv(r, 1)

Description

Calculates similarity sensitive raw subcommunity alpha diversity (an estimate of naive-community metacommunity diversity). This measure may be calculated for a series of orders, represented as a vector of qs.

Usage

raw_sub_alpha(meta, qs)

Arguments

Value

raw_sub_alpha returns a standard output of class rdiv

References

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

Examples

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 raw subcommunity alpha diversity
raw_sub_alpha(meta, 0:2)

Description

Calculates similarity-sensitive raw subcommunity beta diversity (the distinctiveness of subcommunity j). This measure may be calculated for a series of orders, represented as a vector of qs.

Usage

raw_sub_beta(meta, qs)

Arguments

Value

raw_sub_beta returns a standard output of class rdiv

References

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

Examples

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 raw subcommunity beta diversity
raw_sub_beta(meta, 0:2)

Description

Calculates similarity-sensitive raw subcommunity rho diversity (the redundancy of subcommunity j. This measure may be calculated for a series of orders, represented as a vector of qs.

Usage

raw_sub_rho(meta, qs)

Arguments

Value

raw_sub_rho returns a standard output of class rdiv

References

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

Examples

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 raw subcommunity rho diversity
raw_sub_rho(meta, 0:2)

Description

Container for class relativeentropy.

Fields

results

data.frame containing rdiversity output

measure

object of class character naming the diversity measure being calculated

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

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 describing the class of distance / similarity being used, e.g. "naive", "taxonomic", and so on

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)

Description

Randomly reshuffles the relative abundance of types (e.g. species) in a metacommunity (whilst maintaining the relationship between the relative abundance of a particular species across subcommunities). In the case of a phylogenetic metacommunity, the relative abundance of terminal taxa are randomly reshuffled and the relative abundance of types (historical species) are calculated from the resulting partition.

Usage

repartition(meta, new_partition)

Arguments

Value

repartition() returns an object of class metacommunity

Description

Helper function save Attributes

Usage

saveAttributes(x)

Arguments

Description

Container for class similarity.

Usage

similarity(similarity, dat_id)

## S4 method for signature 'matrix,character'
similarity(similarity, dat_id)

## S4 method for signature 'matrix,missing'
similarity(similarity, dat_id)

Arguments

Value

similarity() returns an object of class similarity.

Description

Container for class similarity.

Usage

## S4 method for signature 'similarity'
show(object)

Arguments

Fields

similarity

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

dat_id

object of class character describing the class of distance / similarity being used, e.g. "naive", "taxonomic", and so on

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

parameters

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

Description

Calculates similarity-sensitive subcommunity gamma diversity (the contribution per individual toward metacommunity diversity). This measure may be calculated for a series of orders, represented as a vector of qs.

Usage

sub_gamma(meta, qs)

Arguments

Value

sub_gamma returns a standard output of class rdiv

References

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

Examples

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
sub_gamma(meta, 0:2)

Description

Generic function for calculating subcommunity-level diversity.

Usage

subdiv(data, qs)

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

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

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

Arguments

Details

data may be input as one of three different classes:

• powermean: raw or normalised metacommunity alpha, rho or gamma diversity components; will calculate subcommunity-level raw or normalised metacommunity alpha, rho or gamma diversity

• relativeentropy: raw or normalised metacommunity beta diversity components; will calculate subcommunity-level raw or normalised metacommunity beta diversity

• metacommunity: will calculate all subcommunity measures of diversity

Value

subdiv() 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

inddiv for type-level diversity and metadiv for metacommunity-level diversity.

Examples

# 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)
subdiv(g, 0:2)

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

# Calculate all measures of subcommunity diversity
subdiv(meta, 0:2)

Description

This method provides information about the attributes of the binary number.

Usage

## S3 method for class 'binary'
summary(object, ...)

Arguments

Value

Contains the following information:

• Signedness : unsigned or signed

• Endianess : Big-Endian or Little-Endian

• value<0 : negative or positve number

• Size[bit] : Size in bit

• Base10 : Decimal(Base10) number.

See Also

print.binary

Description

Switch little-endian to big-endian and vice versa.

Usage

switchEndianess(x, stickyBits=FALSE)

Arguments

Value

switch little-endian to big-endian and vice versa.

See Also

fillUpToByte.

Examples

x <- as.binary(c(1,1,0,0), logic=TRUE); print(x); summary(x);
y <- rdiversity:::switchEndianess(x); print(y); summary(y);
y <- rdiversity:::switchEndianess(x, stickyBits=TRUE); print(y); summary(y);

Description

Calculates taxonomic distances between species.

Usage

tax2dist(lookup, tax_distance, precompute_dist = TRUE)

Arguments

Value

tax2dist() returns an object of class distance containing a matrix of pairwise taxonomic distances

References

Shimatani, K. 2001. On the measurement of species diversity incorporating species differences. Oikos 93:135–147.

Examples

# Create Lookup table
Species <- c("tenuifolium", "asterolepis", "simplex var.grandiflora", "simplex var.ochnacea")
Genus <- c("Protium", "Quararibea", "Swartzia", "Swartzia")
Family <- c("Burseraceae", "Bombacaceae", "Fabaceae", "Fabaceae")
Subclass <- c("Sapindales", "Malvales", "Fabales", "Fabales")
lookup <- cbind.data.frame(Species, Genus, Family, Subclass)

# Assign values for each level (Shimatani's taxonomic distance)
tax_distance <- c(Species = 0, Genus = 1, Family = 2, Subclass = 3, Other = 4)

# Generate pairwise distances
distance <- tax2dist(lookup, tax_distance)
similarity <- dist2sim(distance, "linear")