ahn_gen {AnimalHabitatNetwork}

Generate networks characterising habitat physical configurations

Description

Generate undirected networks (weighted or unweighted, connected or disconnected) characterising the physical attributes and spatial organizations (or distributions) of habitat components (i.e. habitat configurations).

Usage

ahn_gen(N, L, mu, lamda, Connected = TRUE, Weighted = TRUE, eta = 1,
  A = 25, X = NULL, Y = NULL, U = NULL, V = NULL)

Arguments

N

The number of nodes

L

A side length of the rectangle landscape within which nodes are anchored

mu

the critical Dij (i.e. Euclidean distance between node i and j) at which the link removing probability curve P(Dij, mu, lamda) transits from concave to convex (see ahn_prob)

lamda

the steepness of the link removing probability curve P(Dij, mu, lamda), see ahn_prob

Connected

TRUE for connected while FALSE ignores whether the networks are connected or not

Weighted

TRUE for weighted while FALSE for unweighted networks

eta

mediates the weight, i.e. (Dij)^-eta, of the link rewiring node i from one network component and node j from another network component (i and j are with an Euclidean distance of Dij) when the network becomes disconnected after removing links from the initial complete network with the probability P(Dij, mu, lamda) = [1 + exp(-lamda(Dij - mu))]^-1 when both Connected = TRUE and Weighted = TRUE

A

The area of the rectangle landscape within which the network is defined

X

A vector of X coordinates for the N nodes (sampled from [0, L] uniformly at random if NULL)

Y

A vector of Y coordinates for the N nodes (sampled from [0, A/L] uniformly at random if NULL)

U

A vector with N elements specifying node attributes (qualitative or quantitive), by default NULL

V

A vector with N elements specifying node attributes (qualitative or quantitive), by default NULL

Value

Return an animal habitat network (an igraph object)

Examples

# generate a connected and weighted network
ahn_gen(N = 10, L = 5, mu = 1, lamda = 5)



N <- 10
x <- runif(N, 0, 5)
ql <- sample(LETTERS, N, replace = TRUE)
qn <- sample(1:20, N, replace = TRUE)

# specify the X coordinates, node attributes U and V for a connected and unweighted network
ahn_gen(N, L = 5, mu = 1, lamda = 5, Weighted = FALSE, X = x, U = ql, V = qn)

# specify the Y coordinates, node attributes U and V for a weighted network, no matter if the
# network will be connected or not
ahn_gen(N, L = 5, mu = 1, lamda = 5, Weighted = TRUE, Connected = FALSE, Y = x, U = ql, V = qn)