NOSM_bip: Compute NOS using a bipartite network

In nos: Compute Node Overlap and Segregation in Ecological Networks

Description

Computation of NOS using a bipartite network (e.g. plant-pollinator network), where nodes can be formally categorized into two distinct categories (e.g. plant-pollinators). All nodes in one category will be considered as potential partners for the nodes in the other category (and vice-versa).

Usage

NOSM_bip(net, perc = 1, sl = 1)

Arguments

net	A network, in the form of an edge list. This should be a matrix or dataframe with two columns. Each value in a column is a node (e.g. a food item in a trophic-web). Nodes can be identified using numbers or characters. For each row (i.e. node pair), the value in the first column is 'consumed' (or pollinated, parasitized etc) by the value in the second column. Data can also be in the format of a frequency interaction matrix, as used in the bipartite R package. In these cases freqMat_2_edge should be used first, to convert the interaction matrix to an edge list.
perc	(default to 1) - the fraction of node pair comparisons to be performed to compute NOS. We recommend performing all possible pair comparisons (perc = 1). However, for exploratory analyses on large sets of networks (or for very large networks), the possibility of using a lower fraction of pair comparisons is a useful option.
sl	(default is 1) Specifies whether cannibalistic interactions should be considered as possible and therefore taken into account and removed during computation ('1') or not ('0').

Value

A list (two elements) of class 'NOSM' with a 'Type' attribute 'bip'. The first element in the list is a vector of overlap values for the "in nodes" and the second element is a vector of overlap values for the "out nodes".

The summary.NOSM methods provides more useful summary statistics.

Examples

data(boreal)
y <-  boreal[sample(rownames(boreal), 100, FALSE),] #subset 100 rows for speed
x <- NOSM_bip(y, perc = 1, sl = 1)
summary(x)

nos documentation built on May 2, 2019, 7:28 a.m.