simulateRicker: Generate time series with the Ricker model

In microbiome/miaSim: Microbiome Data Simulation

Generate time series with the Ricker model

Description

The Ricker model is a discrete version of the generalized Lotka-Volterra model and is implemented here as proposed by Fisher and Mehta in PLoS ONE 2014.

Usage

simulateRicker(
  n_species,
  A,
  names_species = NULL,
  x0 = runif(n_species),
  carrying_capacities = runif(n_species),
  error_variance = 0.05,
  explosion_bound = 10^8,
  t_end = 1000,
  norm = FALSE,
  ...
)

Arguments

n_species	Integer: number of species
A	interaction matrix
names_species	Character: names of species. If NULL, paste0("sp", seq_len(n_species)) is used. (default: names_species = NULL)
x0	Numeric scalar. Indicates the initial abundances of simulated species. If NULL, runif(n = n_species, min = 0, max = 1) is used.
carrying_capacities	Numeric scalar. Indicates carrying capacities. If NULL, runif(n = n_species, min = 0, max = 1) is used.
error_variance	Numeric scalar. Specifies the variance of measurement error. By default it equals to 0, indicating that the result won't contain any measurement error. This value should be non-negative. (Default: 0.05)
explosion_bound	Numeric scalar. Specifies the boundary for explosion. (Default: 10^8)
t_end	Integer scalar. Indicates simulations to be simulated
norm	Logical scalar. Whether normalised abundances (proportions in each generation) is returned. (Default: FALSE)
...	additional parameters, see utils to know more.

Value

simulateRicker returns a TreeSummarizedExperiment class object

References

Fisher & Mehta (2014). Identifying Keystone Species in the Human Gut Microbiome from Metagenomic Timeseries using Sparse Linear Regression. PLoS One 9:e102451

Examples

A <- powerlawA(10, alpha = 1.01)
tse <- simulateRicker(n_species = 10, A, t_end = 100)

microbiome/miaSim documentation built on Oct. 25, 2024, 7:16 p.m.