Package 'miaSim' reference manual

Title:	Microbiome Data Simulation
Description:	Microbiome time series simulation with generalized Lotka-Volterra model, Self-Organized Instability (SOI), and other models. Hubbell's Neutral model is used to determine the abundance matrix. The resulting abundance matrix is applied to (Tree)SummarizedExperiment objects.
Authors:	Yagmur Simsek [cre, aut], Karoline Faust [aut], Yu Gao [aut], Emma Gheysen [aut], Daniel Rios Garza [aut], Tuomas Borman [aut] , Leo Lahti [aut]
Maintainer:	Yagmur Simsek <[email protected]>
License:	Artistic-2.0 \| file LICENSE
Version:	1.13.0
Built:	2024-11-29 07:44:24 UTC
Source:	https://github.com/bioc/miaSim

Generate pairs of interactions according to interaction types

Description

A helper function to be used in combination with .getInteractions()

Usage

.applyInterType(I, pair, interType)
.applyInterType(I, pair, interType)

Arguments

`I`	Matrix: defining the interaction between each pair of species
`pair`	Numeric: a vector with a length of 2, indicating the 2 focusing species in the process of applying the interaction types
`interType`	Character: one of 'mutualism', 'commensalism', 'parasitism', 'amensalism', or 'competition'. Defining the interaction type

Value

A matrix of interaction types with one pair changed

Get the interspecies interaction matrix A using leave-one-out method

Description

generate matrix A from the comparisons between simulations with one absent species and a simulation with complete species (leave-one-out)

Usage

.estimateAFromSimulations(
  simulations,
  simulations2,
  n_instances = 1,
  t_end = NULL,
  scale_off_diagonal = 0.1,
  diagonal = -0.5,
  connectance = 0.2
)
.estimateAFromSimulations(
  simulations,
  simulations2,
  n_instances = 1,
  t_end = NULL,
  scale_off_diagonal = 0.1,
  diagonal = -0.5,
  connectance = 0.2
)

Arguments

`simulations`	A list of simulation(s) with complete species
`simulations2`	A list of simulation(s), each with one absent species
`n_instances`	Integer: number of instances to generate (default: `n_instances = 1`)
`t_end`	Numeric: end time of the simulation. If not identical with t_end in params_list, then it will overwrite t_end in each simulation (default: `t_end = 1000`)
`scale_off_diagonal`	Numeric: scale of the off-diagonal elements compared to the diagonal. Same to the parameter in function `randomA`. (default: `scale_off_diagonal = 0.1`)
`diagonal`	Values defining the strength of self-interactions. Input can be a number (will be applied to all species) or a vector of length n_species. Positive self-interaction values lead to exponential growth. Same to the parameter in function `randomA`. (default: `diagonal = -0.5`)
`connectance`	Numeric frequency of inter-species interactions. i.e. proportion of non-zero off-diagonal terms. Should be in the interval 0 <= connectance <= 1. Same to the parameter in function `randomA`. (default: `connectance = 0.2`)

Value

a matrix A with dimensions (n_species x n_species) where n_species equals to the number of elements in simulations2

generate a vector of times when events is happening

Description

generate a vector of times when events is happening

Usage

.eventTimes(t_events = NULL, t_duration = NULL, t_end = 1000, ...)
.eventTimes(t_events = NULL, t_duration = NULL, t_end = 1000, ...)

Arguments

`t_events`, `t_duration`	Numeric: vector of starting time and duration of the events
`t_end`	Numeric: end time of the simulation
`...`	: additional parameters to pass to simulationTimes, including t_start, t_step, and t_store.

Value

A vector of time points in the simulation

Examples

tEvent <- .eventTimes(
    t_events = c(10, 50, 100),
    t_duration = c(1, 2, 3),
    t_end = 100,
    t_store = 100,
    t_step = 1
)
tEvent <- .eventTimes(
    t_events = c(10, 50, 100),
    t_duration = c(1, 2, 3),
    t_end = 100,
    t_store = 100,
    t_step = 1
)

Generate interactions according to five types of interactions and their weights

Description

Generate interactions according to five types of interactions and their weights

Usage

.getInteractions(n_species, weights, connectance)
.getInteractions(n_species, weights, connectance)

Arguments

`n_species`	Integer: defining the dimension of matrix of interaction
`weights`	Numeric: defining the weights of mutualism, commensalism, parasitism, amensalism, and competition in all interspecies interactions.
`connectance`	Numeric: defining the density of the interaction network. Ranging from 0 to 1

Value

A matrix of interactions with all interactions changed according to the weights and connectance.

check whether a number is a positive integer

Description

check whether a number is a positive integer

Usage

.isPosInt(x, tol = .Machine$double.eps^0.5)
.isPosInt(x, tol = .Machine$double.eps^0.5)

Arguments

`x`	Numeric number to test
`tol`	Numeric tolerance of detection

Value

A logical value: whether the number is a positive integer.

Generate dirichlet random deviates

Description

Generate dirichlet random deviates

Usage

.rdirichlet(n, alpha)
.rdirichlet(n, alpha)

Arguments

`n`	Number of random vectors to generate.
`alpha`	Vector containing shape parameters.

Value

a vector containing the Dirichlet density

Examples

dirichletExample <- .rdirichlet(1, c(1, 2, 3))

dirichletExample <- .rdirichlet(1, c(1, 2, 3))

Replace one element with zero in a list.

Description

If the list contains m elements, then lengths of each element must be m, too. This function is intended to generate a list of x0 (the initial community) with one missing species, to prepare the parameter simulations_compare in estimateAFromSimulations.

Usage

.replaceByZero(input_list)
.replaceByZero(input_list)

Arguments

input_list

A list containing m elements, and lengths of each element must be m, too.

Value

A list of same dimension as input_list, but with 0 at specific positions in the elements of the list.

Interaction matrix with Power-Law network adjacency matrix

Description

N is the an Interspecific Interaction matrix with values drawn from a normal distribution H the interaction strength heterogeneity drawn from a power-law distribution with the parameter alpha, and G the adjacency matrix of with out-degree that reflects the heterogeneity of the powerlaw. A scaling factor s may be used to constrain the values of the interaction matrix to be within a desired range. Diagonal elements of A are defined by the parameter d.

Usage

powerlawA(n_species, alpha = 3, stdev = 1, s = 0.1, d = -1, symmetric = FALSE)
powerlawA(n_species, alpha = 3, stdev = 1, s = 0.1, d = -1, symmetric = FALSE)

Arguments

`n_species`	integer number of species
`alpha`	numeric power-law distribution parameter. Should be > 1. (default: `alpha = 3.0`) Larger values will give lower interaction strength heterogeneity, whereas values closer to 1 give strong heterogeneity in interaction strengths between the species. In other words, values of alpha close to 1 will give Strongly Interacting Species (SIS).
`stdev`	numeric standard deviation parameter of the normal distribution with mean 0 from which the elements of the nominal interspecific interaction matrix N are drawn. (default: `stdev = 1`)
`s`	numeric scaling parameter with which the final global interaction matrix A is multiplied. (default: `s = 0.1`)
`d`	numeric diagonal values, indicating self-interactions (use negative values for stability). (default: `s = 1.0`)
`symmetric`	logical scalar returning a symmetric interaction matrix (default: `symmetric=FALSE`)

Value

The interaction matrix A with dimensions (n_species x n_species)

References

Gibson TE, Bashan A, Cao HT, Weiss ST, Liu YY (2016) On the Origins and Control of Community Types in the Human Microbiome. PLOS Computational Biology 12(2): e1004688. https://doi.org/10.1371/journal.pcbi.1004688

Examples

# Low interaction heterogeneity
A_low <- powerlawA(n_species = 10, alpha = 3)
# Strong interaction heterogeneity
A_strong <- powerlawA(n_species = 10, alpha = 1.01)

# Low interaction heterogeneity
A_low <- powerlawA(n_species = 10, alpha = 3)
# Strong interaction heterogeneity
A_strong <- powerlawA(n_species = 10, alpha = 1.01)

Generate random interaction matrix for GLV model

Description

Generates a random interaction matrix for Generalized Lotka-Volterra (GLV) model.

Usage

randomA(
  n_species,
  names_species = NULL,
  diagonal = -0.5,
  connectance = 0.2,
  scale_off_diagonal = 0.1,
  mutualism = 1,
  commensalism = 1,
  parasitism = 1,
  amensalism = 1,
  competition = 1,
  interactions = NULL,
  symmetric = FALSE,
  list_A = NULL
)
randomA(
  n_species,
  names_species = NULL,
  diagonal = -0.5,
  connectance = 0.2,
  scale_off_diagonal = 0.1,
  mutualism = 1,
  commensalism = 1,
  parasitism = 1,
  amensalism = 1,
  competition = 1,
  interactions = NULL,
  symmetric = FALSE,
  list_A = NULL
)

Arguments

`n_species`	Integer: number of species
`names_species`	Character: names of species. If NULL, `paste0("sp", seq_len(n_species))` is used. (default: `names_species = NULL`)
`diagonal`	Values defining the strength of self-interactions. Input can be a number (will be applied to all species) or a vector of length n_species. Positive self-interaction values lead to exponential growth. (default: `diagonal = -0.5`)
`connectance`	Numeric frequency of inter-species interactions. i.e. proportion of non-zero off-diagonal terms. Should be in the interval 0 <= connectance <= 1. (default: `connectance = 0.2`)
`scale_off_diagonal`	Numeric: scale of the off-diagonal elements compared to the diagonal. (default: `scale_off_diagonal = 0.1`)
`mutualism`	Numeric: relative proportion of interactions terms consistent with mutualism (positive <-> positive) (default: `mutualism = 1`)
`commensalism`	Numeric: relative proportion of interactions terms consistent with commensalism (positive <-> neutral) (default: `commensalism = 1`)
`parasitism`	Numeric: relative proportion of interactions terms consistent with parasitism (positive <-> negative) (default: `parasitism = 1`)
`amensalism`	Numeric: relative proportion of interactions terms consistent with amensalism (neutral <-> negative) (default: `amensalism = 1`)
`competition`	Numeric: relative proportion of interactions terms consistent with competition (negative <-> negative) (default: `competition = 1`)
`interactions`	Numeric: values of the n_species^2 pairwise interaction strengths. Diagonal terms will be replaced by the 'diagonal' parameter If NULL, interactions are drawn from `runif(n_species^2, min=0, max=abs(diagonal))`. Negative values are first converted to positive then the signs are defined by the relative weights of the biological interactions (i.e. mutualism, commensalism, parasitism, amensalism, competition) (default: `interactions = NULL`)
`symmetric`	Logical: whether the strength of mutualistic and competitive interactions are symmetric. This is implemented by overwrite a half of the matrix, so the proportions of interactions might deviate from expectations. (default: `symmetric=FALSE`)
`list_A`	List: a list of matrices generated by randomA. Used to support different groups of interactions. If NULL (by default), no group is considered. Otherwise the given list of matrices will overwrite values around the diagonal. (default: `list_A = NULL`)

Value

randomA returns a matrix A with dimensions (n_species x n_species)

Examples


dense_A <- randomA(
    n_species = 10,
    scale_off_diagonal = 1,
    diagonal = -1.0,
    connectance = 0.9
)

sparse_A <- randomA(
    n_species = 10,
    diagonal = -1.0,
    connectance = 0.09
)

user_interactions <- rbeta(n = 10^2, .5, .5)
user_A <- randomA(n_species = 10, interactions = user_interactions)

competitive_A <- randomA(
    n_species = 10,
    mutualism = 0,
    commensalism = 0,
    parasitism = 0,
    amensalism = 0,
    competition = 1,
    connectance = 1,
    scale_off_diagonal = 1
)

parasitism_A <- randomA(
    n_species = 10,
    mutualism = 0,
    commensalism = 0,
    parasitism = 1,
    amensalism = 0,
    competition = 0,
    connectance = 1,
    scale_off_diagonal = 1,
    symmetric = TRUE
)

list_A <- list(dense_A, sparse_A, competitive_A, parasitism_A)
groupA <- randomA(n_species = 40, list_A = list_A)

dense_A <- randomA(
    n_species = 10,
    scale_off_diagonal = 1,
    diagonal = -1.0,
    connectance = 0.9
)

sparse_A <- randomA(
    n_species = 10,
    diagonal = -1.0,
    connectance = 0.09
)

user_interactions <- rbeta(n = 10^2, .5, .5)
user_A <- randomA(n_species = 10, interactions = user_interactions)

competitive_A <- randomA(
    n_species = 10,
    mutualism = 0,
    commensalism = 0,
    parasitism = 0,
    amensalism = 0,
    competition = 1,
    connectance = 1,
    scale_off_diagonal = 1
)

parasitism_A <- randomA(
    n_species = 10,
    mutualism = 0,
    commensalism = 0,
    parasitism = 1,
    amensalism = 0,
    competition = 0,
    connectance = 1,
    scale_off_diagonal = 1,
    symmetric = TRUE
)

list_A <- list(dense_A, sparse_A, competitive_A, parasitism_A)
groupA <- randomA(n_species = 40, list_A = list_A)

Generate random efficiency matrix

Description

Generate random efficiency matrix for consumer resource model from Dirichlet distribution, where positive efficiencies indicate the consumption of resources, whilst negatives indicate that the species would produce the resource.

Usage

randomE(
  n_species,
  n_resources,
  names_species = NULL,
  names_resources = NULL,
  mean_consumption = n_resources/4,
  mean_production = n_resources/6,
  maintenance = 0.5,
  trophic_levels = NULL,
  trophic_preferences = NULL,
  exact = FALSE
)
randomE(
  n_species,
  n_resources,
  names_species = NULL,
  names_resources = NULL,
  mean_consumption = n_resources/4,
  mean_production = n_resources/6,
  maintenance = 0.5,
  trophic_levels = NULL,
  trophic_preferences = NULL,
  exact = FALSE
)

Arguments

`n_species`	Integer: number of species
`n_resources`	Integer: number of resources
`names_species`	Character: names of species. If NULL, `paste0("sp", seq_len(n_species))` is used. (default: `names_species = NULL`)
`names_resources`	Character: names of resources. If NULL, `paste0("res", seq_len(n_resources))` is used.
`mean_consumption`	Numeric: mean number of resources consumed by each species drawn from a poisson distribution (default: `mean_consumption = n_resources/4`)
`mean_production`	Numeric: mean number of resources produced by each species drawn from a poisson distribution (default: `mean_production = n_resources/6`)
`maintenance`	Numeric: proportion of resources that cannot be converted into products between 0~1 the proportion of resources used to maintain the living of microorganisms. 0 means all the resources will be used for the reproduction of microorganisms, and 1 means all the resources would be used to maintain the living of organisms and no resources would be left for their growth(reproduction). (default: `maintenance = 0.5`)
`trophic_levels`	Integer: number of species in microbial trophic levels. If NULL, by default, microbial trophic levels would not be considered. (default: `trophic_levels = NULL`)
`trophic_preferences`	List: preferred resources and productions of each trophic level. Positive values indicate the consumption of resources, whilst negatives indicate that the species would produce the resource.
`exact`	Logical: whether to set the number of consumption/production to be exact as mean_consumption/mean_production or to set them using a Poisson distribution. (default: `exact = FALSE`) If `length(trophic_preferences)` is smaller than `length(trophic_levels)`, then NULL values would be appended to lower trophic levels. If NULL, by default, the consumption preference will be defined randomly. (default: `trophic_preferences = NULL`)

Value

randomE returns a matrix E with dimensions (n_species x n_resources), and each row represents a species.

Examples

# example with minimum parameters
ExampleEfficiencyMatrix <- randomE(n_species = 5, n_resources = 12)

# examples with specific parameters
ExampleEfficiencyMatrix <- randomE(
    n_species = 3, n_resources = 6,
    names_species = letters[1:3],
    names_resources = paste0("res", LETTERS[1:6]),
    mean_consumption = 3, mean_production = 1
)
ExampleEfficiencyMatrix <- randomE(
    n_species = 3, n_resources = 6,
    maintenance = 0.4
)
ExampleEfficiencyMatrix <- randomE(
    n_species = 3, n_resources = 6,
    mean_consumption = 3, mean_production = 1, maintenance = 0.4
)

# examples with microbial trophic levels
ExampleEfficiencyMatrix <- randomE(
    n_species = 10, n_resources = 15,
    trophic_levels = c(6, 3, 1),
    trophic_preferences = list(
        c(rep(1, 5), rep(-1, 5), rep(0, 5)),
        c(rep(0, 5), rep(1, 5), rep(-1, 5)),
        c(rep(0, 10), rep(1, 5))
    )
)
ExampleEfficiencyMatrix <- randomE(
    n_species = 10, n_resources = 15,
    trophic_levels = c(6, 3, 1),
    trophic_preferences = list(c(rep(1, 5), rep(-1, 5), rep(0, 5)), NULL, NULL)
)
ExampleEfficiencyMatrix <- randomE(
    n_species = 10, n_resources = 15,
    trophic_levels = c(6, 3, 1)
)

# example with minimum parameters
ExampleEfficiencyMatrix <- randomE(n_species = 5, n_resources = 12)

# examples with specific parameters
ExampleEfficiencyMatrix <- randomE(
    n_species = 3, n_resources = 6,
    names_species = letters[1:3],
    names_resources = paste0("res", LETTERS[1:6]),
    mean_consumption = 3, mean_production = 1
)
ExampleEfficiencyMatrix <- randomE(
    n_species = 3, n_resources = 6,
    maintenance = 0.4
)
ExampleEfficiencyMatrix <- randomE(
    n_species = 3, n_resources = 6,
    mean_consumption = 3, mean_production = 1, maintenance = 0.4
)

# examples with microbial trophic levels
ExampleEfficiencyMatrix <- randomE(
    n_species = 10, n_resources = 15,
    trophic_levels = c(6, 3, 1),
    trophic_preferences = list(
        c(rep(1, 5), rep(-1, 5), rep(0, 5)),
        c(rep(0, 5), rep(1, 5), rep(-1, 5)),
        c(rep(0, 10), rep(1, 5))
    )
)
ExampleEfficiencyMatrix <- randomE(
    n_species = 10, n_resources = 15,
    trophic_levels = c(6, 3, 1),
    trophic_preferences = list(c(rep(1, 5), rep(-1, 5), rep(0, 5)), NULL, NULL)
)
ExampleEfficiencyMatrix <- randomE(
    n_species = 10, n_resources = 15,
    trophic_levels = c(6, 3, 1)
)

Consumer-resource model simulation

Description

Simulates time series with the consumer-resource model.

Usage

simulateConsumerResource(
  n_species,
  n_resources,
  names_species = NULL,
  names_resources = NULL,
  E = NULL,
  x0 = NULL,
  resources = NULL,
  resources_dilution = NULL,
  growth_rates = NULL,
  monod_constant = NULL,
  sigma_drift = 0.001,
  sigma_epoch = 0.1,
  sigma_external = 0.3,
  sigma_migration = 0.01,
  epoch_p = 0.001,
  t_external_events = NULL,
  t_external_durations = NULL,
  stochastic = FALSE,
  migration_p = 0.01,
  metacommunity_probability = NULL,
  error_variance = 0,
  norm = FALSE,
  t_end = 1000,
  trophic_priority = NULL,
  inflow_rate = 0,
  outflow_rate = 0,
  volume = 1000,
  ...
)
simulateConsumerResource(
  n_species,
  n_resources,
  names_species = NULL,
  names_resources = NULL,
  E = NULL,
  x0 = NULL,
  resources = NULL,
  resources_dilution = NULL,
  growth_rates = NULL,
  monod_constant = NULL,
  sigma_drift = 0.001,
  sigma_epoch = 0.1,
  sigma_external = 0.3,
  sigma_migration = 0.01,
  epoch_p = 0.001,
  t_external_events = NULL,
  t_external_durations = NULL,
  stochastic = FALSE,
  migration_p = 0.01,
  metacommunity_probability = NULL,
  error_variance = 0,
  norm = FALSE,
  t_end = 1000,
  trophic_priority = NULL,
  inflow_rate = 0,
  outflow_rate = 0,
  volume = 1000,
  ...
)

Arguments

`n_species`	Integer: number of species
`n_resources`	Integer: number of resources
`names_species`	Character: names of species. If NULL, `paste0("sp", seq_len(n_species))` is used. (default: `names_species = NULL`)
`names_resources`	Character: names of resources. If NULL, `paste0("res", seq_len(n_resources))` is used.
`E`	matrix: matrix of efficiency. A matrix defining the efficiency of resource-to-biomass conversion (positive values) and the relative conversion of metabolic by-products (negative values). If NULL, `randomE(n_species, n_resources)` is used. (default: `E = NULL`)
`x0`	Numeric: initial abundances of simulated species. If NULL, `runif(n = n_species, min = 0.1, max = 10)` is used. (default: `x0 = NULL`)
`resources`	Numeric: initial concentrations of resources. If NULL, `runif(n = n_resources, min = 1, max = 100)` is used. (default: `resources = NULL`)
`resources_dilution`	Numeric: concentrations of resources in the continuous inflow (applicable when inflow_rate > 0). If NULL, `resources` is used. (default: `resources_dilution = NULL`)
`growth_rates`	Numeric: vector of maximum growth rates(mu) of species. If NULL, `rep(1, n_species)` is used. (default: `growth_rates = NULL`)
`monod_constant`	matrix: the constant of additive monod growth of n_species consuming n_resources. If NULL, `matrix(rgamma(n = n_speciesn_resources, shape = 50max(resources), rate = 1), nrow = n_species)` is used. (default: `monod_constant = NULL`)
`sigma_drift`	Numeric: standard deviation of a normally distributed noise applied in each time step (t_step) (default: `sigma_drift = 0.001`)
`sigma_epoch`	Numeric: standard deviation of a normally distributed noise applied to random periods of the community composition with frequency defined by the epoch_p parameter (default: `sigma_epoch = 0.1`)
`sigma_external`	Numeric: standard deviation of a normally distributed noise applied to user-defined external events/disturbances (default: `sigma_external = 0.3`)
`sigma_migration`	Numeric: standard deviation of a normally distributed variable that defines the intensity of migration at each time step (t_step) (default: `sigma_migration = 0.01`)
`epoch_p`	Numeric: the probability/frequency of random periodic changes introduced to the community composition (default: `epoch_p = 0.001`)
`t_external_events`	Numeric: the starting time points of defined external events that introduce random changes to the community composition (default: `t_external_events = NULL`)
`t_external_durations`	Numeric: respective duration of the external events that are defined in the 't_external_events' (times) and sigma_external (std). (default: `t_external_durations = NULL`)
`stochastic`	Logical: whether to introduce noise in the simulation. If False, sigma_drift, sigma_epoch, and sigma_external are ignored. (default: `stochastic = FALSE`)
`migration_p`	Numeric: the probability/frequency of migration from a metacommunity. (default: `migration_p = 0.01`)
`metacommunity_probability`	Numeric: Normalized probability distribution of the likelihood that species from the metacommunity can enter the community during the simulation. If NULL, `rdirichlet(1, alpha = rep(1,n_species))` is used. (default: `metacommunity_probability = NULL`)
`error_variance`	Numeric: 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: `error_variance = 0`)
`norm`	Logical: whether the time series should be returned with the abundances as proportions (`norm = TRUE`) or the raw counts (default: `norm = FALSE`) (default: `norm = FALSE`)
`t_end`	Numeric: the end time of the simulationTimes, defining the modeled time length of the community. (default: `t_end = 1000`)
`trophic_priority`	Matrix: a matrix defining the orders of resources to be consumed by each species. If NULL, by default, this feature won't be turned on, and species will consume all resources simultaneously to grow. The dimension should be identical to matrix E. (default: `trophic_priority = NULL`)
`inflow_rate`, `outflow_rate`	Numeric: the inflow and outflow rate of a culture process. By default, inflow_rate and outflow_rate are 0, indicating a batch culture process. By setting them equally larger than 0, we can simulate a continuous culture(e.g. chemostat).
`volume`	Numeric: the volume of the continuous cultivation. This parameter is important for simulations where inflow_rate or outflow_rate are not 0. (default: `volume = 1000`)
`...`	additional parameters, see `utils` to know more.

Value

an TreeSummarizedExperiment class object

Examples

n_species <- 2
n_resources <- 4
tse <- simulateConsumerResource(
    n_species = n_species,
    n_resources = n_resources
)

# example with user-defined values (names_species, names_resources, E, x0,
# resources, growth_rates, error_variance, t_end, t_step)

ExampleE <- randomE(
    n_species = n_species, n_resources = n_resources,
    mean_consumption = 3, mean_production = 1, maintenance = 0.4
)
ExampleResources <- rep(100, n_resources)
tse1 <- simulateConsumerResource(
    n_species = n_species,
    n_resources = n_resources, names_species = letters[seq_len(n_species)],
    names_resources = paste0("res", LETTERS[seq_len(n_resources)]), E = ExampleE,
    x0 = rep(0.001, n_species), resources = ExampleResources,
    growth_rates = runif(n_species),
    error_variance = 0.01,
    t_end = 5000,
    t_step = 1
)

# example with trophic levels
n_species <- 10
n_resources <- 15
ExampleEfficiencyMatrix <- randomE(
    n_species = 10, n_resources = 15,
    trophic_levels = c(6, 3, 1),
    trophic_preferences = list(
        c(rep(1, 5), rep(-1, 5), rep(0, 5)),
        c(rep(0, 5), rep(1, 5), rep(-1, 5)),
        c(rep(0, 10), rep(1, 5))
    )
)

ExampleResources <- c(rep(500, 5), rep(200, 5), rep(50, 5))
tse2 <- simulateConsumerResource(
    n_species = n_species,
    n_resources = n_resources,
    names_species = letters[1:n_species],
    names_resources = paste0(
        "res", LETTERS[1:n_resources]
    ),
    E = ExampleEfficiencyMatrix,
    x0 = rep(0.001, n_species),
    resources = ExampleResources,
    growth_rates = rep(1, n_species),
    # error_variance = 0.001,
    t_end = 5000, t_step = 1
)

# example with trophic priority
n_species <- 4
n_resources <- 6
ExampleE <- randomE(
    n_species = n_species,
    n_resources = n_resources,
    mean_consumption = n_resources,
    mean_production = 0
)
ExampleTrophicPriority <- t(apply(
    matrix(sample(n_species * n_resources),
        nrow = n_species
    ),
    1, order
))
# make sure that for non-consumables resources for each species,
# the priority is 0 (smaller than any given priority)
ExampleTrophicPriority <- (ExampleE > 0) * ExampleTrophicPriority
tse3 <- simulateConsumerResource(
    n_species = n_species,
    n_resources = n_resources,
    E = ExampleE,
    trophic_priority = ExampleTrophicPriority,
    t_end = 2000
)

n_species <- 2
n_resources <- 4
tse <- simulateConsumerResource(
    n_species = n_species,
    n_resources = n_resources
)

# example with user-defined values (names_species, names_resources, E, x0,
# resources, growth_rates, error_variance, t_end, t_step)

ExampleE <- randomE(
    n_species = n_species, n_resources = n_resources,
    mean_consumption = 3, mean_production = 1, maintenance = 0.4
)
ExampleResources <- rep(100, n_resources)
tse1 <- simulateConsumerResource(
    n_species = n_species,
    n_resources = n_resources, names_species = letters[seq_len(n_species)],
    names_resources = paste0("res", LETTERS[seq_len(n_resources)]), E = ExampleE,
    x0 = rep(0.001, n_species), resources = ExampleResources,
    growth_rates = runif(n_species),
    error_variance = 0.01,
    t_end = 5000,
    t_step = 1
)

# example with trophic levels
n_species <- 10
n_resources <- 15
ExampleEfficiencyMatrix <- randomE(
    n_species = 10, n_resources = 15,
    trophic_levels = c(6, 3, 1),
    trophic_preferences = list(
        c(rep(1, 5), rep(-1, 5), rep(0, 5)),
        c(rep(0, 5), rep(1, 5), rep(-1, 5)),
        c(rep(0, 10), rep(1, 5))
    )
)

ExampleResources <- c(rep(500, 5), rep(200, 5), rep(50, 5))
tse2 <- simulateConsumerResource(
    n_species = n_species,
    n_resources = n_resources,
    names_species = letters[1:n_species],
    names_resources = paste0(
        "res", LETTERS[1:n_resources]
    ),
    E = ExampleEfficiencyMatrix,
    x0 = rep(0.001, n_species),
    resources = ExampleResources,
    growth_rates = rep(1, n_species),
    # error_variance = 0.001,
    t_end = 5000, t_step = 1
)

# example with trophic priority
n_species <- 4
n_resources <- 6
ExampleE <- randomE(
    n_species = n_species,
    n_resources = n_resources,
    mean_consumption = n_resources,
    mean_production = 0
)
ExampleTrophicPriority <- t(apply(
    matrix(sample(n_species * n_resources),
        nrow = n_species
    ),
    1, order
))
# make sure that for non-consumables resources for each species,
# the priority is 0 (smaller than any given priority)
ExampleTrophicPriority <- (ExampleE > 0) * ExampleTrophicPriority
tse3 <- simulateConsumerResource(
    n_species = n_species,
    n_resources = n_resources,
    E = ExampleE,
    trophic_priority = ExampleTrophicPriority,
    t_end = 2000
)

Generalized Lotka-Volterra (gLV) simulation

Description

Simulates time series with the generalized Lotka-Volterra model.

Usage

simulateGLV(
  n_species,
  names_species = NULL,
  A = NULL,
  x0 = NULL,
  growth_rates = NULL,
  sigma_drift = 0.001,
  sigma_epoch = 0.1,
  sigma_external = 0.3,
  sigma_migration = 0.01,
  epoch_p = 0.001,
  t_external_events = NULL,
  t_external_durations = NULL,
  stochastic = TRUE,
  migration_p = 0.01,
  metacommunity_probability = NULL,
  error_variance = 0,
  norm = FALSE,
  t_end = 1000,
  ...
)
simulateGLV(
  n_species,
  names_species = NULL,
  A = NULL,
  x0 = NULL,
  growth_rates = NULL,
  sigma_drift = 0.001,
  sigma_epoch = 0.1,
  sigma_external = 0.3,
  sigma_migration = 0.01,
  epoch_p = 0.001,
  t_external_events = NULL,
  t_external_durations = NULL,
  stochastic = TRUE,
  migration_p = 0.01,
  metacommunity_probability = NULL,
  error_variance = 0,
  norm = FALSE,
  t_end = 1000,
  ...
)

Arguments

`n_species`	Integer: number of species
`names_species`	Character: names of species. If NULL, `paste0("sp", seq_len(n_species))` is used. (default: `names_species = NULL`)
`A`	matrix: interaction matrix defining the positive and negative interactions between n_species. If NULL, `randomA(n_species)` is used. (default: `A = NULL`)
`x0`	Numeric: initial abundances of simulated species. If NULL, `runif(n = n_species, min = 0, max = 1)` is used. (default: `x0 = NULL`)
`growth_rates`	Numeric: growth rates of simulated species. If NULL, `runif(n = n_species, min = 0, max = 1)` is used. (default: `growth_rates = NULL`)
`sigma_drift`	Numeric: standard deviation of a normally distributed noise applied in each time step (t_step) (default: `sigma_drift = 0.001`)
`sigma_epoch`	Numeric: standard deviation of a normally distributed noise applied to random periods of the community composition with frequency defined by the epoch_p parameter (default: `sigma_epoch = 0.1`)
`sigma_external`	Numeric: standard deviation of a normally distributed noise applied to user-defined external events/disturbances (default: `sigma_external = 0.3`)
`sigma_migration`	Numeric: standard deviation of a normally distributed variable that defines the intensity of migration at each time step (t_step) (default: `sigma_migration = 0.01`)
`epoch_p`	Numeric: the probability/frequency of random periodic changes introduced to the community composition (default: `epoch_p = 0.001`)
`t_external_events`	Numeric: the starting time points of defined external events that introduce random changes to the community composition (default: `t_external_events = NULL`)
`t_external_durations`	Numeric: respective duration of the external events that are defined in the 't_external_events' (times) and sigma_external (std). (default: `t_external_durations = NULL`)
`stochastic`	Logical: whether to introduce noise in the simulation. If False, sigma_drift, sigma_epoch, and sigma_external are ignored. (default: `stochastic = FALSE`)
`migration_p`	Numeric: the probability/frequency of migration from a metacommunity. (default: `migration_p = 0.01`)
`metacommunity_probability`	Numeric: Normalized probability distribution of the likelihood that species from the metacommunity can enter the community during the simulation. If NULL, `rdirichlet(1, alpha = rep(1,n_species))` is used. (default: `metacommunity_probability = NULL`)
`error_variance`	Numeric: 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: `error_variance = 0`)
`norm`	Logical: whether the time series should be returned with the abundances as proportions (`norm = TRUE`) or the raw counts (default: `norm = FALSE`) (default: `norm = FALSE`)
`t_end`	Numeric: the end time of the simulationTimes, defining the modeled time length of the community. (default: `t_end = 1000`)
`...`	additional parameters, see `utils` to know more.

Details

Simulates a community time series using the generalized Lotka-Volterra model, defined as dx/dt = x(b+Ax), where x is the vector of species abundances, diag(x) is a diagonal matrix with the diagonal values set to x. A is the interaction matrix and b is the vector of growth rates.

Value

simulateGLV returns a TreeSummarizedExperiment class object

Examples


# generate a random interaction matrix
ExampleA <- randomA(n_species = 4, diagonal = -1)

# run the model with default values (only stochastic migration considered)
tse <- simulateGLV(n_species = 4, A = ExampleA)

# run the model with two external disturbances at time points 240 and 480
# with durations equal to 1 (10 time steps when t_step by default is 0.1).
ExampleGLV <- simulateGLV(
    n_species = 4, A = ExampleA,
    t_external_events = c(0, 240, 480), t_external_durations = c(0, 1, 1)
)

# run the model with no perturbation nor migration
set.seed(42)
tse1 <- simulateGLV(
    n_species = 4, A = ExampleA, stochastic = FALSE,
    sigma_migration = 0
)

# run the model with no perturbation nor migration but with measurement error
set.seed(42)
tse2 <- simulateGLV(
    n_species = 4, A = ExampleA, stochastic = FALSE,
    error_variance = 0.001, sigma_migration = 0
)

# generate a random interaction matrix
ExampleA <- randomA(n_species = 4, diagonal = -1)

# run the model with default values (only stochastic migration considered)
tse <- simulateGLV(n_species = 4, A = ExampleA)

# run the model with two external disturbances at time points 240 and 480
# with durations equal to 1 (10 time steps when t_step by default is 0.1).
ExampleGLV <- simulateGLV(
    n_species = 4, A = ExampleA,
    t_external_events = c(0, 240, 480), t_external_durations = c(0, 1, 1)
)

# run the model with no perturbation nor migration
set.seed(42)
tse1 <- simulateGLV(
    n_species = 4, A = ExampleA, stochastic = FALSE,
    sigma_migration = 0
)

# run the model with no perturbation nor migration but with measurement error
set.seed(42)
tse2 <- simulateGLV(
    n_species = 4, A = ExampleA, stochastic = FALSE,
    error_variance = 0.001, sigma_migration = 0
)

Hubbell's neutral model simulation

Description

Neutral species abundances simulation according to the Hubbell model.

Usage

simulateHubbell(
  n_species,
  M,
  carrying_capacity = 1000,
  k_events = 10,
  migration_p = 0.02,
  t_skip = 0,
  t_end,
  norm = FALSE
)
simulateHubbell(
  n_species,
  M,
  carrying_capacity = 1000,
  k_events = 10,
  migration_p = 0.02,
  t_skip = 0,
  t_end,
  norm = FALSE
)

Arguments

`n_species`	integer amount of different species initially in the local community
`M`	integer amount of different species in the metacommunity, including those of the local community
`carrying_capacity`	integer value of fixed amount of individuals in the local community (default: `carrying_capacity = 1000`)
`k_events`	integer value of fixed amount of deaths of local community individuals in each generation (default: `k_events = 10`)
`migration_p`	numeric immigration rate: the probability that a death in the local community is replaced by a migrant of the metacommunity rather than by the birth of a local community member (default: `migration_p = 0.02`)
`t_skip`	integer number of generations that should not be included in the outputted species abundance matrix. (default: `t_skip = 0`)
`t_end`	integer number of simulations to be simulated
`norm`	logical scalar choosing whether the time series should be returned with the abundances as proportions (`norm = TRUE`) or the raw counts (default: `norm = FALSE`)

Value

simulateHubbell returns a TreeSummarizedExperiment class object

References

Rosindell, James et al. "The unified neutral theory of biodiversity and biogeography at age ten." Trends in ecology & evolution vol. 26,7 (2011).

Examples

tse <- simulateHubbell(
    n_species = 8, M = 10, carrying_capacity = 1000, k_events = 50,
    migration_p = 0.02, t_end = 100
)

tse <- simulateHubbell(
    n_species = 8, M = 10, carrying_capacity = 1000, k_events = 50,
    migration_p = 0.02, t_end = 100
)

Hubbell's neutral model simulation applied to time series

Description

Neutral species abundances simulation according to the Hubbell model. This model shows that losses in society can be replaced either by the birth of individuals or by immigration depending on their probabilities. The specific time between the events of birth or migration is calculated and time effect is considered to determine the next event.

Usage

simulateHubbellRates(
  n_species = NULL,
  x0 = NULL,
  names_species = NULL,
  migration_p = 0.01,
  metacommunity_probability = NULL,
  k_events = 1,
  growth_rates = NULL,
  error_variance = 0,
  norm = FALSE,
  t_end = 1000,
  ...
)
simulateHubbellRates(
  n_species = NULL,
  x0 = NULL,
  names_species = NULL,
  migration_p = 0.01,
  metacommunity_probability = NULL,
  k_events = 1,
  growth_rates = NULL,
  error_variance = 0,
  norm = FALSE,
  t_end = 1000,
  ...
)

Arguments

`n_species`	Integer: number of species
`x0`	Numeric: initial species composition. If NULL, `rep(100, n_species)` is used.
`names_species`	Character: names of species. If NULL, `paste0("sp", seq_len(n_species))` is used. (default: `names_species = NULL`)
`migration_p`	Numeric: the probability/frequency of migration from a metacommunity. (default: `migration_p = 0.01`)
`metacommunity_probability`	Numeric: Normalized probability distribution of the likelihood that species from the metacommunity can enter the community during the simulation. If NULL, `rdirichlet(1, alpha = rep(1,n_species))` is used. (default: `metacommunity_probability = NULL`)
`k_events`	Integer: number of events to simulate before updating the sampling distributions. (default: `k_events = 1`)
`growth_rates`	Numeric: maximum growth rates(mu) of species. If NULL, `rep(1, n_species)` is used. (default: `growth_rates = NULL`)
`error_variance`	Numeric: 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: `error_variance = 0`)
`norm`	Logical: whether the time series should be returned with the abundances as proportions (`norm = TRUE`) or the raw counts (default: `norm = FALSE`) (default: `norm = FALSE`)
`t_end`	Numeric: the end time of the simulationTimes, defining the modeled time length of the community. (default: `t_end = 1000`)
`...`	additional parameters, see `utils` to know more.

Value

simulateHubbellRates returns a TreeSummarizedExperiment class object

References

Rosindell, James et al. "The unified neutral theory of biodiversity and biogeography at age ten." Trends in ecology & evolution vol. 26,7 (2011).

Examples

set.seed(42)
tse <- simulateHubbellRates(n_species = 5)

miaViz::plotSeries(tse, x = "time")

# no migration, all stochastic birth and death
set.seed(42)
tse1 <- simulateHubbellRates(n_species = 5, migration_p = 0)

# all migration, no stochastic birth and death
set.seed(42)
tse2 <- simulateHubbellRates(
    n_species = 5,
    migration_p = 1,
    metacommunity_probability = c(0.1, 0.15, 0.2, 0.25, 0.3),
    t_end = 20,
    t_store = 200
)

# all migration, no stochastic birth and death, but with measurement errors
set.seed(42)
tse3 <- simulateHubbellRates(
    n_species = 5,
    migration_p = 1,
    metacommunity_probability = c(0.1, 0.15, 0.2, 0.25, 0.3),
    t_end = 20,
    t_store = 200,
    error_variance = 100
)

# model with specified inputs
set.seed(42)
tse4 <- simulateHubbellRates(
    n_species = 5,
    migration_p = 0.1,
    metacommunity_probability = c(0.1, 0.15, 0.2, 0.25, 0.3),
    t_end = 200,
    t_store = 1000,
    k_events = 5,
    growth_rates = c(1.1, 1.05, 1, 0.95, 0.9)
)

set.seed(42)
tse <- simulateHubbellRates(n_species = 5)

miaViz::plotSeries(tse, x = "time")

# no migration, all stochastic birth and death
set.seed(42)
tse1 <- simulateHubbellRates(n_species = 5, migration_p = 0)

# all migration, no stochastic birth and death
set.seed(42)
tse2 <- simulateHubbellRates(
    n_species = 5,
    migration_p = 1,
    metacommunity_probability = c(0.1, 0.15, 0.2, 0.25, 0.3),
    t_end = 20,
    t_store = 200
)

# all migration, no stochastic birth and death, but with measurement errors
set.seed(42)
tse3 <- simulateHubbellRates(
    n_species = 5,
    migration_p = 1,
    metacommunity_probability = c(0.1, 0.15, 0.2, 0.25, 0.3),
    t_end = 20,
    t_store = 200,
    error_variance = 100
)

# model with specified inputs
set.seed(42)
tse4 <- simulateHubbellRates(
    n_species = 5,
    migration_p = 0.1,
    metacommunity_probability = c(0.1, 0.15, 0.2, 0.25, 0.3),
    t_end = 200,
    t_store = 1000,
    k_events = 5,
    growth_rates = c(1.1, 1.05, 1, 0.95, 0.9)
)

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,
  ...
)
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: initial abundances of simulated species. If NULL, `runif(n = n_species, min = 0, max = 1)` is used.
`carrying_capacities`	numeric carrying capacities. If NULL, `runif(n = n_species, min = 0, max = 1)` is used.
`error_variance`	Numeric: 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: `error_variance = 0.05`)
`explosion_bound`	numeric value of boundary for explosion (default: `explosion_bound = 10^8`)
`t_end`	integer number of simulations to be simulated
`norm`	logical scalar returning normalised abundances (proportions in each generation) (default: `norm = 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)

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

Self-Organised Instability model (SOI) simulation

Description

Generate time-series with The Self-Organised Instability (SOI) model. Implements a K-leap method for accelerating stochastic simulation.

Usage

simulateSOI(
  n_species,
  x0 = NULL,
  names_species = NULL,
  carrying_capacity = 1000,
  A = NULL,
  k_events = 5,
  t_end = 1000,
  metacommunity_probability = runif(n_species, min = 0.1, max = 0.8),
  death_rates = runif(n_species, min = 0.01, max = 0.08),
  norm = FALSE
)
simulateSOI(
  n_species,
  x0 = NULL,
  names_species = NULL,
  carrying_capacity = 1000,
  A = NULL,
  k_events = 5,
  t_end = 1000,
  metacommunity_probability = runif(n_species, min = 0.1, max = 0.8),
  death_rates = runif(n_species, min = 0.01, max = 0.08),
  norm = FALSE
)

Arguments

`n_species`	Integer: number of species
`x0`	a vector of initial community abundances If (default: `x0 = NULL`), based on migration rates
`names_species`	Character: names of species. If NULL, `paste0("sp", seq_len(n_species))` is used. (default: `names_species = NULL`)
`carrying_capacity`	integer community size, number of available sites (individuals)
`A`	matrix: interaction matrix defining the positive and negative interactions between n_species. If NULL, `powerlawA(n_species)` is used. (default: `A = NULL`)
`k_events`	integer number of transition events that are allowed to take place during one leap. (default: `k_events = 5`). Higher values reduce runtime, but also accuracy of the simulation.
`t_end`	Numeric: the end time of the simulation, defining the modeled time length of the community. (default: `t_end = 1000`)
`metacommunity_probability`	Numeric: Normalized probability distribution of the likelihood that species from the metacommunity can enter the community during the simulation. By default, `runif(n_species, min = 0.1, max = 0.8)` is used. (default: `metacommunity_probability = runif(n_species, min = 0.1, max = 0.8)`)
`death_rates`	Numeric: death rates of each species. By default, `runif(n_species, min = 0.01, max = 0.08)` is used. (default: `death_rates = runif(n_species, min = 0.01, max = 0.08)`)
`norm`	logical scalar indicating whether the time series should be returned with the abundances as proportions (`norm = TRUE`) or the raw counts (default: `norm = FALSE`)

Value

simulateSOI returns a TreeSummarizedExperiment class object

Examples

# Generate interaction matrix
A <- miaSim::powerlawA(10, alpha = 1.2)
# Simulate data from the SOI model
tse <- simulateSOI(
    n_species = 10, carrying_capacity = 1000, A = A,
    k_events = 5, x0 = NULL, t_end = 150, norm = TRUE
)

# Generate interaction matrix
A <- miaSim::powerlawA(10, alpha = 1.2)
# Simulate data from the SOI model
tse <- simulateSOI(
    n_species = 10, carrying_capacity = 1000, A = A,
    k_events = 5, x0 = NULL, t_end = 150, norm = TRUE
)

Stochastic Logistic simulation

Description

Simulates time series with the (stochastic) logistic model

Usage

simulateStochasticLogistic(
  n_species,
  names_species = NULL,
  growth_rates = NULL,
  carrying_capacities = NULL,
  death_rates = NULL,
  x0 = NULL,
  sigma_drift = 0.001,
  sigma_epoch = 0.1,
  sigma_external = 0.3,
  sigma_migration = 0.01,
  epoch_p = 0.001,
  t_external_events = NULL,
  t_external_durations = NULL,
  migration_p = 0.01,
  metacommunity_probability = NULL,
  stochastic = TRUE,
  error_variance = 0,
  norm = FALSE,
  t_end = 1000,
  ...
)
simulateStochasticLogistic(
  n_species,
  names_species = NULL,
  growth_rates = NULL,
  carrying_capacities = NULL,
  death_rates = NULL,
  x0 = NULL,
  sigma_drift = 0.001,
  sigma_epoch = 0.1,
  sigma_external = 0.3,
  sigma_migration = 0.01,
  epoch_p = 0.001,
  t_external_events = NULL,
  t_external_durations = NULL,
  migration_p = 0.01,
  metacommunity_probability = NULL,
  stochastic = TRUE,
  error_variance = 0,
  norm = FALSE,
  t_end = 1000,
  ...
)

Arguments

`n_species`	Integer: number of species
`names_species`	Character: names of species. If NULL, `paste0("sp", seq_len(n_species))` is used. (default: `names_species = NULL`)
`growth_rates`	Numeric: growth rates of simulated species. If NULL, `runif(n = n_species, min = 0.1, max = 0.2)` is used. (default: `growth_rates = NULL`)
`carrying_capacities`	Numeric: The max population of species supported in the community. If NULL, `runif(n = n_species, min = 1000, max = 2000)` is used. (default: `carrying_capacities = NULL`)
`death_rates`	Numeric: death rates of each species. If NULL, `runif(n = n_species, min = 0.0005, max = 0.0025)` is used. (default: `death_rates = NULL`)
`x0`	Numeric: initial abundances of simulated species. If NULL, `runif(n = n_species, min = 0.1, max = 10)` is used. (default: `x0 = NULL`)
`sigma_drift`	Numeric: standard deviation of a normally distributed noise applied in each time step (t_step) (default: `sigma_drift = 0.001`)
`sigma_epoch`	Numeric: standard deviation of a normally distributed noise applied to random periods of the community composition with frequency defined by the epoch_p parameter (default: `sigma_epoch = 0.1`)
`sigma_external`	Numeric: standard deviation of a normally distributed noise applied to user-defined external events/disturbances (default: `sigma_external = 0.3`)
`sigma_migration`	Numeric: standard deviation of a normally distributed variable that defines the intensity of migration at each time step (t_step) (default: `sigma_migration = 0.01`)
`epoch_p`	Numeric: the probability/frequency of random periodic changes introduced to the community composition (default: `epoch_p = 0.001`)
`t_external_events`	Numeric: the starting time points of defined external events that introduce random changes to the community composition (default: `t_external_events = NULL`)
`t_external_durations`	Numeric: respective duration of the external events that are defined in the 't_external_events' (times) and sigma_external (std). (default: `t_external_durations = NULL`)
`migration_p`	Numeric: the probability/frequency of migration from a metacommunity. (default: `migration_p = 0.01`)
`metacommunity_probability`	Numeric: Normalized probability distribution of the likelihood that species from the metacommunity can enter the community during the simulation. If NULL, `rdirichlet(1, alpha = rep(1,n_species))` is used. (default: `metacommunity_probability = NULL`)
`stochastic`	Logical: whether to introduce noise in the simulation. If False, sigma_drift, sigma_epoch, and sigma_external are ignored. (default: `stochastic = TRUE`)
`error_variance`	Numeric: 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: `error_variance = 0`)
`norm`	Logical: whether the time series should be returned with the abundances as proportions (`norm = TRUE`) or the raw counts (default: `norm = FALSE`) (default: `norm = FALSE`)
`t_end`	Numeric: the end time of the simulationTimes, defining the modeled time length of the community. (default: `t_end = 1000`)
`...`	additional parameters, see `utils` to know more.

Details

The change rate of the species was defined as dx/dt = b*x*(1-(x/k))*rN - dr*x, where b is the vector of growth rates, x is the vector of initial species abundances, k is the vector of maximum carrying capacities, rN is a random number ranged from 0 to 1 which changes in each time step, dr is the vector of constant death rates. Also, the vectors of initial dead species abundances can be set. The number of species will be set to 0 if the dead species abundances surpass the alive species abundances.

Value

simulateStochasticLogistic returns a TreeSummarizedExperiment class object

Examples

# Example of logistic model without stochasticity, death rates, or external
# disturbances
set.seed(42)
tse <- simulateStochasticLogistic(
    n_species = 5,
    stochastic = FALSE, death_rates = rep(0, 5)
)

# Adding a death rate
set.seed(42)
tse1 <- simulateStochasticLogistic(
    n_species = 5,
    stochastic = FALSE, death_rates = rep(0.01, 5)
)

# Example of stochastic logistic model with measurement error
set.seed(42)
tse2 <- simulateStochasticLogistic(
    n_species = 5,
    error_variance = 1000
)

# example with all the initial parameters defined by the user
set.seed(42)
tse3 <- simulateStochasticLogistic(
    n_species = 2,
    names_species = c("species1", "species2"),
    growth_rates = c(0.2, 0.1),
    carrying_capacities = c(1000, 2000),
    death_rates = c(0.001, 0.0015),
    x0 = c(3, 0.1),
    sigma_drift = 0.001,
    sigma_epoch = 0.3,
    sigma_external = 0.5,
    sigma_migration = 0.002,
    epoch_p = 0.001,
    t_external_events = c(100, 200, 300),
    t_external_durations = c(0.1, 0.2, 0.3),
    migration_p = 0.01,
    metacommunity_probability = miaSim::.rdirichlet(1, alpha = rep(1, 2)),
    stochastic = TRUE,
    error_variance = 0,
    norm = FALSE, # TRUE,
    t_end = 400,
    t_start = 0, t_step = 0.01,
    t_store = 1500
)

# Example of logistic model without stochasticity, death rates, or external
# disturbances
set.seed(42)
tse <- simulateStochasticLogistic(
    n_species = 5,
    stochastic = FALSE, death_rates = rep(0, 5)
)

# Adding a death rate
set.seed(42)
tse1 <- simulateStochasticLogistic(
    n_species = 5,
    stochastic = FALSE, death_rates = rep(0.01, 5)
)

# Example of stochastic logistic model with measurement error
set.seed(42)
tse2 <- simulateStochasticLogistic(
    n_species = 5,
    error_variance = 1000
)

# example with all the initial parameters defined by the user
set.seed(42)
tse3 <- simulateStochasticLogistic(
    n_species = 2,
    names_species = c("species1", "species2"),
    growth_rates = c(0.2, 0.1),
    carrying_capacities = c(1000, 2000),
    death_rates = c(0.001, 0.0015),
    x0 = c(3, 0.1),
    sigma_drift = 0.001,
    sigma_epoch = 0.3,
    sigma_external = 0.5,
    sigma_migration = 0.002,
    epoch_p = 0.001,
    t_external_events = c(100, 200, 300),
    t_external_durations = c(0.1, 0.2, 0.3),
    migration_p = 0.01,
    metacommunity_probability = miaSim::.rdirichlet(1, alpha = rep(1, 2)),
    stochastic = TRUE,
    error_variance = 0,
    norm = FALSE, # TRUE,
    t_end = 400,
    t_start = 0, t_step = 0.01,
    t_store = 1500
)

Package 'miaSim'

Help Index

Generate pairs of interactions according to interaction types

Description

Usage

Arguments

Value

Get the interspecies interaction matrix A using leave-one-out method

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generate a vector of times when events is happening

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Generate interactions according to five types of interactions and their weights

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check whether a number is a positive integer

Description

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Generate dirichlet random deviates

Description

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Examples

Replace one element with zero in a list.

Description

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Interaction matrix with Power-Law network adjacency matrix

Description

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References

Examples

Generate random interaction matrix for GLV model

Description

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Examples

Generate random efficiency matrix

Description

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Value

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Consumer-resource model simulation

Description

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Generalized Lotka-Volterra (gLV) simulation

Description

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Details

Value

Examples

Hubbell's neutral model simulation

Description

Usage

Arguments

Value

References

Examples

Hubbell's neutral model simulation applied to time series

Description