simulate_data-methods: Simulate data

In kpj/dce: Pathway Enrichment Based on Differential Causal Effects

Simulate data

Description

Generate data for given DAG. The flexible framework allows for different distributions for source and child nodes. Default distributions are negative binomial (with mean = 100 and 1/dispersion = 100), and poisson, respectively.

Usage

simulate_data(
  graph,
  n = 100,
  dist_fun = rnbinom,
  dist_args = list(mu = 1000, size = 100),
  child_fun = rpois,
  child_args = list(),
  child_dep = "lambda",
  link_fun = negative.binomial.special()$linkfun,
  link_args = list(offset = 1),
  pop_size = 0,
  latent = 0,
  latent_fun = "unif"
)

## S4 method for signature 'igraph'
simulate_data(
  graph,
  n = 100,
  dist_fun = rnbinom,
  dist_args = list(mu = 1000, size = 100),
  child_fun = rpois,
  child_args = list(),
  child_dep = "lambda",
  link_fun = negative.binomial.special()$linkfun,
  link_args = list(offset = 1),
  pop_size = 0,
  latent = 0,
  latent_fun = "unif"
)

## S4 method for signature 'graphNEL'
simulate_data(
  graph,
  n = 100,
  dist_fun = rnbinom,
  dist_args = list(mu = 1000, size = 100),
  child_fun = rpois,
  child_args = list(),
  child_dep = "lambda",
  link_fun = negative.binomial.special()$linkfun,
  link_args = list(offset = 1),
  pop_size = 0,
  latent = 0,
  latent_fun = "unif"
)

## S4 method for signature 'matrix'
simulate_data(
  graph,
  n = 100,
  dist_fun = rnbinom,
  dist_args = list(mu = 1000, size = 100),
  child_fun = rpois,
  child_args = list(),
  child_dep = "lambda",
  link_fun = negative.binomial.special()$linkfun,
  link_args = list(offset = 1),
  pop_size = 0,
  latent = 0,
  latent_fun = "unif"
)

Arguments

graph	Graph to simulate on
n	Number of samples
dist_fun	distribution function for nodes without parents
dist_args	list of arguments for dist_fun
child_fun	distribution function for nodes with parents
child_args	list of arguments for child_fun
child_dep	link_fun computes an output for the expression of nodes without parents. this output is than used as input for child_fun. child_dep defines the parameter (a a string) of child_fun, which is used for the input. E.g., the link_fun is the identity and the child_fun is rnorm, we usually set child_dep = "mean".
link_fun	special link function for the negative binomial distribution
link_args	list of arguments for link_fun
pop_size	numeric for the population size, e.g., pop_size=1000 adds 1000-n random genes not in the graph
latent	number of latent variables
latent_fun	uniform "unif" or exponential "exp" distribution of latent coefficients

Value

graph

Examples

dag <- create_random_DAG(30, 0.2)
X <- simulate_data(dag)

kpj/dce documentation built on Oct. 29, 2022, 1:40 a.m.