Simulates data under the PLN model with control on the dependency structure

data_from_scratch(
  type,
  p = 20,
  n = 50,
  r = 5,
  covariates = NULL,
  dens = log(p)/p,
  k = 3,
  norm = FALSE,
  signed = FALSE,
  v = 0.01,
  draw = FALSE
)

Arguments

type	type of graph, either "tree", "erdos", "cluster" or "scale-free"
p	wanted number of columns (species)
n	wanted number of rows (samples/observations)
r	within/between connectivity ratio for cluster graphs
covariates	a data.frame or matrix containing data covariates.
dens	density of edges for cluster graphs or edge probability for Erdös graphs
k	number of groups for the cluster structure.
norm	should the Gaussian parameters be normalized ?
signed	boolean: should the graph be composed of positive and negative partial correlations ?
v	noise parameter of the precision matrix
draw	boolean, plots the graph if set to TRUE

Value

a list containing

data: simulated counts
omega: the precision matrix

Examples

set.seed(1)
p<-30
Y1<-data_from_scratch("tree",p=p,draw=TRUE)
str(Y1)
#> List of 2
#>  $ data : int [1:50, 1:30] 4 1 0 0 87 0 0 2 21 21 ...
#>  $ omega: num [1:30, 1:30] 3.01 0 0 0 0 0 0 0 0 1 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : NULL
Y2<-data_from_scratch("cluster",p=p,r=10, dens=10/p, k=3,draw=TRUE)