Simulate data and parameters including missing actors.

generate_missing_data(n, p, r = 1, type, plot = FALSE, dens = 2/p)

Arguments

n	number of samples
p	number of observed species
r	number of missing actors
type	type of conditional dependency structure (graph), either "erdos", "cluster", or "scale-free".
plot	should the simulated graph be plotted ? default to FALSE
dens	density parameter for cluster and erdos graphs. For erdos graphs, this corresponds to the edges probability of connectance

Value

Y: Observed count data
UH: Hidden latent Gaussian parameters
Sigma: Observed bloc of the variance-covariance matrix
Omega: Observed bloc of the precision matrix
G: The generated conditional dependency structure
TC: The true clique of neighbors of the missing actors (a list if r>1)
H: Indexes corresponding to missing actors in the original data

Examples

n=100
p=10
r=1
type="scale-free"
data=generate_missing_data(n,p,r,type, plot=TRUE)
str(data)
#> List of 7
#>  $ Y    : int [1:100, 1:10] 8 14 2 6 9 7 2 13 9 16 ...
#>  $ UH   : num [1:100] -1.32 -1.019 2.041 -0.243 -1.548 ...
#>  $ Sigma: num [1:10, 1:10] 0.822 -0.748 -0.748 -0.748 0.342 ...
#>  $ Omega: num [1:11, 1:11] 4.15 7.19e-01 -5.07e-16 -2.87e-16 4.10e-18 ...
#>  $ G    : num [1:11, 1:11] 0 1 1 1 0 0 0 0 0 0 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : NULL
#>  $ TC   :List of 1
#>   ..$ : int [1:6] 1 5 6 7 8 10
#>  $ H    : int 1