Find initial cliques using blockmodels on the initial marginalized network

This function aims at clustering the marginal network, inferred from edges probabilities obtained with either nestorFit() or EMtree().

init_blockmodels(sigma_O, MO, SO, k = 3, alpha = 0.1, cores = 1)

Arguments

sigma_O	PLNmodels output: covariance matrix estimate.
MO	PLNmodels output: observed means estimate.
SO	PLNmodels output: observed marginal variances estimate.
k	Number of groups to find.
alpha	Tempering parameter.
cores	Number of cores for parallel computation (uses mclapply, not available for Windows).

Value

A list of cliques

Examples

data=generate_missing_data(n=100,p=10,r=1,type="scale-free", plot=TRUE)
data$TC
#> [[1]]
#> [1] 1 2 3 4 8
#> 
PLNfit<-norm_PLN(data$Y)
MO<-PLNfit$MO
SO<-PLNfit$SO
sigma_O=PLNfit$sigma_O
#-- initialize with blockmodels
init_blockmodels(sigma_O, MO, SO, k=2 )
#> 
#> 





#> $cliqueList
#> $cliqueList[[1]]
#> $cliqueList[[1]][[1]]
#> [1] 3
#> 
#> 
#> $cliqueList[[2]]
#> $cliqueList[[2]][[1]]
#> [1]  1  2  4  5  6  7  8  9 10
#> 
#> 
#>