mainVEM_Q: main function of VEM algorithm with fixed number of SBM...

In noisySBM: Noisy Stochastic Block Mode: Graph Inference by Multiple Testing

Description

main function of VEM algorithm with fixed number of SBM blocks

Usage

mainVEM_Q(init, modelFamily, model, data, directed)

Arguments

init	list of initial points for the algorithm
modelFamily	probability distribution for the edges. Possible values: Gauss, Gamma
model	Implemented models: Gauss all Gaussian parameters of the null and the alternative distributions are unknown ; this is the Gaussian model with maximum number of unknown parameters Gauss0 compared to Gauss, the mean of the null distribution is set to 0 Gauss01 compared to Gauss, the null distribution is set to N(0,1) GaussEqVar compared to Gauss, all Gaussian variances (of both the null and the alternative) are supposed to be equal, but unknown Gauss0EqVar compared to GaussEqVar, the mean of the null distribution is set to 0 Gauss0Var1 compared to Gauss, all Gaussian variances are set to 1 and the null distribution is set to N(0,1) Gauss2distr the alternative distribution is a single Gaussian distribution, i.e. the block memberships of the nodes do not influence on the alternative distribution GaussAffil compared to Gauss, for the alternative distribution, there's a distribution for inter-group and another for intra-group interactions Exp the null and the alternatives are all exponential distributions (i.e. Gamma distributions with shape parameter equal to one) with unknown scale parameters ExpGamma the null distribution is an unknown exponential, the alterantive distribution are Gamma distributions with unknown parameters
data	data vector in the undirected model, data matrix in the directed model
directed	booelan to indicate whether the model is directed or undirected

Value

list of estimated model parameters and a node clustering; like the output of fitNSBM()

noisySBM documentation built on Dec. 16, 2020, 5:09 p.m.