mainVEM_Q {noisySBM}

main function of VEM algorithm with fixed number of SBM blocks

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()