blockcluster (version 3.0.2)

cocluststrategy: Strategy function

Description

This function is used to set all the parameters for Co-clustering. It returns an object of class strategy which can be given as input to cocluster function.

Usage

cocluststrategy(algo = "BEM", initmethod = character(),
    stopcriteria = "Parameter", nbiterationsxem = 50,
    nbiterationsXEM = 500, nbinititerations = 10,
    initepsilon = 0.01, nbiterations_int = 5,
    epsilon_int = 0.01, epsilonxem = 1e-04,
    epsilonXEM = 1e-10, nbtry = 2, nbxem = 5,
    bayesianform = FALSE, hyperparam = numeric(0))

Arguments

algo

The valid values for this parameter are "BEM" (Default), "BCEM" and "BSEM".

stopcriteria

It specifies the stopping criteria. It can be based on either relative change in parameters (preffered due to computation reasons) value or relative change in pseudo log-likelihood. Valid criterion values are "Parameter" and "Likelihood". Default

initmethod

Method to initialize model parameters. The valid values are "CEMInit", "FuzzyCEMInit" and "RandomInit". For now only one kind of initialization exist for every model currently available in the package. Hence default value for initialization is set

nbinititerations

Number of Global iterations used in initialization step. Default value is 10.

initepsilon

Tolerance value used while initialization. Default value is 1e-2.

nbiterations_int

Number of iterations for internal E step. Default value is 5.

epsilon_int

Tolerance value for relative change in Parameter/likelihood for internal E-step. Default value is 1e-2.

nbtry

Number of tries (XEM steps). Default value is 2.

nbxem

Number of xem steps. Default value is 5.

nbiterationsxem

Number of EM iterations used during xem step. Default value is 50.

nbiterationsXEM

Number of EM iterations used during XEM step. Default value is 500.

epsilonxem

Tolerance value used during xem step. Default value is 1e-4.

epsilonXEM

Tolerance value used during XEM step. Default value is 1e-10

bayesianform

Boolean parameter to indicate whether to run algorithms in bayesian settings or not.Default value is false.

hyperparam

Hyper-parameters ("a" and "b") in case of Bayesian settings.

Value

• Object of class strategy

Examples

#Default strategy values

strategy<-cocluststrategy()
summary(strategy)