control-parameters: Set control parameters for various purposes

Description Usage Arguments Details Value See Also Examples

Description

Set control parameters for the EM algorithm for latent class model estimation, multinomial logistic regression estimation in the regression step, and genetic algorithm for variable selection procedure.

Usage

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controlLCA(maxiter = 1e05, tol = 1e-04, nrep = 5)

controlReg(maxiter = 5000, tol = 1e-05)

controlGA(popSize = 20, maxiter = 100, run = maxiter/2,
          pcrossover = 0.8, pmutation = 0.2,
          elitism = base::max(1, round(popSize*0.05)))

Arguments

maxiter

Maximum number of iterations in the EM algorithm, the multinomial logistic regression and the genetic algorithm.

tol

Tolerance value for judging when convergence has been reached. Used in the EM algorithm and the multinomial logistic regression.

nrep

Number of times to estimate the latent class analysis model, using different starting values for the matrix z of a posteriori probabilities. Each time, a different random initialization is used in order to search for the global maximum of the log-likelihood.

popSize

Population size. This number corresponds to the number of different models to be considered at each iteration of the genetic algorithm.

run

Number of consecutive generations without any improvement in the best fitness value of the variable selection procedure before the genetic algorithm is stopped.

pcrossover

Probability of crossover between pairs of models.

pmutation

Probability of mutation in a parent model.

elitism

Number of best fitness models to survive at each iteration of the genetic algorithm in the variable selection procedure.

Details

Function controlLCA is used to set control parameters of the EM algorithm employed to estimate the latent class analysis model.

Function controlReg controls tolerance and maximum number of iterations in the estimation of the multinomial logistic regression. This regression is used to model the conditional distribution of a proposed variable given the current set of clustering variables in the variable selection procedure.

Function controlGA sets parameters of the genetic algorithm used for variable selection.

Value

A list of parameters values.

See Also

poLCA, ga

Examples

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data(carcinoma, package = "poLCA")
# increase number of replicates and decrease tolerance value
fit <- fitLCA(carcinoma, ctrlLCA = controlLCA(nrep = 10, tol = 1e-07))

michaelfop/LCAvarsel documentation built on Nov. 30, 2017, 6:31 p.m.