clustMDparallel {clustMD}

Run multiple clustMD models in parallel

Description

This function allows the user to run multiple clustMD models in parallel. The inputs are similar to clustMD() except G is now a vector containing the the numbers of components the user would like to fit and models is a vector of strings indicating the covariance models the user would like to fit for each element of G. The user can specify the number of cores to be used or let the function detect the number available.

Usage

clustMDparallel(X, CnsIndx, OrdIndx, G, models, Nnorms, MaxIter, store.params,
  scale, startCL = "hc_mclust", Ncores = NULL, autoStop = FALSE,
  ma.band = 50, stop.tol = NA)

Arguments

X

a data matrix where the variables are ordered so that the continuous variables come first, the binary (coded 1 and 2) and ordinal variables (coded 1, 2,...) come second and the nominal variables (coded 1, 2,...) are in last position.

CnsIndx

the number of continuous variables in the data set.

OrdIndx

the sum of the number of continuous, binary and ordinal variables in the data set.

G

a vector containing the numbers of mixture components to be fitted.

models

a vector of strings indicating which clustMD models are to be fitted. This may be one of: EII, VII, EEI, VEI, EVI, VVI or BD.

Nnorms

the number of Monte Carlo samples to be used for the intractable E-step in the presence of nominal data.

MaxIter

the maximum number of iterations for which the (MC)EM algorithm should run.

store.params

a logical variable indicating if the parameter estimates at each iteration should be saved and returned by the clustMD function.

scale

a logical variable indicating if the continuous variables should be standardised.

startCL

a string indicating which clustering method should be used to initialise the (MC)EM algorithm. This may be one of "kmeans" (K means clustering), "hclust" (hierarchical clustering), "mclust" (finite mixture of Gaussian distributions), "hc_mclust" (model-based hierarchical clustering) or "random" (random cluster allocation).

Ncores

the number of cores the user would like to use. Must be less than or equal to the number of cores available.

autoStop

a logical argument indicating whether the (MC)EM algorithm should use a stopping criterion to decide if convergence has been reached. Otherwise the algorithm will run for MaxIter iterations.

If only continuous variables are present the algorithm will use Aitken's acceleration criterion with tolerance stop.tol.

If categorical variables are present, the stopping criterion is based on a moving average of the approximated log likelihood values. let $t$ denote the current interation. The average of the ma.band most recent approximated log likelihood values is compared to the average of another ma.band iterations with a lag of 10 iterations. If this difference is less than the tolerance the algorithm will be said to have converged.

ma.band

the number of iterations to be included in the moving average stopping criterion.

stop.tol

the tolerance of the (MC)EM stopping criterion.

Value

An object of class clustMDparallel is returned. The output components are as follows:

BICarray

A matrix indicating the estimated BIC values for each of the models fitted.

results

A list containing the output for each of the models fitted. Each entry of this list is a clustMD object. If the algorithm failed to fit a particular model, the corresponding entry of results will be NULL.

References

McParland, D. and Gormley, I.C. (2016). Model based clustering for mixed data: clustMD. Advances in Data Analysis and Classification, 10 (2):155-169.

See Also

clustMD

Examples

    data(Byar)

    # Transformation skewed variables
    Byar$Size.of.primary.tumour <- sqrt(Byar$Size.of.primary.tumour)
    Byar$Serum.prostatic.acid.phosphatase <- 
    log(Byar$Serum.prostatic.acid.phosphatase)

    # Order variables (Continuous, ordinal, nominal)
    Y <- as.matrix(Byar[, c(1, 2, 5, 6, 8, 9, 10, 11, 3, 4, 12, 7)])

    # Start categorical variables at 1 rather than 0
    Y[, 9:12] <- Y[, 9:12] + 1

    # Standardise continuous variables
    Y[, 1:8] <- scale(Y[, 1:8])

    # Merge categories of EKG variable for efficiency
    Yekg <- rep(NA, nrow(Y))
    Yekg[Y[,12]==1] <- 1
    Yekg[(Y[,12]==2)|(Y[,12]==3)|(Y[,12]==4)] <- 2
    Yekg[(Y[,12]==5)|(Y[,12]==6)|(Y[,12]==7)] <- 3
    Y[, 12] <- Yekg

    ## Not run: 
    res <- clustMDparallel(X = Y, G = 1:3, CnsIndx = 8, OrdIndx = 11, Nnorms = 20000,
    MaxIter = 500, models = c("EVI", "EII", "VII"), store.params = FALSE, scale = TRUE, 
    startCL = "kmeans", autoStop= TRUE, ma.band=30, stop.tol=0.0001)
  
    res$BICarray

## End(Not run)