sim_lsbclust: Simulate and Analyze LSBCLUST

In lsbclust: Least-Squares Bilinear Clustering for Three-Way Data

Description

Perform a single simulation run for the LSBCLUST model. Multiple data sets are generated for a single set of underlying parameters,

Usage

sim_lsbclust(ndata, nobs, size, nclust, clustsize = NULL,
  delta = rep(1L, 4L), ndim = 2L, alpha = 0.5, fixed = c("none",
  "rows", "columns"), err_sd = 1, svmins = 0.5, svmax = 5,
  seed = NULL, parallel = FALSE, parallel_data = TRUE, verbose = 0,
  nstart_T3 = 20L, nstart_ak = 20L, mc.cores = detectCores() - 1,
  include_fits = FALSE, include_data = FALSE, nstart, nstart.kmeans)

Arguments

ndata	Integer giving the number of data sets to generate with the same underlying parameters.
nobs	Integer giving the number of observations to sample.
size	Vector with two elements giving the number of rows and columns respectively of each simulated observation.
nclust	A vector of length four giving the number of clusters for the overall mean, the row margins, the column margins and the interactions (in that order) respectively. Alternatively, a vector of length one, in which case all components will have the same number of clusters.
clustsize	A list of length four, with each element containing a vector of the same length as the corresponding entry in nclust, indicating the number of elements to contribute to each sample. Naturally, each of these vectors must sum to nobs, or an error will result. Positional matching are used, in the order "overall", "rows", "columns" and "interactions". If NULL, all clusters will be of equal size.
delta	A four-element binary vector (logical or numeric) indicating which sum-to-zero constraints must be enforced.
ndim	The required rank for the approximation of the interactions (a scalar).
alpha	Numeric value in [0, 1] which determines how the singular values are distributed between rows and columns (passed to int.lsbclust).
fixed	One of "none", "rows" or "columns" indicating whether to fix neither sets of coordinates, or whether to fix the row or column coordinates across clusters respectively. If a vector is supplied, only the first element will be used (passed to int.lsbclust).
err_sd	The standard deviation of the error distribution, as passed to rnorm
svmins	Vector of minimum values for the singular values (as passed to simsv). Optionally, if all minima are equal, a single numeric value which will be expanded to the correct length.
svmax	The maximum possible singular value (as passed to simsv)
seed	An optional seed to be set for the random number generator
parallel	Logical indicating whether to parallelize over random starts. Note that parallel_data has precedence over this
parallel_data	Logical indicating whether to parallelize over the data sets. If FALSE, parallelization is done over random starts (depending on parallel).
verbose	Integer giving the number of iterations after which the loss values is printed.
nstart_T3	The number of random starts to use for T3Clusf
nstart_ak	The number of random starts to use for akmeans
mc.cores	The number of cores to use, passed to makeCluster
include_fits	Logical indicating whether to include the model fits, or or only the fit statistics
include_data	Logical indicating whether to include the simulated data fitted on, or only the results
nstart	From lsbclust
nstart.kmeans	From lsbclust

Examples

set.seed(1)
res <- sim_lsbclust(ndata = 5, nobs = 100, size = c(10, 8), nclust = rep(5, 4), 
                    verbose = 0, nstart_T3 = 2, nstart_ak = 1, parallel_data = FALSE,
                    nstart = 2, nstart.kmeans = 5 )

lsbclust documentation built on May 1, 2019, 10:27 p.m.