rlsbclust: Simulate from LSBCLUST Model
In lsbclust: Least-Squares Bilinear Clustering for Three-Way Data

Simulate three-way arrays adhering to the LSBCLUST framework (see lsbclust).

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rlsbclust(ndata = 50L, nobs, size, nclust, clustsize = NULL,
  delta = rep(1L, 4L), ndim = 2L, alpha = 0.5, fixed = c("none",
  "rows", "columns"), err_sd = 1, svmins = 1, svmax = 6)

`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`)

## Nothing fixed, balanced classes
set.seed(1)
dat <- rlsbclust(ndata = 1, nobs = 100, size = c(10, 8), nclust = c(5, 4, 6, 5))
res <- lsbclust(data = dat[[1]]$data, nclust = c(5, 4, 6, 5))
cfsim(res, dat[[1]])

## Rows fixed, balanced classes
set.seed(2)
dat <- rlsbclust(ndata = 1, nobs = 100, size = c(10, 8), nclust = c(5, 4, 6, 5), 
                 fixed = "rows")
res <- lsbclust(data = dat[[1]]$data, nclust = c(5, 4, 6, 5), fixed = "rows")
cfsim(res, dat[[1]])

## Rows fixed, unbalanced classes
set.seed(3)
dat <- rlsbclust(ndata = 1, nobs = 100, size = c(10, 8), nclust = c(5, 4, 6, 5), 
                 fixed = "columns", 
                 clustsize = list(NULL, NULL, c(40, 25, 15, 10, 5, 5), c(40, 25, 15, 10, 10)))
res <- lsbclust(data = dat[[1]]$data, nclust = c(5, 4, 6, 5), fixed = "columns")
cfsim(res, dat[[1]])