lsbclust (version 1.1)

rlsbclust: Simulate from LSBCLUST Model

Description

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

Usage

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)

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)

Examples

# NOT RUN {
## 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]])
# }