data.simulation.factors: Simulates subspace clustering data with shared factors
In varclust: Variables Clustering

Description Usage Arguments Value Examples

Generating data for simulation with a low-rank subspace structure: variables are clustered and each cluster has a low-rank representation. Factors that span subspaces are shared between clusters.

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data.simulation.factors(n = 100, SNR = 1, K = 10, numb.vars = 30,
  numb.factors = 10, min.dim = 1, max.dim = 2, equal.dims = TRUE,
  separation.parameter = 0.1)

`n`	An integer, number of individuals.
`SNR`	A numeric, signal to noise ratio measured as variance of the variable, element of a subspace, to the variance of noise.
`K`	An integer, number of subspaces.
`numb.vars`	An integer, number of variables in each subspace.
`numb.factors`	An integer, number of factors from which subspaces basis will be drawn.
`min.dim`	An integer, minimal dimension of subspace .
`max.dim`	An integer, if equal.dims is TRUE then max.dim is dimension of each subspace. If equal.dims is FALSE then subspaces dimensions are drawn from uniform distribution on [min.dim,max.dim].
`equal.dims`	A boolean, if TRUE (value set by default) all clusters are of the same dimension.
`separation.parameter`	a numeric, coefficients of variables in each subspace basis are drawn from range [separation.parameter,1]

A list consisting of:

`X`	matrix, generated data
`signals`	matrix, data without noise
`factors`	matrix, columns of which span subspaces
`indices`	list of vectors, indices of factors that span subspaces
`dims`	vector, dimensions of subspaces
`s`	vector, true partiton of variables

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sim.data <- data.simulation.factors()
sim.data2 <- data.simulation.factors(n = 30, SNR = 2, K = 5, numb.vars = 20,
             numb.factors = 10, max.dim = 3, equal.dims = FALSE, separation.parameter = 0.2)