IMIFA (version 1.3.1)

sim_IMIFA_data: Simulating Data from a Mixture of Factor Analysers Structure

Description

Function to simulate data of any size and dimension from a mixture of (infinite) factor analysers structure.

Usage

sim_IMIFA_data(N = 300L, G = 3L, P = 50L, Q = rep(floor(log(P)), G),
  pis = rep(1/G, G), psi = NULL, nn = NULL, loc.diff = 1L,
  method = c("conditional", "marginal"))

Arguments

N

Desired overall number of observations in the simulated data set - a single integer.

G

Desired number of clusters in the simulated data set - a single integer.

P

Desired number of variables in the simulated dataset - a single integer.

Q

Desired number of cluster-specific latent factors in the simulated data set. Can be specified either as a single integer if all clusters are to have the same number of factors, or a vector of length G. Defaults to floor(log(P)) in each group.

pis

Mixing proportions of the clusters in the dataset if G > 1. Must sum to 1. Defaults to rep(1/G, G).

psi

True values of uniqueness parameters, either as a single value, a vector of length G, a vector of length P, or a G * P matrix: as such the user can specify uniquenesses as a diagonal or isotropic matrix, and further constrain uniquenesses across groups if desired. If psi is missing, uniquenesses are simulated via rgamma(P, 1, 1) within each group.

nn

An alternative way to specify the size of each cluster, by giving the exact number of observations in each group explicitly. Must sum to N.

loc.diff

A parameter to control the closeness of the clusters in terms of the difference in their location vectors. Defaults to 1.

method

A switch indicating whether the mixture to be simulated from is the conditional distribution of the data given the latent variables (default), or simply the marginal distribution of the data.

Value

Invisibly returns a data.frame with N observations (rows) of P variables (columns). The true values of the parameters which generated these data are also stored.

See Also

The function mcmc_IMIFA for fitting an IMIFA related model to the simulated data set.

Examples

# NOT RUN {
# Simulate 100 observations from 3 balanced groups with cluster-specific numbers of latent factors
# Specify isotropic uniquenesses within each cluster
sim_data <- sim_IMIFA_data(N=100, G=3, P=20, Q=c(2, 2, 5), psi=1:3)
names(attributes(sim_data))
labels   <- attr(sim_data, "Labels")

# Visualise the data in two-dimensions
plot(cmdscale(dist(sim_data), k=2), col=labels)

# Fit a MIFA model to this data
# tmp      <- mcmc_IMIFA(sim_data, method="MIFA", range.G=3, n.iters=5000)
# }