sgdGMF (version 1.0)

sim.gmf.data: Simulate non-Gaussian data from a GMF model

Description

Simulate synthetic non-Gaussian data from a generalized matrix factorization (GMF) model.

Usage

sim.gmf.data(n = 100, m = 20, ncomp = 5, family = gaussian(), dispersion = 1)

Value

A list containing the following objects:

• Y: simulated response matrix

• U: simulated factor matrix

• V: simulated loading matrix

• eta: linear predictor matrix

• mu: conditional mean matrix

• phi: scalar dispersion parameter

• family: model family

• ncomp: rank of the latent matrix factorization

• param: a list containing time, phase, frequency and amplitude vectors used to generate U

Arguments

n

number of observations

m

number of variables

ncomp

rank of the latent matrix factorization

family

a glm family (see family for more details)

dispersion

a positive dispersion parameter

Details

The loadings, V, are independently sampled from a standard normal distribution. The scores, U, are simulated according to sinusoidal signals evaluated at different phases, frequencies and amplitudes. These parameters are randomly sampled from independent uniform distributions.

Examples

library(sgdGMF)

# Set the data dimensions
n = 100; m = 20; d = 5

# Generate data using Poisson, Binomial and Gamma models
data_pois = sim.gmf.data(n = n, m = m, ncomp = d, family = poisson())
data_bin = sim.gmf.data(n = n, m = m, ncomp = d, family = binomial())
data_gam = sim.gmf.data(n = n, m = m, ncomp = d, family = Gamma(link = "log"), dispersion = 0.25)

# Compare the results
oldpar = par(no.readonly = TRUE)
par(mfrow = c(3,3), mar = c(1,1,3,1))
image(data_pois$Y, axes = FALSE, main = expression(Y[Pois]))
image(data_pois$mu, axes = FALSE, main = expression(mu[Pois]))
image(data_pois$U, axes = FALSE, main = expression(U[Pois]))
image(data_bin$Y, axes = FALSE, main = expression(Y[Bin]))
image(data_bin$mu, axes = FALSE, main = expression(mu[Bin]))
image(data_bin$U, axes = FALSE, main = expression(U[Bin]))
image(data_gam$Y, axes = FALSE, main = expression(Y[Gam]))
image(data_gam$mu, axes = FALSE, main = expression(mu[Gam]))
image(data_gam$U, axes = FALSE, main = expression(U[Gam]))
par(oldpar)