simulate_gvm: Simulate a Gamma Variability Model

In varian: Variability Analysis in R

Description

This function facilitates simulation of a Gamma Variability Model and allows the number of units and repeated measures to be varied as well as the degree of variability.

Usage

simulate_gvm(n, k, mu, mu.sigma, sigma.shape, sigma.rate, seed = 5346)

Arguments

n	The number of repeated measures on each unit
k	The number of units
mu	The grand mean of the variable
mu.sigma	The standard deviation of the random mean of the variable
sigma.shape	the shape (alpha) parameter of the Gamma distribution controlling the residual variability
sigma.rate	the rate (beta) parameter of the Gamma distribution controlling the residual variability
seed	the random seed, used to make simulations reproductible. Defaults to 5346 (arbitrarily).

Value

a list of the data, IDs, and the parameters used for the simulation

Author(s)

Joshua F. Wiley <[e-mail]>

Examples

raw.sim <- simulate_gvm(12, 140, 0, 1, 4, .1, 94367)
sim.data <- with(raw.sim, {
  set.seed(265393)
  x2 <- MASS::mvrnorm(k, c(0, 0), matrix(c(1, .3, .3, 1), 2))
  y2 <- rnorm(k, cbind(Int = 1, x2) %*% matrix(c(3, .5, .7)) + sigma, sd = 3)
  data.frame(
    y = Data$y,
    y2 = y2[Data$ID2],
    x1 = x2[Data$ID2, 1],
    x2 = x2[Data$ID2, 2],
    ID = Data$ID2)
})

varian documentation built on May 2, 2019, 6:09 a.m.