# 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

 `1` ```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 <[email protected]>

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```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 29, 2017, 7:14 p.m.