simulate: Simulate data and statistical models.
In jeff-hughes/simulateR: Test Statistical Models Through Simulation

Description Usage Arguments Value See Also Examples

simulate simulates data by generating samples according to a user- defined function. These data can then be used to examine whatever statistic you wish. This function is intentionally kept general and flexible, to allow for a wide variety of applications.

simulate(func, params = NULL, n.sims = 5000, output = c("list",
  "dataframe", "vector"), boot = FALSE, bootParams = NULL,
  parallel = c("no", "multicore", "snow"), ncpus = 1, cl = NULL,
  beep = NULL, ...)

`func`	A user-defined function that generates data and performs a statistical test on that data.
`params`	A list of parameters to be passed to `func`. Each of these parameters can be a vector of multiple values, and `simulate` will test each combination of these values.
`n.sims`	Number of simulations (per combination of params).
`output`	Specifies how the function provides the ultimate results of the simulations: can return a "list", a "dataframe", or a "vector". Note that the output from the supplied function must be able to be coerced into this output type.
`boot`	Whether or not to use bootstrapped data to pass along to your simulation.
`bootParams`	If `boot=TRUE`, then use `bootParams` to pass along a named list of arguments to the `boot` function. The statistic and R parameters will be filled automatically, but at minimum you will need to pass along data. Information about parallel processing will also be passed along automatically.
`parallel`	The type of parallel operation to be used (if any).
`ncpus`	Integer: the number of processes to be used in parallel operation.
`cl`	An optional `parallel` or `snow` cluster for use if `parallel = 'snow'`. If not supplied, a cluster on the local machine is created for the duration of the simulations.
`beep`	Include a numeric value or character vector indicating the sound you wish to play once the simulation is done running. Requires the "beepr" package, and information about supported values is available in the documentation for that package.
`...`	Additional arguments to be passed to `func`. If you do not need to vary certain parameters in your model, you can pass them to `func` here.

Returns a list (by default) with one element per simulation. If output is specified as "dataframe", then func must return a (named) vector with the results you wish to capture; if output is specified as "vector", then func must return a one-element vector.

boot

lm_test <- function(N, b0, b1) {
x <- rnorm(N, 0, 1)
y <- rnorm(N, b0 + b1*x, sqrt(1 - b1^2))
data <- data.frame(y, x)
model <- lm(y ~ x, data)

# capture output from model summary
est <- coef(summary(model))['x', 'Estimate']
se <- coef(summary(model))['x', 'Std. Error']
p <- coef(summary(model))['x', 'Pr(>|t|)']

return(c(xm=mean(x), xsd=sd(x), ym=mean(y), ysd=sd(y), est=est, se=se, p=p,
    sig=est > 0 & p <= .05))
}

# test power for two sample sizes: N=200 and N=300, with 5000 sims for each
power_sim <- simulate(lm_test, params=list(N=c(200, 300)), n.sims=5000, b0=0, b1=.15)