run_test: Run a function iteratively, with options for parallel...
In jeff-hughes/paramtest: Run a Function Iteratively While Varying Parameters

Description Usage Arguments Value See Also Examples

run_test runs a user-defined function iteratively. This function is intentionally kept general and flexible, to allow for a wide variety of applications. This function is the general-purpose function called by functions such as grid_search and random_search, which provide different methods for generating the parameters to be tested.

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run_test(func, params = NULL, n.iter = 1, output = c("list",
  "data.frame"), boot = FALSE, bootParams = NULL, parallel = c("no",
  "multicore", "snow"), ncpus = 1, cl = NULL, beep = NULL, ...)

`func`	A user-defined function. The first argument to this function will be the iteration number.
`params`	A list or data frame of parameters to be passed to `func`. Each set of parameters will be passed to `func` in turn.
`n.iter`	Number of iterations (per set of params).
`output`	Specifies how `run_test` provides the ultimate output from `func`: can return a "list" or a "data.frame". Note that if "data.frame" is specified, the supplied function must return a vector, matrix, or data frame, so it can be coerced into the data frame format. The "list" option will accept any type of output.
`boot`	Whether or not to use bootstrapped data to pass along to `func`. Using this option instead of bootstrapping within `func` is preferable to take advantage of parallelization.
`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 iterations.
`beep`	Include a numeric value or character vector indicating the sound you wish to play once the tests are done running. If set to TRUE, a random sound will be played. 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 iteration. If output is specified as "data.frame", then func must return a (named) vector with the results you wish to capture.

boot

lm_test <- function(iter, 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 sample size N=200 and N=300, with 500 iterations for each
power_sim <- run_test(lm_test, params=data.frame(N=c(200, 300)),
    n.iter=500, b0=0, b1=.15)