# Run a function iteratively using a random search approach for parameter values, with options for parallel processing. `random_search` runs a user-defined function iteratively. Lower and upper bounds for parameter values can be given to `random_search`, which will then (uniformly) randomly select values within those bounds on each iteration. ```r random_search(func, params = NULL, n.sample = 1, n.iter = 1, output = c("list", "data.frame"), boot = FALSE, bootParams = NULL, parallel = c("no", "multicore", "snow"), ncpus = 1, cl = NULL, beep = NULL, ...) ``` ## Arguments - `func`: A user-defined function. The first argument to this function will be the iteration number. - `params`: A named list of parameters to be passed to `func`. For continuous numeric values, a parameter must provide a two-element named vector with names "lower" and "upper" to specify the lower and upper bounds within which to sample. For parameters with integer values, provide a sequence, e.g., `seq(5, 10)`. For parameters with non-numeric values, provide a vector with the values from which to sample. On each iteration, the `random_search` function will select a uniformly random value for each parameter and pass this set of parameter values to `func`. - `n.sample`: Number of times to sample from the parameter values. - `n.iter`: Number of iterations (per set of params). - `output`: Specifies how `random_search` 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. 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 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. ## Examples ```r 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 sizes between N=200 and N=300, with 500 iterations total power_sim <- random_search(lm_test, params=list(N=c(200, 300)), n.iter=500, b0=0, b1=.15) ``` ## See Also `boot`

random_search function