paramtest (version 0.1.0)

random_search: Run a function iteratively using a random search approach for parameter values, with options for parallel processing.

Description

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.

Usage

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.

Value

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.

See Also

boot

Examples

# NOT RUN {
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)
# }