vary_element: Vary sample size for simulations

In aosmith16/simpow: Simulation-based power analysis

Description

This is a convenience function that allows the user to vary one parameter or design element and perform a simulated power analysis for each provided value. All other parameters/design elements will be held fixed for all simulations.

Usage

vary_element(simfun = "lmm_f", tovary = "nrep", values, ...)

Arguments

simfun	Character. The name of the function you want to use for the simulations. Currently can only be "lmm_f" (the default) or "lm_2f".
tovary	Character. The name of the argument for the study design element you'd like to allow to vary in the function you defined in simfun. At the moment only a single element can vary at any one time. Currently defaults to "nrep".
values	Numeric vector or list of vectors. The values you want to use for the tovary argument. If tovary takes a single value per simulation, provide a numeric vector. If tovary takes a vector, provide a list of vectors. A separate power analysis done for each provided value or set of values in the vector or list.
...	Other arguments to the function you defined in simfun.

Details

This function is a simple wrapper around a loop to run through the values from your chosen parameter/design element and repeat the power analysis for each one. This is not a particularly efficient function, and you may want to use a different approach if exploring the power for a large number of values.

Value

A data.frame with a row for each simulation done containing the estimated power along with the study design information and true treatment means your defined.

See Also

lmm_f() and lm_2f() for the function arguments you'll need to define in ....

Examples

# Here's an example to explore the change in power
    # for 5 vs 15 blocks in lmm_f()
vary_element(tovary = "nblock",
          values = c(5, 15),
          nsim = 10,
          trtmeans = c(1, 2),
          sd_block = 2,
          sd_resid = 2)

# Allow for more reps per treatment group
    # in each block
vary_element(tovary = "nrep",
          values = c(6, 10),
          nsim = 10,
          trtmeans = c(1, 2),
          sd_block = 2,
          sd_resid = 2)

# Vary effect size by providing different treatment means
vary_element(tovary = "trtmeans",
          values = list(c(5, 15), c(5, 5)),
          nsim = 10,
          nblock = 5,
          sd_block = 2,
          sd_resid = 2)

# Here is an example varying
    # the overall residual standard deviations
vary_element(tovary = "sd_resid",
          values = c(2, 10),
          nsim = 20,
          trtmeans = c(1, 2),
          nblock = 15,
          sd_block = 2)

aosmith16/simpow documentation built on Dec. 19, 2021, 3:41 a.m.