Solve a power curve for the sample size that reaches a target power

The sample-size case of solve_curve(), and the number a power analysis is usually run to obtain. Takes the curve a sweep over sample size has produced and returns the size at which power reaches target, rounded up to a whole number of units alongside the exact solution.

Usage

target_n(curve, target = 0.8, ...)

Arguments

curve: A power curve, as returned by power_curve_mixed() or by sweep_spec() over units$subject$n.
target: The power to reach. Defaults to 0.8, the convention this package's plots draw a line at.
...: Further arguments passed to solve_curve(), such as effect to pick one focal effect out of a curve holding several, or level for the interval.

Value

The list solve_curve() returns, with n, n_lo and n_hi added: value, lo and hi rounded up to whole numbers.

Details

Everything solve_curve() does applies here, including its refusals: a curve that never reaches the target within the sizes it swept is refused outright, and the reported interval can extend past the largest size simulated, which means the sweep was too narrow to settle the question.

The whole-number fields round up rather than to nearest, because a design cannot recruit a fraction of a subject and rounding down would leave the study short of the target it was sized for.

Examples

spec <- build_spec(list(name = "s", seed = 1, design_kind = "between", n_subject = 40,
  factor_name = "group", lev1 = "a", lev2 = "b", intercept = 0, effect = 0.7,
  family = "gaussian", resp_name = "score", sigma = 1))
# n_sims is small so the example runs quickly. Use 200 or more for real planning.
curve <- sweep_spec(spec, "units$subject$n", c(20, 40, 60, 80), power_design, n_sims = 50)
solved <- target_n(curve)
unlist(solved[c("n", "n_lo", "n_hi")])
#>    n n_lo n_hi 
#>   63   53   74