Define cells for a means-based unbalanced ANOVA design

Creates the complete cell table used by power_unbalanced(). Each cell is defined by its factor levels, sample size (n), and population mean (m). End each cell after both reserved values have been supplied. The common population standard deviation belongs in unbalanced_covariance().

Usage

cell_design(..., within = NULL, default_n = NULL, default_m = NULL)

Arguments

...

Repeated named cell definitions. Each cell must contain the same factor names in the same order, plus n and m. Every factor must have at least 2 observed levels, and every combination of factor levels must appear exactly once (or be filled automatically; see default_n).

within

Character vector naming factors in ... that are measured within subjects, or NULL for a purely between-subject design. Stored on the returned object and read by power_unbalanced(). Within-cell names used by unbalanced_covariance() join level values with _; these names must be unique, and within-factor levels must not contain :.

default_n, default_m

Optional scalars used to fill any missing cells in the complete factorial design. Supply both to auto-fill missing cells with these values; supply none to require every cell to be defined explicitly (the default). Supplying only one is an error. When cells are auto-filled, a message reports their count and exact factor-level combinations so that unintended levels can be spotted.

Value

An anovapowersim_cell_design tibble with one row per design cell.

Details

The m values are literal population cell means: their magnitudes and all effects they contain are used as supplied. This differs from means_pattern(), whose values specify only a relative shape that balanced simulation functions project onto the tested term, normalize, and rescale to target_pes.

Lifecycle

cell_design() is experimental and is available only in the development version of anovapowersim. Its API may change.

See also

means_pattern() for shape-only patterns used by balanced simulation functions.

Examples

design <- cell_design(
  group = "control", time = "pre",  n = 22, m = 10.0,
  group = "control", time = "post", n = 22, m = 11.0,
  group = "treatment", time = "pre",  n = 31, m = 10.1,
  group = "treatment", time = "post", n = 31, m = 12.4,
  within = "time"
)
design
#> # A tibble: 4 × 4
#>   group     time      n     m
#>   <chr>     <chr> <int> <dbl>
#> 1 control   pre      22  10  
#> 2 control   post     22  11  
#> 3 treatment pre      31  10.1
#> 4 treatment post     31  12.4