Build a grid of fixed-effect coefficient sets

Produce the conditions needed to sweep an effect size with sweep_spec(), including a condition in which every named effect is zero, so that the same run shows both what a design detects and how often it declares something when there is nothing to find.

Usage

design_conditions(..., .null = TRUE, .base = NULL)

Arguments

...

Named numeric vectors, one per coefficient to vary.

.null

Whether to prepend a condition with every named effect set to zero. TRUE by default.

.base

Optional named list of coefficients to include in every condition, the null condition among them. sweep_spec() already keeps the coefficients a condition does not name. .base is therefore needed only to hold a coefficient at a value other than the specification's, or for a condition used outside sweep_spec(), such as one assigned to fixed$coefficients by hand.

Value

A list of class pilotr_conditions holding one named list of coefficients per condition. The class is how sweep_spec() recognises conditions to merge.

Details

Named arguments give the values each effect should take, and are recycled to a common length, so design_conditions(cond = c(0.02, 0.05), age = 0.1) produces two conditions, both with age at 0.1. The all-zero condition comes first and is shared, since the Type I error rate is a property of the design rather than of any one effect size.

sweep_spec() merges each condition into the specification's own coefficients and keeps their order, so any coefficient not named here keeps its value and a sweep varies only the effects it names. It refuses a condition that names a coefficient the specification lacks. Subsetting the result with [ keeps its class, and so does c() when the result comes first. The three conditions of c(design_conditions(cond = 0.02), design_conditions(age = 0.1, .null = FALSE)) are therefore merged in the same way. A list assembled by other means replaces the coefficients wholesale.

See also

sweep_spec(), which consumes this.

Examples

design_conditions(effect = c(0.03, 0.06))
#> [[1]]
#> [[1]]$effect
#> [1] 0
#> 
#> 
#> [[2]]
#> [[2]]$effect
#> [1] 0.03
#> 
#> 
#> [[3]]
#> [[3]]$effect
#> [1] 0.06
#> 
#> 
design_conditions(cond = c(0.02, 0.05), age = 0.1, .null = FALSE)
#> [[1]]
#> [[1]]$cond
#> [1] 0.02
#> 
#> [[1]]$age
#> [1] 0.1
#> 
#> 
#> [[2]]
#> [[2]]$cond
#> [1] 0.05
#> 
#> [[2]]$age
#> [1] 0.1
#> 
#>