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Calculate ANOVA effect-size sensitivity at a fixed sample size

Source: R/power_effect_calc.R
power_sensitivity_calc.Rd

Calculation-only counterpart to power_sensitivity(). The function searches for the minimum partial eta squared that reaches target power using calculated noncentral-F power, without simulating data or fitting ANOVA models.

Usage

power_sensitivity_calc(
  between = NULL,
  within = NULL,
  term,
  n,
  power = 0.9,
  alpha = 0.05,
  pes_min = 1e-06,
  pes_max = 0.99,
  pes_tol = 0.001,
  gpower = FALSE,
  epsilon = 1
)

Arguments

between

Named integer vector of between-subject factor level counts, e.g. c(group = 2). Use NULL for no between-subject factors.

within

Named integer vector of within-subject factor level counts, e.g. c(time = 3, condition = 4). Use NULL for no within-subject factors.

term

Character scalar naming the ANOVA term to test, e.g. "group:time". Interaction terms are order-insensitive; "time:group" resolves to "group:time" when that is the design's factor order.

n

Number of subjects per between-subject cell. For a purely within-subject design, this is the total sample size.

power

Desired target power.

alpha

Significance threshold.

pes_min

Lower bound of the partial eta-squared search interval.

pes_max

Upper bound of the partial eta-squared search interval.

pes_tol

Maximum width of the final calculated partial eta-squared bracket.

gpower

Logical; if TRUE, use the GPower-style noncentrality convention lambda = total_n * f^2. The default FALSE uses lambda = den_df * f^2. GPower's estimates can differ from target_pes, especially for small samples or terms with more degrees of freedom; a warning is issued when gpower = TRUE. The default gpower = FALSE is recommended.

epsilon

Population nonsphericity correction for the within-subject component of term. Must lie between the theoretical lower bound 1 / within_term_df and 1. The default 1 assumes sphericity. Values below 1 multiply the numerator degrees of freedom, denominator degrees of freedom, and noncentrality parameter. Nonsphericity corrections do not apply to purely between-subject terms.

Value

An anovapowersim_sensitivity object. $pes_needed is the calculated upper effect-size bracket, or NA when pes_max does not achieve target power. $results contains every effect size evaluated by the calculated-power search; simulation-specific result columns are always NA.

Lifecycle

[Experimental]

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

Examples

power_sensitivity_calc(
  between = c(group = 2),
  within = c(time = 3),
  term = "group:time",
  n = 30,
  power = 0.90,
  pes_tol = 0.001,
  gpower = TRUE,
  epsilon = 0.80
)
#> Warning: `gpower = TRUE` calibrates means to G*Power's noncentrality convention, so the partial eta squared actually achieved can differ from `target_pes` -- this is more pronounced for small samples and terms with more degrees of freedom. The default `gpower = FALSE` is recommended if you want `target_pes` to match your reported or expected partial eta squared exactly.
#> <anovapowersim_sensitivity>
#>   term:             'group:time'
#>   fixed n per cell: 30
#>   fixed total N:    60
#>   target power:     0.900
#>   alpha:            0.05
#>   detectable pes:   0.203995
#>   search interval:  [1e-06, 0.99]
#>   requested width:  0.001
#>   calculated points: 12
#>   final width:      0.0009667959
#>   converged:        yes
#>   calculation:      calculated power only
#>   G*Power convention: TRUE
#>   epsilon:          0.8
#> 
#>  target_pes n_per_cell total_n n_sims valid_sims failed_sims epsilon num_df
#>    0.000001         30      60     NA         NA          NA     0.8    1.6
#>    0.123751         30      60     NA         NA          NA     0.8    1.6
#>    0.185626         30      60     NA         NA          NA     0.8    1.6
#>    0.201095         30      60     NA         NA          NA     0.8    1.6
#>    0.203028         30      60     NA         NA          NA     0.8    1.6
#>    0.203995         30      60     NA         NA          NA     0.8    1.6
#>    0.204962         30      60     NA         NA          NA     0.8    1.6
#>    0.208829         30      60     NA         NA          NA     0.8    1.6
#>    0.216563         30      60     NA         NA          NA     0.8    1.6
#>    0.247501         30      60     NA         NA          NA     0.8    1.6
#>    0.495000         30      60     NA         NA          NA     0.8    1.6
#>    0.990000         30      60     NA         NA          NA     0.8    1.6
#>  den_df      ncp power_calc power_sim
#>    92.8    0.000      0.050      <NA>
#>    92.8    6.779      0.662      <NA>
#>    92.8   10.941      0.863      <NA>
#>    92.8   12.082      0.896      <NA>
#>    92.8   12.228      0.899      <NA>
#>    92.8   12.301      0.901      <NA>
#>    92.8   12.374      0.903      <NA>
#>    92.8   12.670      0.910      <NA>
#>    92.8   13.269      0.922      <NA>
#>    92.8   15.787      0.959      <NA>
#>    92.8   47.050      1.000      <NA>
#>    92.8 4752.000      1.000      <NA>