Getting started with anovapowersim

Search for the required sample size

Use power_n() to search for the sample size needed to reach the requested power. This example is a mixed design with one two-level between-subject factor (cond) and one four-level within-subject factor (stim). It tests the cond:stim interaction with 90% power to detect a partial eta squared of 0.14.

power_n(
  between = c(cond = 2), # cond has 2 levels
  within = c(stim = 4), # stim has 4 levels
  term = "cond:stim",
  target_pes = 0.14, 
  alpha = 0.05,
  power = 0.90,
  n_sims = 1000, # use 5000+ for a more precise estimate
  seed = 123 # for reproducibility
)

#> <anovapowersim_curve>
#>   term:          'cond:stim'
#>   target power:  0.900
#>   alpha:         0.05
#>   effect size:   pes = 0.14
#>   n values:      6 per-cell sample sizes visited
#>   sims per cell size: 1000
#>   SS type:       III
#>   n needed for between-subjects cell: 17
#>   total N needed: 34
#> 
#>  n_per_cell total_n n_sims num_df den_df    ncp power_calc power_sim
#>          13      26   1000      3     72 11.721      0.808     0.795
#>          16      32   1000      3     90 14.651      0.897     0.885
#>          17      34   1000      3     96 15.628      0.917     0.912
#>          18      36   1000      3    102 16.605      0.934     0.936
#>          20      40   1000      3    114 18.558      0.958     0.946
#>          26      52   1000      3    150 24.419      0.991     0.991

power_n() reports the required number of subjects per between-subject cell and the corresponding total N. For a purely within-subject design, the reported n_per_cell is the total sample size.

The output table uses compact column names: num_df and den_df are the ANOVA degrees of freedom, ncp is the noncentrality parameter, power_calc is the calculated noncentral-F power, and power_sim is the simulation estimate.

The example uses 1000 simulations for speed. Use at least 5000 simulations for a more stable estimate; the package default is 10000.

power_n(
  between = c(cond = 2, age = 3),
  within = c(stim = 4),
  term = "cond:stim:age",
  target_pes = f_to_pes(0.50)
  power = 0.90,
  n_sims = 5000,
  seed = 123
)

Simulate a power curve

Use power_curve() to estimate power across explicitly chosen sample sizes. The result is a tidy table that can be passed to plot_power_curve().

pc <- power_curve(
  between = c(cond = 2),
  within = c(stim = 2),
  term = "cond:stim",
  target_pes = 0.14,
  n_range = c(16, 20, 23, 28),
  n_sims = 1000,
  seed = 123
)
pc

#> <anovapowersim_curve>
#>   term:          'cond:stim'
#>   target power:  <not specified>
#>   alpha:         0.05
#>   effect size:   pes = 0.14
#>   n values:      4 per-cell sample sizes visited
#>   sims per cell size: 1000
#>   SS type:       III
#>   n needed for between-subjects cell: <not reached>
#>   total N needed: <not reached>
#> 
#>  n_per_cell total_n n_sims num_df den_df   ncp power_calc power_sim
#>          16      32   1000      1     30 4.884      0.571     0.557
#>          20      40   1000      1     38 6.186      0.679     0.666
#>          23      46   1000      1     44 7.163      0.745     0.735
#>          28      56   1000      1     54 8.791      0.829     0.831

plot_power_curve(
  pc,
  power_lines = c(0.80, 0.90)
)

Run simulations in parallel

For larger simulation runs, set parallel = TRUE. If cores is omitted, anovapowersim uses one fewer than the available cores and reports the number selected. Set cores explicitly when a fixed number of workers is required.

power_n(
  between = c(cond = 2),
  within = c(stim = 2),
  term = "cond:stim",
  target_pes = 0.14,
  power = 0.90,
  n_sims = 5000,
  parallel = TRUE,
  cores = 4,
  seed = 123
)

Next steps

• Fixed-sample power and sensitivity
• Calculated power without simulations
• Power for unbalanced designs
• Covariance and nonsphericity
• Comparison with G*Power