power_achieved() and power_sensitivity()
are experimental and available only in the development version of
anovapowersim. They use the same balanced design
construction, ANOVA fitting, covariance handling, and simulation
controls as power_n().
Use power_achieved() when the sample size and partial
eta squared are fixed. The n argument is the number of
subjects per between-subject cell, or total N for a purely
within-subject design.
power_achieved(
between = c(group = 2),
within = c(time = 2),
term = "group:time",
target_pes = 0.14,
n = 20,
n_sims = 5000,
parallel = TRUE,
seed = 123
)The result reports simulated achieved power as the primary estimate and calculated power as a diagnostic.
Use power_sensitivity() when the sample size is fixed
and the minimum detectable partial eta squared is unknown. It simulates
effect sizes until it finds a bracket around the requested power, then
reports an explicitly simulated upper bracket as
pes_needed.
power_sensitivity(
between = c(group = 2),
within = c(time = 2),
term = "group:time",
n = 20,
power = 0.90,
n_sims = 5000,
pes_tol = 0.001,
parallel = TRUE,
seed = 123
)Because simulated power has Monte Carlo variability, use enough
simulations for the precision you need and inspect all visited effect
sizes in $results.
Both functions accept a within_covariance() object for
repeated-measures designs. See Covariance and
nonsphericity for cell naming, custom standard deviations and
correlations, and Greenhouse–Geisser handling.
To skip simulations, use power_achieved_calc() or
power_sensitivity_calc(). These are covered in the Calculated power guide.