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power_achieved() and power_sensitivity()
are experimental. 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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.