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This vignette describes the mp_power() workflow and how
to summarize results with summary().
d <- mp_design(clusters = list(subject = 20), trials_per_cell = 4)
a <- mp_assumptions(
fixed_effects = list(`(Intercept)` = 0, condition = 0.4),
residual_sd = 1,
icc = list(subject = 0.1)
)
scn <- mp_scenario_lme4(
y ~ condition + (1 | subject),
design = d,
assumptions = a,
test_method = "wald"
)
res <- mp_power(scn, nsim = 10, seed = 42)
summary(res)
#> $power
#> [1] 0
#>
#> $mcse
#> [1] 0
#>
#> $ci
#> [1] 0 0
#>
#> $diagnostics
#> $diagnostics$fail_rate
#> [1] 0
#>
#> $diagnostics$singular_rate
#> [1] 0
#>
#>
#> $nsim
#> [1] 10
#>
#> $alpha
#> [1] 0.05
#>
#> $failure_policy
#> [1] "count_as_nondetect"
#>
#> $conf_level
#> [1] 0.95These 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.