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Analysis Modes

ggpower supports five analysis modes. Each mode solves for a different unknown given the others.

Mode Solves for When to use
a_priori Sample size Planning before data collection
post_hoc Power Fixed sample size, retrospective
criterion Alpha Choose significance level
sensitivity Effect size Minimum detectable effect
compromise Alpha and beta Balance \(\alpha\) and \(\beta\) via ratio \(q = \beta/\alpha\)

Restrictions: t_generic has no a_priori. simon_two_stage supports only post_hoc and sensitivity.

A priori — sample size

power_compute("t_two_sample", "a_priori", d = 0.5, alpha = 0.05,
              power = 0.8, tails = "two", allocation_ratio = 1)
#> ggpower result
#> Test: t test: Means - difference between two independent means (two groups)
#> Analysis: a_priori
#> 
#> Input parameters
#>   tails: two
#>   effect_size_d: 0.5
#>   alpha: 0.05
#>   sample_size_group_1: 64
#>   sample_size_group_2: 64
#>   target_power: 0.8
#> 
#> 
#> Output parameters
#>   noncentrality_parameter: 2.828427
#>   critical_t: -1.978971,  1.978971
#>   df: 126
#>   total_sample_size: 128
#>   actual_power: 0.8014596
#> 
#> 
#> Notes
#> - A priori sample sizes are rounded up to integer values and actual power is recomputed.

Post hoc — achieved power

power_compute("t_one_sample", "post_hoc", d = 0.625, n = 30,
              alpha = 0.05, tails = "one")
#> ggpower result
#> Test: t test: Means - difference from constant (one sample case)
#> Analysis: post_hoc
#> 
#> Input parameters
#>   tails: greater
#>   effect_size_d: 0.625
#>   alpha: 0.05
#>   total_sample_size: 30
#> 
#> 
#> Output parameters
#>   noncentrality_parameter: 3.423266
#>   critical_t: 1.699127
#>   df: 29
#>   power: 0.9551444

Criterion — alpha

power_compute("t_one_sample", "criterion", d = 0.5, n = 40,
              power = 0.8, tails = "two")
#> ggpower result
#> Test: t test: Means - difference from constant (one sample case)
#> Analysis: criterion
#> 
#> Input parameters
#>   tails: two
#>   effect_size_d: 0.5
#>   alpha: 0.02642633
#>   total_sample_size: 40
#>   target_power: 0.8
#> 
#> 
#> Output parameters
#>   noncentrality_parameter: 3.162278
#>   critical_t: -2.307422,  2.307422
#>   df: 39
#>   power: 0.8
#>   alpha: 0.02642633
#>   beta: 0.2

Sensitivity — effect size

power_compute("f_mreg_omnibus", "sensitivity", alpha = 0.05, power = 0.8,
              total_n = 100, predictors = 3)
#> ggpower result
#> Test: F test: Multiple Regression - omnibus (deviation of R2 from zero), fixed model
#> Analysis: sensitivity
#> 
#> Input parameters
#>   effect_size_f2: 0.1135624
#>   alpha: 0.05
#>   total_sample_size: 100
#>   predictors: 3
#>   target_power: 0.8
#> 
#> 
#> Output parameters
#>   noncentrality_parameter: 11.35624
#>   critical_f: 2.699393
#>   numerator_df: 3
#>   denominator_df: 96
#>   power: 0.8
#>   f2: 0.1135624

Compromise — alpha and beta ratio

power_compute("t_one_sample", "compromise", d = 0.5, n = 40, q = 1, tails = "two")
#> ggpower result
#> Test: t test: Means - difference from constant (one sample case)
#> Analysis: compromise
#> 
#> Input parameters
#>   tails: two
#>   effect_size_d: 0.5
#>   alpha: 0.0844535
#>   total_sample_size: 40
#> 
#> 
#> Output parameters
#>   noncentrality_parameter: 3.162278
#>   critical_t: -1.770542,  1.770542
#>   df: 39
#>   power: 0.9155465
#>   alpha: 0.0844535
#>   beta: 0.08445349
#>   beta_alpha_ratio: 1
#> 
#> 
#> Notes
#> - Compromise analysis solves alpha so beta / alpha matches the requested ratio as closely as possible.

Effect size conversions

Helper functions convert study parameters into effect sizes used by power_compute().

effect_size_d(mean_h1 = 15, mean_h0 = 10, sd = 8)
#> [1] 0.625
effect_size_f2(r2 = 0.1)
#> [1] 0.1111111
effect_size_w(p0 = c(0.25, 0.25, 0.25, 0.25), p1 = c(0.4, 0.3, 0.2, 0.1))
#> [1] 0.4472136

See the pkgdown site for the full effect size conversions article.

Calculator

The Calculator module evaluates distribution-function scripts via ggpower_calculator().

ggpower_calculator("zinv(0.975)")
#> [1] 1.959964

See the pkgdown site for the full calculator article.

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.