Exact and proportion tests for binary outcomes.
Generic binomial
power_compute("exact_binomial", "post_hoc", p0 = 0.5, p1 = 0.65,
n = 80, alpha = 0.05, tails = "one")
#> ggpower result
#> Test: Exact: Generic binomial test
#> Analysis: post_hoc
#>
#> Input parameters
#> tails: greater
#> p_h0: 0.5
#> p_h1: 0.65
#> alpha: 0.05
#> total_sample_size: 80
#>
#>
#> Output parameters
#> power: 0.8540286
#>
#>
#> Notes
#> - Exact binomial power sums probabilities for outcomes whose exact binomial-test p-value is at or below alpha.One proportion vs constant
power_compute("exact_one_proportion", "a_priori", p0 = 0.5, p1 = 0.7,
alpha = 0.05, power = 0.8, tails = "two")
#> ggpower result
#> Test: Exact: Proportion: Difference from constant
#> Analysis: a_priori
#>
#> Input parameters
#> tails: two.sided
#> p_h0: 0.5
#> p_h1: 0.7
#> alpha: 0.05
#> total_sample_size: 54
#> target_power: 0.8
#>
#>
#> Output parameters
#> actual_power: 0.8367644
#>
#>
#> Notes
#> - Exact binomial power sums probabilities for outcomes whose exact binomial-test p-value is at or below alpha.
#> - A priori sample sizes are rounded up to integer values and actual power is recomputed.Sign test
power_compute("exact_sign", "post_hoc", p0 = 0.5, p1 = 0.65, n = 50, alpha = 0.05)
#> ggpower result
#> Test: Exact: Proportion: Sign test
#> Analysis: post_hoc
#>
#> Input parameters
#> tails: two.sided
#> p_h0: 0.5
#> p_h1: 0.65
#> alpha: 0.05
#> total_sample_size: 50
#>
#>
#> Output parameters
#> power: 0.5059799
#>
#>
#> Notes
#> - Exact binomial power sums probabilities for outcomes whose exact binomial-test p-value is at or below alpha.Fisher exact (two proportions)
power_compute("exact_fisher", "post_hoc", p0 = 0.4, p1 = 0.7,
n1 = 12, n2 = 12, alpha = 0.05, tails = "greater")
#> ggpower result
#> Test: Exact: Proportions - inequality of two independent groups (Fisher exact)
#> Analysis: post_hoc
#>
#> Input parameters
#> tails: less
#> p_group_1: 0.4
#> p_group_2: 0.7
#> alpha: 0.05
#> sample_size_group_1: 12
#> sample_size_group_2: 12
#>
#>
#> Output parameters
#> effect_size_h: 0.6128748
#> total_sample_size: 24
#> power: 0.3571413
#>
#>
#> Notes
#> - Fisher exact power enumerates all two-binomial outcome pairs and sums outcomes rejected by Fisher's exact test.McNemar (approximation)
power_compute("exact_mcnemar", "post_hoc", p0 = 0.5, p1 = 0.65, n = 60, alpha = 0.05)
#> ggpower result
#> Test: Exact: McNemar test approximation through discordant-pair binomial test
#> Analysis: post_hoc
#>
#> Input parameters
#> tails: two.sided
#> p_h0: 0.5
#> p_h1: 0.65
#> alpha: 0.05
#> total_sample_size: 60
#>
#>
#> Output parameters
#> power: 0.5590343
#>
#>
#> Notes
#> - Exact binomial power sums probabilities for outcomes whose exact binomial-test p-value is at or below alpha.Chi-square contingency (Cohen’s w)
w <- effect_size_w(c(.25, .25, .25, .25), c(.35, .15, .30, .20))
power_compute("chisq_contingency", "a_priori", w = w, alpha = 0.05, power = 0.8,
groups = 4)
#> ggpower result
#> Test: chi-square test: Contingency tables
#> Analysis: a_priori
#>
#> Input parameters
#> effect_size_w: 0.3162278
#> alpha: 0.05
#> total_sample_size: 79
#> df: 1
#> target_power: 0.8
#>
#>
#> Output parameters
#> noncentrality_parameter: 7.9
#> critical_chisq: 3.841459
#> actual_power: 0.8025412
#>
#>
#> Notes
#> - A priori sample sizes are rounded up to integer values and actual power is recomputed.