The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
The Power Workspace module registers classical test families. This vignette summarizes ANOVA/regression, exact proportions, correlations, GLM, and nonparametric tests. See t Tests for the t-test family.
power_compute("f_anova_one_way", "a_priori", f = 0.25, alpha = 0.05,
power = 0.8, groups = 4)
#> ggpower result
#> Test: F test: Fixed effects ANOVA - one way
#> Analysis: a_priori
#>
#> Input parameters
#> effect_size_f: 0.25
#> alpha: 0.05
#> total_sample_size: 179
#> groups: 4
#> target_power: 0.8
#>
#>
#> Output parameters
#> noncentrality_parameter: 11.1875
#> critical_f: 2.656234
#> numerator_df: 3
#> denominator_df: 175
#> actual_power: 0.8015073
#>
#>
#> Notes
#> - A priori sample sizes are rounded up to integer values and actual power is recomputed.f2 <- effect_size_f2(0.10)
power_compute("f_mreg_omnibus", "post_hoc", f2 = f2,
total_n = 95, predictors = 5)
#> ggpower result
#> Test: F test: Multiple Regression - omnibus (deviation of R2 from zero), fixed model
#> Analysis: post_hoc
#>
#> Input parameters
#> effect_size_f2: 0.1111111
#> alpha: 0.05
#> total_sample_size: 95
#> predictors: 5
#>
#>
#> Output parameters
#> noncentrality_parameter: 10.55556
#> critical_f: 2.316858
#> numerator_df: 5
#> denominator_df: 89
#> power: 0.6735858power_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.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.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.q <- effect_size_q(0.75, 0.88)
power_compute("z_corr_independent", "post_hoc", q_effect = q,
n1 = 51, n2 = 260, alpha = 0.05)
#> ggpower result
#> Test: z test: Correlation - inequality of two independent Pearson r's
#> Analysis: post_hoc
#>
#> Input parameters
#> tails: two
#> effect_size_q: -0.4028126
#> alpha: 0.05
#> sample_size_group_1: 51
#> sample_size_group_2: 260
#>
#>
#> Output parameters
#> critical_z: -1.959964, 1.959964
#> total_sample_size: 311
#> power: 0.7263517power_compute("z_logistic", "a_priori", odds_ratio = 1.5, p0 = 0.5,
alpha = 0.05, power = 0.95, total_n = 300,
r2_other = 0, x_variance = 1)
#> ggpower result
#> Test: z test: Multiple logistic regression
#> Analysis: a_priori
#>
#> Input parameters
#> tails: two
#> odds_ratio: 1.5
#> p_h0: 0.5
#> alpha: 0.05
#> total_sample_size: 317
#> r2_other_x: 0
#> x_variance: 1
#> target_power: 0.95
#>
#>
#> Output parameters
#> critical_z: -1.959964, 1.959964
#> beta1: 0.4054651
#> actual_power: 0.9504862
#>
#>
#> Notes
#> - Logistic regression support uses a large-sample Wald approximation suitable for planning; enumeration and Demidenko variants can be added later.
#> - A priori sample sizes are rounded up to integer values and actual power is recomputed.power_compute("z_poisson", "a_priori", exp_beta1 = 1.3,
base_rate = 0.85, exposure = 1, alpha = 0.05,
power = 0.95, r2_other = 0, x_variance = 0.25)
#> ggpower result
#> Test: z test: Poisson regression
#> Analysis: a_priori
#>
#> Input parameters
#> tails: two
#> exp_beta1: 1.3
#> base_rate: 0.85
#> exposure: 1
#> alpha: 0.05
#> total_sample_size: 889
#> r2_other_x: 0
#> x_variance: 0.25
#> target_power: 0.95
#>
#>
#> Output parameters
#> critical_z: -1.959964, 1.959964
#> beta1: 0.2623643
#> actual_power: 0.9501298
#>
#>
#> Notes
#> - Poisson regression support uses a large-sample Wald approximation; exact enumeration is a future refinement.
#> - A priori sample sizes are rounded up to integer values and actual power is recomputed.Nonparametric tests map rank-test planning to t-test noncentrality via ARE: \(d_{\text{eff}} = d \cdot \sqrt{\text{ARE}}\).
power_compute("wilcoxon_signed", "post_hoc", d = 0.5, n = 40,
alpha = 0.05, are = 3 / pi)
#> ggpower result
#> Test: Wilcoxon signed-rank test: Means - difference from constant or matched pairs
#> Analysis: post_hoc
#>
#> Input parameters
#> tails: two
#> effect_size_d: 0.5
#> alpha: 0.05
#> total_sample_size: 38
#> asymptotic_relative_efficiency: 0.9549297
#>
#>
#> Output parameters
#> noncentrality_parameter: 3.082207
#> critical_t: -2.026192, 2.026192
#> df: 37
#> power: 0.8511398
#>
#>
#> Notes
#> - Wilcoxon signed-rank support uses the A.R.E. method and reuses the matched/one-sample t-test kernel.power_compute("wilcoxon_mann_whitney", "post_hoc", d = 0.5,
n1 = 30, n2 = 30, alpha = 0.05, are = 3 / pi)
#> ggpower result
#> Test: Wilcoxon-Mann-Whitney test of a difference between two independent means
#> Analysis: post_hoc
#>
#> Input parameters
#> tails: two
#> effect_size_d: 0.5
#> alpha: 0.05
#> sample_size_group_1: 28
#> sample_size_group_2: 28
#> asymptotic_relative_efficiency: 0.9549297
#>
#>
#> Output parameters
#> noncentrality_parameter: 1.870829
#> critical_t: -2.004879, 2.004879
#> df: 54
#> total_sample_size: 56
#> power: 0.4513506
#>
#>
#> Notes
#> - Wilcoxon-Mann-Whitney support uses the A.R.E. method and reuses the two-sample t-test kernel.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.
Health stats visible at Monitor.