z tests for correlations and GLM coefficients.
Independent correlations
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: 0.7263517Dependent correlations (common index)
power_compute("z_corr_dependent_common", "a_priori", rho_ab = 0.4,
rho_ac = 0.2, rho_bc = 0.5, alpha = 0.05,
power = 0.8, tails = "one")
#> ggpower result
#> Test: z test: Correlation - inequality of two dependent Pearson r's (common index)
#> Analysis: a_priori
#>
#> Input parameters
#> tails: greater
#> rho_ab: 0.4
#> rho_ac: 0.2
#> rho_bc: 0.5
#> alpha: 0.05
#> total_sample_size: 144
#> target_power: 0.8
#>
#>
#> Output parameters
#> critical_z: 1.644854
#> actual_power: 0.8011611
#>
#>
#> Notes
#> - A priori sample sizes are rounded up to integer values and actual power is recomputed.Dependent correlations (no common index)
power_compute("z_corr_dependent_no_common", "post_hoc", rho_ab = 0.1,
rho_cd = 0.2, rho_ac = 0.5, rho_ad = 0.4,
rho_bc = -0.4, rho_bd = 0.8, n = 200)
#> ggpower result
#> Test: z test: Correlation - inequality of two dependent Pearson r's (no common index)
#> Analysis: post_hoc
#>
#> Input parameters
#> tails: two
#> rho_ab: 0.1
#> rho_cd: 0.2
#> rho_ac: 0.5
#> rho_ad: 0.4
#> rho_bc: -0.4
#> rho_bd: 0.8
#> alpha: 0.05
#> total_sample_size: 200
#>
#>
#> Output parameters
#> critical_z: -1.959964, 1.959964
#> power: 0.2166397Exact correlation (Fisher-Z approximation)
power_compute("exact_correlation", "post_hoc", rho = 0.3, rho0 = 0,
n = 50, alpha = 0.05, tails = "two")
#> ggpower result
#> Test: Exact: Correlation - difference from constant (one sample case)
#> Analysis: post_hoc
#>
#> Input parameters
#> tails: two
#> rho_h0: 0
#> rho_h1: 0.3
#> alpha: 0.05
#> total_sample_size: 50
#>
#>
#> Output parameters
#> effect_size_q: 0.3095196
#> critical_z: -1.959964, 1.959964
#> power: 0.5643676
#>
#>
#> Notes
#> - Correlation support uses Fisher Z approximation; exact small-sample correlation distribution is planned.Tetrachoric correlation (approximation)
power_compute("z_tetrachoric", "post_hoc", rho = 0.4, n = 100, alpha = 0.05)
#> ggpower result
#> Test: z test: Tetrachoric correlation
#> Analysis: post_hoc
#>
#> Input parameters
#> tails: two
#> rho_h0: 0
#> rho_h1: 0.4
#> alpha: 0.05
#> total_sample_size: 100
#>
#>
#> Output parameters
#> critical_z: -1.959964, 1.959964
#> power: 0.9865337
#>
#>
#> Notes
#> - Tetrachoric correlation currently uses the large-sample Fisher-Z style planning approximation.Logistic regression
power_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.Poisson regression
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.