tests/testthat/_snaps/calc_effect_sizes.md

In MeikeSteinhilber/sprtt: Sequential Probability Ratio Tests Toolbox

snaphot: effect_size

Code
  effect_sizes(formula, df_no_effect)
Output
  $cohens_f
  [1] 0.02783414

  $ci_cohens_f_lower
  [1] NA

  $ci_cohens_f_upper
  [1] 0.04623125

  $cohens_f_adj
  [1] 0.01997895

  $cohens_f_median
  [1] 0.02269439

  $eta_squared
  [1] 0.0007741396

  $partial_eta_squared
  [1] 0.0007741396

  $adjusted_eta_squared
  [1] 0.0003992425

  $ci_ncp_lower
  [1] NA

  $ci_ncp_upper
  [1] 17.09863

Code
  effect_sizes(formula, df_no_effect_extreme)
Output
  $cohens_f
  [1] 0.007884077

  $ci_cohens_f_lower
  [1] NA

  $ci_cohens_f_upper
  [1] 0.01359347

  $cohens_f_adj
  [1] 0.00496528

  $cohens_f_median
  [1] 0.005989646

  $eta_squared
  [1] 6.215481e-05

  $partial_eta_squared
  [1] 6.215481e-05

  $adjusted_eta_squared
  [1] 2.465526e-05

  $ci_ncp_lower
  [1] NA

  $ci_ncp_upper
  [1] 14.7826

Code
  seq_anova(formula, 1, data = df_no_effect_extreme)
Condition
  [1m[33mWarning[39m in `calc_likelihoods_anova()`:[22m
  At least one likelihood is equal to 0.
              The test works with the logarithm of the likelihoods.
Output

  *****  Sequential ANOVA *****

  formula: formula
  test statistic:
   log-likelihood ratio = -39375.2, decision = accept H0
  SPRT thresholds:
   lower log(B) = -2.944, upper log(A) = 2.944
  Log-Likelihood of the:
   alternative hypothesis = -39376.7
   null hypothesis = -1.505
  alternative hypothesis: true difference in means is not equal to 0.
  specified effect size: Cohen's f = 1
  empirical Cohen's f = 0.007884077, 95% CI[0, 0.01359347]
  Cohen's f adjusted = 0.005
  degrees of freedom: df1 = 3, df2 = 79996
  SS effect = 4.9679, SS residual = 79922.87, SS total = 79927.84
  *Note: to get access to the object of the results use the @ or [] instead of the $ operator.

Code
  seq_anova(formula, 0.01, data = df_0.01_effect)
Output

  *****  Sequential ANOVA *****

  formula: formula
  test statistic:
   log-likelihood ratio = -0.052, decision = continue sampling
  SPRT thresholds:
   lower log(B) = -2.944, upper log(A) = 2.944
  Log-Likelihood of the:
   alternative hypothesis = -0.412
   null hypothesis = -0.36
  alternative hypothesis: true difference in means is not equal to 0.
  specified effect size: Cohen's f = 0.01
  empirical Cohen's f = 0.02693014, 95% CI[NA, 0.05742471]
  Cohen's f adjusted = 0
  degrees of freedom: df1 = 3, df2 = 1996
  SS effect = 1.405763, SS residual = 1938.363, SS total = 1939.768
  *Note: to get access to the object of the results use the @ or [] instead of the $ operator.

Code
  effect_sizes(formula, df_0.01_effect)
Output
  $cohens_f
  [1] 0.02693014

  $ci_cohens_f_lower
  [1] NA

  $ci_cohens_f_upper
  [1] 0.05742471

  $cohens_f_adj
  [1] 0

  $cohens_f_median
  [1] 0

  $eta_squared
  [1] 0.0007247067

  $partial_eta_squared
  [1] 0.0007247067

  $adjusted_eta_squared
  [1] -0.00077721

  $ci_ncp_lower
  [1] NA

  $ci_ncp_upper
  [1] 6.595196

Code
  effect_sizes(formula, df_0.10_effect)
Output
  $cohens_f
  [1] 0.08548456

  $ci_cohens_f_lower
  [1] NA

  $ci_cohens_f_upper
  [1] 0.1432724

  $cohens_f_adj
  [1] 0.05918448

  $cohens_f_median
  [1] 0.06829665

  $eta_squared
  [1] 0.007254596

  $partial_eta_squared
  [1] 0.007254596

  $adjusted_eta_squared
  [1] 0.003513093

  $ci_ncp_lower
  [1] NA

  $ci_ncp_upper
  [1] 16.42159

Code
  effect_sizes(formula, df_0.25_effect)
Output
  $cohens_f
  [1] 0.3951891

  $ci_cohens_f_lower
  [1] 0.212692

  $ci_cohens_f_upper
  [1] 0.5496487

  $cohens_f_adj
  [1] 0.3709923

  $cohens_f_median
  [1] 0.3818288

  $eta_squared
  [1] 0.1350786

  $partial_eta_squared
  [1] 0.1350786

  $adjusted_eta_squared
  [1] 0.123311

  $ci_ncp_lower
  [1] 6.785684

  $ci_ncp_upper
  [1] 45.31705

Code
  effect_sizes(formula, df_0.40_effect)
Output
  $cohens_f
  [1] 0.5759504

  $ci_cohens_f_lower
  [1] 0.1342421

  $ci_cohens_f_upper
  [1] 0.7988566

  $cohens_f_adj
  [1] 0.4530764

  $cohens_f_median
  [1] 0.4838036

  $eta_squared
  [1] 0.2490908

  $partial_eta_squared
  [1] 0.2490908

  $adjusted_eta_squared
  [1] 0.1823433

  $ci_ncp_lower
  [1] 0.9010465

  $ci_ncp_upper
  [1] 31.90859

Code
  effect_sizes(formula, df_2_effect)
Output
  $cohens_f
  [1] 1.710485

  $ci_cohens_f_lower
  [1] 0.928781

  $ci_cohens_f_upper
  [1] 2.297912

  $cohens_f_adj
  [1] 1.513475

  $cohens_f_median
  [1] 1.595753

  $eta_squared
  [1] 0.7452722

  $partial_eta_squared
  [1] 0.7452722

  $adjusted_eta_squared
  [1] 0.7311207

  $ci_ncp_lower
  [1] 17.25268

  $ci_ncp_upper
  [1] 105.608

Code
  effect_sizes(formula, df_no_effect)
Output
  $cohens_f
  [1] 0.02783414

  $ci_cohens_f_lower
  [1] NA

  $ci_cohens_f_upper
  [1] 0.04623125

  $cohens_f_adj
  [1] 0.01997895

  $cohens_f_median
  [1] 0.02269439

  $eta_squared
  [1] 0.0007741396

  $partial_eta_squared
  [1] 0.0007741396

  $adjusted_eta_squared
  [1] 0.0003992425

  $ci_ncp_lower
  [1] NA

  $ci_ncp_upper
  [1] 17.09863

Code
  effect_sizes(formula, df_0.01_effect)
Output
  $cohens_f
  [1] 0.02693014

  $ci_cohens_f_lower
  [1] NA

  $ci_cohens_f_upper
  [1] 0.05742471

  $cohens_f_adj
  [1] 0

  $cohens_f_median
  [1] 0

  $eta_squared
  [1] 0.0007247067

  $partial_eta_squared
  [1] 0.0007247067

  $adjusted_eta_squared
  [1] -0.00077721

  $ci_ncp_lower
  [1] NA

  $ci_ncp_upper
  [1] 6.595196

Code
  effect_sizes(formula, df_0.10_effect)
Output
  $cohens_f
  [1] 0.08548456

  $ci_cohens_f_lower
  [1] NA

  $ci_cohens_f_upper
  [1] 0.1432724

  $cohens_f_adj
  [1] 0.05918448

  $cohens_f_median
  [1] 0.06829665

  $eta_squared
  [1] 0.007254596

  $partial_eta_squared
  [1] 0.007254596

  $adjusted_eta_squared
  [1] 0.003513093

  $ci_ncp_lower
  [1] NA

  $ci_ncp_upper
  [1] 16.42159

Code
  effect_sizes(formula, df_0.25_effect)
Output
  $cohens_f
  [1] 0.3951891

  $ci_cohens_f_lower
  [1] 0.212692

  $ci_cohens_f_upper
  [1] 0.5496487

  $cohens_f_adj
  [1] 0.3709923

  $cohens_f_median
  [1] 0.3818288

  $eta_squared
  [1] 0.1350786

  $partial_eta_squared
  [1] 0.1350786

  $adjusted_eta_squared
  [1] 0.123311

  $ci_ncp_lower
  [1] 6.785684

  $ci_ncp_upper
  [1] 45.31705

Code
  effect_sizes(formula, df_0.40_effect)
Output
  $cohens_f
  [1] 0.5759504

  $ci_cohens_f_lower
  [1] 0.1342421

  $ci_cohens_f_upper
  [1] 0.7988566

  $cohens_f_adj
  [1] 0.4530764

  $cohens_f_median
  [1] 0.4838036

  $eta_squared
  [1] 0.2490908

  $partial_eta_squared
  [1] 0.2490908

  $adjusted_eta_squared
  [1] 0.1823433

  $ci_ncp_lower
  [1] 0.9010465

  $ci_ncp_upper
  [1] 31.90859

Code
  effect_sizes(formula, df_2_effect)
Output
  $cohens_f
  [1] 1.710485

  $ci_cohens_f_lower
  [1] 0.928781

  $ci_cohens_f_upper
  [1] 2.297912

  $cohens_f_adj
  [1] 1.513475

  $cohens_f_median
  [1] 1.595753

  $eta_squared
  [1] 0.7452722

  $partial_eta_squared
  [1] 0.7452722

  $adjusted_eta_squared
  [1] 0.7311207

  $ci_ncp_lower
  [1] 17.25268

  $ci_ncp_upper
  [1] 105.608

MeikeSteinhilber/sprtt documentation built on Jan. 19, 2024, 1:56 a.m.