Nothing
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)
Warning <simpleWarning>
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
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