Power calculations for emmeans analyses

Frederik Aust

14 April, 2021

library("Superpower")
library("emmeans")

When conducting exact ANOVA power analyses with Superpower it is possible to calculate the power for both the omnibus \(F\)-tests and planned contrasts or post-hoc comparisons. It is possible to use this approach to calculate power for standard contrasts, such as pairwise contrasts between cells. Here I provide a brief overview of how the output of ANOVA_exact() can be used to perform power analyses for tailored planned contrasts and follow-up tests.

A note of caution

All power analyses for emmeans-objects are based on the \(F\)- and \(t\)-values from the analyses of the dataset simulated by ANOVA_exact() assuming two-sided testing. Thus, the emmeans_power() does not honor adjustments of the testing procedure due to either one-sided testing (including two one-sided tests) or corrections for multiple comparisons via the adjust option in emmeans. As noted below, for the Bonferroni-adjustment this limitation can be overcome by manually adjusting alpha_level.

Post hoc pairwise comparisons

First, we will set up a 2 \(\times\) 3 repeated measures design. When calling ANOVA_exact() pairwise comparisons of expected marginal means are added by setting emm = TRUE and contrast_type = "pairwise" (default).

# Set up a within design with 2 factors, each with 2 and 3 levels
design_result <- ANOVA_design(
  design = "2w*3w",
  n = 40,
  mu = c(0.3, 0, 0.5, 0.3, 0, 0),
  sd = 2,
  r = 0.8, 
  labelnames = c("condition", "cheerful", "sad", "voice", "human", "robot", "cartoon")
)

exact_result <- ANOVA_exact(
  design_result,
  alpha_level = 0.05,
  verbose = FALSE,
  emm = TRUE,
  contrast_type = "pairwise"
)

The result contains the power calculations for both the omnibus \(F\)-tests and pairwise post-hoc comparisons.

exact_result$main_results
power partial_eta_squared cohen_f non_centrality
condition 29.08380 0.0507099 0.2311251 2.083333
voice 50.12584 0.0621242 0.2573700 5.166667
condition:voice 41.62968 0.0507099 0.2311251 4.166667
head(exact_result$emm_results)
contrast power partial_eta_squared cohen_f non_centrality
condition_cheerful voice_cartoon - condition_sad voice_cartoon 68.37160 0.1381215 0.4003204 6.25
condition_cheerful voice_cartoon - condition_cheerful voice_human 16.41134 0.0250000 0.1601282 1.00
condition_cheerful voice_cartoon - condition_sad voice_human 16.41134 0.0250000 0.1601282 1.00
condition_cheerful voice_cartoon - condition_cheerful voice_robot 68.37160 0.1381215 0.4003204 6.25
condition_cheerful voice_cartoon - condition_sad voice_robot 68.37160 0.1381215 0.4003204 6.25
condition_sad voice_cartoon - condition_cheerful voice_human 30.99652 0.0545455 0.2401922 2.25

The output also contains the emmeans-object on which these power calculations are based. By manipulating this object it is possible to tailor the power analyses to the contrasts desired for the planned study. That is, based on the dataset simulated with ANOVA_exact() we can write out the analysis code for emmeans-contrasts, just as we would if we were to analyze the empirical data, and use the output to perform the corresponding power analysis.

Customized emmeans contrasts

The emmeans reference grid and contrasts are included in the output of ANOVA_exact().

knitr::kable(exact_result$emmeans$emmeans)
condition voice emmean SE df lower.CL upper.CL
condition_cheerful voice_cartoon 0.5 0.3162278 39 -0.139631 1.139631
condition_sad voice_cartoon 0.0 0.3162278 39 -0.639631 0.639631
condition_cheerful voice_human 0.3 0.3162278 39 -0.339631 0.939631
condition_sad voice_human 0.3 0.3162278 39 -0.339631 0.939631
condition_cheerful voice_robot 0.0 0.3162278 39 -0.639631 0.639631
condition_sad voice_robot 0.0 0.3162278 39 -0.639631 0.639631
knitr::kable(exact_result$emmeans$contrasts)
contrast estimate SE df t.ratio p.value
condition_cheerful voice_cartoon - condition_sad voice_cartoon 0.5 0.2 39 2.5 0.0167338
condition_cheerful voice_cartoon - condition_cheerful voice_human 0.2 0.2 39 1.0 0.3234749
condition_cheerful voice_cartoon - condition_sad voice_human 0.2 0.2 39 1.0 0.3234749
condition_cheerful voice_cartoon - condition_cheerful voice_robot 0.5 0.2 39 2.5 0.0167338
condition_cheerful voice_cartoon - condition_sad voice_robot 0.5 0.2 39 2.5 0.0167338
condition_sad voice_cartoon - condition_cheerful voice_human -0.3 0.2 39 -1.5 0.1416670
condition_sad voice_cartoon - condition_sad voice_human -0.3 0.2 39 -1.5 0.1416670
condition_sad voice_cartoon - condition_cheerful voice_robot 0.0 0.2 39 0.0 1.0000000
condition_sad voice_cartoon - condition_sad voice_robot 0.0 0.2 39 0.0 1.0000000
condition_cheerful voice_human - condition_sad voice_human 0.0 0.2 39 0.0 1.0000000
condition_cheerful voice_human - condition_cheerful voice_robot 0.3 0.2 39 1.5 0.1416670
condition_cheerful voice_human - condition_sad voice_robot 0.3 0.2 39 1.5 0.1416670
condition_sad voice_human - condition_cheerful voice_robot 0.3 0.2 39 1.5 0.1416670
condition_sad voice_human - condition_sad voice_robot 0.3 0.2 39 1.5 0.1416670
condition_cheerful voice_robot - condition_sad voice_robot 0.0 0.2 39 0.0 1.0000000

By using emmeans_power() on the contrasts, we can reproduce the results of the previous power analysis for the pairwise comparisons.

head(emmeans_power(exact_result$emmeans$contrasts))
contrast power partial_eta_squared cohen_f non_centrality
condition_cheerful voice_cartoon - condition_sad voice_cartoon 68.37160 0.1381215 0.4003204 6.25
condition_cheerful voice_cartoon - condition_cheerful voice_human 16.41134 0.0250000 0.1601282 1.00
condition_cheerful voice_cartoon - condition_sad voice_human 16.41134 0.0250000 0.1601282 1.00
condition_cheerful voice_cartoon - condition_cheerful voice_robot 68.37160 0.1381215 0.4003204 6.25
condition_cheerful voice_cartoon - condition_sad voice_robot 68.37160 0.1381215 0.4003204 6.25
condition_sad voice_cartoon - condition_cheerful voice_human 30.99652 0.0545455 0.2401922 2.25

Now, we can manipulate the emmeans reference grid to perform additional power analyses. In the following example, we calculate the power for the contrasts between sad and cheerful condition for each voice.

simple_condition_effects <- emmeans(
  exact_result$emmeans$emmeans,
  specs = ~ condition | voice
)

emmeans_power(pairs(simple_condition_effects))
contrast voice power partial_eta_squared cohen_f non_centrality
condition_cheerful - condition_sad voice_cartoon 68.3716 0.1381215 0.4003204 6.25
condition_cheerful - condition_sad voice_human 5.0000 0.0000000 0.0000000 0.00
condition_cheerful - condition_sad voice_robot 5.0000 0.0000000 0.0000000 0.00

We may also calculate the power for testing all condition means against an arbitrary constant.

emmeans_power(test(simple_condition_effects, null = 0.5))
condition voice power partial_eta_squared cohen_f non_centrality
condition_cheerful voice_cartoon 5.000000 0.0000000 0.0000000 0.0
condition_sad voice_cartoon 33.831141 0.0602410 0.2531848 2.5
condition_cheerful voice_human 9.462673 0.0101523 0.1012739 0.4
condition_sad voice_human 9.462673 0.0101523 0.1012739 0.4
condition_cheerful voice_robot 33.831141 0.0602410 0.2531848 2.5
condition_sad voice_robot 33.831141 0.0602410 0.2531848 2.5

Finally, we can calculate the power for custom contrasts between any linear combination of conditions.

custom_contrast <- contrast(
  exact_result$emmeans$emmeans,
  list(robot_vs_sad_human = c(0, 0, 0, 1, -0.5, -0.5))
)

emmeans_power(custom_contrast)
contrast power partial_eta_squared cohen_f non_centrality
robot_vs_sad_human 39.34971 0.0714286 0.2773501 3

Although emmeans_power() currently ignores adjustments for multiple comparisons, it is possible to calculate the power for Bonferroni-corrected tests by adjusting alpha_level.

n_contrasts <- nrow(as.data.frame(simple_condition_effects))

emmeans_power(
  pairs(simple_condition_effects),
  alpha_level = 0.05 / n_contrasts
)
contrast voice power partial_eta_squared cohen_f non_centrality
condition_cheerful - condition_sad voice_cartoon 40.1555499 0.1381215 0.4003204 6.25
condition_cheerful - condition_sad voice_human 0.8333333 0.0000000 0.0000000 0.00
condition_cheerful - condition_sad voice_robot 0.8333333 0.0000000 0.0000000 0.00

Similarly, if we want to calculate power for a one-sided test, we can doubling alpha_level.

emmeans_power(
  pairs(simple_condition_effects)[1],
  alpha_level = 2 * 0.05
)
contrast voice power partial_eta_squared cohen_f non_centrality
condition_cheerful - condition_sad voice_cartoon 79.14507 0.1381215 0.4003204 6.25

Note, that because power is calculated from the squared \(t\)-value, power is only calculated correctly if the alternative hypothesis is true in the simulated dataset. That is, the difference of the condition means is consistent with the tested directional hypothesis.

Equivalence and non-superiority/-inferiority tests

Because emmeans can perform equivalence, non-superiority, and -inferiority tests, emmeans_power() can calculate the corresponding power for these tests.

emmeans_power(
  pairs(simple_condition_effects, side = "equivalence", delta = 0.3)[2]
)
contrast voice power partial_eta_squared cohen_f non_centrality
2 condition_cheerful - condition_sad voice_human 30.99652 0.0545455 0.2401922 2.25

Note, that because power is calculated from the squared \(t\)-value, power is only calculated correctly if the alternative hypothesis is true in the simulated dataset. That is, the difference between the condition means is consistent with the tested directional hypothesis (smaller than delta).

Joint tests

Another useful application of emmeans_power() is to joint tests. Lets assume we plan to test the main effect of voice for each of the two conditions separately using joint_tests(). We can then calculate the power for each Bonferroni-corrected \(F\)-test as follows.

voice_by_condition <- joint_tests(
  exact_result$emmeans$emmeans,
  by = "condition"
)

emmeans_power(voice_by_condition, alpha_level = 0.05 / 2)
model term condition power partial_eta_squared cohen_f non_centrality
1 voice condition_cheerful 21.80102 0.0751061 0.2849651 3.167
3 voice condition_sad 10.39583 0.0370370 0.1961161 1.500