Skip to main content
added 1 character in body
Source Link
Russ Lenth
  • 21.3k
  • 33
  • 70

I guess if a Cohen's d makes senssense, you can do something like

CON <- contrast(emm, interaction = TRUE, "pairwise", adjust="mvt")
eff_size(CON, sigma = ???, edf = ???, method = "identity")

(see the examples in the help page for eff_size) but you need to replace the ???s with reasonable values -- and be able to explain and defend them. You apparently have a mixed model, and I'm not sure Cohen's d is even defined because there are multiple sigma values involved. But I suppose that for edf you can use #groups - 1 for the coarsest grouping. For sigma, possibly you can do VarCorr(model) to see all the variance estimates; and then combine them. For instance, if there are three SDs sd1, sd2, sd3 you might use

sigma <- sqrt(sd1^2 + sd2^2 + sd3^2)

I am very leery of all of this because you can't find an example where this was actually ever done with interaction contrasts, and because in any but the simplest situations, the whole idea of effect size is flaky; people improvise something such as what I suggest, and then nobody really knows what you're talking about. You wind up with numbers, but they answer a question that can't be stated clearly.

I guess if a Cohen's d makes sens, you can do something like

CON <- contrast(emm, interaction = TRUE, "pairwise", adjust="mvt")
eff_size(CON, sigma = ???, edf = ???, method = "identity")

(see the examples in the help page for eff_size) but you need to replace the ???s with reasonable values -- and be able to explain and defend them. You apparently have a mixed model, and I'm not sure Cohen's d is even defined because there are multiple sigma values involved. But I suppose that for edf you can use #groups - 1 for the coarsest grouping. For sigma, possibly you can do VarCorr(model) to see all the variance estimates; and then combine them. For instance, if there are three SDs sd1, sd2, sd3 you might use

sigma <- sqrt(sd1^2 + sd2^2 + sd3^2)

I am very leery of all of this because you can't find an example where this was actually ever done with interaction contrasts, and because in any but the simplest situations, the whole idea of effect size is flaky; people improvise something such as what I suggest, and then nobody really knows what you're talking about. You wind up with numbers, but they answer a question that can't be stated clearly.

I guess if a Cohen's d makes sense, you can do something like

CON <- contrast(emm, interaction = TRUE, "pairwise", adjust="mvt")
eff_size(CON, sigma = ???, edf = ???, method = "identity")

(see the examples in the help page for eff_size) but you need to replace the ???s with reasonable values -- and be able to explain and defend them. You apparently have a mixed model, and I'm not sure Cohen's d is even defined because there are multiple sigma values involved. But I suppose that for edf you can use #groups - 1 for the coarsest grouping. For sigma, possibly you can do VarCorr(model) to see all the variance estimates; and then combine them. For instance, if there are three SDs sd1, sd2, sd3 you might use

sigma <- sqrt(sd1^2 + sd2^2 + sd3^2)

I am very leery of all of this because you can't find an example where this was actually ever done with interaction contrasts, and because in any but the simplest situations, the whole idea of effect size is flaky; people improvise something such as what I suggest, and then nobody really knows what you're talking about. You wind up with numbers, but they answer a question that can't be stated clearly.

Source Link
Russ Lenth
  • 21.3k
  • 33
  • 70

I guess if a Cohen's d makes sens, you can do something like

CON <- contrast(emm, interaction = TRUE, "pairwise", adjust="mvt")
eff_size(CON, sigma = ???, edf = ???, method = "identity")

(see the examples in the help page for eff_size) but you need to replace the ???s with reasonable values -- and be able to explain and defend them. You apparently have a mixed model, and I'm not sure Cohen's d is even defined because there are multiple sigma values involved. But I suppose that for edf you can use #groups - 1 for the coarsest grouping. For sigma, possibly you can do VarCorr(model) to see all the variance estimates; and then combine them. For instance, if there are three SDs sd1, sd2, sd3 you might use

sigma <- sqrt(sd1^2 + sd2^2 + sd3^2)

I am very leery of all of this because you can't find an example where this was actually ever done with interaction contrasts, and because in any but the simplest situations, the whole idea of effect size is flaky; people improvise something such as what I suggest, and then nobody really knows what you're talking about. You wind up with numbers, but they answer a question that can't be stated clearly.