Calculating 95% confidence interval for Repeated Measurement ANOVA output

Question

I'm conducting a repeated measurement ANOVA analysis below (both factors are within-subject factors):

SUN_REPORTED_RM <- 
  aov_car(
    RT ~ condition*Proportion + 
    Error(subject / (condition * Proportion)), 
    data = Sun_Contrast3, 
    anova_table = list(es = "pes", correction = "GG")
  )
knitr::kable(nice(SUN_REPORTED_RM))

Here is the output:

Effect  df  MSE F   pes p.value
condition   1, 30   0.98    0.93    .03 .34
Proportion  1.59, 47.58 0.40    29.92 ***   .50 <.0001
condition:Proportion    1.89, 56.60 0.33    1.22    .04 .30

I wonder if it's possible to calculate 95% confidence intervals for both the main effects and the interaction? If yes, how to do it? If not, could you justify why?

Thanks!

Answer 1

Well. I found an answer myself. See this link https://easystats.github.io/effectsize/articles/anovaES.html