Power analysis to support EPS small grant submission

ectva_power_sims.R

library(tidyverse)
library(Superpower)

# set means & standard deviations for idealised data
# (note experiment 1 data comes from the pilot experiment)
exp_1_means <- c(1334, 1594, 1588, 1725)
exp_1_sds <- c(308, 295, 338, 343)
exp_2_means <- c(1030, 1080, 1120, 1150)
exp_2_sds <- c(200, 200, 200, 200)

# plot the idealised data at population level
pd <- position_dodge(0.4)

tibble(
  csi = c("short", "short", "short", "short", 
          "long", "long", "long", "long"), 
  response = c("rep", "rep", "sw", "sw",
               "rep", "rep", "sw", "sw"), 
  stimulus = c("rep", "sw", "rep", "sw", 
               "rep", "sw", "rep", "sw"), 
  rt = c(exp_1_means, exp_2_means), 
  sd = c(exp_1_sds, exp_2_sds)) %>% 
  ggplot(aes(x = response, y = rt, group = stimulus)) +
  geom_point(aes(colour = stimulus), 
             position = pd) + 
  geom_errorbar(aes(colour = stimulus,
                    ymax = rt + sd, 
                    ymin = rt - sd), 
                position = pd) + 
  geom_line(aes(colour = stimulus), 
            position = pd) + 
  facet_wrap(~csi) + 
  theme_bw()

# power analysis
test <- Superpower::ANOVA_design(
  design = "2w*2w*2w",
  n = 100,
  mu = data$rt,
  sd = data$sd,
  r = 0.80,
  labelnames = c("csi", "short", "long",
                 "response", "rep", "sw",
                 "stimulus", "rep", "sw")
)

result <- Superpower::ANOVA_power(test, nsims = 10000)
# Power and Effect sizes for ANOVA tests
# power effect_size
# anova_csi                   100.00     0.92040
# anova_response              100.00     0.71464
# anova_stimulus              100.00     0.66611
# anova_csi:response          100.00     0.30111
# anova_csi:stimulus          100.00     0.46830
# anova_response:stimulus      98.67     0.15790
# anova_csi:response:stimulus  85.16     0.09256