eff plotter

plot_eff_against_power.R

+library(ggplot2)
+
+# specify how many simulations you want to run
+nSimul <- 10000
+
+# specify how many effects size you want to test
+effects <- seq(.1,.9,.2)
+
+# specify how many sample sizes you want to test
+samples <- seq(10,150,10)
+
+# Storage for plotting
+results <- list()
+
+# Simulation loop
+for (effect in effects) {
+  sample.storage <- numeric(length(samples))
+  
+  for (ii in seq_along(samples)) {
+    
+    p.storage <- numeric(nSimul)
+    
+    for (simulation in seq(nSimul)) {
+      
+      # run statistical test
+      x <- rnorm(n = samples[ii], mean = 0, sd = 1)
+      y <- rnorm(n = samples[ii], mean = effect, sd = 1)
+      
+      #extract significance
+      z <- t.test(x, y)
+      p.storage[simulation] <- z$p.value
+    }
+    
+    achieved.power <- mean(p.storage < 0.05)
+    sample.storage[ii] <- achieved.power
+  }
+  
+  # Store results for the current effect size
+  results[[paste0("effect_", effect)]] <- data.frame(
+    sample_size = samples,
+    achieved_power = sample.storage
+  )
+}
+
+# Combine results into a single data frame for plotting
+plot_data <- do.call(rbind, lapply(names(results), function(effect) {
+  data <- results[[effect]]
+  data$effect <- effect
+  data
+}))
+
+# Convert effect to numeric for better plotting
+plot_data$effect <- as.numeric(gsub("effect_", "", plot_data$effect))
+
+# Create the plot
+ggplot(plot_data, aes(x = sample_size, y = achieved_power, color = factor(effect))) +
+  geom_line(linewidth = 1) +
+  labs(
+    title = "Achieved Power vs. Sample Size",
+    x = "Sample Size",
+    y = "Achieved Power",
+    color = "Effect Size"
+  ) +
+  theme_minimal()