Update title in README; rebuild

03_anova/powersim-anova.md

@@ -35,7 +35,7 @@ dat <- data.frame(
   A = factor(rep(1:2, each = n/2), labels = c("low", "high")),
   B = factor(rep(rep(1:2, each = n/4), 2), labels = c("low", "high"))
 )
-X <- model.matrix(~ A*B, dat)
+X <- model.matrix(~ A * B, dat)
 unique(X)
 ```
 
@@ -73,7 +73,7 @@ boxplot(t(out))
 ``` r
 pval <- replicate(2000, {
   y <- means + rnorm(n, sd = 10)
-  m <- aov(y ~ A*B, dat)
+  m <- aov(y ~ A * B, dat)
   summary(m)[[1]]$"Pr(>F)"[3]      # test of interaction
 })
 mean(pval < 0.05)

04_ancova/exercises-ancova2.md

@@ -4,9 +4,7 @@ measurements
 
 ## MASS anorexia data
 
-1.  Analyze the original data:
+Data preparation
-    - In R, see ?MASS::anorexia
-    - Data preparation
 
 ``` r
 data(anorexia, package = "MASS")
@@ -20,11 +18,13 @@ lattice::xyplot(Postwt ~ Prewt, dat, groups = Treat,
                 type = c("g", "r", "p"), auto.key = TRUE)
 ```
 
-- Estimate the average treatment effect (ATE) for FT relative to CBT.
+1.  Analyze the original data:
-- What is the 95% CI for the ATE?
+    - In R, see ?MASS::anorexia
-- What are the pre- and post-weight means for the two groups?
+    - Estimate the average treatment effect (ATE) for FT relative to
-- What are the baseline-adjusted means for the two groups?
+      CBT.
-
+    - What is the 95% CI for the ATE?
+    - What are the pre- and post-weight means for the two groups?
+    - What are the baseline-adjusted means for the two groups?
 2.  Run a power simulation for a replication study:
     - Draw plausible pre-weights.
     - Specify the minimum relevant effect.
@@ -41,5 +41,3 @@ lattice::xyplot(Postwt ~ Prewt, dat, groups = Treat,
     - the R code, output, and plots (if any)
 
     Render the R or Rmd file to HTML.
-
-### Reference

05_mixed1/powersim-lmm.md

@@ -3,8 +3,8 @@ Power for mixed-effects models
 Last modified: 2026-01-09
 
 ``` r
-library(lattice)
+library("lattice")
-library(lme4)
+library("lme4")
 ```
 
 # Reanalysis

06_mixed2/exercises-datsimlmm.md

@@ -8,8 +8,8 @@ Exercises: Data simulation for crossed random-effects models
   “manually”
 
 ``` r
-library(lattice)
+library("lattice")
-library(lme4)
+library("lme4")
 
 #--------------- (1) Create data frame ----------------------------------------
 datsim <- expand.grid(subject = factor(c("s1" , "s2" , "s3" )),
@@ -40,9 +40,9 @@ e <- rnorm(18, mean = 0, sd = se)
 sig <- matrix(c(sy0^2, ry * sy0 * sy1, ry * sy0 * sy1, sy1^2), 2, 2)
 y01 <- MASS::mvrnorm(3, mu = c(0, 0), Sigma = sig)
 y0 <- rep(y01[,1], each = 6)
-y1 <- rep(c(0, y01[1,2],
+y1 <- rep(c(0, y01[1, 2],
-            0, y01[2,2],
+            0, y01[2, 2],
-            0, y01[3,2]), each = 3)
+            0, y01[3, 2]), each = 3)
 
 datsim$rt <- b0 + b1 + w + y0 + y1 + e
 

README.md

@@ -1,6 +1,5 @@
-Power simulations
+Methods Workshop WS 2025/26: Power simulations
 ================
-Nora Wickelmaier
 January, 21-22, 2026
 
 ## Schedule