diff --git a/docs/2024-01-DE/index.html b/docs/2024-01-DE/index.html
index fd3d806..1660fd1 100644
--- a/docs/2024-01-DE/index.html
+++ b/docs/2024-01-DE/index.html
@@ -97,6 +97,7 @@ <h3>R scripts</h3>
 	<ul>
 	  <li><p><a href="scripts/survey-script.R">Preliminary analysis of survey data</a></p></li>
 	  <li><p><a href="scripts/bivariate.R">Summarizing bivariate data</a></p></li>
+	  <li><p><a href="scripts/lad-optimization.R">Optimization for LAD regression</a></p></li>
 	</ul>
 	
 	<h3>Resources</h3>
diff --git a/docs/2024-01-DE/scripts/bivariate.R b/docs/2024-01-DE/scripts/bivariate.R
index ac32f20..6a2529b 100644
--- a/docs/2024-01-DE/scripts/bivariate.R
+++ b/docs/2024-01-DE/scripts/bivariate.R
@@ -239,5 +239,23 @@ xyplot(BPSysAve ~ Age, data = nhSubF, grid = TRUE,
        type = c("p", "smooth"), abline = abNH.FLAD, col.line = "red")
 
 
+## Is R^2 == r^2
+
+R2 <- function(x, y) {
+  if (length(x) != length(y)) stop("Lengths and x and y do not match")
+  skip <- is.na(x) | is.na(y)
+  x <- x[!skip]
+  y <- y[!skip]
+  ab <- lsfit(x, y)
+  r <- cor(x, y)
+  T2 <- sum((y - mean(y))^2)
+  S2 <- sum((y - ab[1] - ab[2] * x)^2)
+  R2 <- 1 - S2 / T2
+  c(r2 = r^2, R2 = R2)
+}
+
+R2(runif(20), runif(20))
+R2(runif(20), rnorm(20))
+
 
 
diff --git a/docs/2024-01-DE/scripts/lad-optimization.R b/docs/2024-01-DE/scripts/lad-optimization.R
new file mode 100644
index 0000000..7cf9c5b
--- /dev/null
+++ b/docs/2024-01-DE/scripts/lad-optimization.R
@@ -0,0 +1,85 @@
+
+## optimization for LAD regression
+
+lad_loss_function <- function(x, y) {
+  if (length(x) != length(y)) stop("Lengths and x and y do not match")
+  skip <- is.na(x) | is.na(y)
+  x <- x[!skip]
+  y <- y[!skip]
+  lambda <- function(a, b) {
+    if (length(a) != 1) stop("a must be scalar")
+    if (length(b) != 1) stop("b must be scalar")
+    e <- abs(y - a - b * x)
+    sum(e)
+  }
+  lambda
+}
+
+
+DavisLoss <- with(Davis, lad_loss_function(x = height, y = weight))
+
+DavisLoss
+DavisLoss(2, 4)
+
+## scope: x and y are found (in scope) because x and y are available 
+## in the function that defined this function. This kind of scoping 
+## is known as "lexical scoping".
+
+## Question: How to find (a, b) which minimizes DavisLoss(a, b)?
+
+## fix a=0, and vary b
+
+bseq <- seq(0, 10, length.out = 21)
+
+## loops (for vs sapply)
+
+f <- numeric(length(bseq))
+for (i in seq_along(f)) {
+  f[i] <- DavisLoss(a = 0, b = bseq[i])
+} 
+
+## alternative using sapply() / vapply()
+
+
+sapply(bseq, function(b) DavisLoss(a = 0, b = b))
+
+sapply(bseq, DavisLoss, a = 0)
+
+## safer version of sapply where expected result type is specified
+vapply(bseq, DavisLoss, a = 0, FUN.VALUE = numeric(1))
+
+
+bseq <- seq(0, 2, length.out = 201)
+f <- vapply(bseq, DavisLoss, a = 0, FUN.VALUE = numeric(1))
+
+plot(f ~ bseq, type = "l")
+
+g <- 
+  expand.grid(a = seq(-500, 500, length.out = 200),
+              b = seq(-3, 3, length.out = 200))
+
+g$loss <- 0
+
+for (i in seq_along(g$loss)) {
+  g$loss[i] <- DavisLoss(g$a[i], g$b[i])
+}
+
+g[which.min(g$loss), ]
+
+## contour plots / image plots or level plots
+
+library(lattice)
+
+contourplot(loss ~ a + b, data = g)
+
+levelplot(loss ~ a + b, data = g)
+
+wireframe(loss ~ a + b, data = g, shade = TRUE)
+
+plot(weight ~ height, Davis)
+abline(abDavisLAD)
+abline(-113.0653, 1.040201, col = "red")
+
+
+
+