Update cdf weights plot

assets/crps_learning/weights_plot/index.qmd

@@ -1,5 +1,5 @@
 ---
-title: "Knots-Demo"
+title: "CDF Weights"
 date: 2025-07-10
 format:
     revealjs:
@@ -15,7 +15,7 @@ d3 = require("d3@7")
 ```
 
 ```{ojs}
-bsplineData = FileAttachment("basis_functions.csv").csv({ typed: true })
+cdf_data = FileAttachment("cdf_data.csv").csv({ typed: true })
 ```
 
 ```{ojs}
@@ -47,9 +47,9 @@ function updateChartInner(g, x, y, linesGroup, color, line, data) {
 
 chart = {
   // State variable for selected mu parameter
-  let selectedMu = 0.5;
+  let selectedMu = 1;
   
-  const filteredData = () => bsplineData.filter(d =>
+  const filteredData = () => cdf_data.filter(d =>
     Math.abs(selectedMu - d.mu) < 0.001
   );
   
@@ -84,7 +84,11 @@ chart = {
       muDisplay.text(selectedMu.toFixed(1));
       updateChart(filteredData());
     })
-    .style('width', '100%');
+    .style('width', '100%')
+    //.style('-webkit-appearance', 'none')
+    .style('appearance', 'none')
+    .style('height', '8px')
+    .style('background', '#BDBDBDFF');
     
   const muDisplay = sliderContainer.append('span')
     .text(selectedMu.toFixed(1))
@@ -104,7 +108,7 @@ chart = {
     });
 
    // Build SVG
-   const width = 1200;
+   const width = 800;
    const height = 450;
    const margin = {top: 40, right: 20, bottom: 40, left: 40};
    const innerWidth = width - margin.left - margin.right;

assets/crps_learning/weights_plot/make_cdf_data.R

@@ -0,0 +1,78 @@
+library(tidyverse)
+
+source("assets/01_common.R")
+
+set.seed(2002)
+# Experts
+N <- 2
+# Observations
+T <- 2^5
+# Size of probability grid
+P <- 999
+prob_grid <- 1:P / (P + 1)
+
+# Realized observations
+y <- rnorm(T)
+
+# Deviation of the experts
+dev <- c(-1, 3)
+experts_sd <- c(1, sqrt(4))
+
+# Expert predictions
+experts <- array(dim = c(P, N))
+
+seq(-5, 10, length.out = P) -> x_grid
+
+experts[, 1] <- qnorm(prob_grid, mean = dev[1], sd = experts_sd[1])
+experts[, 2] <- qnorm(prob_grid, mean = dev[2], sd = experts_sd[2])
+
+experts <- rbind(c(rep(min(experts), N)), experts)
+experts <- rbind(experts, c(rep(max(experts), N)))
+prob_grid <- c(0, prob_grid, 1)
+
+naive <- 1
+
+df <- data.frame(
+  x = rep(prob_grid, each = N),
+  y = c(t(experts)),
+  expert = rep(1:N, (P + 2)),
+  naive = rep(naive, (P + 2) * N)
+)
+
+naive <- seq(0, 1, length.out = 11)
+
+dfs <- list()
+
+df_old <- df
+
+for (i in seq_along(naive)) {
+  df_old$naive <- naive[i]
+
+  df_new <- data.frame(
+    x = prob_grid,
+    y = (experts[, 1] * (naive[i] * (0.5) + (1 - naive[i]) * (1 - prob_grid)) + (naive[i] * 0.5 + (1 - naive[i]) * (prob_grid)) * experts[, 2]),
+    expert = 3,
+    naive = rep(naive[i], (P + 2))
+  )
+
+  dfs[[i + 1]] <- bind_rows(df_old, df_new)
+}
+
+dfs <- reduce(dfs, bind_rows)
+
+colnames(dfs) <- c("y", "x", "b", "mu")
+
+dfs %>%
+  ggplot(aes(x = x, y = y, color = factor(b))) +
+  geom_line() +
+  labs(
+    title = "Expert Predictions",
+    x = "Probability Grid",
+    y = "Predicted Value"
+  ) +
+  theme_minimal() +
+  scale_color_brewer(palette = "Set1") +
+  theme(legend.position = "top") +
+  facet_wrap(. ~ mu, ncol = 3)
+
+write_csv(dfs, "assets/crps_learning/weights_plot/cdf_data.csv")