Update app.R

app.R

@@ -1,5 +1,6 @@
 # setwd('~/Dropbox/ImageSeq/')
 
+options(error = NULL)
 library(shiny)
 library(dplyr)
 library(fields)  # For image.plot in heatMap
@@ -104,7 +105,8 @@ ui <- fluidPage(
       checkboxInput("compareToBest", "Compare to best single scale", value = FALSE)
     ),
     mainPanel(
-      plotOutput("heatmapPlot", height = "600px")
+      plotOutput("heatmapPlot", height = "600px"),
+      div(style = "margin-top: 10px; font-style: italic;", uiOutput("contextNote"))
     )
   )
 )
@@ -230,12 +232,13 @@ server <- function(input, output) {
     if (input$compareToBest) {
       z <- grouped_data$improvement
       main <- paste(input$application, "-", input$metric, "improvement over best single scale")
-      max_abs <- max(abs(z), na.rm = TRUE)
-      zlim <- if (!is.na(max_abs)) c(-max_abs, max_abs) else NULL
+      #max_abs <- max(abs(z), na.rm = TRUE)
+      #zlim <- if (!is.na(max_abs)) c(-max_abs, max_abs) else NULL
+      zlim <- range(z, na.rm = TRUE) 
     } else {
       z <- grouped_data$mean_metric
       main <- paste(input$application, "-", input$metric)
-      zlim <- range(z, na.rm = TRUE)  # Changed from zlim <- NULL
+      zlim <- range(z, na.rm = TRUE) 
     }
     
     customPalette <- colorRampPalette(c("blue", "white", "red"))(50)
@@ -245,13 +248,57 @@ server <- function(input, output) {
             N = 50,
             main = main,
             xlab = "Image Dimension 1",
-            ylab = "Image Dimensions 2",
+            ylab = "Image Dimension 2",
             useLog = "xy",
             myCol = customPalette,
             cex.lab = 1.4,
             zlim = zlim,
             optimal_point = interp_data$optimal_point)
   })
+  
+  # Contextual Note Output
+  output$contextNote <- renderText({
+    if (input$compareToBest) {
+      paste("This heatmap shows the improvement in", input$metric, 
+            "over the best single scale for", input$application, 
+            "using the", input$model, "model. The green star marks the optimal point.",
+            "The Peru RCT involves a multifaceted graduation program treatment to reduce poverty outcomes.",
+            "The Uganda RCT involves a cash grant program to stimulate human capital and living conditions among the poor.",
+            "For more information, see <a href='https://arxiv.org/abs/2411.02134' target='_blank'>https://arxiv.org/abs/2411.02134</a>", 
+            "<div style='font-size: 10px; line-height: 1.5;'>",
+            "<b>Glossary:</b><br>",
+            "• <b>Model:</b> The neural-network backbone (e.g., clip-rsicd) transforming satellite images into numerical representations.<br>",
+            "• <b>Metric:</b> The criterion (e.g., RATE Ratio, RMSE) measuring performance or heterogeneity detection.<br>",
+            "• <b>Compare to best single-scale:</b> Toggle showing metric improvement relative to the best single-scale baseline.<br>",
+            "• <b>ImageDim1, ImageDim2:</b> Image sizes (e.g., 64×64, 128×128) for multi-scale analysis.<br>",
+            "• <b>RATE Ratio:</b> Statistic indicating how well the model detects treatment-effect variation.<br>",
+            "• <b>PC:</b> Principal Components; a compression step of neural representations.<br>",
+            "• <b>MeanDiff, MeanDiff_pc:</b> Gain in RATE Ratio from multi-scale vs. single-scale, with '_pc' for compressed data.<br>",
+            "• <b>RMSE:</b> Root Mean Squared Error, measuring prediction accuracy in simulations.<br>",
+            "</div>"
+            )
+    } else {
+      paste("This heatmap displays", input$metric, 
+            "for", input$application, 
+            "using the", input$model, 
+            "model across different image dimension combinations. The green star marks the optimal point.",
+            "The Peru RCT involves a multifaceted graduation program treatment to reduce poverty outcomes.",
+            "The Uganda RCT involves a cash grant program to stimulate human capital and living conditions among the poor.",
+            "For more information, see <a href='https://arxiv.org/abs/2411.02134' target='_blank'>https://arxiv.org/abs/2411.02134</a>", 
+            "<div style='font-size: 10px; line-height: 1.5;'>",
+            "<b>Glossary:</b><br>",
+            "• <b>Model:</b> The neural-network backbone (e.g., clip-rsicd) transforming satellite images into numerical representations.<br>",
+            "• <b>Metric:</b> The criterion (e.g., RATE Ratio, RMSE) measuring performance or heterogeneity detection.<br>",
+            "• <b>Compare to best single-scale:</b> Toggle showing metric improvement relative to the best single-scale baseline.<br>",
+            "• <b>ImageDim1, ImageDim2:</b> Image sizes (e.g., 64×64, 128×128) for multi-scale analysis.<br>",
+            "• <b>RATE Ratio:</b> Statistic indicating how well the model detects treatment-effect variation.<br>",
+            "• <b>PC:</b> Principal Components; a compression step of neural representations.<br>",
+            "• <b>MeanDiff, MeanDiff_pc:</b> Gain in RATE Ratio from multi-scale vs. single-scale, with '_pc' for compressed data.<br>",
+            "• <b>RMSE:</b> Root Mean Squared Error, measuring prediction accuracy in simulations.<br>",
+            "</div>"
+            )
+    }
+  })
 }
 
 # Run the Shiny App