Update app.R

app.R

@@ -31,10 +31,12 @@ plot_factor <- function(pi_star_list,
   # Manual dodging: Create numeric x-positions with offsets
   df$Level_num <- as.numeric(as.factor(df$Level))  # Convert Level to numeric (1, 2, ...)
   if (n_strategies == 1) {
-    df$x_dodged <- df$Level_num  # No dodging for single strategy
+    df$x_dodged <- df$Level_num   # No dodging for single strategy
   } else {
     # Apply ±offset for Democrat/Republican
-    df$x_dodged <- df$Level_num + ifelse(df$Strategy == "Democrat", -0.05, 0.05)
+    df$x_dodged <- df$Level_num  + 
+                  ifelse(df$Strategy == "Democrat",
+                          -0.05, 0.05)
   }
   
   # Plot with ggplot2
@@ -53,19 +55,24 @@ plot_factor <- function(pi_star_list,
     ) +
     # Text label above the point
     geom_text(
-      aes(x = x_dodged, label = sprintf("%.2f", Probability)),
+      aes(x = x_dodged, 
+          label = sprintf("%.2f", Probability)),
       vjust = -0.7,
       size = 3
     ) +
     # Set x-axis with original Level labels
     scale_x_continuous(
       breaks = unique(df$Level_num),
-      labels = unique(df$Level)
+      labels = unique(df$Level),
+      limits = c(min(df$x_dodged)-0.20,
+                 max(df$x_dodged)+0.20)
     ) +
     # Labels
     labs(
-      title = sprintf("Optimal Distribution for *%s*", 
-                      gsub(factor_name, pattern = "\\.", replace = " ")),
+      title = "Optimal Distribution for:",
+      subtitle = sprintf("*%s*", gsub(factor_name, 
+                                      pattern = "\\.", 
+                                      replace = " ")),
       x = "Level",
       y = "Probability"
     ) +
@@ -85,7 +92,7 @@ plot_factor <- function(pi_star_list,
     # Manual color scale for different strategies
     scale_color_manual(values = c("Democrat" = "#89cff0",
                                   "Republican" = "red",
-                                  "Optimal" = "black"))
+                                  "Optimal" = "black")) 
   
   return(p)
 }
@@ -236,19 +243,21 @@ ui <- fluidPage(
                  plotOutput("strategy_plot", height = "600px")),
         tabPanel("Q Value",
                  verbatimTextOutput("q_value"),
-                 p("Q represents the estimated outcome (e.g., strategy probability)
+                 p("Q represents the estimated outcome
                    under the optimal strategy, with 95% confidence interval.")),
         tabPanel("About",
                  h3("About this page"),
                  p("This page app explores the ",
                    a("strategize R package", href = "https://github.com/cjerzak/strategize-software/", target = "_blank"), 
-                   " R package using Ono forced conjoint experimental data. 
+                   " using Ono forced conjoint experimental data. 
                    It computes optimal strategies for Average (optimizing for a respondent group) 
                    and Adversarial (optimizing for both parties in competition) cases on the fly."),
                  p(strong("Average Case:"), 
                    "Optimizes candidate characteristics for a selected respondent group."),
-                 p(strong("Adversarial Case"),
-                   "Finds equilibrium strategies for Democrats and Republicans.")
+                 p(strong("Adversarial Case:"),
+                   "Finds equilibrium strategies for Democrats and Republicans."),
+                 p(strong("More information:"),
+                   a("strategizelab.org", href = "https://strategizelab.org", target = "_blank"))
         )
       ),
       br(),
@@ -353,7 +362,6 @@ server <- function(input, output, session) {
           conda_env = params$conda_env,
           conda_env_required = params$conda_env_required
         )
-        Qoptimized <- Qoptimized[1] # select first 
         Qoptimized$n_strategies <- 1L
       } else {
         # Adversarial case
@@ -450,6 +458,12 @@ server <- function(input, output, session) {
         Qoptimized$n_strategies <- 2L
       }
       
+      Qoptimized <- Qoptimized[c("pi_star_point",
+                                 "pi_star_se",
+                                 "Q_point",
+                                 "Q_se", 
+                                 "n_strategies")]
+      
       incProgress(0.8, detail = "Finalizing results...")
       
       # Store in the reactiveValues cache
@@ -479,16 +493,20 @@ server <- function(input, output, session) {
     n_strategies <- selectedResult()$n_strategies
     plot_factor(pi_star_list = pi_star_list, 
                 pi_star_se_list = pi_star_se_list, 
-                factor_name =factor_name, 
+                factor_name = factor_name, 
                 n_strategies = n_strategies)
   })
   
   # Render Q value
   output$q_value <- renderText({
     req(selectedResult())
-    q_point <- selectedResult()$Q_point_mEst
-    q_se <- selectedResult()$Q_se_mEst
-    paste("Estimated Q Value: ", sprintf("%.3f ± %.3f", q_point, 1.96 * q_se))
+    q_point <- selectedResult()$Q_point
+    q_se <- selectedResult()$Q_se
+    show_se <- length(q_se) > 0
+    if(show_se){ show_se <- q_se > 0 }
+    if(!show_se){ render_text <- paste("Estimated Q Value:", sprintf("%.3f", q_point)) }
+    if(show_se){ render_text <- paste("Estimated Q Value:", sprintf("%.3f ± %.3f", q_point, 1.96 * q_se)) }
+    render_text
   })
   
   # Show which set of parameters (label) is currently selected