Update app.R

app.R

@@ -9,33 +9,38 @@ library(strategize)
 library(dplyr)
 
 # Custom plotting function for optimal strategy distributions
-plot_factor <- function(pi_star_list, pi_star_se_list, factor_name, zStar = 1.96) {
+plot_factor <- function(pi_star_list, 
+                        pi_star_se_list, 
+                        factor_name, 
+                        zStar = 1.96,
+                        n_strategies = 1L) {
   probs <- lapply(pi_star_list, function(x) x[[factor_name]])
   ses <- lapply(pi_star_se_list, function(x) x[[factor_name]])
   levels <- names(probs[[1]])
-  n_strategies <- length(probs)
   
   # Create data frame for plotting
   df <- do.call(rbind, lapply(1:n_strategies, function(i) {
     data.frame(
       Strategy = if (n_strategies == 1) "Optimal" else c("Democrat", "Republican")[i],
       Level = levels,
-      Probability = probs[[i]],
-      SE = ses[[i]]
+      Probability = probs[[i]]
+      #SE = ses[[i]]
     )
   }))
   
   # Plot with ggplot2
   p <- ggplot(df, aes(x = Level, y = Probability, fill = Strategy)) +
     geom_bar(stat = "identity", position = position_dodge(width = 0.9), width = 0.8) +
-    geom_errorbar(aes(ymin = Probability - zStar * SE, ymax = Probability + zStar * SE),
-                  position = position_dodge(width = 0.9), width = 0.25) +
+    #geom_errorbar(aes(ymin = Probability - zStar * SE, ymax = Probability + zStar * SE),
+                  #position = position_dodge(width = 0.9), width = 0.25) +
     labs(title = paste("Optimal Distribution for", factor_name),
          x = "Level", y = "Probability") +
     theme_minimal() +
     theme(axis.text.x = element_text(angle = 45, hjust = 1),
           legend.position = "top") +
-    scale_fill_manual(values = c("Democrat" = "#89cff0", "Republican" = "red", "Optimal" = "black"))
+    scale_fill_manual(values = c("Democrat" = "#89cff0", 
+                                 "Republican" = "red",
+                                 "Optimal" = "black"))
   
   return(p)
 }
@@ -71,14 +76,14 @@ ui <- fluidPage(
       numericInput("lambda_input", "Lambda (regularization):", 
                    value = 0.01, min = 1e-6, max = 10, step = 0.01),
       actionButton("compute", "Compute Results", class = "btn-primary"),
-      br(),
-      selectInput("previousResults", "View Previous Results:",
-                  choices = NULL),
       hr(),
       h4("Visualization"),
       selectInput("factor", "Select Factor to Display:",
                   choices = NULL),
       hr(),
+      selectInput("previousResults", "View Previous Results:",
+                  choices = NULL),
+      hr(),
       h5("Instructions:"),
       p("1. Select a case type and, for Average case, a respondent group."),
       p("2. Specify the single lambda to be used by strategize."),
@@ -170,14 +175,16 @@ server <- function(input, output, session) {
         }
         
         FACTOR_MAT <- FACTOR_MAT_FULL[indices, 
-                                      !colnames(FACTOR_MAT_FULL) %in% c("Office","Party.affiliation","Party.competition")]
+                                      !colnames(FACTOR_MAT_FULL) %in% 
+                                        c("Office","Party.affiliation","Party.competition")]
         Yobs <- Yobs_FULL[indices]
         X <- X_FULL[indices, ]
         log_pr_w <- log_pr_w_FULL[indices]
         pair_id <- pair_id_FULL[indices]
-        assignmentProbList <- assignmentProbList_FULL[!names(assignmentProbList_FULL) %in% "Office"]
+        assignmentProbList <- assignmentProbList_FULL[names(FACTOR_MAT)]
         
-        incProgress(0.4, detail = "Running strategize...")
+        incProgress(0.4, 
+                    detail = "Running strategize...")
         
         # Compute with strategize
         Qoptimized <- strategize(
@@ -201,7 +208,8 @@ 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
         
@@ -292,20 +300,7 @@ server <- function(input, output, session) {
           conda_env = params$conda_env,
           conda_env_required = params$conda_env_required
         )
-        
-        # Identify Democrat vs Republican based on "Pro-life" stance
-        prolife_probs <- c(
-          Qoptimized$pi_star_point$Democrat$Position.on.abortion["Pro-life"],
-          Qoptimized$pi_star_point$Republican$Position.on.abortion["Pro-life"]
-        )
-        which_repub <- which.max(prolife_probs)
-        if (which_repub == 1) {
-          # Swap if the first is actually "Republican"
-          Qoptimized$pi_star_point <- list(k1 = Qoptimized$pi_star_point$k2, 
-                                           k2 = Qoptimized$pi_star_point$k1)
-          Qoptimized$pi_star_se <- list(k1 = Qoptimized$pi_star_se$k2, 
-                                        k2 = Qoptimized$pi_star_se$k1)
-        }
+        Qoptimized$n_strategies <- 2L
       }
       
       incProgress(0.8, detail = "Finalizing results...")
@@ -334,7 +329,11 @@ server <- function(input, output, session) {
     factor_name <- input$factor
     pi_star_list <- selectedResult()$pi_star_point
     pi_star_se_list <- selectedResult()$pi_star_se
-    plot_factor(pi_star_list, pi_star_se_list, factor_name)
+    n_strategies <- selectedResult()$n_strategies
+    plot_factor(pi_star_list = pi_star_list, 
+                pi_star_se_list = pi_star_se_list, 
+                factor_name =factor_name, 
+                n_strategies = n_strategies)
   })
   
   # Render Q value