Update app.R

app.R

@@ -1,5 +1,7 @@
+# Script: app_ono.R
 # setwd("~/Dropbox/OptimizingSI/Analysis/ono")
 
+options(error = NULL)
 library(shiny)
 library(ggplot2)
 library(strategize)
@@ -128,15 +130,17 @@ server <- function(input, output, session) {
       if (input$case_type == "Average") {
         # Subset data for Average case
         if (input$respondent_group == "All") {
-          indices <- 1:nrow(my_data_FULL)
+          indices <- which( my_data$Office == "President" )
         } else {
-          indices <- which(my_data_FULL$R_Partisanship == input$respondent_group)
+          indices <- which(my_data_FULL$R_Partisanship == input$respondent_group & 
+                             my_data$Office == "President")
         }
         
         FACTOR_MAT <- FACTOR_MAT_FULL[indices, !colnames(FACTOR_MAT_FULL) %in% "Office"]
         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"]
         
         incProgress(0.4, detail = "Running strategize...")
@@ -146,8 +150,11 @@ server <- function(input, output, session) {
           Y = Yobs,
           W = FACTOR_MAT,
           X = X,
+          pair_id = pair_id,
+          
           p_list = assignmentProbList,
           lambda = my_lambda,
+          diff = TRUE, 
           adversarial = FALSE,
           K = 1L, # Base analysis
           nSGD = params$nSGD,
@@ -195,25 +202,54 @@ server <- function(input, output, session) {
         
         slate_list <- list("Democratic" = Primary_D_slate, "Republican" = Primary_R_slate)
         
+        # subset data 
+        indices <- which( my_data$R_Partisanship %in% c("Republican","Democrat") & 
+                           my_data$Office == "President" )
+        FACTOR_MAT <- FACTOR_MAT_FULL[indices, 
+                                      !colnames(FACTOR_MAT_FULL) %in% c("Office",
+                                                                        "Party.competition",
+                                                                        "Party.affiliation")]
+        Yobs <- Yobs_FULL[indices]
+        my_data_red <- my_data_FULL[indices,]
+        pair_id <- pair_id_FULL[indices]
+        cluster_var <- cluster_var_FULL[ indices ]
+        my_data_red$Party.affiliation_clean <- ifelse(my_data_red$Party.affiliation == "Republican Party",
+                                                      yes = "Republican", no = ifelse(my_data_red$Party.affiliation == "Democratic Party",
+                                                                                      yes = "Democrat",no = "Independent"))
+        
+        # subset cols 
+        assignmentProbList <- assignmentProbList_FULL[colnames(FACTOR_MAT)]
+        slate_list$Democratic <- slate_list$Democratic[names(assignmentProbList)]
+        slate_list$Republican <- slate_list$Republican[names(assignmentProbList)]
+        
         incProgress(0.4, detail = "Running strategize...")
         
         # Compute with strategize using a single lambda
         Qoptimized <- strategize(
           Y = Yobs,
           W = FACTOR_MAT,
-          X = X,
+          #X = X,
+          X = NULL,
           p_list = assignmentProbList,
           slate_list = slate_list,
-          competing_group_variable_respondent = my_data_FULL$R_Partisanship,
-          competing_group_variable_candidate = ifelse(my_data_FULL$Party.affiliation == "Republican Party", "Republican",
-                                                      ifelse(my_data_FULL$Party.affiliation == "Democratic Party", "Democrat", "Independent")),
+          varcov_cluster_variable = cluster_var,
+          competing_group_variable_respondent = my_data_red$R_Partisanship,
+          competing_group_variable_candidate = my_data_red$Party.affiliation_clean,
+          competing_group_competition_variable_candidate = my_data_red$Party.competition,
+          pair_id = pair_id,
+          respondent_id = my_data_red$respondentIndex,
+          respondent_task_id = my_data_red$task,
+          profile_order = my_data_red$profile,
+          
           lambda = my_lambda,
+          diff = TRUE, 
+          force_gaussian = FALSE,
           adversarial = TRUE,
+          nFolds_glm = 3L, 
           K = 1L,
           nMonte_adversarial = 100L,
           nSGD = params$nSGD,
           penalty_type = params$penalty_type,
-          folds = params$nFolds,
           use_optax = params$use_optax,
           compute_se = params$compute_se,
           conf_level = params$conf_level,