Render code harmless

R/randomized_saturation_designer.R

@@ -1,93 +1,93 @@
-N_networks <- 10
-max_network_size <- 20
-min_network_size <- 10
-ate <- .5
-outcome_sd <- 1
-ate_per_neighbor_treated <- .01
-saturations <- c(0,.25,.5,.75)
-icc <- .1
-
-population <- declare_population(
-  networks = add_level(
-    N = N_networks,
-    network_size = round(runif(N,min_network_size,max_network_size))
-  ),
-  individuals = add_level(
-    N = network_size,
-    u_0 = draw_normal_icc(mean = 0,
-                          clusters = networks,
-                          sd = outcome_sd,
-                          ICC = icc)
-  )
-)
-
-assign_S_to_clusters <- declare_assignment(
-  clusters = networks, 
-  conditions = saturations, 
-  assignment_variable = "S"
-)
-assign_Z_to_individuals <- declare_assignment(
-  handler = function(data){
-    data$Z <- with(data,block_ra(
-      blocks = networks, block_prob = tapply(S,networks,mean)))
-    return(data)
-  }
-)
-
-within_cluster_spillover <- declare_step(
-  individuals = modify_level(
-    n_neighbors_treated = ave(x = Z, networks, FUN = sum)),
-  label = "Add spillover variables")
-
-potential_outcomes_no_spillover <- 
-  declare_potential_outcomes(Y ~ u_0 + ate * Z)
-potential_outcomes_spillover <- declare_potential_outcomes(
-    Y_S ~ u_0 + ate * Z + 
-      n_neighbors_treated * ate_per_neighbor_treated)
-
-ate <- declare_estimand(ate = mean(Y_Z_1 - Y_Z_0))
-spillover <- declare_estimand(spillover = ate_per_neighbor_treated)
-
-reveal_Y_S <- declare_reveal(Y_S,Z)
-reveal_Y <- declare_reveal(Y,Z)
-
-estimator <- declare_estimator(
-  formula = Y_S ~ Z + n_neighbors_treated, 
-  model = lm_robust,
-  clusters = networks,
-  coefficients = c(Z,n_neighbors_treated),
-  estimand = c(ate,spillover),
-  label = "OLS"
-)
-
-design <- declare_design(
-  population,
-  assign_S_to_clusters,
-  assign_Z_to_individuals,
-  potential_outcomes_no_spillover,
-  within_cluster_spillover,
-  potential_outcomes_spillover,
-  estimands,
-  reveal_Y_S,
-  reveal_Y,
-  estimator
-)
-
-
-diagnose_design(design,sims = 1000,bootstrap = F)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+# N_networks <- 10
+# max_network_size <- 20
+# min_network_size <- 10
+# ate <- .5
+# outcome_sd <- 1
+# ate_per_neighbor_treated <- .01
+# saturations <- c(0,.25,.5,.75)
+# icc <- .1
+# 
+# population <- declare_population(
+#   networks = add_level(
+#     N = N_networks,
+#     network_size = round(runif(N,min_network_size,max_network_size))
+#   ),
+#   individuals = add_level(
+#     N = network_size,
+#     u_0 = draw_normal_icc(mean = 0,
+#                           clusters = networks,
+#                           sd = outcome_sd,
+#                           ICC = icc)
+#   )
+# )
+# 
+# assign_S_to_clusters <- declare_assignment(
+#   clusters = networks, 
+#   conditions = saturations, 
+#   assignment_variable = "S"
+# )
+# assign_Z_to_individuals <- declare_assignment(
+#   handler = function(data){
+#     data$Z <- with(data,block_ra(
+#       blocks = networks, block_prob = tapply(S,networks,mean)))
+#     return(data)
+#   }
+# )
+# 
+# within_cluster_spillover <- declare_step(
+#   individuals = modify_level(
+#     n_neighbors_treated = ave(x = Z, networks, FUN = sum)),
+#   label = "Add spillover variables")
+# 
+# potential_outcomes_no_spillover <- 
+#   declare_potential_outcomes(Y ~ u_0 + ate * Z)
+# potential_outcomes_spillover <- declare_potential_outcomes(
+#     Y_S ~ u_0 + ate * Z + 
+#       n_neighbors_treated * ate_per_neighbor_treated)
+# 
+# ate <- declare_estimand(ate = mean(Y_Z_1 - Y_Z_0))
+# spillover <- declare_estimand(spillover = ate_per_neighbor_treated)
+# 
+# reveal_Y_S <- declare_reveal(Y_S,Z)
+# reveal_Y <- declare_reveal(Y,Z)
+# 
+# estimator <- declare_estimator(
+#   formula = Y_S ~ Z + n_neighbors_treated, 
+#   model = lm_robust,
+#   clusters = networks,
+#   coefficients = c(Z,n_neighbors_treated),
+#   estimand = c(ate,spillover),
+#   label = "OLS"
+# )
+# 
+# design <- declare_design(
+#   population,
+#   assign_S_to_clusters,
+#   assign_Z_to_individuals,
+#   potential_outcomes_no_spillover,
+#   within_cluster_spillover,
+#   potential_outcomes_spillover,
+#   estimands,
+#   reveal_Y_S,
+#   reveal_Y,
+#   estimator
+# )
+# 
+# 
+# diagnose_design(design,sims = 1000,bootstrap = F)
+# 
+# 
+# 
+# 
+# 
+# 
+# 
+# 
+# 
+# 
+# 
+# 
+# 
+# 
+# 
+#