cleaning up + implementing robustness checks

[ml_measurement_error_public.git] / simulations / summarize_estimator.R

diff --git a/simulations/summarize_estimator.R b/simulations/summarize_estimator.R
index 8199c0619496f81607dc22c5d7e5736f2ab106f6..3e4209f42f4c3486a89c1499e221c297b424b643 100644 (file)
--- a/simulations/summarize_estimator.R
+++ b/simulations/summarize_estimator.R
@@ -13,10 +13,11 @@ summarize.estimator <- function(df, suffix='naive', coefname='x'){
                   'accuracy_imbalance_difference'
                   ),
                with=FALSE]
                   'accuracy_imbalance_difference'
                   ),
                with=FALSE]

-    
+
+

     true.in.ci <- as.integer((part$Bxy >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (part$Bxy <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]]))
     zero.in.ci <- as.integer(0 >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (0 <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]])
     true.in.ci <- as.integer((part$Bxy >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (part$Bxy <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]]))
     zero.in.ci <- as.integer(0 >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (0 <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]])

-    bias <- part$Bxy - part[[paste0('B',coefname,'y.est.',suffix)]]
+    bias <- part[[paste0('B',coefname,'y')]] - part[[paste0('B',coefname,'y.est.',suffix)]]

     sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))
 
     part <- part[,':='(true.in.ci = true.in.ci,
     sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))
 
     part <- part[,':='(true.in.ci = true.in.ci,


@@ -28,8 +29,15 @@ summarize.estimator <- function(df, suffix='naive', coefname='x'){
                           mean.bias = mean(bias),
                           mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
                           var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
                           mean.bias = mean(bias),
                           mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
                           var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]),

-                          est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.95,na.rm=T),
-                          est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.05,na.rm=T),
+                          est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.975,na.rm=T),
+                          est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.025,na.rm=T),
+                          mean.ci.upper = mean(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]],na.rm=T),
+                          mean.ci.lower = mean(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]],na.rm=T),
+                          ci.upper.975 = quantile(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]],0.975,na.rm=T),
+                          ci.upper.025 = quantile(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]],0.025,na.rm=T),
+                          ci.lower.975 = quantile(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]],0.975,na.rm=T),
+                          ci.lower.025 = quantile(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]],0.025,na.rm=T),
+                          N.ci.is.NA = sum(is.na(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]])),

                           N.sims = .N,
                           p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
                           variable=coefname,
                           N.sims = .N,
                           p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
                           variable=coefname,