update simulation base from hyak

diff --git a/simulations/simulation_base.R b/simulations/simulation_base.R
index 73544e9aee194800d6ae7a9dd0e4f279db978869..bafd7d34bba08ccd43513e3d699f4e3090e8fa30 100644 (file)
--- a/simulations/simulation_base.R
+++ b/simulations/simulation_base.R
@@ -89,7 +89,7 @@ my.mle <- function(df){
     return(mlefit)
 }
 
-run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formula=w_pred~y){
+run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formula=w_pred~y, confint_method='quad'){
 
     (accuracy <- df[,mean(w_pred==y)])
     result <- append(result, list(accuracy=accuracy))
@@ -150,11 +150,23 @@ run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formu
 
     temp.df <- copy(df)
     temp.df[,y:=y.obs]
-    mod.caroll.lik <- measerr_mle_dv(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula)
-    fischer.info <- solve(mod.caroll.lik$hessian)
-    coef <- mod.caroll.lik$par
-    ci.upper <- coef + sqrt(diag(fischer.info)) * 1.96
-    ci.lower <- coef - sqrt(diag(fischer.info)) * 1.96
+
+    if(confint_method=='quad'){
+        mod.caroll.lik <- measerr_mle_dv(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula)
+        fischer.info <- solve(mod.caroll.lik$hessian)
+        coef <- mod.caroll.lik$par
+        ci.upper <- coef + sqrt(diag(fischer.info)) * 1.96
+        ci.lower <- coef - sqrt(diag(fischer.info)) * 1.96
+    }
+    else{ ## confint_method is 'profile'
+
+        mod.caroll.lik <- measerr_mle_dv(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula, method='bbmle')
+        coef <- coef(mod.caroll.lik)
+        ci <- confint(mod.caroll.lik, method='spline')
+        ci.lower <- ci[,'2.5 %']
+        ci.upper <- ci[,'97.5 %']
+    }
+
     result <- append(result,
                      list(Bxy.est.mle = coef['x'],
                           Bxy.ci.upper.mle = ci.upper['x'],
@@ -216,7 +228,7 @@ run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formu
 
 
 ## outcome_formula, proxy_formula, and truth_formula are passed to measerr_mle 
-run_simulation <-  function(df, result, outcome_formula=y~x+z, proxy_formula=NULL, truth_formula=NULL){
+run_simulation <-  function(df, result, outcome_formula=y~x+z, proxy_formula=NULL, truth_formula=NULL, confint_method='quad'){
 
     accuracy <- df[,mean(w_pred==x)]
     accuracy.y0 <- df[y<=0,mean(w_pred==x)]
@@ -328,11 +340,20 @@ run_simulation <-  function(df, result, outcome_formula=y~x+z, proxy_formula=NUL
     tryCatch({
         temp.df <- copy(df)
         temp.df <- temp.df[,x:=x.obs]
-        mod.caroll.lik <- measerr_mle(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula, truth_formula=truth_formula)
-        fischer.info <- solve(mod.caroll.lik$hessian)
-        coef <- mod.caroll.lik$par
-        ci.upper <- coef + sqrt(diag(fischer.info)) * 1.96
-        ci.lower <- coef - sqrt(diag(fischer.info)) * 1.96
+        if(confint_method=='quad'){
+            mod.caroll.lik <- measerr_mle(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula, truth_formula=truth_formula, method='optim')
+            fischer.info <- solve(mod.caroll.lik$hessian)
+            coef <- mod.caroll.lik$par
+            ci.upper <- coef + sqrt(diag(fischer.info)) * 1.96
+            ci.lower <- coef - sqrt(diag(fischer.info)) * 1.96
+        } else { # confint_method == 'bbmle'
+
+            mod.caroll.lik <- measerr_mle(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula, truth_formula=truth_formula, method='bbmle')
+            coef <- coef(mod.caroll.lik)
+            ci <- confint(mod.caroll.lik, method='spline')
+            ci.lower <- ci[,'2.5 %']
+            ci.upper <- ci[,'97.5 %']
+        }
         mle_result <- list(Bxy.est.mle = coef['x'],
                            Bxy.ci.upper.mle = ci.upper['x'],
                            Bxy.ci.lower.mle = ci.lower['x'],