update simulation and mle code

[ml_measurement_error_public.git] / simulations / irr_simulation_base.R

diff --git a/simulations/irr_simulation_base.R b/simulations/irr_simulation_base.R
new file mode 100644 (file)
index 0000000..ebb215b
--- /dev/null
+++ b/simulations/irr_simulation_base.R
@@ -0,0 +1,106 @@
+library(matrixStats) # for numerically stable logsumexps
+
+options(amelia.parallel="no",
+        amelia.ncpus=1)
+library(Amelia)
+
+source("measerr_methods.R") ## for my more generic function.
+
+run_simulation <- function(df, result, outcome_formula = y ~ x + z, proxy_formula = w_pred ~ x, truth_formula = x ~ z){
+
+    accuracy <- df[,mean(w_pred==x)]
+    result <- append(result, list(accuracy=accuracy))
+
+    (model.true <- lm(y ~ x + z, data=df))
+    true.ci.Bxy <- confint(model.true)['x',]
+    true.ci.Bzy <- confint(model.true)['z',]
+
+    result <- append(result, list(Bxy.est.true=coef(model.true)['x'],
+                                  Bzy.est.true=coef(model.true)['z'],
+                                  Bxy.ci.upper.true = true.ci.Bxy[2],
+                                  Bxy.ci.lower.true = true.ci.Bxy[1],
+                                  Bzy.ci.upper.true = true.ci.Bzy[2],
+                                  Bzy.ci.lower.true = true.ci.Bzy[1]))
+
+
+
+    loa0.feasible <- lm(y ~ x.obs.0 + z, data = df[!(is.na(x.obs.1))])
+
+    loa0.ci.Bxy <- confint(loa0.feasible)['x.obs.0',]
+    loa0.ci.Bzy <- confint(loa0.feasible)['z',]
+
+    result <- append(result, list(Bxy.est.loa0.feasible=coef(loa0.feasible)['x.obs.0'],
+                                  Bzy.est.loa0.feasible=coef(loa0.feasible)['z'],
+                                  Bxy.ci.upper.loa0.feasible = loa0.ci.Bxy[2],
+                                  Bxy.ci.lower.loa0.feasible = loa0.ci.Bxy[1],
+                                  Bzy.ci.upper.loa0.feasible = loa0.ci.Bzy[2],
+                                  Bzy.ci.lower.loa0.feasible = loa0.ci.Bzy[1]))
+
+
+    df.loa0.mle <- copy(df)
+    df.loa0.mle[,x:=x.obs.0]
+    loa0.mle <- measerr_mle(df.loa0.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula, truth_formula=truth_formula)
+    fisher.info <- solve(loa0.mle$hessian)
+    coef <- loa0.mle$par
+    ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
+    ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
+
+    result <- append(result, list(Bxy.est.loa0.mle=coef['x'],
+                                  Bzy.est.loa0.mle=coef['z'],
+                                  Bxy.ci.upper.loa0.mle = ci.upper['x'],
+                                  Bxy.ci.lower.loa0.mle = ci.lower['x'],
+                                  Bzy.ci.upper.loa0.mle = ci.upper['z'],
+                                  Bzy.ci.lower.loa0.mle = ci.upper['z']))
+
+    loco.feasible <- lm(y ~ x.obs.1 + z, data = df[(!is.na(x.obs.1)) & (x.obs.1 == x.obs.0)])
+
+    loco.feasible.ci.Bxy <- confint(loco.feasible)['x.obs.1',]
+    loco.feasible.ci.Bzy <- confint(loco.feasible)['z',]
+
+    result <- append(result, list(Bxy.est.loco.feasible=coef(loco.feasible)['x.obs.1'],
+                                  Bzy.est.loco.feasible=coef(loco.feasible)['z'],
+                                  Bxy.ci.upper.loco.feasible = loco.feasible.ci.Bxy[2],
+                                  Bxy.ci.lower.loco.feasible = loco.feasible.ci.Bxy[1],
+                                  Bzy.ci.upper.loco.feasible = loco.feasible.ci.Bzy[2],
+                                  Bzy.ci.lower.loco.feasible = loco.feasible.ci.Bzy[1]))
+
+
+    df.loco.mle <- copy(df)
+    df.loco.mle[,x.obs:=NA]
+    df.loco.mle[(x.obs.0)==(x.obs.1),x.obs:=x.obs.0]
+    df.loco.mle[,x.true:=x]
+    df.loco.mle[,x:=x.obs]
+    print(df.loco.mle[!is.na(x.obs.1),mean(x.true==x,na.rm=TRUE)])
+    loco.mle <- measerr_mle(df.loco.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula, truth_formula=truth_formula)
+    fisher.info <- solve(loco.mle$hessian)
+    coef <- loco.mle$par
+    ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
+    ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
+
+    result <- append(result, list(Bxy.est.loco.mle=coef['x'],
+                                  Bzy.est.loco.mle=coef['z'],
+                                  Bxy.ci.upper.loco.mle = ci.upper['x'],
+                                  Bxy.ci.lower.loco.mle = ci.lower['x'],
+                                  Bzy.ci.upper.loco.mle = ci.upper['z'],
+                                  Bzy.ci.lower.loco.mle = ci.upper['z']))
+
+    ## print(rater_formula)
+    ## print(proxy_formula)
+    ## mle.irr <- measerr_irr_mle( df, outcome_formula = outcome_formula, rater_formula = rater_formula, proxy_formula=proxy_formula, truth_formula=truth_formula)
+
+    ## fisher.info <- solve(mle.irr$hessian)
+    ## coef <- mle.irr$par
+    ## ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
+    ## ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
+    
+    ## result <- append(result,
+    ##                  list(Bxy.est.mle = coef['x'],
+    ##                       Bxy.ci.upper.mle = ci.upper['x'],
+    ##                       Bxy.ci.lower.mle = ci.lower['x'],
+    ##                       Bzy.est.mle = coef['z'],
+    ##                       Bzy.ci.upper.mle = ci.upper['z'],
+    ##                       Bzy.ci.lower.mle = ci.lower['z']))
+
+    return(result)
+
+}