options(amelia.parallel="no",
amelia.ncpus=1)
library(Amelia)
+source("measerr_methods.R")
+source("pl_methods.R")
-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){
+run_simulation <- function(df, result, outcome_formula = y ~ x + z, proxy_formula = w_pred ~ x, coder_formulas=list(x.obs.1~x, x.obs.0~x), truth_formula = x ~ z){
accuracy <- df[,mean(w_pred==x)]
result <- append(result, list(accuracy=accuracy))
+
+
loa0.feasible <- lm(y ~ x.obs.0 + z, data = df[!(is.na(x.obs.1))])
loa0.ci.Bxy <- confint(loa0.feasible)['x.obs.0',]
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]))
-
+ print("fitting loa0 model")
df.loa0.mle <- copy(df)
df.loa0.mle[,x:=x.obs.0]
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',]
Bzy.ci.lower.loco.feasible = loco.feasible.ci.Bzy[1]))
+ (model.naive <- lm(y~w_pred+z, data=df))
+
+ naive.ci.Bxy <- confint(model.naive)['w_pred',]
+ naive.ci.Bzy <- confint(model.naive)['z',]
+
+ result <- append(result, list(Bxy.est.naive=coef(model.naive)['w_pred'],
+ Bzy.est.naive=coef(model.naive)['z'],
+ Bxy.ci.upper.naive = naive.ci.Bxy[2],
+ Bxy.ci.lower.naive = naive.ci.Bxy[1],
+ Bzy.ci.upper.naive = naive.ci.Bzy[2],
+ Bzy.ci.lower.naive = naive.ci.Bzy[1]))
+
+ print("fitting loco model")
+
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.accuracy <- df.loco.mle[(!is.na(x.obs.1)) & (x.obs.1 == x.obs.0),mean(x.obs.1 == x.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'],
+ result <- append(result, list(loco.accuracy=loco.accuracy,
+ 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.lower['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)
+ df.double.proxy.mle <- copy(df)
+ df.double.proxy.mle[,x.obs:=NA]
+ print("fitting double proxy model")
+
+ double.proxy.mle <- measerr_irr_mle(df.double.proxy.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula, coder_formulas=coder_formulas[1], truth_formula=truth_formula)
+ fisher.info <- solve(double.proxy.mle$hessian)
+ coef <- double.proxy.mle$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.double.proxy=coef['x'],
+ Bzy.est.double.proxy=coef['z'],
+ Bxy.ci.upper.double.proxy = ci.upper['x'],
+ Bxy.ci.lower.double.proxy = ci.lower['x'],
+ Bzy.ci.upper.double.proxy = ci.upper['z'],
+ Bzy.ci.lower.double.proxy = ci.lower['z']))
+
+ df.triple.proxy.mle <- copy(df)
+ df.triple.proxy.mle[,x.obs:=NA]
+
+ print("fitting triple proxy model")
+ triple.proxy.mle <- measerr_irr_mle(df.triple.proxy.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula, coder_formulas=coder_formulas, truth_formula=truth_formula)
+ fisher.info <- solve(triple.proxy.mle$hessian)
+ coef <- triple.proxy.mle$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.triple.proxy=coef['x'],
+ Bzy.est.triple.proxy=coef['z'],
+ Bxy.ci.upper.triple.proxy = ci.upper['x'],
+ Bxy.ci.lower.triple.proxy = ci.lower['x'],
+ Bzy.ci.upper.triple.proxy = ci.upper['z'],
+ Bzy.ci.lower.triple.proxy = ci.lower['z']))
+ tryCatch({
+ amelia.out.k <- amelia(df.loco.mle, m=200, p2s=0, idvars=c('x.true','w','x.obs.1','x.obs.0','x'))
+ mod.amelia.k <- zelig(y~x.obs+z, model='ls', data=amelia.out.k$imputations, cite=FALSE)
+ (coefse <- combine_coef_se(mod.amelia.k, messages=FALSE))
+
+ est.x.mi <- coefse['x.obs','Estimate']
+ est.x.se <- coefse['x.obs','Std.Error']
+ result <- append(result,
+ list(Bxy.est.amelia.full = est.x.mi,
+ Bxy.ci.upper.amelia.full = est.x.mi + 1.96 * est.x.se,
+ Bxy.ci.lower.amelia.full = est.x.mi - 1.96 * est.x.se
+ ))
+
+ est.z.mi <- coefse['z','Estimate']
+ est.z.se <- coefse['z','Std.Error']
+
+ result <- append(result,
+ list(Bzy.est.amelia.full = est.z.mi,
+ Bzy.ci.upper.amelia.full = est.z.mi + 1.96 * est.z.se,
+ Bzy.ci.lower.amelia.full = est.z.mi - 1.96 * est.z.se
+ ))
+
+ },
+ error = function(e){
+ message("An error occurred:\n",e)
+ result$error <-paste0(result$error,'\n', e)
+ }
+ )
+
+ tryCatch({
+
+ mod.zhang.lik <- zhang.mle.iv(df.loco.mle)
+ coef <- coef(mod.zhang.lik)
+ ci <- confint(mod.zhang.lik,method='quad')
+ result <- append(result,
+ list(Bxy.est.zhang = coef['Bxy'],
+ Bxy.ci.upper.zhang = ci['Bxy','97.5 %'],
+ Bxy.ci.lower.zhang = ci['Bxy','2.5 %'],
+ Bzy.est.zhang = coef['Bzy'],
+ Bzy.ci.upper.zhang = ci['Bzy','97.5 %'],
+ Bzy.ci.lower.zhang = ci['Bzy','2.5 %']))
+ },
+
+ error = function(e){
+ message("An error occurred:\n",e)
+ result$error <- paste0(result$error,'\n', e)
+ })
+
+ df <- df.loco.mle
+ N <- nrow(df)
+ m <- nrow(df[!is.na(x.obs)])
+ p <- v <- train <- rep(0,N)
+ M <- m
+ p[(M+1):(N)] <- 1
+ v[1:(M)] <- 1
+ df <- df[order(x.obs)]
+ y <- df[,y]
+ x <- df[,x.obs]
+ z <- df[,z]
+ w <- df[,w_pred]
+ # gmm gets pretty close
+ (gmm.res <- predicted_covariates(y, x, z, w, v, train, p, max_iter=100, verbose=TRUE))
+
+ result <- append(result,
+ list(Bxy.est.gmm = gmm.res$beta[1,1],
+ Bxy.ci.upper.gmm = gmm.res$confint[1,2],
+ Bxy.ci.lower.gmm = gmm.res$confint[1,1],
+ gmm.ER_pval = gmm.res$ER_pval
+ ))
+
+ result <- append(result,
+ list(Bzy.est.gmm = gmm.res$beta[2,1],
+ Bzy.ci.upper.gmm = gmm.res$confint[2,2],
+ Bzy.ci.lower.gmm = gmm.res$confint[2,1]))
+
- ## 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)
projects / ml_measurement_error_public.git / blobdiff