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