213 lines
10 KiB
R
213 lines
10 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("pl_methods.R")
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source("measerr_methods_2.R") ## for my more generic function.
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run_simulation_depvar <- function(df, result, outcome_formula = y ~ x + z, coder_formulas = list(y.obs.0 ~ 1, y.obs.1 ~ 1), proxy_formula = w_pred ~ y.obs.1+y.obs.0+y){
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(accuracy <- df[,mean(w_pred==y)])
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result <- append(result, list(accuracy=accuracy))
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(error.cor.z <- cor(df$x, df$w_pred - df$z))
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(error.cor.x <- cor(df$x, df$w_pred - df$y))
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(error.cor.y <- cor(df$y, df$y - df$w_pred))
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result <- append(result, list(error.cor.x = error.cor.x,
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error.cor.z = error.cor.z,
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error.cor.y = error.cor.y))
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model.null <- glm(y~1, data=df,family=binomial(link='logit'))
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(model.true <- glm(y ~ x + z, data=df,family=binomial(link='logit')))
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(lik.ratio <- exp(logLik(model.true) - logLik(model.null)))
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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(lik.ratio=lik.ratio))
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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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(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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loa0.feasible <- glm(y.obs.0 ~ x + z, data = df[!(is.na(y.obs.0))], family=binomial(link='logit'))
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loa0.ci.Bxy <- confint(loa0.feasible)['x',]
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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'],
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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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## df.loa0.mle <- copy(df)
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## df.loa0.mle[,y:=y.obs.0]
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## loa0.mle <- measerr_mle_dv(df.loa0.mle, outcome_formula=outcome_formula, proxy_formula=proxy_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 <- glm(y.obs.0 ~ x + z, data = df[(!is.na(y.obs.0)) & (y.obs.1 == y.obs.0)], family=binomial(link='logit'))
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loco.feasible.ci.Bxy <- confint(loco.feasible)['x',]
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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'],
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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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## df.double.proxy <- copy(df)
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## df.double.proxy <- df.double.proxy[,y.obs:=NA]
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## df.double.proxy <- df.double.proxy[,y:=NA]
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## double.proxy.mle <- measerr_irr_mle_dv(df.double.proxy, outcome_formula=y~x+z, outcome_family=binomial(link='logit'), coder_formulas=list(y.obs.0 ~ y), proxy_formula=w_pred ~ y.obs.0 + y, proxy_family=binomial(link='logit'))
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## print(double.proxy.mle$hessian)
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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(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 <- copy(df)
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df.triple.proxy <- df.triple.proxy[,y.obs:=NA]
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df.triple.proxy <- df.triple.proxy[,y:=NA]
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triple.proxy.mle <- measerr_irr_mle_dv(df.triple.proxy, outcome_formula=outcome_formula, outcome_family=binomial(link='logit'), coder_formulas=coder_formulas, proxy_formula=proxy_formula, proxy_family=binomial(link='logit'))
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print(triple.proxy.mle$hessian)
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fisher.info <- solve(triple.proxy.mle$hessian)
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print(fisher.info)
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coef <- triple.proxy.mle$par
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ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
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ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
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result <- append(result, list(Bxy.est.triple.proxy=coef['x'],
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Bzy.est.triple.proxy=coef['z'],
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Bxy.ci.upper.triple.proxy = ci.upper['x'],
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Bxy.ci.lower.triple.proxy = ci.lower['x'],
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Bzy.ci.upper.triple.proxy = ci.upper['z'],
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Bzy.ci.lower.triple.proxy = ci.lower['z']))
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## df.loco.mle <- copy(df)
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## df.loco.mle[,y.obs:=NA]
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## df.loco.mle[(y.obs.0)==(y.obs.1),y.obs:=y.obs.0]
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## df.loco.mle[,y.true:=y]
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## df.loco.mle[,y:=y.obs]
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## print(df.loco.mle[!is.na(y.obs.1),mean(y.true==y,na.rm=TRUE)])
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## loco.mle <- measerr_mle_dv(df.loco.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula)
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## fisher.info <- solve(loco.mle$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(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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## my implementatoin of liklihood based correction
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mod.zhang <- zhang.mle.dv(df.loco.mle)
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coef <- coef(mod.zhang)
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ci <- confint(mod.zhang,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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print(df.loco.mle)
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# amelia says use normal distribution for binary variables.
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tryCatch({
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amelia.out.k <- amelia(df.loco.mle, m=200, p2s=0, idvars=c('y','ystar','w','y.obs.1','y.obs.0','y.true'))
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mod.amelia.k <- zelig(y.obs~x+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','Estimate']
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est.x.se <- coefse['x','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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## mle.irr <- measerr_irr_mle( df, outcome_formula = outcome_formula, rater_formula = rater_formula, proxy_formula=proxy_formula, truth_formula=truth_formula)
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## fisher.info <- solve(mle.irr$hessian)
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## coef <- mle.irr$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,
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## list(Bxy.est.mle = coef['x'],
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## Bxy.ci.upper.mle = ci.upper['x'],
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## Bxy.ci.lower.mle = ci.lower['x'],
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## Bzy.est.mle = coef['z'],
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## Bzy.ci.upper.mle = ci.upper['z'],
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## Bzy.ci.lower.mle = ci.lower['z']))
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return(result)
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}
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