108 lines
4.8 KiB
R
108 lines
4.8 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") ## for my more generic function.
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run_simulation_depvar <- function(df, result, outcome_formula = y ~ x + z, rater_formula = y.obs ~ x, proxy_formula = w_pred ~ 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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(model.true <- glm(y ~ x + z, data=df, family=binomial(link='logit')))
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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 <- 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.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.upper['z']))
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print(rater_formula)
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print(proxy_formula)
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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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