amelia.ncpus=1)
library(Amelia)
-source("measerr_methods.R") ## for my more generic function.
+source("pl_methods.R")
+source("measerr_methods_2.R") ## for my more generic function.
-run_simulation_depvar <- function(df, result, outcome_formula = y ~ x + z, rater_formula = y.obs ~ x, proxy_formula = w_pred ~ y){
-
- accuracy <- df[,mean(w_pred==y)]
+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){
+ (accuracy <- df[,mean(w_pred==y)])
result <- append(result, list(accuracy=accuracy))
+ (error.cor.z <- cor(df$x, df$w_pred - df$z))
+ (error.cor.x <- cor(df$x, df$w_pred - df$y))
+ (error.cor.y <- cor(df$y, df$y - df$w_pred))
+ result <- append(result, list(error.cor.x = error.cor.x,
+ error.cor.z = error.cor.z,
+ error.cor.y = error.cor.y))
+
+ model.null <- glm(y~1, data=df,family=binomial(link='logit'))
+ (model.true <- glm(y ~ x + z, data=df,family=binomial(link='logit')))
+ (lik.ratio <- exp(logLik(model.true) - logLik(model.null)))
- (model.true <- glm(y ~ x + z, data=df, family=binomial(link='logit')))
true.ci.Bxy <- confint(model.true)['x',]
true.ci.Bzy <- confint(model.true)['z',]
+
+ result <- append(result, list(lik.ratio=lik.ratio))
+
result <- append(result, list(Bxy.est.true=coef(model.true)['x'],
Bzy.est.true=coef(model.true)['z'],
Bxy.ci.upper.true = true.ci.Bxy[2],
Bxy.ci.lower.true = true.ci.Bxy[1],
Bzy.ci.upper.true = true.ci.Bzy[2],
Bzy.ci.lower.true = true.ci.Bzy[1]))
+
+ (model.naive <- lm(y~w_pred+z, data=df))
+
+ naive.ci.Bxy <- confint(model.naive)['w_pred',]
+ naive.ci.Bzy <- confint(model.naive)['z',]
+
+ result <- append(result, list(Bxy.est.naive=coef(model.naive)['w_pred'],
+ Bzy.est.naive=coef(model.naive)['z'],
+ Bxy.ci.upper.naive = naive.ci.Bxy[2],
+ Bxy.ci.lower.naive = naive.ci.Bxy[1],
+ Bzy.ci.upper.naive = naive.ci.Bzy[2],
+ Bzy.ci.lower.naive = naive.ci.Bzy[1]))
Bzy.ci.lower.loa0.feasible = loa0.ci.Bzy[1]))
- df.loa0.mle <- copy(df)
- df.loa0.mle[,y:=y.obs.0]
- loa0.mle <- measerr_mle_dv(df.loa0.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula)
- fisher.info <- solve(loa0.mle$hessian)
- coef <- loa0.mle$par
- ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
- ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
+ ## df.loa0.mle <- copy(df)
+ ## df.loa0.mle[,y:=y.obs.0]
+ ## loa0.mle <- measerr_mle_dv(df.loa0.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula)
+ ## fisher.info <- solve(loa0.mle$hessian)
+ ## coef <- loa0.mle$par
+ ## ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
+ ## ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
- result <- append(result, list(Bxy.est.loa0.mle=coef['x'],
- Bzy.est.loa0.mle=coef['z'],
- Bxy.ci.upper.loa0.mle = ci.upper['x'],
- Bxy.ci.lower.loa0.mle = ci.lower['x'],
- Bzy.ci.upper.loa0.mle = ci.upper['z'],
- Bzy.ci.lower.loa0.mle = ci.upper['z']))
+ ## result <- append(result, list(Bxy.est.loa0.mle=coef['x'],
+ ## Bzy.est.loa0.mle=coef['z'],
+ ## Bxy.ci.upper.loa0.mle = ci.upper['x'],
+ ## Bxy.ci.lower.loa0.mle = ci.lower['x'],
+ ## Bzy.ci.upper.loa0.mle = ci.upper['z'],
+ ## Bzy.ci.lower.loa0.mle = ci.upper['z']))
loco.feasible <- glm(y.obs.0 ~ x + z, data = df[(!is.na(y.obs.0)) & (y.obs.1 == y.obs.0)], family=binomial(link='logit'))
Bzy.ci.upper.loco.feasible = loco.feasible.ci.Bzy[2],
Bzy.ci.lower.loco.feasible = loco.feasible.ci.Bzy[1]))
+
+ ## df.double.proxy <- copy(df)
+ ## df.double.proxy <- df.double.proxy[,y.obs:=NA]
+ ## df.double.proxy <- df.double.proxy[,y:=NA]
+
+ ## double.proxy.mle <- measerr_irr_mle_dv(df.double.proxy, outcome_formula=y~x+z, outcome_family=binomial(link='logit'), coder_formulas=list(y.obs.0 ~ y), proxy_formula=w_pred ~ y.obs.0 + y, proxy_family=binomial(link='logit'))
+ ## print(double.proxy.mle$hessian)
+ ## 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.loco.mle <- copy(df)
- df.loco.mle[,y.obs:=NA]
- df.loco.mle[(y.obs.0)==(y.obs.1),y.obs:=y.obs.0]
- df.loco.mle[,y.true:=y]
- df.loco.mle[,y:=y.obs]
- print(df.loco.mle[!is.na(y.obs.1),mean(y.true==y,na.rm=TRUE)])
- loco.mle <- measerr_mle_dv(df.loco.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula)
- fisher.info <- solve(loco.mle$hessian)
- coef <- loco.mle$par
+ df.triple.proxy <- copy(df)
+ df.triple.proxy <- df.triple.proxy[,y.obs:=NA]
+ df.triple.proxy <- df.triple.proxy[,y:=NA]
+
+ 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'))
+ print(triple.proxy.mle$hessian)
+ fisher.info <- solve(triple.proxy.mle$hessian)
+ print(fisher.info)
+ coef <- triple.proxy.mle$par
ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
- result <- append(result, list(Bxy.est.loco.mle=coef['x'],
- Bzy.est.loco.mle=coef['z'],
- Bxy.ci.upper.loco.mle = ci.upper['x'],
- Bxy.ci.lower.loco.mle = ci.lower['x'],
- Bzy.ci.upper.loco.mle = ci.upper['z'],
- Bzy.ci.lower.loco.mle = ci.upper['z']))
+ result <- append(result, list(Bxy.est.triple.proxy=coef['x'],
+ Bzy.est.triple.proxy=coef['z'],
+ Bxy.ci.upper.triple.proxy = ci.upper['x'],
+ Bxy.ci.lower.triple.proxy = ci.lower['x'],
+ Bzy.ci.upper.triple.proxy = ci.upper['z'],
+ Bzy.ci.lower.triple.proxy = ci.lower['z']))
+
+ ## df.loco.mle <- copy(df)
+ ## df.loco.mle[,y.obs:=NA]
+ ## df.loco.mle[(y.obs.0)==(y.obs.1),y.obs:=y.obs.0]
+ ## df.loco.mle[,y.true:=y]
+ ## df.loco.mle[,y:=y.obs]
+ ## print(df.loco.mle[!is.na(y.obs.1),mean(y.true==y,na.rm=TRUE)])
+ ## loco.mle <- measerr_mle_dv(df.loco.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula)
+ ## fisher.info <- solve(loco.mle$hessian)
+ ## coef <- loco.mle$par
+ ## ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
+ ## ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
+
+ ## result <- append(result, list(Bxy.est.loco.mle=coef['x'],
+ ## Bzy.est.loco.mle=coef['z'],
+ ## Bxy.ci.upper.loco.mle = ci.upper['x'],
+ ## Bxy.ci.lower.loco.mle = ci.lower['x'],
+ ## Bzy.ci.upper.loco.mle = ci.upper['z'],
+ ## Bzy.ci.lower.loco.mle = ci.lower['z']))
+
+
- print(rater_formula)
- print(proxy_formula)
+ ## my implementatoin of liklihood based correction
+ mod.zhang <- zhang.mle.dv(df.loco.mle)
+ coef <- coef(mod.zhang)
+ ci <- confint(mod.zhang,method='quad')
+
+ result <- append(result,
+ list(Bxy.est.zhang = coef['Bxy'],
+ Bxy.ci.upper.zhang = ci['Bxy','97.5 %'],
+ Bxy.ci.lower.zhang = ci['Bxy','2.5 %'],
+ Bzy.est.zhang = coef['Bzy'],
+ Bzy.ci.upper.zhang = ci['Bzy','97.5 %'],
+ Bzy.ci.lower.zhang = ci['Bzy','2.5 %']))
+
+
+ print(df.loco.mle)
+
+ # amelia says use normal distribution for binary variables.
+ tryCatch({
+ amelia.out.k <- amelia(df.loco.mle, m=200, p2s=0, idvars=c('y','ystar','w','y.obs.1','y.obs.0','y.true'))
+ mod.amelia.k <- zelig(y.obs~x+z, model='ls', data=amelia.out.k$imputations, cite=FALSE)
+ (coefse <- combine_coef_se(mod.amelia.k, messages=FALSE))
+ est.x.mi <- coefse['x','Estimate']
+ est.x.se <- coefse['x','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)
+ })
## 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)
projects / ml_measurement_error_public.git / blobdiff