546 lines
22 KiB
R
546 lines
22 KiB
R
library(predictionError)
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library(mecor)
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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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library(Zelig)
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library(bbmle)
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library(matrixStats) # for numerically stable logsumexps
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source("measerr_methods.R") ## for my more generic function.
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## This uses the pseudolikelihood approach from Carroll page 349.
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## assumes MAR
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## assumes differential error, but that only depends on Y
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## inefficient, because pseudolikelihood
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## This uses the pseudo-likelihood approach from Carroll page 346.
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my.pseudo.mle <- function(df){
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p1.est <- mean(df[w_pred==1]$y.obs==1,na.rm=T)
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p0.est <- mean(df[w_pred==0]$y.obs==0,na.rm=T)
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nll <- function(B0, Bxy, Bzy){
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pw <- vector(mode='numeric',length=nrow(df))
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dfw1 <- df[w_pred==1]
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dfw0 <- df[w_pred==0]
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pw[df$w_pred==1] <- plogis(B0 + Bxy * dfw1$x + Bzy * dfw1$z, log=T)
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pw[df$w_pred==0] <- plogis(B0 + Bxy * dfw0$x + Bzy * dfw0$z, lower.tail=FALSE, log=T)
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probs <- colLogSumExps(rbind(log(1 - p0.est), log(p1.est + p0.est - 1) + pw))
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return(-1*sum(probs))
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}
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mlefit <- mle2(minuslogl = nll, start = list(B0=0.0, Bxy=0.0, Bzy=0.0), control=list(maxit=1e6),method='L-BFGS-B')
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return(mlefit)
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}
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## model from Zhang's arxiv paper, with predictions for y
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## Zhang got this model from Hausman 1998
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### I think this is actually eqivalent to the pseudo.mle method
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zhang.mle.iv <- function(df){
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df.obs <- df[!is.na(x.obs)]
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df.unobs <- df[is.na(x.obs)]
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tn <- df.obs[(w_pred == 0) & (x.obs == w_pred),.N]
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pn <- df.obs[(w_pred==0), .N]
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npv <- tn / pn
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tp <- df.obs[(w_pred==1) & (x.obs == w_pred),.N]
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pp <- df.obs[(w_pred==1),.N]
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ppv <- tp / pp
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nll <- function(B0=0, Bxy=0, Bzy=0, sigma_y=0.1){
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## fpr = 1 - TNR
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### Problem: accounting for uncertainty in ppv / npv
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## fnr = 1 - TPR
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ll.y.obs <- with(df.obs, dnorm(y, B0 + Bxy * x + Bzy * z, sd=sigma_y,log=T))
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ll <- sum(ll.y.obs)
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# unobserved case; integrate out x
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ll.x.1 <- with(df.unobs, dnorm(y, B0 + Bxy + Bzy * z, sd = sigma_y, log=T))
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ll.x.0 <- with(df.unobs, dnorm(y, B0 + Bzy * z, sd = sigma_y,log=T))
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## case x == 1
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lls.x.1 <- colLogSumExps(rbind(log(ppv) + ll.x.1, log(1-ppv) + ll.x.0))
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## case x == 0
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lls.x.0 <- colLogSumExps(rbind(log(1-npv) + ll.x.1, log(npv) + ll.x.0))
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lls <- colLogSumExps(rbind(df.unobs$w_pred * lls.x.1, (1-df.unobs$w_pred) * lls.x.0))
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ll <- ll + sum(lls)
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return(-ll)
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}
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mlefit <- mle2(minuslogl = nll, control=list(maxit=1e6), lower=list(sigma_y=0.0001, B0=-Inf, Bxy=-Inf, Bzy=-Inf),
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upper=list(sigma_y=Inf, B0=Inf, Bxy=Inf, Bzy=Inf),method='L-BFGS-B')
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return(mlefit)
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}
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## this is equivalent to the pseudo-liklihood model from Caroll
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## zhang.mle.dv <- function(df){
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## nll <- function(B0=0, Bxy=0, Bzy=0, ppv=0.9, npv=0.9){
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## df.obs <- df[!is.na(y.obs)]
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## ## fpr = 1 - TNR
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## ll.w0y0 <- with(df.obs[y.obs==0],dbinom(1-w_pred,1,npv,log=TRUE))
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## ll.w1y1 <- with(df.obs[y.obs==1],dbinom(w_pred,1,ppv,log=TRUE))
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## # observed case
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## ll.y.obs <- vector(mode='numeric', length=nrow(df.obs))
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## ll.y.obs[df.obs$y.obs==1] <- with(df.obs[y.obs==1], plogis(B0 + Bxy * x + Bzy * z,log=T))
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## ll.y.obs[df.obs$y.obs==0] <- with(df.obs[y.obs==0], plogis(B0 + Bxy * x + Bzy * z,log=T,lower.tail=FALSE))
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## ll <- sum(ll.y.obs) + sum(ll.w0y0) + sum(ll.w1y1)
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## # unobserved case; integrate out y
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## ## case y = 1
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## ll.y.1 <- vector(mode='numeric', length=nrow(df))
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## pi.y.1 <- with(df,plogis(B0 + Bxy * x + Bzy*z, log=T))
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## ## P(w=1| y=1)P(y=1) + P(w=0|y=1)P(y=1) = P(w=1,y=1) + P(w=0,y=1)
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## lls.y.1 <- colLogSumExps(rbind(log(ppv) + pi.y.1, log(1-ppv) + pi.y.1))
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## ## case y = 0
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## ll.y.0 <- vector(mode='numeric', length=nrow(df))
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## pi.y.0 <- with(df,plogis(B0 + Bxy * x + Bzy*z, log=T,lower.tail=FALSE))
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## ## P(w=1 | y=0)P(y=0) + P(w=0|y=0)P(y=0) = P(w=1,y=0) + P(w=0,y=0)
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## lls.y.0 <- colLogSumExps(rbind(log(npv) + pi.y.0, log(1-npv) + pi.y.0))
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## lls <- colLogSumExps(rbind(lls.y.1, lls.y.0))
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## ll <- ll + sum(lls)
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## return(-ll)
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## }
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## mlefit <- mle2(minuslogl = nll, control=list(maxit=1e6),method='L-BFGS-B',lower=list(B0=-Inf, Bxy=-Inf, Bzy=-Inf, ppv=0.001,npv=0.001),
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## upper=list(B0=Inf, Bxy=Inf, Bzy=Inf,ppv=0.999,npv=0.999))
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## return(mlefit)
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## }
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zhang.mle.dv <- function(df){
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df.obs <- df[!is.na(y.obs)]
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df.unobs <- df[is.na(y.obs)]
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fp <- df.obs[(w_pred==1) & (y.obs != w_pred),.N]
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p <- df.obs[(w_pred==1),.N]
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fpr <- fp / p
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fn <- df.obs[(w_pred==0) & (y.obs != w_pred), .N]
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n <- df.obs[(w_pred==0),.N]
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fnr <- fn / n
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nll <- function(B0=0, Bxy=0, Bzy=0){
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## observed case
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ll.y.obs <- vector(mode='numeric', length=nrow(df.obs))
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ll.y.obs[df.obs$y.obs==1] <- with(df.obs[y.obs==1], plogis(B0 + Bxy * x + Bzy * z,log=T))
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ll.y.obs[df.obs$y.obs==0] <- with(df.obs[y.obs==0], plogis(B0 + Bxy * x + Bzy * z,log=T,lower.tail=FALSE))
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ll <- sum(ll.y.obs)
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pi.y.1 <- with(df,plogis(B0 + Bxy * x + Bzy*z, log=T))
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pi.y.0 <- with(df,plogis(B0 + Bxy * x + Bzy*z, log=T,lower.tail=FALSE))
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lls <- with(df.unobs, colLogSumExps(rbind(w_pred * colLogSumExps(rbind(log(fpr), log(1 - fnr - fpr)+pi.y.1)),
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(1-w_pred) * colLogSumExps(rbind(log(1-fpr), log(1 - fnr - fpr)+pi.y.0)))))
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ll <- ll + sum(lls)
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return(-ll)
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}
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mlefit <- mle2(minuslogl = nll, control=list(maxit=1e6),method='L-BFGS-B',lower=c(B0=-Inf, Bxy=-Inf, Bzy=-Inf),
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upper=c(B0=Inf, Bxy=Inf, Bzy=Inf))
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return(mlefit)
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}
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## This uses the likelihood approach from Carroll page 353.
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## assumes that we have a good measurement error model
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my.mle <- function(df){
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## liklihood for observed responses
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nll <- function(B0, Bxy, Bzy, gamma0, gamma_y, gamma_z, gamma_yz){
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df.obs <- df[!is.na(y.obs)]
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yobs0 <- df.obs$y==0
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yobs1 <- df.obs$y==1
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p.y.obs <- vector(mode='numeric', length=nrow(df.obs))
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p.y.obs[yobs1] <- plogis(B0 + Bxy * df.obs[yobs1]$x + Bzy*df.obs[yobs1]$z,log=T)
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p.y.obs[yobs0] <- plogis(B0 + Bxy * df.obs[yobs0]$x + Bzy*df.obs[yobs0]$z,lower.tail=FALSE,log=T)
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wobs0 <- df.obs$w_pred==0
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wobs1 <- df.obs$w_pred==1
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p.w.obs <- vector(mode='numeric', length=nrow(df.obs))
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p.w.obs[wobs1] <- plogis(gamma0 + gamma_y * df.obs[wobs1]$y + gamma_z*df.obs[wobs1]$z + df.obs[wobs1]$z*df.obs[wobs1]$y* gamma_yz, log=T)
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p.w.obs[wobs0] <- plogis(gamma0 + gamma_y * df.obs[wobs0]$y + gamma_z*df.obs[wobs0]$z + df.obs[wobs0]$z*df.obs[wobs0]$y* gamma_yz, lower.tail=FALSE, log=T)
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p.obs <- p.w.obs + p.y.obs
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df.unobs <- df[is.na(y.obs)]
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p.unobs.0 <- vector(mode='numeric',length=nrow(df.unobs))
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p.unobs.1 <- vector(mode='numeric',length=nrow(df.unobs))
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wunobs.0 <- df.unobs$w_pred == 0
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wunobs.1 <- df.unobs$w_pred == 1
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p.unobs.0[wunobs.1] <- plogis(B0 + Bxy * df.unobs[wunobs.1]$x + Bzy*df.unobs[wunobs.1]$z, log=T) + plogis(gamma0 + gamma_y + gamma_z*df.unobs[wunobs.1]$z + df.unobs[wunobs.1]$z*gamma_yz, log=T)
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p.unobs.0[wunobs.0] <- plogis(B0 + Bxy * df.unobs[wunobs.0]$x + Bzy*df.unobs[wunobs.0]$z, log=T) + plogis(gamma0 + gamma_y + gamma_z*df.unobs[wunobs.0]$z + df.unobs[wunobs.0]$z*gamma_yz, lower.tail=FALSE, log=T)
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p.unobs.1[wunobs.1] <- plogis(B0 + Bxy * df.unobs[wunobs.1]$x + Bzy*df.unobs[wunobs.1]$z, log=T, lower.tail=FALSE) + plogis(gamma0 + gamma_z*df.unobs[wunobs.1]$z, log=T)
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p.unobs.1[wunobs.0] <- plogis(B0 + Bxy * df.unobs[wunobs.0]$x + Bzy*df.unobs[wunobs.0]$z, log=T, lower.tail=FALSE) + plogis(gamma0 + gamma_z*df.unobs[wunobs.0]$z, lower.tail=FALSE, log=T)
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p.unobs <- colLogSumExps(rbind(p.unobs.1, p.unobs.0))
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p <- c(p.obs, p.unobs)
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return(-1*(sum(p)))
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}
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mlefit <- mle2(minuslogl = nll, start = list(B0=0, Bxy=0,Bzy=0, gamma0=0, gamma_y=0, gamma_z=0, gamma_yz=0), control=list(maxit=1e6),method='L-BFGS-B')
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return(mlefit)
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}
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run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, 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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error.cor.x <- cor(df$x, df$w - df$x)
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result <- append(result, list(error.cor.x = error.cor.x))
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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.feasible <- glm(y.obs~x+z,data=df,family=binomial(link='logit')))
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feasible.ci.Bxy <- confint(model.feasible)['x',]
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result <- append(result, list(Bxy.est.feasible=coef(model.feasible)['x'],
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Bxy.ci.upper.feasible = feasible.ci.Bxy[2],
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Bxy.ci.lower.feasible = feasible.ci.Bxy[1]))
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feasible.ci.Bzy <- confint(model.feasible)['z',]
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result <- append(result, list(Bzy.est.feasible=coef(model.feasible)['z'],
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Bzy.ci.upper.feasible = feasible.ci.Bzy[2],
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Bzy.ci.lower.feasible = feasible.ci.Bzy[1]))
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(model.naive <- glm(w_pred~x+z, data=df, family=binomial(link='logit')))
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naive.ci.Bxy <- confint(model.naive)['x',]
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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)['x'],
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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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(model.naive.cont <- lm(w~x+z, data=df))
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naivecont.ci.Bxy <- confint(model.naive.cont)['x',]
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naivecont.ci.Bzy <- confint(model.naive.cont)['z',]
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## my implementation of liklihood based correction
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temp.df <- copy(df)
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temp.df[,y:=y.obs]
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mod.caroll.lik <- measerr_mle_dv(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula)
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fisher.info <- solve(mod.caroll.lik$hessian)
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coef <- mod.caroll.lik$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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## my implementatoin of liklihood based correction
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mod.zhang <- zhang.mle.dv(df)
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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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# amelia says use normal distribution for binary variables.
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tryCatch({
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amelia.out.k <- amelia(df, m=200, p2s=0, idvars=c('y','ystar','w'))
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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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return(result)
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}
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## outcome_formula, proxy_formula, and truth_formula are passed to measerr_mle
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run_simulation <- function(df, result, outcome_formula=y~x+z, proxy_formula=NULL, truth_formula=NULL){
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accuracy <- df[,mean(w_pred==x)]
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accuracy.y0 <- df[y<=0,mean(w_pred==x)]
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accuracy.y1 <- df[y>=0,mean(w_pred==x)]
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cor.y.xi <- cor(df$x - df$w_pred, df$y)
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fnr <- df[w_pred==0,mean(w_pred!=x)]
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fnr.y0 <- df[(w_pred==0) & (y<=0),mean(w_pred!=x)]
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fnr.y1 <- df[(w_pred==0) & (y>=0),mean(w_pred!=x)]
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fpr <- df[w_pred==1,mean(w_pred!=x)]
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fpr.y0 <- df[(w_pred==1) & (y<=0),mean(w_pred!=x)]
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fpr.y1 <- df[(w_pred==1) & (y>=0),mean(w_pred!=x)]
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cor.resid.w_pred <- cor(resid(lm(y~x+z,df)),df$w_pred)
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result <- append(result, list(accuracy=accuracy,
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accuracy.y0=accuracy.y0,
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accuracy.y1=accuracy.y1,
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cor.y.xi=cor.y.xi,
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fnr=fnr,
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fnr.y0=fnr.y0,
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fnr.y1=fnr.y1,
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fpr=fpr,
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fpr.y0=fpr.y0,
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fpr.y1=fpr.y1,
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cor.resid.w_pred=cor.resid.w_pred
|
|
))
|
|
|
|
result <- append(result, list(cor.xz=cor(df$x,df$z)))
|
|
(model.true <- lm(y ~ x + z, data=df))
|
|
true.ci.Bxy <- confint(model.true)['x',]
|
|
true.ci.Bzy <- confint(model.true)['z',]
|
|
|
|
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.feasible <- lm(y~x.obs+z,data=df))
|
|
|
|
feasible.ci.Bxy <- confint(model.feasible)['x.obs',]
|
|
result <- append(result, list(Bxy.est.feasible=coef(model.feasible)['x.obs'],
|
|
Bxy.ci.upper.feasible = feasible.ci.Bxy[2],
|
|
Bxy.ci.lower.feasible = feasible.ci.Bxy[1]))
|
|
|
|
feasible.ci.Bzy <- confint(model.feasible)['z',]
|
|
result <- append(result, list(Bzy.est.feasible=coef(model.feasible)['z'],
|
|
Bzy.ci.upper.feasible = feasible.ci.Bzy[2],
|
|
Bzy.ci.lower.feasible = 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]))
|
|
|
|
|
|
tryCatch({
|
|
amelia.out.k <- amelia(df, m=200, p2s=0, idvars=c('x','w'))
|
|
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$error,'\n', e)
|
|
}
|
|
)
|
|
|
|
tryCatch({
|
|
temp.df <- copy(df)
|
|
temp.df <- temp.df[,x:=x.obs]
|
|
mod.caroll.lik <- measerr_mle(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula, truth_formula=truth_formula)
|
|
fisher.info <- solve(mod.caroll.lik$hessian)
|
|
coef <- mod.caroll.lik$par
|
|
ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
|
|
ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
|
|
|
|
|
|
result <- append(result,
|
|
list(Bxy.est.mle = coef['x'],
|
|
Bxy.ci.upper.mle = ci.upper['x'],
|
|
Bxy.ci.lower.mle = ci.lower['x'],
|
|
Bzy.est.mle = coef['z'],
|
|
Bzy.ci.upper.mle = ci.upper['z'],
|
|
Bzy.ci.lower.mle = ci.lower['z']))
|
|
},
|
|
|
|
error = function(e){
|
|
message("An error occurred:\n",e)
|
|
result$error <- paste0(result$error,'\n', e)
|
|
})
|
|
|
|
tryCatch({
|
|
|
|
mod.zhang.lik <- zhang.mle.iv(df)
|
|
coef <- coef(mod.zhang.lik)
|
|
ci <- confint(mod.zhang.lik,method='quad')
|
|
result <- append(result,
|
|
list(Bxy.est.zhang = coef['Bxy'],
|
|
Bxy.ci.upper.zhang = ci['Bxy','97.5 %'],
|
|
Bxy.ci.lower.zhang = ci['Bxy','2.5 %'],
|
|
Bzy.est.zhang = coef['Bzy'],
|
|
Bzy.ci.upper.zhang = ci['Bzy','97.5 %'],
|
|
Bzy.ci.lower.zhang = ci['Bzy','2.5 %']))
|
|
},
|
|
|
|
error = function(e){
|
|
message("An error occurred:\n",e)
|
|
result$error <- paste0(result$error,'\n', e)
|
|
})
|
|
|
|
## What if we can't observe k -- most realistic scenario. We can't include all the ML features in a model.
|
|
## amelia.out.nok <- amelia(df, m=200, p2s=0, idvars=c("x","w_pred"), noms=noms)
|
|
## mod.amelia.nok <- zelig(y~x.obs+g, model='ls', data=amelia.out.nok$imputations, cite=FALSE)
|
|
## (coefse <- combine_coef_se(mod.amelia.nok, messages=FALSE))
|
|
|
|
## est.x.mi <- coefse['x.obs','Estimate']
|
|
## est.x.se <- coefse['x.obs','Std.Error']
|
|
## result <- append(result,
|
|
## list(Bxy.est.amelia.nok = est.x.mi,
|
|
## Bxy.ci.upper.amelia.nok = est.x.mi + 1.96 * est.x.se,
|
|
## Bxy.ci.lower.amelia.nok = est.x.mi - 1.96 * est.x.se
|
|
## ))
|
|
|
|
## est.g.mi <- coefse['g','Estimate']
|
|
## est.g.se <- coefse['g','Std.Error']
|
|
|
|
## result <- append(result,
|
|
## list(Bgy.est.amelia.nok = est.g.mi,
|
|
## Bgy.ci.upper.amelia.nok = est.g.mi + 1.96 * est.g.se,
|
|
## Bgy.ci.lower.amelia.nok = est.g.mi - 1.96 * est.g.se
|
|
## ))
|
|
|
|
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$confint[2,1]))
|
|
|
|
|
|
tryCatch({
|
|
mod.calibrated.mle <- mecor(y ~ MeasError(w_pred, reference = x.obs) + z, df, B=400, method='efficient')
|
|
(mod.calibrated.mle)
|
|
(mecor.ci <- summary(mod.calibrated.mle)$c$ci['x.obs',])
|
|
result <- append(result, list(
|
|
Bxy.est.mecor = mecor.ci['Estimate'],
|
|
Bxy.ci.upper.mecor = mecor.ci['UCI'],
|
|
Bxy.ci.lower.mecor = mecor.ci['LCI'])
|
|
)
|
|
|
|
(mecor.ci <- summary(mod.calibrated.mle)$c$ci['z',])
|
|
|
|
result <- append(result, list(
|
|
Bzy.est.mecor = mecor.ci['Estimate'],
|
|
Bzy.ci.upper.mecor = mecor.ci['UCI'],
|
|
Bzy.ci.lower.mecor = mecor.ci['LCI'])
|
|
)
|
|
},
|
|
error = function(e){
|
|
message("An error occurred:\n",e)
|
|
result$error <- paste0(result$error, '\n', e)
|
|
}
|
|
)
|
|
## clean up memory
|
|
## rm(list=c("df","y","x","g","w","v","train","p","amelia.out.k","amelia.out.nok", "mod.calibrated.mle","gmm.res","mod.amelia.k","mod.amelia.nok", "model.true","model.naive","model.feasible"))
|
|
|
|
## gc()
|
|
return(result)
|
|
}
|