library(predictionError)
library(mecor)
options(amelia.parallel="no",
        amelia.ncpus=1)
library(Amelia)
library(Zelig)
library(bbmle)
library(matrixStats) # for numerically stable logsumexps

source("pl_methods.R")
source("measerr_methods.R") ## for my more generic function.

## This uses the pseudolikelihood approach from Carroll page 349.
## assumes MAR
## assumes differential error, but that only depends on Y
## inefficient, because pseudolikelihood
    
## This uses the pseudo-likelihood approach from Carroll page 346.
my.pseudo.mle <- function(df){
    p1.est <- mean(df[w_pred==1]$y.obs==1,na.rm=T)
    p0.est <- mean(df[w_pred==0]$y.obs==0,na.rm=T)
    
    nll <- function(B0, Bxy, Bzy){

        pw <- vector(mode='numeric',length=nrow(df))
        dfw1 <- df[w_pred==1]
        dfw0 <- df[w_pred==0]
        pw[df$w_pred==1] <- plogis(B0 + Bxy * dfw1$x + Bzy * dfw1$z, log=T)
        pw[df$w_pred==0] <- plogis(B0 + Bxy * dfw0$x + Bzy * dfw0$z, lower.tail=FALSE, log=T)
        
        probs <- colLogSumExps(rbind(log(1 - p0.est), log(p1.est + p0.est - 1) + pw))
        return(-1*sum(probs))
    }
    
    mlefit <- mle2(minuslogl = nll, start = list(B0=0.0, Bxy=0.0, Bzy=0.0), control=list(maxit=1e6),method='L-BFGS-B')
    return(mlefit)

}

 
## This uses the likelihood approach from Carroll page 353.
## assumes that we have a good measurement error model
my.mle <- function(df){
    
    ## liklihood for observed responses
    nll <- function(B0, Bxy, Bzy, gamma0, gamma_y, gamma_z, gamma_yz){
        df.obs <- df[!is.na(y.obs)]
        yobs0 <- df.obs$y==0 
        yobs1 <- df.obs$y==1
        p.y.obs <- vector(mode='numeric', length=nrow(df.obs))
        
        p.y.obs[yobs1] <- plogis(B0 + Bxy * df.obs[yobs1]$x + Bzy*df.obs[yobs1]$z,log=T)
        p.y.obs[yobs0] <- plogis(B0 + Bxy * df.obs[yobs0]$x + Bzy*df.obs[yobs0]$z,lower.tail=FALSE,log=T)

        wobs0 <- df.obs$w_pred==0
        wobs1 <- df.obs$w_pred==1
        p.w.obs <- vector(mode='numeric', length=nrow(df.obs))

        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)
        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)
        
        p.obs <- p.w.obs + p.y.obs

        df.unobs <- df[is.na(y.obs)]

        p.unobs.0 <- vector(mode='numeric',length=nrow(df.unobs))
        p.unobs.1 <- vector(mode='numeric',length=nrow(df.unobs))

        wunobs.0 <- df.unobs$w_pred == 0
        wunobs.1 <- df.unobs$w_pred == 1
        
        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)

        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)

        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)

        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)

        p.unobs <- colLogSumExps(rbind(p.unobs.1, p.unobs.0))

        p <- c(p.obs, p.unobs)

        return(-1*(sum(p)))
    }

    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')

    return(mlefit)
}

run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formula=w_pred~y, confint_method='quad'){

    (accuracy <- df[,mean(w_pred==y)])
    result <- append(result, list(accuracy=accuracy))
    (error.cor.z <- cor(df$z, df$y - df$w_pred))
    (error.cor.x <- cor(df$x, df$y - df$w_pred))
    (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)))

    true.ci.Bxy <- confint(model.true)['x',]
    true.ci.Bzy <- confint(model.true)['z',]

    result <- append(result, list(cor.xz=cor(df$x,df$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.feasible <- glm(y.obs~x+z,data=df,family=binomial(link='logit')))

    feasible.ci.Bxy <- confint(model.feasible)['x',]
    result <- append(result, list(Bxy.est.feasible=coef(model.feasible)['x'],
                                  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 <- glm(w_pred~x+z, data=df, family=binomial(link='logit')))

    naive.ci.Bxy <- confint(model.naive)['x',]
    naive.ci.Bzy <- confint(model.naive)['z',]

    result <- append(result, list(Bxy.est.naive=coef(model.naive)['x'],
                                  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]))


    (model.naive.cont <- lm(w~x+z, data=df))
    naivecont.ci.Bxy <- confint(model.naive.cont)['x',]
    naivecont.ci.Bzy <- confint(model.naive.cont)['z',]

    ## my implementation of liklihood based correction

    temp.df <- copy(df)
    temp.df[,y:=y.obs]

    if(confint_method=='quad'){
        mod.caroll.lik <- measerr_mle_dv(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula)
        fischer.info <- solve(mod.caroll.lik$hessian)
        coef <- mod.caroll.lik$par
        ci.upper <- coef + sqrt(diag(fischer.info)) * 1.96
        ci.lower <- coef - sqrt(diag(fischer.info)) * 1.96
    }
    else{ ## confint_method is 'profile'

        mod.caroll.lik <- measerr_mle_dv(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula, method='bbmle')
        coef <- coef(mod.caroll.lik)
        ci <- confint(mod.caroll.lik, method='spline')
        ci.lower <- ci[,'2.5 %']
        ci.upper <- ci[,'97.5 %']
    }

    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']))


    ## my implementatoin of liklihood based correction
    mod.zhang <- zhang.mle.dv(df)
    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 %']))

    

    # amelia says use normal distribution for binary variables.
    amelia_result <- list(Bxy.est.amelia.full = NA,
                          Bxy.ci.upper.amelia.full = NA,
                          Bxy.ci.lower.amelia.full = NA,
                          Bzy.est.amelia.full = NA,
                          Bzy.ci.upper.amelia.full = NA,
                          Bzy.ci.lower.amelia.full = NA
                          )

    tryCatch({
        amelia.out.k <- amelia(df, m=200, p2s=0, idvars=c('y','ystar','w'))
        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']

        est.z.mi <- coefse['z','Estimate']
        est.z.se <- coefse['z','Std.Error']
        amelia_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,
                          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){
    result[['error']] <- e}
    )
    result <- append(result,amelia_result)

    return(result)

}


## outcome_formula, proxy_formula, and truth_formula are passed to measerr_mle 
run_simulation <-  function(df, result, outcome_formula=y~x+z, proxy_formula=NULL, truth_formula=NULL, confint_method='quad'){

    accuracy <- df[,mean(w_pred==x)]
    accuracy.y0 <- df[y<=0,mean(w_pred==x)]
    accuracy.y1 <- df[y>=0,mean(w_pred==x)]
    cor.y.xi <- cor(df$x - df$w_pred, df$y)

    fnr <- df[w_pred==0,mean(w_pred!=x)]
    fnr.y0 <- df[(w_pred==0) & (y<=0),mean(w_pred!=x)]
    fnr.y1 <- df[(w_pred==0) & (y>=0),mean(w_pred!=x)]

    fpr <- df[w_pred==1,mean(w_pred!=x)]
    fpr.y0 <- df[(w_pred==1) & (y<=0),mean(w_pred!=x)]
    fpr.y1 <- df[(w_pred==1) & (y>=0),mean(w_pred!=x)]
    cor.resid.w_pred <- cor(resid(lm(y~x+z,df)),df$w_pred)

    result <- append(result, list(accuracy=accuracy,
                                  accuracy.y0=accuracy.y0,
                                  accuracy.y1=accuracy.y1,
                                  cor.y.xi=cor.y.xi,
                                  fnr=fnr,
                                  fnr.y0=fnr.y0,
                                  fnr.y1=fnr.y1,
                                  fpr=fpr,
                                  fpr.y0=fpr.y0,
                                  fpr.y1=fpr.y1,
                                  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]))

    amelia_result <- list(
        Bxy.est.amelia.full = NULL,
        Bxy.ci.upper.amelia.full = NULL,
        Bxy.ci.lower.amelia.full = NULL,
        Bzy.est.amelia.full = NULL,
        Bzy.ci.upper.amelia.full = NULL,
        Bzy.ci.lower.amelia.full = NULL
        )

    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))

        est.x.mi <- coefse['x.obs','Estimate']
        est.x.se <- coefse['x.obs','Std.Error']
        est.z.mi <- coefse['z','Estimate']
        est.z.se <- coefse['z','Std.Error']

        amelia_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,
                              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){
        result[['error']] <- e}
    )


    result <- append(result, amelia_result)


   mle_result <- list(Bxy.est.mle = NULL,
                      Bxy.ci.upper.mle = NULL,
                      Bxy.ci.lower.mle = NULL,
                      Bzy.est.mle = NULL,
                      Bzy.ci.upper.mle = NULL,
                      Bzy.ci.lower.mle = NULL)

    tryCatch({
        temp.df <- copy(df)
        temp.df <- temp.df[,x:=x.obs]
        if(confint_method=='quad'){
            mod.caroll.lik <- measerr_mle(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula, truth_formula=truth_formula, method='optim')
            fischer.info <- solve(mod.caroll.lik$hessian)
            coef <- mod.caroll.lik$par
            ci.upper <- coef + sqrt(diag(fischer.info)) * 1.96
            ci.lower <- coef - sqrt(diag(fischer.info)) * 1.96
        } else { # confint_method == 'bbmle'

            mod.caroll.lik <- measerr_mle(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula, truth_formula=truth_formula, method='bbmle')
            coef <- coef(mod.caroll.lik)
            ci <- confint(mod.caroll.lik, method='spline')
            ci.lower <- ci[,'2.5 %']
            ci.upper <- ci[,'97.5 %']
        }
        mle_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) {result[['error']] <- as.character(e)
    })

        
    result <- append(result, mle_result)

    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 %']))

    ## 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)
}