X-Git-Url: https://code.communitydata.science/ml_measurement_error_public.git/blobdiff_plain/003733f22f42b435315803fd5f47d483c712d72d..588bdd7ed74cf8fe8fd0f15df58a6a40c26ebae5:/simulations/simulation_base.R diff --git a/simulations/simulation_base.R b/simulations/simulation_base.R index 345d14e..a73ed79 100644 --- a/simulations/simulation_base.R +++ b/simulations/simulation_base.R @@ -4,9 +4,164 @@ options(amelia.parallel="no", amelia.ncpus=1) library(Amelia) library(Zelig) +library(stats4) + + +## This uses the pseudolikelihood approach from Carroll page 349. +## assumes MAR +## assumes differential error, but that only depends on Y +## inefficient, because pseudolikelihood +logistic.correction.pseudo <- 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, Bgy){ + probs <- (1 - p0.est) + (p1.est + p0.est - 1)*plogis(B0 + Bxy * df$x + Bgy * df$g) + + part1 = sum(log(probs[df$w_pred == 1])) + part2 = sum(log(1-probs[df$w_pred == 0])) + + return(-1*(part1 + part2)) + } + + mlefit <- stats4::mle(minuslogl = nll, start = list(B0=0, Bxy=0.0, Bgy=0.0)) + return(mlefit) + +} + +## This uses the likelihood approach from Carroll page 353. +## assumes that we have a good measurement error model +logistic.correction.liklihood <- function(df){ + + ## liklihood for observed responses + nll <- function(B0, Bxy, Bgy, gamma0, gamma_y, gamma_g){ + df.obs <- df[!is.na(y.obs)] + p.y.obs <- plogis(B0 + Bxy * df.obs$x + Bgy*df.obs$g) + p.y.obs[df.obs$y==0] <- 1-p.y.obs[df.obs$y==0] + p.s.obs <- plogis(gamma0 + gamma_y * df.obs$y + gamma_g*df.obs$g) + p.s.obs[df.obs$w_pred==0] <- 1 - p.s.obs[df.obs$w_pred==0] + + p.obs <- p.s.obs * p.y.obs + + df.unobs <- df[is.na(y.obs)] + + p.unobs.1 <- plogis(B0 + Bxy * df.unobs$x + Bgy*df.unobs$g)*plogis(gamma0 + gamma_y + gamma_g*df.unobs$g) + p.unobs.0 <- (1-plogis(B0 + Bxy * df.unobs$x + Bgy*df.unobs$g))*plogis(gamma0 + gamma_g*df.unobs$g) + p.unobs <- p.unobs.1 + p.unobs.0 + p.unobs[df.unobs$w_pred==0] <- 1 - p.unobs[df.unobs$w_pred==0] + + p <- c(p.obs, p.unobs) + + return(-1*(sum(log(p)))) + } + + mlefit <- stats4::mle(minuslogl = nll, start = list(B0=1, Bxy=0,Bgy=0, gamma0=5, gamma_y=0, gamma_g=0)) + + return(mlefit) +} + logistic <- function(x) {1/(1+exp(-1*x))} +run_simulation_depvar <- function(df, result){ + + accuracy <- df[,mean(w_pred==y)] + result <- append(result, list(accuracy=accuracy)) + + (model.true <- glm(y ~ x + g, data=df,family=binomial(link='logit'))) + true.ci.Bxy <- confint(model.true)['x',] + true.ci.Bgy <- confint(model.true)['g',] + + result <- append(result, list(Bxy.est.true=coef(model.true)['x'], + Bgy.est.true=coef(model.true)['g'], + Bxy.ci.upper.true = true.ci.Bxy[2], + Bxy.ci.lower.true = true.ci.Bxy[1], + Bgy.ci.upper.true = true.ci.Bgy[2], + Bgy.ci.lower.true = true.ci.Bgy[1])) + + (model.feasible <- glm(y.obs~x+g,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.Bgy <- confint(model.feasible)['g',] + result <- append(result, list(Bgy.est.feasible=coef(model.feasible)['g'], + Bgy.ci.upper.feasible = feasible.ci.Bgy[2], + Bgy.ci.lower.feasible = feasible.ci.Bgy[1])) + + (model.naive <- glm(w_pred~x+g, data=df, family=binomial(link='logit'))) + + naive.ci.Bxy <- confint(model.naive)['x',] + naive.ci.Bgy <- confint(model.naive)['g',] + + result <- append(result, list(Bxy.est.naive=coef(model.naive)['x'], + Bgy.est.naive=coef(model.naive)['g'], + Bxy.ci.upper.naive = naive.ci.Bxy[2], + Bxy.ci.lower.naive = naive.ci.Bxy[1], + Bgy.ci.upper.naive = naive.ci.Bgy[2], + Bgy.ci.lower.naive = naive.ci.Bgy[1])) + + + (model.naive.cont <- lm(w~x+g, data=df)) + naivecont.ci.Bxy <- confint(model.naive.cont)['x',] + naivecont.ci.Bgy <- confint(model.naive.cont)['g',] + + ## my implementatoin of liklihood based correction + mod.caroll.lik <- logistic.correction.liklihood(df) + coef <- coef(mod.caroll.lik) + ci <- confint(mod.caroll.lik) + + result <- append(result, + list(Bxy.est.mle = coef['Bxy'], + Bxy.ci.upper.mle = ci['Bxy','97.5 %'], + Bxy.ci.lower.mle = ci['Bxy','2.5 %'], + Bgy.est.mle = coef['Bgy'], + Bgy.ci.upper.mle = ci['Bgy','97.5 %'], + Bgy.ci.lower.mle = ci['Bgy','2.5 %'])) + + + ## my implementatoin of liklihood based correction + mod.caroll.pseudo <- logistic.correction.pseudo(df) + coef <- coef(mod.caroll.pseudo) + ci <- confint(mod.caroll.pseudo) + + result <- append(result, + list(Bxy.est.pseudo = coef['Bxy'], + Bxy.ci.upper.pseudo = ci['Bxy','97.5 %'], + Bxy.ci.lower.pseudo = ci['Bxy','2.5 %'], + Bgy.est.pseudo = coef['Bgy'], + Bgy.ci.upper.pseudo = ci['Bgy','97.5 %'], + Bgy.ci.lower.pseudo = ci['Bgy','2.5 %'])) + + + # amelia says use normal distribution for binary variables. + amelia.out.k <- amelia(df, m=200, p2s=0, idvars=c('y','ystar','w_pred')) + mod.amelia.k <- zelig(y.obs~x+g, model='ls', data=amelia.out.k$imputations, cite=FALSE) + (coefse <- combine_coef_se(mod.amelia.k, messages=FALSE)) + + est.x.mi <- coefse['x','Estimate'] + est.x.se <- coefse['x','Std.Error'] + result <- append(result, + list(Bxy.est.amelia.full = est.x.mi, + Bxy.ci.upper.amelia.full = est.x.mi + 1.96 * est.x.se, + Bxy.ci.lower.amelia.full = est.x.mi - 1.96 * est.x.se + )) + + est.g.mi <- coefse['g','Estimate'] + est.g.se <- coefse['g','Std.Error'] + + result <- append(result, + list(Bgy.est.amelia.full = est.g.mi, + Bgy.ci.upper.amelia.full = est.g.mi + 1.96 * est.g.se, + Bgy.ci.lower.amelia.full = est.g.mi - 1.96 * est.g.se + )) + + return(result) + +} + run_simulation <- function(df, result){ accuracy <- df[,mean(w_pred==x)] @@ -48,19 +203,7 @@ run_simulation <- function(df, result){ Bgy.ci.lower.naive = naive.ci.Bgy[1])) - ## multiple imputation when k is observed - ## amelia does great at this one. - noms <- c() - if(length(unique(df$x.obs)) <=2){ - noms <- c(noms, 'x.obs') - } - - if(length(unique(df$g)) <=2){ - noms <- c(noms, 'g') - } - - - amelia.out.k <- amelia(df, m=200, p2s=0, idvars=c('x','w_pred'),noms=noms) + amelia.out.k <- amelia(df, m=200, p2s=0, idvars=c('x','w_pred')) mod.amelia.k <- zelig(y~x.obs+g, model='ls', data=amelia.out.k$imputations, cite=FALSE) (coefse <- combine_coef_se(mod.amelia.k, messages=FALSE))