From 5c931a7198452ff3ce0ace5b1f68046bfb33d352 Mon Sep 17 00:00:00 2001 From: Nathan TeBlunthuis Date: Wed, 2 Nov 2022 17:46:04 -0700 Subject: [PATCH] git-annex in nathante@n3246:/gscratch/comdata/users/nathante/ml_measurement_error_public --- simulations/.Rhistory | 91 +++++ simulations/01_two_covariates.R | 34 +- simulations/02_indep_differential.R | 15 +- simulations/03_depvar.R | 13 +- simulations/04_depvar_differential.R | 33 +- simulations/05_irr_indep.R | 25 +- simulations/06_irr_dv.R | 58 +-- simulations/Makefile | 83 ++-- simulations/example_2_B.feather | Bin 671194 -> 0 bytes simulations/example_3.feather | Bin 999506 -> 1447282 bytes simulations/irr_dv_simulation_base.R | 175 +++++++-- simulations/irr_simulation_base.R | 156 +++++++- simulations/measerr_methods.R | 562 ++++++++++++++++----------- simulations/plot_dv_example.R | 4 +- simulations/plot_example.R | 13 +- simulations/plot_irr_dv_example.R | 12 +- simulations/plot_irr_example.R | 41 +- simulations/run_simulation.sbatch | 5 +- simulations/simulation_base.R | 241 +++--------- simulations/summarize_estimator.R | 4 +- 20 files changed, 964 insertions(+), 601 deletions(-) delete mode 100644 simulations/example_2_B.feather diff --git a/simulations/.Rhistory b/simulations/.Rhistory index 6de9aa0..f0a3609 100644 --- a/simulations/.Rhistory +++ b/simulations/.Rhistory @@ -63,3 +63,94 @@ list.files() install.packages("filelock") q() n +df +df +outcome_formula <- y ~ x + z +outcome_family=gaussian() +proxy_formula <- w_pred ~ x +truth_formula <- x ~ z +params <- start +ll.y.obs.x0 +ll.y.obs.x1 +rater_formula <- x.obs ~ x +rater_formula +rater.modle.matrix.obs.x0 +rater.model.matrix.obs.x0 +names(rater.model.matrix.obs.x0) +head(rater.model.matrix.obs.x0) +df.obs +ll.x.obs.0 +rater.params +rater.params %*% t(rater.model.matrix.x.obs.0[df.obs$xobs.0==1]) +df.obs$xobs.0==1 +df.obs$x.obs.0==1 +ll.x.obs.0[df.obs$x.obs.0==1] +rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,] +df.obs$x.obs.0==1 +n.rater.model.covars <- dim(rater.model.matrix.x.obs.0)[2] + rater.params <- params[param.idx:n.rater.model.covars] +rater.params + ll.x.obs.0[df.obs$x.obs.0==1] <- plogis(rater.params %*% t(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,]), log=TRUE) +t(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,] +) +dimt(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,]) +dim(t(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,])) +dim(ll.x.obs.0[df.obs$x.obs.0==1]) +rater.params +rater.params +rater.params +rater_formula +rater.params +) +1+1 +q() +n +outcome_formula <- y ~ x + z +proxy_formula <- w_pred ~ x + z + y +truth_formula <- x ~ z +proxy_formula +eyboardio Model 01 - Kaleidoscope locally built +df <- df.triple.proxy.mle +outcome_family='gaussian' +outcome_family=gaussian() +proxy_formulas=list(proxy_formula,x.obs.0~x, x.obs.1~x) +proxy_formulas +proxy_familites <- rep(binomial(link='logit'),3) +proxy_families = rep(binomial(link='logit'),3) +proxy_families +proxy_families = list(binomial(link='logit'),binomial(link='logit'),binomial(link='logit')) +proxy_families +proxy_families[[1]] +proxy.params +i +proxy_params +proxy.params +params +params <- start +df.triple.proxy.mle +df +coder.formulas <- c(x.obs.0 ~ x, x.obs.1 ~x) +outcome.formula +outcome_formula +depvar(outcome_formula +) +outcome_formula$terms +terms(outcome_formula) +q() +n +df.triple.proxy.mle +triple.proxy.mle +df +df <- df.triple.proxy +outcome_family <- binomial(link='logit') +outcome_formula <- y ~x+z +proxy_formula <- w_pred ~ y +coder_formulas=list(y.obs.1~y,y.obs.2~y); proxy_formula=w_pred~y; proxy_family=binomial(link='logit')) +coder_formulas=list(y.obs.1~y,y.obs.2~y); proxy_formula=w_pred~y; proxy_family=binomial(link='logit') +coder_formulas=list(y.obs.0~y,y.obs.1~y) +traceback() +df +df +outcome.model.matrix +q() +n diff --git a/simulations/01_two_covariates.R b/simulations/01_two_covariates.R index 3fd6914..b8f9317 100644 --- a/simulations/01_two_covariates.R +++ b/simulations/01_two_covariates.R @@ -32,7 +32,7 @@ source("simulation_base.R") simulate_data <- function(N, m, B0=0, Bxy=0.2, Bzy=-0.2, Bzx=0.2, y_explained_variance=0.025, prediction_accuracy=0.73, seed=1){ set.seed(seed) - z <- rbinom(N, 1, 0.5) + z <- rnorm(N,sd=0.5) # x.var.epsilon <- var(Bzx *z) * ((1-zx_explained_variance)/zx_explained_variance) xprime <- Bzx * z #+ x.var.epsilon x <- rbinom(N,1,plogis(xprime)) @@ -77,7 +77,7 @@ parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy va parser <- add_argument(parser, "--truth_formula", help='formula for the true variable', default="x~z") parser <- add_argument(parser, "--Bzx", help='Effect of z on x', default=0.3) parser <- add_argument(parser, "--Bzy", help='Effect of z on y', default=-0.3) -parser <- add_argument(parser, "--Bxy", help='Effect of z on y', default=0.3) +parser <- add_argument(parser, "--Bxy", help='Effect of x on y', default=0.3) args <- parse_args(parser) B0 <- 0 @@ -85,23 +85,21 @@ Bxy <- args$Bxy Bzy <- args$Bzy Bzx <- args$Bzx -if (args$m < args$N){ +df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, Bzx, seed=args$seed + 500, y_explained_variance = args$y_explained_variance, prediction_accuracy=args$prediction_accuracy) - df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, Bzx, seed=args$seed + 500, y_explained_variance = args$y_explained_variance, prediction_accuracy=args$prediction_accuracy) +result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy, Bzx=Bzx, 'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'accuracy_imbalance_difference'=args$accuracy_imbalance_difference, 'outcome_formula'=args$outcome_formula, 'proxy_formula'=args$proxy_formula,truth_formula=args$truth_formula, error='') - result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy, Bzx=Bzx, 'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'accuracy_imbalance_difference'=args$accuracy_imbalance_difference, 'outcome_formula'=args$outcome_formula, 'proxy_formula'=args$proxy_formula,truth_formula=args$truth_formula, error='') - - outline <- run_simulation(df, result, outcome_formula=as.formula(args$outcome_formula), proxy_formula=as.formula(args$proxy_formula), truth_formula=as.formula(args$truth_formula)) +outline <- run_simulation(df, result, outcome_formula=as.formula(args$outcome_formula), proxy_formula=as.formula(args$proxy_formula), truth_formula=as.formula(args$truth_formula)) - outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE) - if(file.exists(args$outfile)){ - logdata <- read_feather(args$outfile) - logdata <- rbind(logdata,as.data.table(outline),fill=TRUE) - } else { - logdata <- as.data.table(outline) - } - - print(outline) - write_feather(logdata, args$outfile) - unlock(outfile_lock) +outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE) +if(file.exists(args$outfile)){ + logdata <- read_feather(args$outfile) + logdata <- rbind(logdata,as.data.table(outline),fill=TRUE) +} else { + logdata <- as.data.table(outline) } + +print(outline) +write_feather(logdata, args$outfile) +unlock(outfile_lock) + diff --git a/simulations/02_indep_differential.R b/simulations/02_indep_differential.R index bcfad65..6e2732f 100644 --- a/simulations/02_indep_differential.R +++ b/simulations/02_indep_differential.R @@ -31,11 +31,11 @@ source("simulation_base.R") ## one way to do it is by adding correlation to x.obs and y that isn't in w. ## in other words, the model is missing an important feature of x.obs that's related to y. -simulate_data <- function(N, m, B0, Bxy, Bzx, Bzy, seed, y_explained_variance=0.025, prediction_accuracy=0.73, y_bias=-0.8,accuracy_imbalance_difference=0.3){ +simulate_data <- function(N, m, B0, Bxy, Bzx, Bzy, seed, y_explained_variance=0.025, prediction_accuracy=0.73, y_bias=-0.8,z_bias=0,accuracy_imbalance_difference=0.3){ set.seed(seed) # make w and y dependent - z <- rbinom(N, 1, plogis(qlogis(0.5))) - x <- rbinom(N, 1, plogis(Bzx * z + qlogis(0.5))) + z <- rnorm(N,sd=0.5) + x <- rbinom(N, 1, plogis(Bzx * z)) y.var.epsilon <- (var(Bzy * z) + var(Bxy *x) + 2*cov(Bzy*z,Bxy*x)) * ((1-y_explained_variance)/y_explained_variance) y.epsilon <- rnorm(N, sd = sqrt(y.var.epsilon)) @@ -105,8 +105,8 @@ simulate_data <- function(N, m, B0, Bxy, Bzx, Bzy, seed, y_explained_variance=0. ## print(mean(df$w_pred == df$x)) resids <- resid(lm(y~x + z)) - odds.x1 <- qlogis(prediction_accuracy) + y_bias*qlogis(pnorm(resids[x==1])) - odds.x0 <- qlogis(prediction_accuracy,lower.tail=F) + y_bias*qlogis(pnorm(resids[x==0])) + odds.x1 <- qlogis(prediction_accuracy) + y_bias*qlogis(pnorm(resids[x==1])) + z_bias * qlogis(pnorm(z,sd(z))) + odds.x0 <- qlogis(prediction_accuracy,lower.tail=F) + y_bias*qlogis(pnorm(resids[x==0])) + z_bias * qlogis(pnorm(z,sd(z))) ## acc.x0 <- p.correct[df[,x==0]] ## acc.x1 <- p.correct[df[,x==1]] @@ -129,14 +129,15 @@ parser <- add_argument(parser, "--m", default=500, help="m the number of ground parser <- add_argument(parser, "--seed", default=51, help='seed for the rng') parser <- add_argument(parser, "--outfile", help='output file', default='example_2.feather') parser <- add_argument(parser, "--y_explained_variance", help='what proportion of the variance of y can be explained?', default=0.1) -parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.8) +parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.75) parser <- add_argument(parser, "--accuracy_imbalance_difference", help='how much more accurate is the predictive model for one class than the other?', default=0.3) parser <- add_argument(parser, "--Bzx", help='Effect of z on x', default=0.3) parser <- add_argument(parser, "--Bzy", help='Effect of z on y', default=-0.3) parser <- add_argument(parser, "--Bxy", help='Effect of z on y', default=0.3) parser <- add_argument(parser, "--outcome_formula", help='formula for the outcome variable', default="y~x+z") parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~y*z*x") -parser <- add_argument(parser, "--y_bias", help='coefficient of y on the probability a classification is correct', default=-1) +parser <- add_argument(parser, "--y_bias", help='coefficient of y on the probability a classification is correct', default=-0.5) +parser <- add_argument(parser, "--z_bias", help='coefficient of z on the probability a classification is correct', default=0) parser <- add_argument(parser, "--truth_formula", help='formula for the true variable', default="x~z") args <- parse_args(parser) diff --git a/simulations/03_depvar.R b/simulations/03_depvar.R index 79a516f..dde1bee 100644 --- a/simulations/03_depvar.R +++ b/simulations/03_depvar.R @@ -31,13 +31,13 @@ source("simulation_base.R") ## one way to do it is by adding correlation to x.obs and y that isn't in w. ## in other words, the model is missing an important feature of x.obs that's related to y. -simulate_data <- function(N, m, B0, Bxy, Bzy, seed, prediction_accuracy=0.73, log.likelihood.gain = 0.1){ +simulate_data <- function(N, m, B0, Bxy, Bzy, Bzx, seed, prediction_accuracy=0.73, log.likelihood.gain = 0.1){ set.seed(seed) set.seed(seed) # make w and y dependent - z <- rbinom(N, 1, 0.5) - x <- rbinom(N, 1, 0.5) + z <- rnorm(N, sd=0.5) + x <- rbinom(N, 1, plogis(Bzx*z)) ystar <- Bzy * z + Bxy * x + B0 y <- rbinom(N,1,plogis(ystar)) @@ -75,6 +75,7 @@ parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is th ## parser <- add_argument(parser, "--x_bias_y0", help='how is the classifier biased when y = 0 ?', default=0.75) parser <- add_argument(parser, "--Bxy", help='coefficient of x on y', default=0.01) parser <- add_argument(parser, "--Bzy", help='coeffficient of z on y', default=-0.01) +parser <- add_argument(parser, "--Bzx", help='coeffficient of z on x', default=-0.5) parser <- add_argument(parser, "--outcome_formula", help='formula for the outcome variable', default="y~x+z") parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~y") @@ -83,13 +84,13 @@ args <- parse_args(parser) B0 <- 0 Bxy <- args$Bxy Bzy <- args$Bzy - +Bzx <- args$Bzx if(args$m < args$N){ - df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, args$seed, args$prediction_accuracy) + df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, Bzx, args$seed, args$prediction_accuracy) # result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'x_bias_y0'=args$x_bias_y0,'x_bias_y1'=args$x_bias_y1,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula) - result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula) + result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'Bzx'=Bzx,'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula) outline <- run_simulation_depvar(df, result, outcome_formula = as.formula(args$outcome_formula), proxy_formula = as.formula(args$proxy_formula)) diff --git a/simulations/04_depvar_differential.R b/simulations/04_depvar_differential.R index 0d436b6..df0e825 100644 --- a/simulations/04_depvar_differential.R +++ b/simulations/04_depvar_differential.R @@ -31,12 +31,12 @@ source("simulation_base.R") ## one way to do it is by adding correlation to x.obs and y that isn't in w. ## in other words, the model is missing an important feature of x.obs that's related to y. -simulate_data <- function(N, m, B0, Bxy, Bzy, seed, prediction_accuracy=0.73, x_bias=-0.75){ +simulate_data <- function(N, m, B0, Bxy, Bzy, seed, prediction_accuracy=0.73, z_bias=-0.75){ set.seed(seed) # make w and y dependent - z <- rbinom(N, 1, 0.5) - x <- rbinom(N, 1, 0.5) + z <- rnorm(N,sd=0.5) + x <- rbinom(N,1,0.5) ystar <- Bzy * z + Bxy * x + B0 y <- rbinom(N,1,plogis(ystar)) @@ -51,8 +51,8 @@ simulate_data <- function(N, m, B0, Bxy, Bzy, seed, prediction_accuracy=0.73, x_ df <- df[, y.obs := y] } - odds.y1 <- qlogis(prediction_accuracy) + x_bias*df[y==1]$x - odds.y0 <- qlogis(prediction_accuracy,lower.tail=F) + x_bias*df[y==0]$x + odds.y1 <- qlogis(prediction_accuracy) + z_bias*df[y==1]$z + odds.y0 <- qlogis(prediction_accuracy,lower.tail=F) + z_bias*df[y==0]$z df[y==0,w:=plogis(rlogis(.N,odds.y0))] df[y==1,w:=plogis(rlogis(.N,odds.y1))] @@ -69,16 +69,15 @@ parser <- arg_parser("Simulate data and fit corrected models") parser <- add_argument(parser, "--N", default=1000, help="number of observations of w") parser <- add_argument(parser, "--m", default=500, help="m the number of ground truth observations") parser <- add_argument(parser, "--seed", default=17, help='seed for the rng') -parser <- add_argument(parser, "--outfile", help='output file', default='example_2.feather') -parser <- add_argument(parser, "--y_explained_variance", help='what proportion of the variance of y can be explained?', default=0.005) -parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.8) -## parser <- add_argument(parser, "--x_bias_y1", help='how is the classifier biased when y = 1?', default=-0.75) -## parser <- add_argument(parser, "--x_bias_y0", help='how is the classifier biased when y = 0 ?', default=0.75) -parser <- add_argument(parser, "--x_bias", help='how is the classifier biased?', default=0.75) -parser <- add_argument(parser, "--Bxy", help='coefficient of x on y', default=0.3) -parser <- add_argument(parser, "--Bzy", help='coeffficient of z on y', default=-0.3) +parser <- add_argument(parser, "--outfile", help='output file', default='example_4.feather') +parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.79) +## parser <- add_argument(parser, "--z_bias_y1", help='how is the classifier biased when y = 1?', default=-0.75) +## parser <- add_argument(parser, "--z_bias_y0", help='how is the classifier biased when y = 0 ?', default=0.75) +parser <- add_argument(parser, "--z_bias", help='how is the classifier biased?', default=1.5) +parser <- add_argument(parser, "--Bxy", help='coefficient of x on y', default=0.1) +parser <- add_argument(parser, "--Bzy", help='coeffficient of z on y', default=-0.1) parser <- add_argument(parser, "--outcome_formula", help='formula for the outcome variable', default="y~x+z") -parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~y+x") +parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~y+z") args <- parse_args(parser) @@ -88,10 +87,10 @@ Bzy <- args$Bzy if(args$m < args$N){ - df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, args$seed, args$prediction_accuracy, args$x_bias) + df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, args$seed, args$prediction_accuracy, args$z_bias) -# result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'x_bias_y0'=args$x_bias_y0,'x_bias_y1'=args$x_bias_y1,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula) - result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'x_bias'=args$x_bias,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula) +# result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'z_bias_y0'=args$z_bias_y0,'z_bias_y1'=args$z_bias_y1,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula) + result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'z_bias'=args$z_bias,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula) outline <- run_simulation_depvar(df, result, outcome_formula = as.formula(args$outcome_formula), proxy_formula = as.formula(args$proxy_formula)) diff --git a/simulations/05_irr_indep.R b/simulations/05_irr_indep.R index 4c3a109..ebee715 100644 --- a/simulations/05_irr_indep.R +++ b/simulations/05_irr_indep.R @@ -39,7 +39,7 @@ simulate_data <- function(N, m, B0=0, Bxy=0.2, Bzy=-0.2, Bzx=0.2, y_explained_va y.var.epsilon <- (var(Bzy * z) + var(Bxy *x) + 2*cov(Bxy*x,Bzy*z)) * ((1-y_explained_variance)/y_explained_variance) y.epsilon <- rnorm(N, sd = sqrt(y.var.epsilon)) - y <- Bzy * z + Bxy * x + y.epsilon + y <- Bzy * z + Bxy * x + y.epsilon + B0 df <- data.table(x=x,y=y,z=z) @@ -49,9 +49,12 @@ simulate_data <- function(N, m, B0=0, Bxy=0.2, Bzy=-0.2, Bzx=0.2, y_explained_va df <- df[, x.obs := x] } - df[ (!is.na(x.obs)) ,x.obs.0 := abs(x.obs - rbinom(.N, 1, 1-coder_accuracy))] - df[ (!is.na(x.obs)) ,x.obs.1 := abs(x.obs - rbinom(.N, 1, 1-coder_accuracy))] - + coder.0.correct <- rbinom(m, 1, coder_accuracy) + coder.1.correct <- rbinom(m, 1, coder_accuracy) + + df[!is.na(x.obs),x.obs.0 := as.numeric((x.obs & coder.0.correct) | (!x.obs & !coder.0.correct))] + df[!is.na(x.obs),x.obs.1 := as.numeric((x.obs & coder.1.correct) | (!x.obs & !coder.1.correct))] + ## how can you make a model with a specific accuracy? w0 =(1-x)**2 + (-1)**(1-x) * prediction_accuracy @@ -69,21 +72,21 @@ simulate_data <- function(N, m, B0=0, Bxy=0.2, Bzy=-0.2, Bzx=0.2, y_explained_va parser <- arg_parser("Simulate data and fit corrected models") parser <- add_argument(parser, "--N", default=1000, help="number of observations of w") -parser <- add_argument(parser, "--m", default=500, help="m the number of ground truth observations") -parser <- add_argument(parser, "--seed", default=57, help='seed for the rng') +parser <- add_argument(parser, "--m", default=150, help="m the number of ground truth observations") +parser <- add_argument(parser, "--seed", default=1, help='seed for the rng') parser <- add_argument(parser, "--outfile", help='output file', default='example_1.feather') -parser <- add_argument(parser, "--y_explained_variance", help='what proportion of the variance of y can be explained?', default=0.05) +parser <- add_argument(parser, "--y_explained_variance", help='what proportion of the variance of y can be explained?', default=0.1) # parser <- add_argument(parser, "--zx_explained_variance", help='what proportion of the variance of x can be explained by z?', default=0.3) parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.73) -parser <- add_argument(parser, "--coder_accuracy", help='how accurate is the predictive model?', default=0.8) +parser <- add_argument(parser, "--coder_accuracy", help='how accurate are the human coders?', default=0.85) parser <- add_argument(parser, "--outcome_formula", help='formula for the outcome variable', default="y~x+z") parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~x") # parser <- add_argument(parser, "--rater_formula", help='formula for the true variable', default="x.obs~x") parser <- add_argument(parser, "--truth_formula", help='formula for the true variable', default="x~z") parser <- add_argument(parser, "--Bzx", help='Effect of z on x', default=-0.3) -parser <- add_argument(parser, "--Bzy", help='Effect of z on y', default=-0.3) -parser <- add_argument(parser, "--Bxy", help='Effect of z on y', default=0.3) +parser <- add_argument(parser, "--Bzy", help='Effect of z on y', default=0.27) +parser <- add_argument(parser, "--Bxy", help='Effect of x on y', default=-0.33) args <- parse_args(parser) B0 <- 0 @@ -93,7 +96,7 @@ Bzx <- args$Bzx if (args$m < args$N){ - df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, Bzx, seed=args$seed + 500, y_explained_variance = args$y_explained_variance, prediction_accuracy=args$prediction_accuracy, coder_accuracy=args$coder_accuracy) + df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, Bzx, seed=args$seed, y_explained_variance = args$y_explained_variance, prediction_accuracy=args$prediction_accuracy, coder_accuracy=args$coder_accuracy) result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy, Bzx=Bzx, 'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'accuracy_imbalance_difference'=args$accuracy_imbalance_difference, 'outcome_formula'=args$outcome_formula, 'truth_formula'=args$truth_formula, 'proxy_formula'=args$proxy_formula,truth_formula=args$truth_formula, 'coder_accuracy'=args$coder_accuracy, error='') diff --git a/simulations/06_irr_dv.R b/simulations/06_irr_dv.R index 0dd13b6..dd8fa72 100644 --- a/simulations/06_irr_dv.R +++ b/simulations/06_irr_dv.R @@ -31,14 +31,13 @@ simulate_data <- function(N, m, B0, Bxy, Bzy, seed, prediction_accuracy=0.73, co df <- data.table(x=x,y=y,ystar=ystar,z=z) - if(m < N){ - df <- df[sample(nrow(df), m), y.obs := y] - } else { - df <- df[, y.obs := y] - } + df <- df[sample(nrow(df), m), y.obs := y] - df[ (!is.na(y.obs)) ,y.obs.0 := abs(y.obs - rbinom(.N, 1, 1-coder_accuracy))] - df[ (!is.na(y.obs)) ,y.obs.1 := abs(y.obs - rbinom(.N, 1, 1-coder_accuracy))] + coder.0.correct <- rbinom(m, 1, coder_accuracy) + coder.1.correct <- rbinom(m, 1, coder_accuracy) + + df[!is.na(y.obs),y.obs.0 := as.numeric((.SD$y.obs & coder.0.correct) | (!.SD$y.obs & !coder.0.correct))] + df[!is.na(y.obs),y.obs.1 := as.numeric((.SD$y.obs & coder.1.correct) | (!.SD$y.obs & !coder.1.correct))] odds.y1 <- qlogis(prediction_accuracy) odds.y0 <- qlogis(prediction_accuracy,lower.tail=F) @@ -48,6 +47,9 @@ simulate_data <- function(N, m, B0, Bxy, Bzy, seed, prediction_accuracy=0.73, co df[,w_pred := as.integer(w > 0.5)] + print(mean(df$y == df$y.obs.0,na.rm=T)) + print(mean(df$y == df$y.obs.1,na.rm=T)) + print(mean(df[x==0]$y == df[x==0]$w_pred)) print(mean(df[x==1]$y == df[x==1]$w_pred)) print(mean(df$w_pred == df$y)) @@ -55,18 +57,18 @@ simulate_data <- function(N, m, B0, Bxy, Bzy, seed, prediction_accuracy=0.73, co } parser <- arg_parser("Simulate data and fit corrected models") -parser <- add_argument(parser, "--N", default=1000, help="number of observations of w") +parser <- add_argument(parser, "--N", default=5000, help="number of observations of w") parser <- add_argument(parser, "--m", default=500, help="m the number of ground truth observations") -parser <- add_argument(parser, "--seed", default=17, help='seed for the rng') +parser <- add_argument(parser, "--seed", default=16, help='seed for the rng') parser <- add_argument(parser, "--outfile", help='output file', default='example_2.feather') -parser <- add_argument(parser, "--y_explained_variance", help='what proportion of the variance of y can be explained?', default=0.005) -parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.72) +parser <- add_argument(parser, "--y_explained_variance", help='what proportion of the variance of y can be explained?', default=0.1) +parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.73) ## parser <- add_argument(parser, "--x_bias_y1", help='how is the classifier biased when y = 1?', default=-0.75) ## parser <- add_argument(parser, "--x_bias_y0", help='how is the classifier biased when y = 0 ?', default=0.75) parser <- add_argument(parser, "--Bxy", help='coefficient of x on y', default=0.3) parser <- add_argument(parser, "--Bzy", help='coeffficient of z on y', default=-0.3) parser <- add_argument(parser, "--outcome_formula", help='formula for the outcome variable', default="y~x+z") -parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~y") +parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~y+y.obs.1+y.obs.0") parser <- add_argument(parser, "--coder_accuracy", help='How accurate are the coders?', default=0.8) args <- parse_args(parser) @@ -76,24 +78,24 @@ Bxy <- args$Bxy Bzy <- args$Bzy -if(args$m < args$N){ - df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, args$seed, args$prediction_accuracy, args$coder_accuracy) - -# result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'x_bias_y0'=args$x_bias_y0,'x_bias_y1'=args$x_bias_y1,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula) - result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula) +df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, args$seed, args$prediction_accuracy, args$coder_accuracy) - outline <- run_simulation_depvar(df, result, outcome_formula = as.formula(args$outcome_formula), proxy_formula = as.formula(args$proxy_formula)) + # result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'x_bias_y0'=args$x_bias_y0,'x_bias_y1'=args$x_bias_y1,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula) +result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula) - outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE) +outline <- run_simulation_depvar(df, result, outcome_formula = as.formula(args$outcome_formula), proxy_formula = as.formula(args$proxy_formula)) - if(file.exists(args$outfile)){ - logdata <- read_feather(args$outfile) - logdata <- rbind(logdata,as.data.table(outline),fill=TRUE) - } else { - logdata <- as.data.table(outline) - } +outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE) - print(outline) - write_feather(logdata, args$outfile) - unlock(outfile_lock) +if(file.exists(args$outfile)){ + logdata <- read_feather(args$outfile) + logdata <- rbind(logdata,as.data.table(outline),fill=TRUE) +} else { + logdata <- as.data.table(outline) } + +print(outline) +write_feather(logdata, args$outfile) +unlock(outfile_lock) + +warnings() diff --git a/simulations/Makefile b/simulations/Makefile index af54727..b3ab77a 100644 --- a/simulations/Makefile +++ b/simulations/Makefile @@ -1,9 +1,9 @@ SHELL=bash -Ns=[1000, 2000, 4000] -ms=[100, 200, 400, 800] -seeds=[$(shell seq -s, 1 250)] +Ns=[1000, 5000, 10000] +ms=[100, 200, 400] +seeds=[$(shell seq -s, 1 500)] explained_variances=[0.1] all:remembr.RDS remember_irr.RDS @@ -23,21 +23,28 @@ joblists:example_1_jobs example_2_jobs example_3_jobs # sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 test_true_z_jobs -example_1_jobs: 01_two_covariates.R simulation_base.R grid_sweep.py - sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 01_two_covariates.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_1.feather"], "y_explained_variance":${explained_variances}, "Bzx":[0.3]}' --outfile example_1_jobs +example_1_jobs: 01_two_covariates.R simulation_base.R grid_sweep.py pl_methods.R + sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 01_two_covariates.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_1.feather"], "y_explained_variance":${explained_variances}, "Bzx":[1]}' --outfile example_1_jobs example_1.feather: example_1_jobs rm -f example_1.feather - sbatch --wait --verbose --array=1-$(shell cat example_1_jobs | wc -l) run_simulation.sbatch 0 example_1_jobs -# sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 example_1_jobs + sbatch --wait --verbose --array=1-1000 run_simulation.sbatch 0 example_1_jobs + sbatch --wait --verbose --array=1001-2000 run_simulation.sbatch 0 example_1_jobs + sbatch --wait --verbose --array=2001-3000 run_simulation.sbatch 0 example_1_jobs + sbatch --wait --verbose --array=3001-4000 run_simulation.sbatch 0 example_1_jobs + sbatch --wait --verbose --array=4001-$(shell cat example_1_jobs | wc -l) run_simulation.sbatch 0 example_1_jobs -example_2_jobs: 02_indep_differential.R simulation_base.R grid_sweep.py - sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 02_indep_differential.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_2.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y*z*x"]}' --outfile example_2_jobs +example_2_jobs: 02_indep_differential.R simulation_base.R grid_sweep.py pl_methods.R + sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 02_indep_differential.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_2.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y*z*x"]}' --outfile example_2_jobs example_2.feather: example_2_jobs rm -f example_2.feather - sbatch --wait --verbose --array=1-$(shell cat example_1_jobs | wc -l) run_simulation.sbatch 0 example_2_jobs -# sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 example_2_jobs + sbatch --wait --verbose --array=1-1000 run_simulation.sbatch 0 example_2_jobs + sbatch --wait --verbose --array=1001-2000 run_simulation.sbatch 0 example_2_jobs + sbatch --wait --verbose --array=2001-3000 run_simulation.sbatch 0 example_2_jobs + sbatch --wait --verbose --array=3001-4000 run_simulation.sbatch 0 example_2_jobs + sbatch --wait --verbose --array=4001-$(shell cat example_2_jobs | wc -l) run_simulation.sbatch 0 example_2_jobs + # example_2_B_jobs: example_2_B.R # sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript example_2_B.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_2_B.feather"]}' --outfile example_2_B_jobs @@ -46,19 +53,28 @@ example_2.feather: example_2_jobs # rm -f example_2_B.feather # sbatch --wait --verbose --array=1-3000 run_simulation.sbatch 0 example_2_B_jobs -example_3_jobs: 03_depvar.R simulation_base.R grid_sweep.py - sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 03_depvar.R" --arg_dict '{"N":${Ns},"m":${ms}, "Bxy":[0.01],"Bzy":[-0.01],"seed":${seeds}, "outfile":["example_3.feather"], "y_explained_variance":${explained_variances}}' --outfile example_3_jobs +example_3_jobs: 03_depvar.R simulation_base.R grid_sweep.py pl_methods.R + sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 03_depvar.R" --arg_dict '{"N":${Ns},"m":${ms}, "Bxy":[0.7],"Bzy":[-0.7],"seed":${seeds}, "outfile":["example_3.feather"], "y_explained_variance":${explained_variances}}' --outfile example_3_jobs example_3.feather: example_3_jobs rm -f example_3.feather - sbatch --wait --verbose --array=1-$(shell cat example_3_jobs | wc -l) run_simulation.sbatch 0 example_3_jobs + sbatch --wait --verbose --array=1-1000 run_simulation.sbatch 0 example_3_jobs + sbatch --wait --verbose --array=1001-2000 run_simulation.sbatch 0 example_3_jobs + sbatch --wait --verbose --array=2001-3000 run_simulation.sbatch 0 example_3_jobs + sbatch --wait --verbose --array=3001-4000 run_simulation.sbatch 0 example_3_jobs + sbatch --wait --verbose --array=4001-$(shell cat example_3_jobs | wc -l) run_simulation.sbatch 0 example_3_jobs -example_4_jobs: 04_depvar_differential.R simulation_base.R grid_sweep.py - sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 04_depvar_differential.R" --arg_dict '{"N":${Ns},"Bxy":[0.01],"Bzy":[-0.01],"m":${ms}, "seed":${seeds}, "outfile":["example_4.feather"], "y_explained_variance":${explained_variances}}' --outfile example_4_jobs +example_4_jobs: 04_depvar_differential.R simulation_base.R grid_sweep.py pl_methods.R + sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 04_depvar_differential.R" --arg_dict '{"N":${Ns},"Bxy":[0.7],"Bzy":[-0.7],"m":${ms}, "seed":${seeds}, "outfile":["example_4.feather"], "z_bias":[0.5]}' --outfile example_4_jobs example_4.feather: example_4_jobs rm -f example_4.feather - sbatch --wait --verbose --array=1-$(shell cat example_4_jobs | wc -l) run_simulation.sbatch 0 example_4_jobs + sbatch --wait --verbose --array=1-1000 run_simulation.sbatch 0 example_4_jobs + sbatch --wait --verbose --array=1001-2000 run_simulation.sbatch 0 example_4_jobs + sbatch --wait --verbose --array=2001-3000 run_simulation.sbatch 0 example_4_jobs + sbatch --wait --verbose --array=3001-4000 run_simulation.sbatch 0 example_4_jobs + sbatch --wait --verbose --array=4001-$(shell cat example_4_jobs | wc -l) run_simulation.sbatch 0 example_4_jobs + remembr.RDS:example_1.feather example_2.feather example_3.feather example_4.feather plot_example.R plot_dv_example.R summarize_estimator.R @@ -69,30 +85,39 @@ remembr.RDS:example_1.feather example_2.feather example_3.feather example_4.feat ${srun} Rscript plot_dv_example.R --infile example_4.feather --name "plot.df.example.4" -irr_Ns = ${Ns} -irr_ms = ${ms} +irr_Ns = [1000] +irr_ms = [150,300,600] irr_seeds=${seeds} irr_explained_variances=${explained_variances} +irr_coder_accuracy=[0.80] -example_5_jobs: 05_irr_indep.R irr_simulation_base.R grid_sweep.py - sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 05_irr_indep.R" --arg_dict '{"N":${irr_Ns},"m":${irr_ms}, "seed":${irr_seeds}, "outfile":["example_5.feather"], "y_explained_variance":${irr_explained_variances}}' --outfile example_5_jobs +example_5_jobs: 05_irr_indep.R irr_simulation_base.R grid_sweep.py pl_methods.R measerr_methods.R + sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 05_irr_indep.R" --arg_dict '{"N":${irr_Ns},"m":${irr_ms}, "seed":${irr_seeds}, "outfile":["example_5.feather"], "y_explained_variance":${irr_explained_variances}, "coder_accuracy":${irr_coder_accuracy}}' --outfile example_5_jobs example_5.feather:example_5_jobs rm -f example_5.feather - sbatch --wait --verbose --array=1-$(shell cat example_5_jobs | wc -l) run_simulation.sbatch 0 example_5_jobs + sbatch --wait --verbose --array=1-1000 run_simulation.sbatch 0 example_5_jobs + sbatch --wait --verbose --array=1001-$(shell cat example_5_jobs | wc -l) run_simulation.sbatch 1000 example_5_jobs + # sbatch --wait --verbose --array=2001-3000 run_simulation.sbatch 2000 example_5_jobs + # sbatch --wait --verbose --array=3001-4000 run_simulation.sbatch 3000 example_5_jobs + # sbatch --wait --verbose --array=2001-$(shell cat example_5_jobs | wc -l) run_simulation.sbatch 4000 example_5_jobs + + +# example_6_jobs: 06_irr_dv.R irr_dv_simulation_base.R grid_sweep.py pl_methods.R +# sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 06_irr_dv.R" --arg_dict '{"N":${irr_Ns},"m":${irr_ms}, "seed":${irr_seeds}, "outfile":["example_6.feather"], "y_explained_variance":${irr_explained_variances},"coder_accuracy":${irr_coder_accuracy}}' --outfile example_6_jobs -example_6_jobs: 06_irr_dv.R irr_dv_simulation_base.R grid_sweep.py - sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 06_irr_dv.R" --arg_dict '{"N":${irr_Ns},"m":${irr_ms}, "seed":${irr_seeds}, "outfile":["example_6.feather"], "y_explained_variance":${irr_explained_variances}}' --outfile example_6_jobs +# example_6.feather:example_6_jobs +# rm -f example_6.feather +# sbatch --wait --verbose --array=1-1000 run_simulation.sbatch 0 example_6_jobs +# sbatch --wait --verbose --array=1001-2000 run_simulation.sbatch 1000 example_6_jobs +# sbatch --wait --verbose --array=2001-$(shell cat example_6_jobs | wc -l) run_simulation.sbatch 2000 example_6_jobs -example_6.feather:example_6_jobs - rm -f example_6.feather - sbatch --wait --verbose --array=1-$(shell cat example_6_jobs | wc -l) run_simulation.sbatch 0 example_6_jobs -remember_irr.RDS: example_5.feather example_6.feather plot_irr_example.R plot_irr_dv_example.R summarize_estimator.R +remember_irr.RDS: example_5.feather plot_irr_example.R plot_irr_dv_example.R summarize_estimator.R rm -f remember_irr.RDS sbatch --wait --verbose run_job.sbatch Rscript plot_irr_example.R --infile example_5.feather --name "plot.df.example.5" - sbatch --wait --verbose run_job.sbatch Rscript plot_irr_dv_example.R --infile example_6.feather --name "plot.df.example.6" +# sbatch --wait --verbose run_job.sbatch Rscript plot_irr_dv_example.R --infile example_6.feather --name "plot.df.example.6" diff --git a/simulations/example_2_B.feather b/simulations/example_2_B.feather deleted file mode 100644 index 8d0ecbd884f2881afaf66a7e456f834facf58374..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 671194 zcmeFZd3;kv7e747y*EkQv`w3~hCM8W$l9`obpo=KJ+ek*DIlRNLMcRpAOzV82v#1J zf+$f@3JR1>0tgLz*ux%HBMGIf2BcV!<^9fWqOHo~`+Gm{AMfY=Jbj)U?%bJk=FFKh 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b/simulations/irr_dv_simulation_base.R index 059473c..3263322 100644 --- a/simulations/irr_dv_simulation_base.R +++ b/simulations/irr_dv_simulation_base.R @@ -4,23 +4,47 @@ options(amelia.parallel="no", 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$w_pred)) + result <- append(result, list(error.cor.x = error.cor.x, + error.cor.z = error.cor.z, + error.cor.y = error.cor.y)) + + model.null <- glm(y~1, data=df,family=binomial(link='logit')) + (model.true <- glm(y ~ x + z, data=df,family=binomial(link='logit'))) + (lik.ratio <- exp(logLik(model.true) - logLik(model.null))) - (model.true <- glm(y ~ x + z, data=df, family=binomial(link='logit'))) true.ci.Bxy <- confint(model.true)['x',] true.ci.Bzy <- confint(model.true)['z',] + + result <- append(result, list(lik.ratio=lik.ratio)) + result <- append(result, list(Bxy.est.true=coef(model.true)['x'], Bzy.est.true=coef(model.true)['z'], Bxy.ci.upper.true = true.ci.Bxy[2], Bxy.ci.lower.true = true.ci.Bxy[1], Bzy.ci.upper.true = true.ci.Bzy[2], Bzy.ci.lower.true = true.ci.Bzy[1])) + + (model.naive <- lm(y~w_pred+z, data=df)) + + naive.ci.Bxy <- confint(model.naive)['w_pred',] + naive.ci.Bzy <- confint(model.naive)['z',] + + result <- append(result, list(Bxy.est.naive=coef(model.naive)['w_pred'], + Bzy.est.naive=coef(model.naive)['z'], + Bxy.ci.upper.naive = naive.ci.Bxy[2], + Bxy.ci.lower.naive = naive.ci.Bxy[1], + Bzy.ci.upper.naive = naive.ci.Bzy[2], + Bzy.ci.lower.naive = naive.ci.Bzy[1])) @@ -37,20 +61,20 @@ run_simulation_depvar <- function(df, result, outcome_formula = y ~ x + z, rater Bzy.ci.lower.loa0.feasible = loa0.ci.Bzy[1])) - df.loa0.mle <- copy(df) - df.loa0.mle[,y:=y.obs.0] - loa0.mle <- measerr_mle_dv(df.loa0.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula) - fisher.info <- solve(loa0.mle$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$par + ## ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96 + ## ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96 - result <- append(result, list(Bxy.est.loa0.mle=coef['x'], - Bzy.est.loa0.mle=coef['z'], - Bxy.ci.upper.loa0.mle = ci.upper['x'], - Bxy.ci.lower.loa0.mle = ci.lower['x'], - Bzy.ci.upper.loa0.mle = ci.upper['z'], - Bzy.ci.lower.loa0.mle = ci.upper['z'])) + ## result <- append(result, list(Bxy.est.loa0.mle=coef['x'], + ## Bzy.est.loa0.mle=coef['z'], + ## Bxy.ci.upper.loa0.mle = ci.upper['x'], + ## Bxy.ci.lower.loa0.mle = ci.lower['x'], + ## Bzy.ci.upper.loa0.mle = ci.upper['z'], + ## Bzy.ci.lower.loa0.mle = ci.upper['z'])) loco.feasible <- glm(y.obs.0 ~ x + z, data = df[(!is.na(y.obs.0)) & (y.obs.1 == y.obs.0)], family=binomial(link='logit')) @@ -64,29 +88,110 @@ run_simulation_depvar <- function(df, result, outcome_formula = y ~ x + z, rater Bzy.ci.upper.loco.feasible = loco.feasible.ci.Bzy[2], Bzy.ci.lower.loco.feasible = loco.feasible.ci.Bzy[1])) + + ## df.double.proxy <- copy(df) + ## df.double.proxy <- df.double.proxy[,y.obs:=NA] + ## df.double.proxy <- df.double.proxy[,y:=NA] + + ## double.proxy.mle <- measerr_irr_mle_dv(df.double.proxy, outcome_formula=y~x+z, outcome_family=binomial(link='logit'), coder_formulas=list(y.obs.0 ~ y), proxy_formula=w_pred ~ y.obs.0 + y, proxy_family=binomial(link='logit')) + ## print(double.proxy.mle$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$par ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96 ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96 - result <- append(result, list(Bxy.est.loco.mle=coef['x'], - Bzy.est.loco.mle=coef['z'], - Bxy.ci.upper.loco.mle = ci.upper['x'], - Bxy.ci.lower.loco.mle = ci.lower['x'], - Bzy.ci.upper.loco.mle = ci.upper['z'], - Bzy.ci.lower.loco.mle = ci.lower['z'])) + result <- append(result, list(Bxy.est.triple.proxy=coef['x'], + Bzy.est.triple.proxy=coef['z'], + Bxy.ci.upper.triple.proxy = ci.upper['x'], + Bxy.ci.lower.triple.proxy = ci.lower['x'], + Bzy.ci.upper.triple.proxy = ci.upper['z'], + Bzy.ci.lower.triple.proxy = ci.lower['z'])) + + ## df.loco.mle <- copy(df) + ## df.loco.mle[,y.obs:=NA] + ## df.loco.mle[(y.obs.0)==(y.obs.1),y.obs:=y.obs.0] + ## df.loco.mle[,y.true:=y] + ## df.loco.mle[,y:=y.obs] + ## print(df.loco.mle[!is.na(y.obs.1),mean(y.true==y,na.rm=TRUE)]) + ## loco.mle <- measerr_mle_dv(df.loco.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula) + ## fisher.info <- solve(loco.mle$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) diff --git a/simulations/irr_simulation_base.R b/simulations/irr_simulation_base.R index ee7112a..f16c96b 100644 --- a/simulations/irr_simulation_base.R +++ b/simulations/irr_simulation_base.R @@ -3,10 +3,10 @@ library(matrixStats) # for numerically stable logsumexps options(amelia.parallel="no", amelia.ncpus=1) library(Amelia) +source("measerr_methods.R") +source("pl_methods.R") -source("measerr_methods.R") ## for my more generic function. - -run_simulation <- function(df, result, outcome_formula = y ~ x + z, proxy_formula = w_pred ~ x, truth_formula = x ~ z){ +run_simulation <- function(df, result, outcome_formula = y ~ x + z, proxy_formula = w_pred ~ x, coder_formulas=list(x.obs.1~x, x.obs.0~x), truth_formula = x ~ z){ accuracy <- df[,mean(w_pred==x)] result <- append(result, list(accuracy=accuracy)) @@ -24,6 +24,8 @@ run_simulation <- function(df, result, outcome_formula = y ~ x + z, proxy_formul + + loa0.feasible <- lm(y ~ x.obs.0 + z, data = df[!(is.na(x.obs.1))]) loa0.ci.Bxy <- confint(loa0.feasible)['x.obs.0',] @@ -35,7 +37,7 @@ run_simulation <- function(df, result, outcome_formula = y ~ x + z, proxy_formul Bxy.ci.lower.loa0.feasible = loa0.ci.Bxy[1], Bzy.ci.upper.loa0.feasible = loa0.ci.Bzy[2], Bzy.ci.lower.loa0.feasible = loa0.ci.Bzy[1])) - + print("fitting loa0 model") df.loa0.mle <- copy(df) df.loa0.mle[,x:=x.obs.0] @@ -52,8 +54,11 @@ run_simulation <- function(df, result, outcome_formula = y ~ x + z, proxy_formul Bzy.ci.upper.loa0.mle = ci.upper['z'], Bzy.ci.lower.loa0.mle = ci.upper['z'])) + + loco.feasible <- lm(y ~ x.obs.1 + z, data = df[(!is.na(x.obs.1)) & (x.obs.1 == x.obs.0)]) + loco.feasible.ci.Bxy <- confint(loco.feasible)['x.obs.1',] loco.feasible.ci.Bzy <- confint(loco.feasible)['z',] @@ -65,41 +70,152 @@ run_simulation <- function(df, result, outcome_formula = y ~ x + z, proxy_formul Bzy.ci.lower.loco.feasible = loco.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])) + + print("fitting loco model") + df.loco.mle <- copy(df) df.loco.mle[,x.obs:=NA] df.loco.mle[(x.obs.0)==(x.obs.1),x.obs:=x.obs.0] df.loco.mle[,x.true:=x] df.loco.mle[,x:=x.obs] print(df.loco.mle[!is.na(x.obs.1),mean(x.true==x,na.rm=TRUE)]) + loco.accuracy <- df.loco.mle[(!is.na(x.obs.1)) & (x.obs.1 == x.obs.0),mean(x.obs.1 == x.true)] loco.mle <- measerr_mle(df.loco.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula, truth_formula=truth_formula) fisher.info <- solve(loco.mle$hessian) coef <- loco.mle$par ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96 ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96 - result <- append(result, list(Bxy.est.loco.mle=coef['x'], + result <- append(result, list(loco.accuracy=loco.accuracy, + Bxy.est.loco.mle=coef['x'], Bzy.est.loco.mle=coef['z'], Bxy.ci.upper.loco.mle = ci.upper['x'], Bxy.ci.lower.loco.mle = ci.lower['x'], Bzy.ci.upper.loco.mle = ci.upper['z'], Bzy.ci.lower.loco.mle = ci.lower['z'])) - ## print(rater_formula) - ## print(proxy_formula) - ## mle.irr <- measerr_irr_mle( df, outcome_formula = outcome_formula, rater_formula = rater_formula, proxy_formula=proxy_formula, truth_formula=truth_formula) + df.double.proxy.mle <- copy(df) + df.double.proxy.mle[,x.obs:=NA] + print("fitting double proxy model") + + double.proxy.mle <- measerr_irr_mle(df.double.proxy.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula, coder_formulas=coder_formulas[1], truth_formula=truth_formula) + fisher.info <- solve(double.proxy.mle$hessian) + coef <- double.proxy.mle$par + ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96 + ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96 + + result <- append(result, list( + Bxy.est.double.proxy=coef['x'], + Bzy.est.double.proxy=coef['z'], + Bxy.ci.upper.double.proxy = ci.upper['x'], + Bxy.ci.lower.double.proxy = ci.lower['x'], + Bzy.ci.upper.double.proxy = ci.upper['z'], + Bzy.ci.lower.double.proxy = ci.lower['z'])) + + df.triple.proxy.mle <- copy(df) + df.triple.proxy.mle[,x.obs:=NA] + + print("fitting triple proxy model") + triple.proxy.mle <- measerr_irr_mle(df.triple.proxy.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula, coder_formulas=coder_formulas, truth_formula=truth_formula) + fisher.info <- solve(triple.proxy.mle$hessian) + coef <- triple.proxy.mle$par + ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96 + ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96 + + result <- append(result, list( + Bxy.est.triple.proxy=coef['x'], + Bzy.est.triple.proxy=coef['z'], + Bxy.ci.upper.triple.proxy = ci.upper['x'], + Bxy.ci.lower.triple.proxy = ci.lower['x'], + Bzy.ci.upper.triple.proxy = ci.upper['z'], + Bzy.ci.lower.triple.proxy = ci.lower['z'])) + tryCatch({ + amelia.out.k <- amelia(df.loco.mle, m=200, p2s=0, idvars=c('x.true','w','x.obs.1','x.obs.0','x')) + mod.amelia.k <- zelig(y~x.obs+z, model='ls', data=amelia.out.k$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({ + + mod.zhang.lik <- zhang.mle.iv(df.loco.mle) + 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) + }) + + df <- df.loco.mle + 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$confint[2,1])) + - ## fisher.info <- solve(mle.irr$hessian) - ## coef <- mle.irr$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'])) return(result) diff --git a/simulations/measerr_methods.R b/simulations/measerr_methods.R index 087c608..63f8bc1 100644 --- a/simulations/measerr_methods.R +++ b/simulations/measerr_methods.R @@ -1,5 +1,6 @@ library(formula.tools) library(matrixStats) +library(optimx) library(bbmle) ## df: dataframe to model ## outcome_formula: formula for y | x, z @@ -113,227 +114,18 @@ measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='lo return(fit) } -## Experimental, and not necessary if errors are independent. -measerr_irr_mle <- function(df, outcome_formula, outcome_family=gaussian(), rater_formula, proxy_formula, proxy_family=binomial(link='logit'), truth_formula, truth_family=binomial(link='logit'),method='optim'){ - ### in this scenario, the ground truth also has measurement error, but we have repeated measures for it. +measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_formula, proxy_family=binomial(link='logit'), truth_formula, truth_family=binomial(link='logit'),method='optim'){ - ## probability of y given observed data. - df.obs <- df[!is.na(x.obs.1)] + df.obs <- model.frame(outcome_formula, df) + response.var <- all.vars(outcome_formula)[1] proxy.variable <- all.vars(proxy_formula)[1] - df.x.obs.1 <- copy(df.obs)[,x:=1] - df.x.obs.0 <- copy(df.obs)[,x:=0] - y.obs <- df.obs[,y] - - nll <- function(params){ - outcome.model.matrix.x.obs.0 <- model.matrix(outcome_formula, df.x.obs.0) - outcome.model.matrix.x.obs.1 <- model.matrix(outcome_formula, df.x.obs.1) - - param.idx <- 1 - n.outcome.model.covars <- dim(outcome.model.matrix.x.obs.0)[2] - outcome.params <- params[param.idx:n.outcome.model.covars] - param.idx <- param.idx + n.outcome.model.covars - - sigma.y <- params[param.idx] - param.idx <- param.idx + 1 - - ll.y.x.obs.0 <- dnorm(y.obs, outcome.params %*% t(outcome.model.matrix.x.obs.0),sd=sigma.y, log=TRUE) - ll.y.x.obs.1 <- dnorm(y.obs, outcome.params %*% t(outcome.model.matrix.x.obs.1),sd=sigma.y, log=TRUE) - - ## assume that the two coders are statistically independent conditional on x - ll.x.obs.0.x0 <- vector(mode='numeric', length=nrow(df.obs)) - ll.x.obs.1.x0 <- vector(mode='numeric', length=nrow(df.obs)) - ll.x.obs.0.x1 <- vector(mode='numeric', length=nrow(df.obs)) - ll.x.obs.1.x1 <- vector(mode='numeric', length=nrow(df.obs)) - - rater.model.matrix.x.obs.0 <- model.matrix(rater_formula, df.x.obs.0) - rater.model.matrix.x.obs.1 <- model.matrix(rater_formula, df.x.obs.1) - - n.rater.model.covars <- dim(rater.model.matrix.x.obs.0)[2] - rater.0.params <- params[param.idx:(n.rater.model.covars + param.idx - 1)] - param.idx <- param.idx + n.rater.model.covars - - rater.1.params <- params[param.idx:(n.rater.model.covars + param.idx - 1)] - param.idx <- param.idx + n.rater.model.covars - - # probability of rater 0 if x is 0 or 1 - ll.x.obs.0.x0[df.obs$x.obs.0==1] <- plogis(rater.0.params %*% t(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,]), log=TRUE) - ll.x.obs.0.x0[df.obs$x.obs.0==0] <- plogis(rater.0.params %*% t(rater.model.matrix.x.obs.0[df.obs$x.obs.0==0,]), log=TRUE, lower.tail=FALSE) - ll.x.obs.0.x1[df.obs$x.obs.0==1] <- plogis(rater.0.params %*% t(rater.model.matrix.x.obs.1[df.obs$x.obs.0==1,]), log=TRUE) - ll.x.obs.0.x1[df.obs$x.obs.0==0] <- plogis(rater.0.params %*% t(rater.model.matrix.x.obs.1[df.obs$x.obs.0==0,]), log=TRUE, lower.tail=FALSE) - - # probability of rater 1 if x is 0 or 1 - ll.x.obs.1.x0[df.obs$x.obs.1==1] <- plogis(rater.1.params %*% t(rater.model.matrix.x.obs.0[df.obs$x.obs.1==1,]), log=TRUE) - ll.x.obs.1.x0[df.obs$x.obs.1==0] <- plogis(rater.1.params %*% t(rater.model.matrix.x.obs.0[df.obs$x.obs.1==0,]), log=TRUE, lower.tail=FALSE) - ll.x.obs.1.x1[df.obs$x.obs.1==1] <- plogis(rater.1.params %*% t(rater.model.matrix.x.obs.1[df.obs$x.obs.1==1,]), log=TRUE) - ll.x.obs.1.x1[df.obs$x.obs.1==0] <- plogis(rater.1.params %*% t(rater.model.matrix.x.obs.1[df.obs$x.obs.1==0,]), log=TRUE, lower.tail=FALSE) - - proxy.model.matrix.x0 <- model.matrix(proxy_formula, df.x.obs.0) - proxy.model.matrix.x1 <- model.matrix(proxy_formula, df.x.obs.1) - - n.proxy.model.covars <- dim(proxy.model.matrix.x0)[2] - proxy.params <- params[param.idx:(n.proxy.model.covars+param.idx-1)] - param.idx <- param.idx + n.proxy.model.covars - - proxy.obs <- with(df.obs, eval(parse(text=proxy.variable))) - - if( (proxy_family$family=="binomial") & (proxy_family$link=='logit')){ - ll.w.obs.x0 <- vector(mode='numeric',length=dim(proxy.model.matrix.x0)[1]) - ll.w.obs.x1 <- vector(mode='numeric',length=dim(proxy.model.matrix.x1)[1]) - - # proxy_formula likelihood using logistic regression - ll.w.obs.x0[proxy.obs==1] <- plogis(proxy.params %*% t(proxy.model.matrix.x0[proxy.obs==1,]),log=TRUE) - ll.w.obs.x0[proxy.obs==0] <- plogis(proxy.params %*% t(proxy.model.matrix.x0[proxy.obs==0,]),log=TRUE, lower.tail=FALSE) - - ll.w.obs.x1[proxy.obs==1] <- plogis(proxy.params %*% t(proxy.model.matrix.x1[proxy.obs==1,]),log=TRUE) - ll.w.obs.x1[proxy.obs==0] <- plogis(proxy.params %*% t(proxy.model.matrix.x1[proxy.obs==0,]),log=TRUE, lower.tail=FALSE) - } - - ## assume that the probability of x is a logistic regression depending on z - truth.model.matrix.obs <- model.matrix(truth_formula, df.obs) - n.truth.params <- dim(truth.model.matrix.obs)[2] - truth.params <- params[param.idx:(n.truth.params + param.idx - 1)] - - ll.obs.x0 <- plogis(truth.params %*% t(truth.model.matrix.obs), log=TRUE) - ll.obs.x1 <- plogis(truth.params %*% t(truth.model.matrix.obs), log=TRUE, lower.tail=FALSE) - - ll.obs <- colLogSumExps(rbind(ll.y.x.obs.0 + ll.x.obs.0.x0 + ll.x.obs.1.x0 + ll.obs.x0 + ll.w.obs.x0, - ll.y.x.obs.1 + ll.x.obs.0.x1 + ll.x.obs.1.x1 + ll.obs.x1 + ll.w.obs.x1)) - - ### NOW FOR THE FUN PART. Likelihood of the unobserved data. - ### we have to integrate out x.obs.0, x.obs.1, and x. - - - ## THE OUTCOME - df.unobs <- df[is.na(x.obs)] - df.x.unobs.0 <- copy(df.unobs)[,x:=0] - df.x.unobs.1 <- copy(df.unobs)[,x:=1] - y.unobs <- df.unobs$y - - outcome.model.matrix.x.unobs.0 <- model.matrix(outcome_formula, df.x.unobs.0) - outcome.model.matrix.x.unobs.1 <- model.matrix(outcome_formula, df.x.unobs.1) - - ll.y.unobs.x0 <- dnorm(y.unobs, outcome.params %*% t(outcome.model.matrix.x.unobs.0), sd=sigma.y, log=TRUE) - ll.y.unobs.x1 <- dnorm(y.unobs, outcome.params %*% t(outcome.model.matrix.x.unobs.1), sd=sigma.y, log=TRUE) - - - ## THE UNLABELED DATA - - - ## assume that the two coders are statistically independent conditional on x - ll.x.unobs.0.x0 <- vector(mode='numeric', length=nrow(df.unobs)) - ll.x.unobs.1.x0 <- vector(mode='numeric', length=nrow(df.unobs)) - ll.x.unobs.0.x1 <- vector(mode='numeric', length=nrow(df.unobs)) - ll.x.unobs.1.x1 <- vector(mode='numeric', length=nrow(df.unobs)) - - df.x.unobs.0[,x.obs := 1] - df.x.unobs.1[,x.obs := 1] - - rater.model.matrix.x.unobs.0 <- model.matrix(rater_formula, df.x.unobs.0) - rater.model.matrix.x.unobs.1 <- model.matrix(rater_formula, df.x.unobs.1) - - - ## # probability of rater 0 if x is 0 or 1 - ## ll.x.unobs.0.x0 <- colLogSumExps(rbind(plogis(rater.0.params %*% t(rater.model.matrix.x.unobs.0), log=TRUE), - ## plogis(rater.0.params %*% t(rater.model.matrix.x.unobs.0), log=TRUE, lower.tail=TRUE))) - - ## ll.x.unobs.0.x1 <- colLogSumExps(rbind(plogis(rater.0.params %*% t(rater.model.matrix.x.unobs.1), log=TRUE), - ## plogis(rater.0.params %*% t(rater.model.matrix.x.unobs.1), log=TRUE, lower.tail=TRUE))) - - ## # probability of rater 1 if x is 0 or 1 - ## ll.x.unobs.1.x0 <- colLogSumExps(rbind(plogis(rater.1.params %*% t(rater.model.matrix.x.unobs.0), log=TRUE), - ## plogis(rater.1.params %*% t(rater.model.matrix.x.unobs.0), log=TRUE, lower.tail=TRUE))) - - ## ll.x.unobs.1.x1 <- colLogSumExps(rbind(plogis(rater.1.params %*% t(rater.model.matrix.x.unobs.1), log=TRUE), - ## plogis(rater.1.params %*% t(rater.model.matrix.x.unobs.1), log=TRUE, lower.tail=TRUE))) - - - proxy.unobs <- with(df.unobs, eval(parse(text=proxy.variable))) - proxy.model.matrix.x0.unobs <- model.matrix(proxy_formula, df.x.unobs.0) - proxy.model.matrix.x1.unobs <- model.matrix(proxy_formula, df.x.unobs.1) - - if( (proxy_family$family=="binomial") & (proxy_family$link=='logit')){ - ll.w.unobs.x0 <- vector(mode='numeric',length=dim(proxy.model.matrix.x0)[1]) - ll.w.unobs.x1 <- vector(mode='numeric',length=dim(proxy.model.matrix.x1)[1]) - - - # proxy_formula likelihood using logistic regression - ll.w.unobs.x0[proxy.unobs==1] <- plogis(proxy.params %*% t(proxy.model.matrix.x0.unobs[proxy.unobs==1,]),log=TRUE) - ll.w.unobs.x0[proxy.unobs==0] <- plogis(proxy.params %*% t(proxy.model.matrix.x0.unobs[proxy.unobs==0,]),log=TRUE, lower.tail=FALSE) - - ll.w.unobs.x1[proxy.unobs==1] <- plogis(proxy.params %*% t(proxy.model.matrix.x1.unobs[proxy.unobs==1,]),log=TRUE) - ll.w.unobs.x1[proxy.unobs==0] <- plogis(proxy.params %*% t(proxy.model.matrix.x1.unobs[proxy.unobs==0,]),log=TRUE, lower.tail=FALSE) - } - - truth.model.matrix.unobs <- model.matrix(truth_formula, df.unobs) - - ll.unobs.x0 <- plogis(truth.params %*% t(truth.model.matrix.unobs), log=TRUE) - ll.unobs.x1 <- plogis(truth.params %*% t(truth.model.matrix.unobs), log=TRUE, lower.tail=FALSE) - - ll.unobs <- colLogSumExps(rbind(ll.unobs.x0 + ll.w.unobs.x0 + ll.y.unobs.x0, - ll.unobs.x1 + ll.w.unobs.x1 + ll.y.unobs.x1)) - - return(-1 *( sum(ll.obs) + sum(ll.unobs))) - } - - outcome.params <- colnames(model.matrix(outcome_formula,df)) - lower <- rep(-Inf, length(outcome.params)) - - if(outcome_family$family=='gaussian'){ - params <- c(outcome.params, 'sigma_y') - lower <- c(lower, 0.00001) - } else { - params <- outcome.params - } - - rater.0.params <- colnames(model.matrix(rater_formula,df)) - params <- c(params, paste0('rater_0',rater.0.params)) - lower <- c(lower, rep(-Inf, length(rater.0.params))) - - rater.1.params <- colnames(model.matrix(rater_formula,df)) - params <- c(params, paste0('rater_1',rater.1.params)) - lower <- c(lower, rep(-Inf, length(rater.1.params))) - - proxy.params <- colnames(model.matrix(proxy_formula, df)) - params <- c(params, paste0('proxy_',proxy.params)) - lower <- c(lower, rep(-Inf, length(proxy.params))) - - truth.params <- colnames(model.matrix(truth_formula, df)) - params <- c(params, paste0('truth_', truth.params)) - lower <- c(lower, rep(-Inf, length(truth.params))) - start <- rep(0.1,length(params)) - names(start) <- params - - - if(method=='optim'){ - fit <- optim(start, fn = nll, lower=lower, method='L-BFGS-B', hessian=TRUE, control=list(maxit=1e6)) - } else { - - quoted.names <- gsub("[\\(\\)]",'',names(start)) - print(quoted.names) - text <- paste("function(", paste0(quoted.names,'=',start,collapse=','),"){params<-c(",paste0(quoted.names,collapse=','),");return(nll(params))}") - - measerr_mle_nll <- eval(parse(text=text)) - names(start) <- quoted.names - names(lower) <- quoted.names - fit <- mle2(minuslogl=measerr_mle_nll, start=start, lower=lower, parnames=params,control=list(maxit=1e6),method='L-BFGS-B') - } - - return(fit) -} - - -measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_formula, proxy_family=binomial(link='logit'), truth_formula, truth_family=binomial(link='logit'),method='optim'){ + truth.variable <- all.vars(truth_formula)[1] + outcome.model.matrix <- model.matrix(outcome_formula, df) + proxy.model.matrix <- model.matrix(proxy_formula, df) + y.obs <- with(df.obs,eval(parse(text=response.var))) measerr_mle_nll <- function(params){ - df.obs <- model.frame(outcome_formula, df) - proxy.variable <- all.vars(proxy_formula)[1] - proxy.model.matrix <- model.matrix(proxy_formula, df) - response.var <- all.vars(outcome_formula)[1] - y.obs <- with(df.obs,eval(parse(text=response.var))) - - outcome.model.matrix <- model.matrix(outcome_formula, df) - param.idx <- 1 n.outcome.model.covars <- dim(outcome.model.matrix)[2] outcome.params <- params[param.idx:n.outcome.model.covars] @@ -343,7 +135,6 @@ measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_fo if(outcome_family$family == "gaussian"){ sigma.y <- params[param.idx] param.idx <- param.idx + 1 - # outcome_formula likelihood using linear regression ll.y.obs <- dnorm(y.obs, outcome.params %*% t(outcome.model.matrix),sd=sigma.y, log=TRUE) } @@ -363,7 +154,7 @@ measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_fo } df.obs <- model.frame(truth_formula, df) - truth.variable <- all.vars(truth_formula)[1] + truth.obs <- with(df.obs, eval(parse(text=truth.variable))) truth.model.matrix <- model.matrix(truth_formula,df) n.truth.model.covars <- dim(truth.model.matrix)[2] @@ -468,3 +259,338 @@ measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_fo return(fit) } +## Experimental, but probably works. +measerr_irr_mle <- function(df, outcome_formula, outcome_family=gaussian(), coder_formulas=list(x.obs.0~x, x.obs.1~x), proxy_formula, proxy_family=binomial(link='logit'), truth_formula, truth_family=binomial(link='logit'),method='optim'){ + + ### in this scenario, the ground truth also has measurement error, but we have repeated measures for it. + # this time we never get to observe the true X + outcome.model.matrix <- model.matrix(outcome_formula, df) + proxy.model.matrix <- model.matrix(proxy_formula, df) + response.var <- all.vars(outcome_formula)[1] + proxy.var <- all.vars(proxy_formula)[1] + param.var <- all.vars(truth_formula)[1] + truth.var<- all.vars(truth_formula)[1] + y <- with(df,eval(parse(text=response.var))) + + nll <- function(params){ + param.idx <- 1 + n.outcome.model.covars <- dim(outcome.model.matrix)[2] + outcome.params <- params[param.idx:n.outcome.model.covars] + param.idx <- param.idx + n.outcome.model.covars + + + if(outcome_family$family == "gaussian"){ + sigma.y <- params[param.idx] + param.idx <- param.idx + 1 + } + + + n.proxy.model.covars <- dim(proxy.model.matrix)[2] + proxy.params <- params[param.idx:(n.proxy.model.covars+param.idx-1)] + param.idx <- param.idx + n.proxy.model.covars + + df.temp <- copy(df) + + if((truth_family$family == "binomial") + & (truth_family$link=='logit')){ + integrate.grid <- expand.grid(replicate(1 + length(coder_formulas), c(0,1), simplify=FALSE)) + ll.parts <- matrix(nrow=nrow(df),ncol=nrow(integrate.grid)) + for(i in 1:nrow(integrate.grid)){ + # setup the dataframe for this row + row <- integrate.grid[i,] + + df.temp[[param.var]] <- row[[1]] + ci <- 2 + for(coder_formula in coder_formulas){ + coder.var <- all.vars(coder_formula)[1] + df.temp[[coder.var]] <- row[[ci]] + ci <- ci + 1 + } + + outcome.model.matrix <- model.matrix(outcome_formula, df.temp) + if(outcome_family$family == "gaussian"){ + ll.y <- dnorm(y, outcome.params %*% t(outcome.model.matrix), sd=sigma.y, log=TRUE) + } + + if(proxy_family$family=="binomial" & (proxy_family$link=='logit')){ + proxy.model.matrix <- model.matrix(proxy_formula, df.temp) + ll.w <- vector(mode='numeric', length=dim(proxy.model.matrix)[1]) + proxyvar <- with(df.temp,eval(parse(text=proxy.var))) + ll.w[proxyvar==1] <- plogis(proxy.params %*% t(proxy.model.matrix[proxyvar==1,]),log=TRUE) + ll.w[proxyvar==0] <- plogis(proxy.params %*% t(proxy.model.matrix[proxyvar==0,]),log=TRUE,lower.tail=FALSE) + } + + ## probability of the coded variables + coder.lls <- matrix(nrow=nrow(df.temp),ncol=length(coder_formulas)) + ci <- 1 + for(coder_formula in coder_formulas){ + coder.model.matrix <- model.matrix(coder_formula, df.temp) + n.coder.model.covars <- dim(coder.model.matrix)[2] + coder.params <- params[param.idx:(n.coder.model.covars + param.idx - 1)] + param.idx <- param.idx + n.coder.model.covars + coder.var <- all.vars(coder_formula)[1] + x.obs <- with(df.temp, eval(parse(text=coder.var))) + true.codervar <- df[[all.vars(coder_formula)[1]]] + + ll.coder <- vector(mode='numeric', length=dim(coder.model.matrix)[1]) + ll.coder[x.obs==1] <- plogis(coder.params %*% t(coder.model.matrix[x.obs==1,]),log=TRUE) + ll.coder[x.obs==0] <- plogis(coder.params %*% t(coder.model.matrix[x.obs==0,]),log=TRUE,lower.tail=FALSE) + + # don't count when we know the observed value, unless we're accounting for observed value + ll.coder[(!is.na(true.codervar)) & (true.codervar != x.obs)] <- NA + coder.lls[,ci] <- ll.coder + ci <- ci + 1 + } + + truth.model.matrix <- model.matrix(truth_formula, df.temp) + n.truth.model.covars <- dim(truth.model.matrix)[2] + truth.params <- params[param.idx:(n.truth.model.covars + param.idx - 1)] + + for(coder_formula in coder_formulas){ + coder.model.matrix <- model.matrix(coder_formula, df.temp) + n.coder.model.covars <- dim(coder.model.matrix)[2] + param.idx <- param.idx - n.coder.model.covars + } + + x <- with(df.temp, eval(parse(text=truth.var))) + ll.truth <- vector(mode='numeric', length=dim(truth.model.matrix)[1]) + ll.truth[x==1] <- plogis(truth.params %*% t(truth.model.matrix[x==1,]), log=TRUE) + ll.truth[x==0] <- plogis(truth.params %*% t(truth.model.matrix[x==0,]), log=TRUE, lower.tail=FALSE) + + true.truthvar <- df[[all.vars(truth_formula)[1]]] + + if(!is.null(true.truthvar)){ + # ll.truth[(!is.na(true.truthvar)) & (true.truthvar != truthvar)] <- -Inf + # ll.truth[(!is.na(true.truthvar)) & (true.truthvar == truthvar)] <- 0 + } + ll.parts[,i] <- ll.y + ll.w + apply(coder.lls,1,sum) + ll.truth + + } + + lls <- rowLogSumExps(ll.parts,na.rm=TRUE) + + ## likelihood of observed data + target <- -1 * sum(lls) + return(target) + } + } + + outcome.params <- colnames(model.matrix(outcome_formula,df)) + lower <- rep(-Inf, length(outcome.params)) + + if(outcome_family$family=='gaussian'){ + params <- c(outcome.params, 'sigma_y') + lower <- c(lower, 0.00001) + } else { + params <- outcome.params + } + + proxy.params <- colnames(model.matrix(proxy_formula, df)) + params <- c(params, paste0('proxy_',proxy.params)) + positive.params <- paste0('proxy_',truth.var) + lower <- c(lower, rep(-Inf, length(proxy.params))) + names(lower) <- params + lower[positive.params] <- 0.01 + ci <- 0 + + for(coder_formula in coder_formulas){ + coder.params <- colnames(model.matrix(coder_formula,df)) + params <- c(params, paste0('coder_',ci,coder.params)) + positive.params <- paste0('coder_', ci, truth.var) + ci <- ci + 1 + lower <- c(lower, rep(-Inf, length(coder.params))) + names(lower) <- params + lower[positive.params] <- 0.01 + } + + truth.params <- colnames(model.matrix(truth_formula, df)) + params <- c(params, paste0('truth_', truth.params)) + lower <- c(lower, rep(-Inf, length(truth.params))) + start <- rep(0.1,length(params)) + names(start) <- params + names(lower) <- params + + if(method=='optim'){ + print(start) + fit <- optim(start, fn = nll, lower=lower, method='L-BFGS-B', hessian=TRUE, control=list(maxit=1e6)) + } else { + + quoted.names <- gsub("[\\(\\)]",'',names(start)) + print(quoted.names) + text <- paste("function(", paste0(quoted.names,'=',start,collapse=','),"){params<-c(",paste0(quoted.names,collapse=','),");return(nll(params))}") + + measerr_mle_nll <- eval(parse(text=text)) + names(start) <- quoted.names + names(lower) <- quoted.names + fit <- mle2(minuslogl=measerr_mle_nll, start=start, lower=lower, method='L-BFGS-B',control=list(maxit=1e6)) + } + + return(fit) +} + +## Experimental, and does not work. +measerr_irr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='logit'), coder_formulas=list(y.obs.0~y+w_pred+y.obs.1,y.obs.1~y+w_pred+y.obs.0), proxy_formula=w_pred~y, proxy_family=binomial(link='logit'),method='optim'){ + integrate.grid <- expand.grid(replicate(1 + length(coder_formulas), c(0,1), simplify=FALSE)) + print(integrate.grid) + + + outcome.model.matrix <- model.matrix(outcome_formula, df) + n.outcome.model.covars <- dim(outcome.model.matrix)[2] + + + ### in this scenario, the ground truth also has measurement error, but we have repeated measures for it. + # this time we never get to observe the true X + nll <- function(params){ + param.idx <- 1 + outcome.params <- params[param.idx:n.outcome.model.covars] + param.idx <- param.idx + n.outcome.model.covars + proxy.model.matrix <- model.matrix(proxy_formula, df) + n.proxy.model.covars <- dim(proxy.model.matrix)[2] + response.var <- all.vars(outcome_formula)[1] + + if(outcome_family$family == "gaussian"){ + sigma.y <- params[param.idx] + param.idx <- param.idx + 1 + } + + proxy.params <- params[param.idx:(n.proxy.model.covars+param.idx-1)] + param.idx <- param.idx + n.proxy.model.covars + + df.temp <- copy(df) + + if((outcome_family$family == "binomial") + & (outcome_family$link=='logit')){ + ll.parts <- matrix(nrow=nrow(df),ncol=nrow(integrate.grid)) + for(i in 1:nrow(integrate.grid)){ + # setup the dataframe for this row + row <- integrate.grid[i,] + + df.temp[[response.var]] <- row[[1]] + ci <- 2 + for(coder_formula in coder_formulas){ + codervar <- all.vars(coder_formula)[1] + df.temp[[codervar]] <- row[[ci]] + ci <- ci + 1 + } + + outcome.model.matrix <- model.matrix(outcome_formula, df.temp) + if(outcome_family$family == "gaussian"){ + ll.y <- dnorm(df.temp[[response.var]], outcome.params %*% t(outcome.model.matrix), sd=sigma.y, log=T) + } + + if(outcome_family$family == "binomial" & (outcome_family$link=='logit')){ + ll.y <- vector(mode='numeric',length=nrow(df.temp)) + ll.y[df.temp[[response.var]]==1] <- plogis( outcome.params %*% t(outcome.model.matrix), log=TRUE) + ll.y[df.temp[[response.var]]==0] <- plogis( outcome.params %*% t(outcome.model.matrix), log=TRUE,lower.tail=FALSE) + } + + if(proxy_family$family=="binomial" & (proxy_family$link=='logit')){ + proxy.model.matrix <- model.matrix(proxy_formula, df.temp) + ll.w <- vector(mode='numeric', length=dim(proxy.model.matrix)[1]) + proxyvar <- with(df.temp,eval(parse(text=all.vars(proxy_formula)[1]))) + ll.w[proxyvar==1] <- plogis(proxy.params %*% t(proxy.model.matrix[proxyvar==1,]),log=TRUE) + ll.w[proxyvar==0] <- plogis(proxy.params %*% t(proxy.model.matrix[proxyvar==0,]),log=TRUE,lower.tail=FALSE) + } + + ## probability of the coded variables + coder.lls <- matrix(nrow=nrow(df.temp),ncol=length(coder_formulas)) + ci <- 1 + for(coder_formula in coder_formulas){ + coder.model.matrix <- model.matrix(coder_formula, df.temp) + n.coder.model.covars <- dim(coder.model.matrix)[2] + coder.params <- params[param.idx:(n.coder.model.covars + param.idx - 1)] + param.idx <- param.idx + n.coder.model.covars + codervar <- with(df.temp, eval(parse(text=all.vars(coder_formula)[1]))) + true.codervar <- df[[all.vars(coder_formula)[1]]] + + ll.coder <- vector(mode='numeric', length=dim(coder.model.matrix)[1]) + ll.coder[codervar==1] <- plogis(coder.params %*% t(coder.model.matrix[codervar==1,]),log=TRUE) + ll.coder[codervar==0] <- plogis(coder.params %*% t(coder.model.matrix[codervar==0,]),log=TRUE,lower.tail=FALSE) + + # don't count when we know the observed value, unless we're accounting for observed value + ll.coder[(!is.na(true.codervar)) & (true.codervar != codervar)] <- NA + coder.lls[,ci] <- ll.coder + ci <- ci + 1 + } + + for(coder_formula in coder_formulas){ + coder.model.matrix <- model.matrix(coder_formula, df.temp) + n.coder.model.covars <- dim(coder.model.matrix)[2] + param.idx <- param.idx - n.coder.model.covars + } + + ll.parts[,i] <- ll.y + ll.w + apply(coder.lls,1,function(x) sum(x)) + + } + + lls <- rowLogSumExps(ll.parts,na.rm=TRUE) + + ## likelihood of observed data + target <- -1 * sum(lls) + print(target) + print(params) + return(target) + } + } + + outcome.params <- colnames(model.matrix(outcome_formula,df)) + response.var <- all.vars(outcome_formula)[1] + lower <- rep(-Inf, length(outcome.params)) + + if(outcome_family$family=='gaussian'){ + params <- c(outcome.params, 'sigma_y') + lower <- c(lower, 0.00001) + } else { + params <- outcome.params + } + + ## constrain the model of the coder and proxy vars + ## this is to ensure identifiability + ## it is a safe assumption because the coders aren't hostile (wrong more often than right) + ## so we can assume that y ~Bw, B is positive + proxy.params <- colnames(model.matrix(proxy_formula, df)) + positive.params <- paste0('proxy_',response.var) + params <- c(params, paste0('proxy_',proxy.params)) + lower <- c(lower, rep(-Inf, length(proxy.params))) + names(lower) <- params + lower[positive.params] <- 0.001 + + ci <- 0 + for(coder_formula in coder_formulas){ + coder.params <- colnames(model.matrix(coder_formula,df)) + latent.coder.params <- coder.params %in% response.var + params <- c(params, paste0('coder_',ci,coder.params)) + positive.params <- paste0('coder_',ci,response.var) + ci <- ci + 1 + lower <- c(lower, rep(-Inf, length(coder.params))) + names(lower) <-params + lower[positive.params] <- 0.001 + } + + ## init by using the "loco model" + temp.df <- copy(df) + temp.df <- temp.df[y.obs.1 == y.obs.0, y:=y.obs.1] + loco.model <- glm(outcome_formula, temp.df, family=outcome_family) + + start <- rep(1,length(params)) + names(start) <- params + start[names(coef(loco.model))] <- coef(loco.model) + names(lower) <- params + if(method=='optim'){ + print(lower) + fit <- optim(start, fn = nll, lower=lower, method='L-BFGS-B', hessian=TRUE,control=list(maxit=1e6)) + } else { + + quoted.names <- gsub("[\\(\\)]",'',names(start)) + print(quoted.names) + text <- paste("function(", paste0(quoted.names,'=',start,collapse=','),"){params<-c(",paste0(quoted.names,collapse=','),");return(nll(params))}") + + measerr_mle_nll <- eval(parse(text=text)) + names(start) <- quoted.names + names(lower) <- quoted.names + fit <- mle2(minuslogl=measerr_mle_nll, start=start, lower=lower, parnames=params,control=list(maxit=1e6),method='L-BFGS-B') + } + + return(fit) +} + diff --git a/simulations/plot_dv_example.R b/simulations/plot_dv_example.R index 71963b1..45a5d51 100644 --- a/simulations/plot_dv_example.R +++ b/simulations/plot_dv_example.R @@ -6,7 +6,7 @@ library(filelock) library(argparser) parser <- arg_parser("Simulate data and fit corrected models.") -parser <- add_argument(parser, "--infile", default="", help="name of the file to read.") +parser <- add_argument(parser, "--infile", default="example_4.feather", help="name of the file to read.") parser <- add_argument(parser, "--remember-file", default="remembr.RDS", help="name of the remember file.") parser <- add_argument(parser, "--name", default="", help="The name to safe the data to in the remember file.") args <- parse_args(parser) @@ -87,6 +87,7 @@ build_plot_dataset <- function(df){ change.remember.file(args$remember_file, clear=TRUE) sims.df <- read_feather(args$infile) sims.df[,Bzx:=NA] +sims.df[,y_explained_variance:=NA] sims.df[,accuracy_imbalance_difference:=NA] plot.df <- build_plot_dataset(sims.df) @@ -97,6 +98,7 @@ set.remember.prefix(gsub("plot.df.","",args$name)) remember(median(sims.df$cor.xz),'med.cor.xz') remember(median(sims.df$accuracy),'med.accuracy') remember(median(sims.df$error.cor.x),'med.error.cor.x') +remember(median(sims.df$error.cor.z),'med.error.cor.z') remember(median(sims.df$lik.ratio),'med.lik.ratio') diff --git a/simulations/plot_example.R b/simulations/plot_example.R index 8e6c477..09d6bf3 100644 --- a/simulations/plot_example.R +++ b/simulations/plot_example.R @@ -9,7 +9,7 @@ source("summarize_estimator.R") parser <- arg_parser("Simulate data and fit corrected models.") -parser <- add_argument(parser, "--infile", default="", help="name of the file to read.") +parser <- add_argument(parser, "--infile", default="example_2.feather", help="name of the file to read.") parser <- add_argument(parser, "--remember-file", default="remembr.RDS", help="name of the remember file.") parser <- add_argument(parser, "--name", default="", help="The name to safe the data to in the remember file.") args <- parse_args(parser) @@ -76,13 +76,13 @@ build_plot_dataset <- function(df){ z.amelia.full <- summarize.estimator(df, 'amelia.full', 'z') - x.mecor <- summarize.estimator(df, 'mecor', 'x') + ## x.mecor <- summarize.estimator(df, 'mecor', 'x') - z.mecor <- summarize.estimator(df, 'mecor', 'z') + ## z.mecor <- summarize.estimator(df, 'mecor', 'z') - x.mecor <- summarize.estimator(df, 'mecor', 'x') + ## x.mecor <- summarize.estimator(df, 'mecor', 'x') - z.mecor <- summarize.estimator(df, 'mecor', 'z') + ## z.mecor <- summarize.estimator(df, 'mecor', 'z') x.mle <- summarize.estimator(df, 'mle', 'x') @@ -97,7 +97,7 @@ build_plot_dataset <- function(df){ z.gmm <- summarize.estimator(df, 'gmm', 'z') accuracy <- df[,mean(accuracy)] - plot.df <- rbindlist(list(x.true,z.true,x.naive,z.naive,x.amelia.full,z.amelia.full,x.mecor, z.mecor, x.gmm, z.gmm, x.feasible, z.feasible,z.mle, x.mle, x.zhang, z.zhang, x.gmm, z.gmm),use.names=T) + plot.df <- rbindlist(list(x.true,z.true,x.naive,z.naive,x.amelia.full,z.amelia.full,x.gmm, z.gmm, x.feasible, z.feasible,z.mle, x.mle, x.zhang, z.zhang, x.gmm, z.gmm),use.names=T) plot.df[,accuracy := accuracy] plot.df <- plot.df[,":="(sd.est=sqrt(var.est)/N.sims)] return(plot.df) @@ -105,6 +105,7 @@ build_plot_dataset <- function(df){ sims.df <- read_feather(args$infile) +unique(sims.df[,.N,by=.(N,m)]) print(unique(sims.df$N)) # df <- df[apply(df,1,function(x) !any(is.na(x)))] diff --git a/simulations/plot_irr_dv_example.R b/simulations/plot_irr_dv_example.R index f5e2c41..46450d5 100644 --- a/simulations/plot_irr_dv_example.R +++ b/simulations/plot_irr_dv_example.R @@ -17,6 +17,10 @@ build_plot_dataset <- function(df){ z.true <- summarize.estimator(df, 'true','z') + x.naive <- summarize.estimator(df, 'naive','x') + + z.naive <- summarize.estimator(df, 'naive','z') + x.loa0.feasible <- summarize.estimator(df, 'loa0.feasible','x') z.loa0.feasible <- summarize.estimator(df,'loa0.feasible','z') @@ -34,8 +38,14 @@ build_plot_dataset <- function(df){ z.loco.mle <- summarize.estimator(df, 'loco.mle', 'z') + z.loco.amelia <- summarize.estimator(df, 'amelia.full', 'z') + x.loco.amelia <- summarize.estimator(df, 'amelia.full', 'x') + + z.loco.zhang <- summarize.estimator(df, 'zhang', 'z') + x.loco.zhang <- summarize.estimator(df, 'zhang', 'x') + accuracy <- df[,mean(accuracy)] - plot.df <- rbindlist(list(x.true,z.true,x.loa0.feasible,z.loa0.feasible,x.loa0.mle,z.loa0.mle,x.loco.feasible, z.loco.feasible, z.loco.mle, x.loco.mle),use.names=T) + plot.df <- rbindlist(list(x.true,z.true,x.loa0.feasible,z.loa0.feasible,x.naive,z.naive,x.loa0.mle,z.loa0.mle,x.loco.feasible, z.loco.feasible, z.loco.mle, x.loco.mle, x.loco.amelia, z.loco.amelia, z.loco.zhang, x.loco.zhang),use.names=T) plot.df[,accuracy := accuracy] plot.df <- plot.df[,":="(sd.est=sqrt(var.est)/N.sims)] return(plot.df) diff --git a/simulations/plot_irr_example.R b/simulations/plot_irr_example.R index bf5e661..4ec79dc 100644 --- a/simulations/plot_irr_example.R +++ b/simulations/plot_irr_example.R @@ -17,6 +17,10 @@ build_plot_dataset <- function(df){ z.true <- summarize.estimator(df, 'true','z') + x.naive <- summarize.estimator(df, 'naive','x') + + z.naive <- summarize.estimator(df, 'naive','z') + x.loa0.feasible <- summarize.estimator(df, 'loa0.feasible','x') z.loa0.feasible <- summarize.estimator(df,'loa0.feasible','z') @@ -33,36 +37,55 @@ build_plot_dataset <- function(df){ z.loco.mle <- summarize.estimator(df, 'loco.mle', 'z') + x.loco.mle <- summarize.estimator(df, 'loco.mle', 'x') + + z.loco.amelia <- summarize.estimator(df, 'amelia.full', 'z') + x.loco.amelia <- summarize.estimator(df, 'amelia.full', 'x') + + z.loco.zhang <- summarize.estimator(df, 'zhang', 'z') + x.loco.zhang <- summarize.estimator(df, 'zhang', 'x') + + + z.loco.gmm <- summarize.estimator(df, 'gmm', 'z') + x.loco.gmm <- summarize.estimator(df, 'gmm', 'x') + + + + ## x.mle <- summarize.estimator(df, 'mle', 'x') ## z.mle <- summarize.estimator(df, 'mle', 'z') accuracy <- df[,mean(accuracy)] - plot.df <- rbindlist(list(x.true,z.true,x.loa0.feasible,z.loa0.feasible,x.loa0.mle,z.loa0.mle,x.loco.feasible, z.loco.feasible, x.loco.mle, z.loco.mle),use.names=T) + plot.df <- rbindlist(list(x.true,z.true,x.loa0.feasible,z.loa0.feasible,x.loa0.mle,z.loa0.mle,x.loco.feasible, z.loco.feasible, x.loco.mle, z.loco.mle, x.loco.amelia, z.loco.amelia,x.loco.zhang, z.loco.zhang,x.loco.gmm, z.loco.gmm,x.naive,z.naive),use.names=T) plot.df[,accuracy := accuracy] plot.df <- plot.df[,":="(sd.est=sqrt(var.est)/N.sims)] return(plot.df) } -plot.df <- read_feather(args$infile) -print(unique(plot.df$N)) +sims.df <- read_feather(args$infile) +print(unique(sims.df$N)) # df <- df[apply(df,1,function(x) !any(is.na(x)))] -if(!('Bzx' %in% names(plot.df))) - plot.df[,Bzx:=NA] +if(!('Bzx' %in% names(sims.df))) + sims.df[,Bzx:=NA] -if(!('accuracy_imbalance_difference' %in% names(plot.df))) - plot.df[,accuracy_imbalance_difference:=NA] +if(!('accuracy_imbalance_difference' %in% names(sims.df))) + sims.df[,accuracy_imbalance_difference:=NA] -unique(plot.df[,'accuracy_imbalance_difference']) +unique(sims.df[,'accuracy_imbalance_difference']) #plot.df <- build_plot_dataset(df[accuracy_imbalance_difference==0.1][N==700]) -plot.df <- build_plot_dataset(plot.df) +plot.df <- build_plot_dataset(sims.df) change.remember.file("remember_irr.RDS",clear=TRUE) remember(plot.df,args$name) + +set.remember.prefix(gsub("plot.df.","",args$name)) +remember(median(sims.df$loco.accuracy),'med.loco.acc') + #ggplot(df,aes(x=Bxy.est.mle)) + geom_histogram() + facet_grid(accuracy_imbalance_difference ~ Bzy) ## ## ## df[gmm.ER_pval<0.05] diff --git a/simulations/run_simulation.sbatch b/simulations/run_simulation.sbatch index 9dce9ea..f5db415 100644 --- a/simulations/run_simulation.sbatch +++ b/simulations/run_simulation.sbatch @@ -1,8 +1,8 @@ #!/bin/bash #SBATCH --job-name="simulate measurement error models" ## Allocation Definition -#SBATCH --account=comdata -#SBATCH --partition=compute-bigmem +#SBATCH --account=comdata-ckpt +#SBATCH --partition=ckpt ## Resources #SBATCH --nodes=1 ## Walltime (4 hours) @@ -18,5 +18,6 @@ source ~/.bashrc TASK_NUM=$(($SLURM_ARRAY_TASK_ID + $1)) TASK_CALL=$(sed -n ${TASK_NUM}p $2) +echo ${TASK_CALL} ${TASK_CALL} diff --git a/simulations/simulation_base.R b/simulations/simulation_base.R index 27f0276..82b17a7 100644 --- a/simulations/simulation_base.R +++ b/simulations/simulation_base.R @@ -7,6 +7,7 @@ library(Zelig) library(bbmle) library(matrixStats) # for numerically stable logsumexps +source("pl_methods.R") source("measerr_methods.R") ## for my more generic function. ## This uses the pseudolikelihood approach from Carroll page 349. @@ -36,124 +37,6 @@ my.pseudo.mle <- function(df){ } - -## model from Zhang's arxiv paper, with predictions for y -## Zhang got this model from Hausman 1998 -### I think this is actually eqivalent to the pseudo.mle method -zhang.mle.iv <- function(df){ - df.obs <- df[!is.na(x.obs)] - df.unobs <- df[is.na(x.obs)] - - tn <- df.obs[(w_pred == 0) & (x.obs == w_pred),.N] - pn <- df.obs[(w_pred==0), .N] - npv <- tn / pn - - tp <- df.obs[(w_pred==1) & (x.obs == w_pred),.N] - pp <- df.obs[(w_pred==1),.N] - ppv <- tp / pp - - nll <- function(B0=0, Bxy=0, Bzy=0, sigma_y=0.1){ - - ## fpr = 1 - TNR - ### Problem: accounting for uncertainty in ppv / npv - - ## fnr = 1 - TPR - ll.y.obs <- with(df.obs, dnorm(y, B0 + Bxy * x + Bzy * z, sd=sigma_y,log=T)) - ll <- sum(ll.y.obs) - - # unobserved case; integrate out x - ll.x.1 <- with(df.unobs, dnorm(y, B0 + Bxy + Bzy * z, sd = sigma_y, log=T)) - ll.x.0 <- with(df.unobs, dnorm(y, B0 + Bzy * z, sd = sigma_y,log=T)) - - ## case x == 1 - lls.x.1 <- colLogSumExps(rbind(log(ppv) + ll.x.1, log(1-ppv) + ll.x.0)) - - ## case x == 0 - lls.x.0 <- colLogSumExps(rbind(log(1-npv) + ll.x.1, log(npv) + ll.x.0)) - - lls <- colLogSumExps(rbind(df.unobs$w_pred * lls.x.1, (1-df.unobs$w_pred) * lls.x.0)) - ll <- ll + sum(lls) - return(-ll) - } - mlefit <- mle2(minuslogl = nll, control=list(maxit=1e6), lower=list(sigma_y=0.0001, B0=-Inf, Bxy=-Inf, Bzy=-Inf), - upper=list(sigma_y=Inf, B0=Inf, Bxy=Inf, Bzy=Inf),method='L-BFGS-B') - return(mlefit) -} - -## this is equivalent to the pseudo-liklihood model from Caroll -## zhang.mle.dv <- function(df){ - -## nll <- function(B0=0, Bxy=0, Bzy=0, ppv=0.9, npv=0.9){ -## df.obs <- df[!is.na(y.obs)] - -## ## fpr = 1 - TNR -## ll.w0y0 <- with(df.obs[y.obs==0],dbinom(1-w_pred,1,npv,log=TRUE)) -## ll.w1y1 <- with(df.obs[y.obs==1],dbinom(w_pred,1,ppv,log=TRUE)) - -## # observed case -## ll.y.obs <- vector(mode='numeric', length=nrow(df.obs)) -## ll.y.obs[df.obs$y.obs==1] <- with(df.obs[y.obs==1], plogis(B0 + Bxy * x + Bzy * z,log=T)) -## ll.y.obs[df.obs$y.obs==0] <- with(df.obs[y.obs==0], plogis(B0 + Bxy * x + Bzy * z,log=T,lower.tail=FALSE)) - -## ll <- sum(ll.y.obs) + sum(ll.w0y0) + sum(ll.w1y1) - -## # unobserved case; integrate out y -## ## case y = 1 -## ll.y.1 <- vector(mode='numeric', length=nrow(df)) -## pi.y.1 <- with(df,plogis(B0 + Bxy * x + Bzy*z, log=T)) -## ## P(w=1| y=1)P(y=1) + P(w=0|y=1)P(y=1) = P(w=1,y=1) + P(w=0,y=1) -## lls.y.1 <- colLogSumExps(rbind(log(ppv) + pi.y.1, log(1-ppv) + pi.y.1)) - -## ## case y = 0 -## ll.y.0 <- vector(mode='numeric', length=nrow(df)) -## pi.y.0 <- with(df,plogis(B0 + Bxy * x + Bzy*z, log=T,lower.tail=FALSE)) - -## ## P(w=1 | y=0)P(y=0) + P(w=0|y=0)P(y=0) = P(w=1,y=0) + P(w=0,y=0) -## lls.y.0 <- colLogSumExps(rbind(log(npv) + pi.y.0, log(1-npv) + pi.y.0)) - -## lls <- colLogSumExps(rbind(lls.y.1, lls.y.0)) -## ll <- ll + sum(lls) -## return(-ll) -## } -## mlefit <- mle2(minuslogl = nll, control=list(maxit=1e6),method='L-BFGS-B',lower=list(B0=-Inf, Bxy=-Inf, Bzy=-Inf, ppv=0.001,npv=0.001), -## upper=list(B0=Inf, Bxy=Inf, Bzy=Inf,ppv=0.999,npv=0.999)) -## return(mlefit) -## } - -zhang.mle.dv <- function(df){ - df.obs <- df[!is.na(y.obs)] - df.unobs <- df[is.na(y.obs)] - - fp <- df.obs[(w_pred==1) & (y.obs != w_pred),.N] - p <- df.obs[(w_pred==1),.N] - fpr <- fp / p - fn <- df.obs[(w_pred==0) & (y.obs != w_pred), .N] - n <- df.obs[(w_pred==0),.N] - fnr <- fn / n - - nll <- function(B0=0, Bxy=0, Bzy=0){ - - - ## observed case - ll.y.obs <- vector(mode='numeric', length=nrow(df.obs)) - ll.y.obs[df.obs$y.obs==1] <- with(df.obs[y.obs==1], plogis(B0 + Bxy * x + Bzy * z,log=T)) - ll.y.obs[df.obs$y.obs==0] <- with(df.obs[y.obs==0], plogis(B0 + Bxy * x + Bzy * z,log=T,lower.tail=FALSE)) - - ll <- sum(ll.y.obs) - - pi.y.1 <- with(df,plogis(B0 + Bxy * x + Bzy*z, log=T)) - pi.y.0 <- with(df,plogis(B0 + Bxy * x + Bzy*z, log=T,lower.tail=FALSE)) - - lls <- with(df.unobs, colLogSumExps(rbind(w_pred * colLogSumExps(rbind(log(fpr), log(1 - fnr - fpr)+pi.y.1)), - (1-w_pred) * colLogSumExps(rbind(log(1-fpr), log(1 - fnr - fpr)+pi.y.0))))) - - ll <- ll + sum(lls) - return(-ll) - } - mlefit <- mle2(minuslogl = nll, control=list(maxit=1e6),method='L-BFGS-B',lower=c(B0=-Inf, Bxy=-Inf, Bzy=-Inf), - upper=c(B0=Inf, Bxy=Inf, Bzy=Inf)) - return(mlefit) -} ## This uses the likelihood approach from Carroll page 353. ## assumes that we have a good measurement error model @@ -208,10 +91,14 @@ my.mle <- function(df){ run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formula=w_pred~y){ - accuracy <- df[,mean(w_pred==y)] + (accuracy <- df[,mean(w_pred==y)]) result <- append(result, list(accuracy=accuracy)) - error.cor.x <- cor(df$x, df$w - df$x) - result <- append(result, list(error.cor.x = error.cor.x)) + (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$w_pred)) + result <- append(result, list(error.cor.x = error.cor.x, + error.cor.z = error.cor.z, + error.cor.y = error.cor.y)) model.null <- glm(y~1, data=df,family=binomial(link='logit')) (model.true <- glm(y ~ x + z, data=df,family=binomial(link='logit'))) @@ -220,7 +107,7 @@ run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formu true.ci.Bxy <- confint(model.true)['x',] true.ci.Bzy <- confint(model.true)['z',] - + result <- append(result, list(cor.xz=cor(df$x,df$z))) result <- append(result, list(lik.ratio=lik.ratio)) result <- append(result, list(Bxy.est.true=coef(model.true)['x'], @@ -293,33 +180,26 @@ run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formu # amelia says use normal distribution for binary variables. - tryCatch({ - amelia.out.k <- amelia(df, m=200, p2s=0, idvars=c('y','ystar','w')) - mod.amelia.k <- zelig(y.obs~x+z, model='ls', data=amelia.out.k$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'] - 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 - )) + 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$imputations, cite=FALSE) + (coefse <- combine_coef_se(mod.amelia.k, messages=FALSE)) + est.x.mi <- coefse['x','Estimate'] + est.x.se <- coefse['x','Std.Error'] + result <- append(result, + list(Bxy.est.amelia.full = est.x.mi, + Bxy.ci.upper.amelia.full = est.x.mi + 1.96 * est.x.se, + Bxy.ci.lower.amelia.full = est.x.mi - 1.96 * est.x.se + )) - }, - error = function(e){ - message("An error occurred:\n",e) - result$error <- paste0(result$error,'\n', e) - }) + est.z.mi <- coefse['z','Estimate'] + est.z.se <- coefse['z','Std.Error'] + result <- append(result, + list(Bzy.est.amelia.full = est.z.mi, + Bzy.ci.upper.amelia.full = est.z.mi + 1.96 * est.z.se, + Bzy.ci.lower.amelia.full = est.z.mi - 1.96 * est.z.se + )) return(result) @@ -393,7 +273,7 @@ run_simulation <- function(df, result, outcome_formula=y~x+z, proxy_formula=NUL Bzy.ci.lower.naive = naive.ci.Bzy[1])) - tryCatch({ + amelia.out.k <- amelia(df, m=200, p2s=0, idvars=c('x','w')) mod.amelia.k <- zelig(y~x.obs+z, model='ls', data=amelia.out.k$imputations, cite=FALSE) (coefse <- combine_coef_se(mod.amelia.k, messages=FALSE)) @@ -415,14 +295,7 @@ run_simulation <- function(df, result, outcome_formula=y~x+z, proxy_formula=NUL 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) @@ -439,14 +312,6 @@ run_simulation <- function(df, result, outcome_formula=y~x+z, proxy_formula=NUL 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) @@ -458,12 +323,6 @@ run_simulation <- function(df, result, outcome_formula=y~x+z, proxy_formula=NUL 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) @@ -514,29 +373,29 @@ run_simulation <- function(df, result, outcome_formula=y~x+z, proxy_formula=NUL 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$error, '\n', e) - } - ) + ## tryCatch({ + ## mod.calibrated.mle <- mecor(y ~ MeasError(w_pred, reference = x.obs) + z, df, B=400, method='efficient') + ## (mod.calibrated.mle) + ## (mecor.ci <- summary(mod.calibrated.mle)$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")) diff --git a/simulations/summarize_estimator.R b/simulations/summarize_estimator.R index e0e7622..3e4209f 100644 --- a/simulations/summarize_estimator.R +++ b/simulations/summarize_estimator.R @@ -31,8 +31,8 @@ summarize.estimator <- function(df, suffix='naive', coefname='x'){ var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]), est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.975,na.rm=T), est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.025,na.rm=T), - mean.ci.upper = mean(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]]), - mean.ci.lower = mean(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]]), + mean.ci.upper = mean(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]],na.rm=T), + mean.ci.lower = mean(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]],na.rm=T), ci.upper.975 = quantile(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]],0.975,na.rm=T), ci.upper.025 = quantile(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]],0.025,na.rm=T), ci.lower.975 = quantile(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]],0.975,na.rm=T), -- 2.39.5

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