parser <- arg_parser("Simulate data and fit corrected models")
parser <- add_argument(parser, "--N", default=1000, help="number of observations of w")
-aparser <- add_argument(parser, "--m", default=500, help="m the number of ground truth observations")
+parser <- add_argument(parser, "--m", default=500, help="m the number of ground truth observations")
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, "--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, "--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=0.72)
## 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)
--- /dev/null
+### EXAMPLE 2_b: demonstrates how measurement error can lead to a type
+### sign error in a covariate This is the same as example 2, only
+### instead of x->k we have k->x. Even when you have a good
+### predictor, if it's biased against a covariate you can get the
+### wrong sign. Even when you include the proxy variable in the
+### regression. But with some ground truth and multiple imputation,
+### you can fix it.
+
+library(argparser)
+library(mecor)
+library(ggplot2)
+library(data.table)
+library(filelock)
+library(arrow)
+library(Amelia)
+library(Zelig)
+
+library(predictionError)
+options(amelia.parallel="no", amelia.ncpus=1)
+
+source("irr_simulation_base.R")
+
+## SETUP:
+### we want to estimate x -> y; x is MAR
+### we have x -> k; k -> w; x -> w is used to predict x via the model w.
+### A realistic scenario is that we have an NLP model predicting something like "racial harassment" in social media comments
+### The labels x are binary, but the model provides a continuous predictor
+
+### simulation:
+#### how much power do we get from the model in the first place? (sweeping N and m)
+####
+
+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, coder_accuracy=0.9, seed=1){
+ set.seed(seed)
+ z <- rbinom(N, 1, 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))
+
+ 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
+
+ df <- data.table(x=x,y=y,z=z)
+
+ if(m < N){
+ df <- df[sample(nrow(df), m), x.obs := x]
+ } else {
+ 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))]
+
+
+ ## how can you make a model with a specific accuracy?
+ w0 =(1-x)**2 + (-1)**(1-x) * prediction_accuracy
+
+ ## how can you make a model with a specific accuracy, with a continuous latent variable.
+ # now it makes the same amount of mistake to each point, probably
+ # add mean0 noise to the odds.
+
+ w.noisey.odds = rlogis(N,qlogis(w0))
+ df[,w := plogis(w.noisey.odds)]
+ df[,w_pred:=as.integer(w > 0.5)]
+ (mean(df$x==df$w_pred))
+ return(df)
+}
+
+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, "--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, "--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, "--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)
+
+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$m, B0, Bxy, Bzy, Bzx, seed=args$seed + 500, 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='')
+
+ 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$outfile)
+ unlock(outfile_lock)
+}
--- /dev/null
+
+library(argparser)
+library(mecor)
+library(ggplot2)
+library(data.table)
+library(filelock)
+library(arrow)
+library(Amelia)
+library(Zelig)
+library(predictionError)
+options(amelia.parallel="no",
+ amelia.ncpus=1)
+setDTthreads(40)
+
+source("irr_dv_simulation_base.R")
+
+
+## one way to do it is by adding correlation to x.obs and y that isn't in w.
+## in other words, the model is missing an important feature of x.obs that's related to y.
+simulate_data <- function(N, m, B0, Bxy, Bzy, seed, prediction_accuracy=0.73, coder_accuracy=0.8){
+ set.seed(seed)
+
+ # make w and y dependent
+ z <- rbinom(N, 1, 0.5)
+ x <- rbinom(N, 1, 0.5)
+
+ ystar <- Bzy * z + Bxy * x + B0
+ y <- rbinom(N,1,plogis(ystar))
+
+ # glm(y ~ x + z, family="binomial")
+
+ df <- data.table(x=x,y=y,ystar=ystar,z=z)
+
+ if(m < N){
+ df <- df[sample(nrow(df), m), y.obs := y]
+ } else {
+ df <- df[, y.obs := y]
+ }
+
+ df[ (!is.na(y.obs)) ,y.obs.0 := abs(y.obs - rbinom(.N, 1, 1-coder_accuracy))]
+ df[ (!is.na(y.obs)) ,y.obs.1 := abs(y.obs - rbinom(.N, 1, 1-coder_accuracy))]
+
+ odds.y1 <- qlogis(prediction_accuracy)
+ odds.y0 <- qlogis(prediction_accuracy,lower.tail=F)
+
+ df[y==0,w:=plogis(rlogis(.N,odds.y0))]
+ df[y==1,w:=plogis(rlogis(.N,odds.y1))]
+
+ df[,w_pred := as.integer(w > 0.5)]
+
+ print(mean(df[x==0]$y == df[x==0]$w_pred))
+ print(mean(df[x==1]$y == df[x==1]$w_pred))
+ print(mean(df$w_pred == df$y))
+ return(df)
+}
+
+parser <- arg_parser("Simulate data and fit corrected models")
+parser <- add_argument(parser, "--N", default=1000, help="number of observations of w")
+parser <- add_argument(parser, "--m", default=500, help="m the number of ground truth observations")
+parser <- add_argument(parser, "--seed", default=17, help='seed for the rng')
+parser <- add_argument(parser, "--outfile", help='output file', default='example_2.feather')
+parser <- add_argument(parser, "--y_explained_variance", help='what proportion of the variance of y can be explained?', default=0.005)
+parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.72)
+## parser <- add_argument(parser, "--x_bias_y1", help='how is the classifier biased when y = 1?', default=-0.75)
+## parser <- add_argument(parser, "--x_bias_y0", help='how is the classifier biased when y = 0 ?', default=0.75)
+parser <- add_argument(parser, "--Bxy", help='coefficient of x on y', default=0.3)
+parser <- add_argument(parser, "--Bzy", help='coeffficient of z on y', default=-0.3)
+parser <- add_argument(parser, "--outcome_formula", help='formula for the outcome variable', default="y~x+z")
+parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~y")
+parser <- add_argument(parser, "--coder_accuracy", help='How accurate are the coders?', default=0.8)
+
+args <- parse_args(parser)
+
+B0 <- 0
+Bxy <- args$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)
+
+ outline <- run_simulation_depvar(df, result, outcome_formula = as.formula(args$outcome_formula), proxy_formula = as.formula(args$proxy_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)
+}
SHELL=bash
-Ns=[1000, 2000, 4000, 8000]
-ms=[100, 200, 400, 800]
-seeds=[$(shell seq -s, 1 100)]
+Ns=[1000, 2000, 4000]
+ms=[200, 400, 800]
+seeds=[$(shell seq -s, 1 250)]
explained_variances=[0.1]
-all:remembr.RDS
+all:remembr.RDS remember_irr.RDS
srun=srun -A comdata -p compute-bigmem --time=6:00:00 --mem 4G -c 1
# sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 example_1_jobs
example_2_jobs: 02_indep_differential.R simulation_base.R
- 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"], "truth_formula":["x~z"]}' --outfile example_2_jobs
+ 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.feather: example_2_jobs
rm -f example_2.feather
rm -f example_4.feather
sbatch --wait --verbose --array=1-$(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
rm -f remembr.RDS
${srun} Rscript plot_example.R --infile example_1.feather --name "plot.df.example.1"
${srun} Rscript plot_dv_example.R --infile example_3.feather --name "plot.df.example.3"
${srun} Rscript plot_dv_example.R --infile example_4.feather --name "plot.df.example.4"
+
+irr_Ns = ${Ns}
+irr_ms = ${ms}
+irr_seeds=${seeds}
+irr_explained_variances=${explained_variances}
+
+example_5_jobs: 05_irr_indep.R irr_simulation_base.R
+ grid_sweep.py --command "Rscript 05_irr_indep.R" --arg_dict '{"N":${irr_Ns},"m":${irr_ms}, "seed":${irr_seeds}, "outfile":["example_5.feather"], "y_explained_variance":${irr_explained_variances}}' --outfile example_5_jobs
+
+example_5.feather:example_5_jobs
+ rm -f example_5.feather
+ sbatch --wait --verbose --array=1-$(shell cat example_5_jobs | wc -l) run_simulation.sbatch 0 example_5_jobs
+
+
+example_6_jobs: 06_irr_dv.R irr_dv_simulation_base.R
+ 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-$(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
+ rm -f remember_irr.RDS
+ ${srun} Rscript plot_irr_example.R --infile example_5.feather --name "plot.df.example.5"
+ ${srun} Rscript plot_irr_dv_example.R --infile example_6.feather --name "plot.df.example.6"
+
clean:
rm *.feather
rm -f remembr.RDS
--- /dev/null
+library(matrixStats) # for numerically stable logsumexps
+
+options(amelia.parallel="no",
+ amelia.ncpus=1)
+library(Amelia)
+
+source("measerr_methods.R") ## for my more generic function.
+
+run_simulation_depvar <- function(df, result, outcome_formula = y ~ x + z, rater_formula = y.obs ~ x, proxy_formula = w_pred ~ y){
+
+ accuracy <- df[,mean(w_pred==y)]
+ result <- append(result, list(accuracy=accuracy))
+
+ (model.true <- glm(y ~ x + z, data=df, family=binomial(link='logit')))
+ true.ci.Bxy <- confint(model.true)['x',]
+ true.ci.Bzy <- confint(model.true)['z',]
+
+ result <- append(result, list(Bxy.est.true=coef(model.true)['x'],
+ Bzy.est.true=coef(model.true)['z'],
+ Bxy.ci.upper.true = true.ci.Bxy[2],
+ Bxy.ci.lower.true = true.ci.Bxy[1],
+ Bzy.ci.upper.true = true.ci.Bzy[2],
+ Bzy.ci.lower.true = true.ci.Bzy[1]))
+
+
+
+ loa0.feasible <- glm(y.obs.0 ~ x + z, data = df[!(is.na(y.obs.0))], family=binomial(link='logit'))
+
+ loa0.ci.Bxy <- confint(loa0.feasible)['x',]
+ loa0.ci.Bzy <- confint(loa0.feasible)['z',]
+
+ result <- append(result, list(Bxy.est.loa0.feasible=coef(loa0.feasible)['x'],
+ Bzy.est.loa0.feasible=coef(loa0.feasible)['z'],
+ Bxy.ci.upper.loa0.feasible = loa0.ci.Bxy[2],
+ Bxy.ci.lower.loa0.feasible = loa0.ci.Bxy[1],
+ Bzy.ci.upper.loa0.feasible = loa0.ci.Bzy[2],
+ Bzy.ci.lower.loa0.feasible = loa0.ci.Bzy[1]))
+
+
+ df.loa0.mle <- copy(df)
+ df.loa0.mle[,y:=y.obs.0]
+ loa0.mle <- measerr_mle_dv(df.loa0.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula)
+ fisher.info <- solve(loa0.mle$hessian)
+ coef <- loa0.mle$par
+ ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
+ ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
+
+ result <- append(result, list(Bxy.est.loa0.mle=coef['x'],
+ Bzy.est.loa0.mle=coef['z'],
+ Bxy.ci.upper.loa0.mle = ci.upper['x'],
+ Bxy.ci.lower.loa0.mle = ci.lower['x'],
+ Bzy.ci.upper.loa0.mle = ci.upper['z'],
+ Bzy.ci.lower.loa0.mle = ci.upper['z']))
+
+ loco.feasible <- glm(y.obs.0 ~ x + z, data = df[(!is.na(y.obs.0)) & (y.obs.1 == y.obs.0)], family=binomial(link='logit'))
+
+ loco.feasible.ci.Bxy <- confint(loco.feasible)['x',]
+ loco.feasible.ci.Bzy <- confint(loco.feasible)['z',]
+
+ result <- append(result, list(Bxy.est.loco.feasible=coef(loco.feasible)['x'],
+ Bzy.est.loco.feasible=coef(loco.feasible)['z'],
+ Bxy.ci.upper.loco.feasible = loco.feasible.ci.Bxy[2],
+ Bxy.ci.lower.loco.feasible = loco.feasible.ci.Bxy[1],
+ Bzy.ci.upper.loco.feasible = loco.feasible.ci.Bzy[2],
+ Bzy.ci.lower.loco.feasible = loco.feasible.ci.Bzy[1]))
+
+
+ df.loco.mle <- copy(df)
+ df.loco.mle[,y.obs:=NA]
+ df.loco.mle[(y.obs.0)==(y.obs.1),y.obs:=y.obs.0]
+ df.loco.mle[,y.true:=y]
+ df.loco.mle[,y:=y.obs]
+ print(df.loco.mle[!is.na(y.obs.1),mean(y.true==y,na.rm=TRUE)])
+ loco.mle <- measerr_mle_dv(df.loco.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula)
+ fisher.info <- solve(loco.mle$hessian)
+ coef <- loco.mle$par
+ ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
+ ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
+
+ result <- append(result, list(Bxy.est.loco.mle=coef['x'],
+ Bzy.est.loco.mle=coef['z'],
+ Bxy.ci.upper.loco.mle = ci.upper['x'],
+ Bxy.ci.lower.loco.mle = ci.lower['x'],
+ Bzy.ci.upper.loco.mle = ci.upper['z'],
+ Bzy.ci.lower.loco.mle = ci.upper['z']))
+
+ print(rater_formula)
+ print(proxy_formula)
+
+ ## mle.irr <- measerr_irr_mle( df, outcome_formula = outcome_formula, rater_formula = rater_formula, proxy_formula=proxy_formula, truth_formula=truth_formula)
+
+ ## fisher.info <- solve(mle.irr$hessian)
+ ## coef <- mle.irr$par
+ ## ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
+ ## ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
+
+ ## result <- append(result,
+ ## list(Bxy.est.mle = coef['x'],
+ ## Bxy.ci.upper.mle = ci.upper['x'],
+ ## Bxy.ci.lower.mle = ci.lower['x'],
+ ## Bzy.est.mle = coef['z'],
+ ## Bzy.ci.upper.mle = ci.upper['z'],
+ ## Bzy.ci.lower.mle = ci.lower['z']))
+
+ return(result)
+
+}
--- /dev/null
+library(matrixStats) # for numerically stable logsumexps
+
+options(amelia.parallel="no",
+ amelia.ncpus=1)
+library(Amelia)
+
+source("measerr_methods.R") ## for my more generic function.
+
+run_simulation <- function(df, result, outcome_formula = y ~ x + z, proxy_formula = w_pred ~ x, truth_formula = x ~ z){
+
+ accuracy <- df[,mean(w_pred==x)]
+ result <- append(result, list(accuracy=accuracy))
+
+ (model.true <- lm(y ~ x + z, data=df))
+ true.ci.Bxy <- confint(model.true)['x',]
+ true.ci.Bzy <- confint(model.true)['z',]
+
+ result <- append(result, list(Bxy.est.true=coef(model.true)['x'],
+ Bzy.est.true=coef(model.true)['z'],
+ Bxy.ci.upper.true = true.ci.Bxy[2],
+ Bxy.ci.lower.true = true.ci.Bxy[1],
+ Bzy.ci.upper.true = true.ci.Bzy[2],
+ Bzy.ci.lower.true = true.ci.Bzy[1]))
+
+
+
+ loa0.feasible <- lm(y ~ x.obs.0 + z, data = df[!(is.na(x.obs.1))])
+
+ loa0.ci.Bxy <- confint(loa0.feasible)['x.obs.0',]
+ loa0.ci.Bzy <- confint(loa0.feasible)['z',]
+
+ result <- append(result, list(Bxy.est.loa0.feasible=coef(loa0.feasible)['x.obs.0'],
+ Bzy.est.loa0.feasible=coef(loa0.feasible)['z'],
+ Bxy.ci.upper.loa0.feasible = loa0.ci.Bxy[2],
+ Bxy.ci.lower.loa0.feasible = loa0.ci.Bxy[1],
+ Bzy.ci.upper.loa0.feasible = loa0.ci.Bzy[2],
+ Bzy.ci.lower.loa0.feasible = loa0.ci.Bzy[1]))
+
+
+ df.loa0.mle <- copy(df)
+ df.loa0.mle[,x:=x.obs.0]
+ loa0.mle <- measerr_mle(df.loa0.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula, truth_formula=truth_formula)
+ fisher.info <- solve(loa0.mle$hessian)
+ coef <- loa0.mle$par
+ ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
+ ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
+
+ result <- append(result, list(Bxy.est.loa0.mle=coef['x'],
+ Bzy.est.loa0.mle=coef['z'],
+ Bxy.ci.upper.loa0.mle = ci.upper['x'],
+ Bxy.ci.lower.loa0.mle = ci.lower['x'],
+ Bzy.ci.upper.loa0.mle = ci.upper['z'],
+ Bzy.ci.lower.loa0.mle = ci.upper['z']))
+
+ loco.feasible <- lm(y ~ x.obs.1 + z, data = df[(!is.na(x.obs.1)) & (x.obs.1 == x.obs.0)])
+
+ loco.feasible.ci.Bxy <- confint(loco.feasible)['x.obs.1',]
+ loco.feasible.ci.Bzy <- confint(loco.feasible)['z',]
+
+ result <- append(result, list(Bxy.est.loco.feasible=coef(loco.feasible)['x.obs.1'],
+ Bzy.est.loco.feasible=coef(loco.feasible)['z'],
+ Bxy.ci.upper.loco.feasible = loco.feasible.ci.Bxy[2],
+ Bxy.ci.lower.loco.feasible = loco.feasible.ci.Bxy[1],
+ Bzy.ci.upper.loco.feasible = loco.feasible.ci.Bzy[2],
+ Bzy.ci.lower.loco.feasible = loco.feasible.ci.Bzy[1]))
+
+
+ df.loco.mle <- copy(df)
+ df.loco.mle[,x.obs:=NA]
+ df.loco.mle[(x.obs.0)==(x.obs.1),x.obs:=x.obs.0]
+ df.loco.mle[,x.true:=x]
+ df.loco.mle[,x:=x.obs]
+ print(df.loco.mle[!is.na(x.obs.1),mean(x.true==x,na.rm=TRUE)])
+ loco.mle <- measerr_mle(df.loco.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula, truth_formula=truth_formula)
+ fisher.info <- solve(loco.mle$hessian)
+ coef <- loco.mle$par
+ ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
+ ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
+
+ result <- append(result, list(Bxy.est.loco.mle=coef['x'],
+ Bzy.est.loco.mle=coef['z'],
+ Bxy.ci.upper.loco.mle = ci.upper['x'],
+ Bxy.ci.lower.loco.mle = ci.lower['x'],
+ Bzy.ci.upper.loco.mle = ci.upper['z'],
+ Bzy.ci.lower.loco.mle = ci.upper['z']))
+
+ ## print(rater_formula)
+ ## print(proxy_formula)
+ ## mle.irr <- measerr_irr_mle( df, outcome_formula = outcome_formula, rater_formula = rater_formula, proxy_formula=proxy_formula, truth_formula=truth_formula)
+
+ ## fisher.info <- solve(mle.irr$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)
+
+}
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')){
+
+ ### in this scenario, the ground truth also has measurement error, but we have repeated measures for it.
+
+ ## probability of y given observed data.
+ df.obs <- df[!is.na(x.obs.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$family=='gaussian'){
+ params <- c(outcome.params, 'sigma_y')
+ lower <- c(lower, 0.00001)
+ } else {
+ params <- outcome.params
+ }
+
+ rater.0.params <- colnames(model.matrix(rater_formula,df))
+ params <- c(params, paste0('rater_0',rater.0.params))
+ lower <- c(lower, rep(-Inf, length(rater.0.params)))
+
+ rater.1.params <- colnames(model.matrix(rater_formula,df))
+ params <- c(params, paste0('rater_1',rater.1.params))
+ lower <- c(lower, rep(-Inf, length(rater.1.params)))
+
+ proxy.params <- colnames(model.matrix(proxy_formula, df))
+ params <- c(params, paste0('proxy_',proxy.params))
+ lower <- c(lower, rep(-Inf, length(proxy.params)))
+
+ truth.params <- colnames(model.matrix(truth_formula, df))
+ params <- c(params, paste0('truth_', truth.params))
+ lower <- c(lower, rep(-Inf, length(truth.params)))
+ start <- rep(0.1,length(params))
+ names(start) <- params
+
+ fit <- optim(start, fn = nll, lower=lower, method='L-BFGS-B', hessian=TRUE, control=list(maxit=1e6))
+ return(fit)
+}
+
+
measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_formula, proxy_family=binomial(link='logit'), truth_formula, truth_family=binomial(link='logit')){
measrr_mle_nll <- function(params){
df.obs <- model.frame(outcome_formula, df)
-
proxy.variable <- all.vars(proxy_formula)[1]
proxy.model.matrix <- model.matrix(proxy_formula, df)
-
response.var <- all.vars(outcome_formula)[1]
y.obs <- with(df.obs,eval(parse(text=response.var)))
-
+
outcome.model.matrix <- model.matrix(outcome_formula, df)
param.idx <- 1
sigma.y <- params[param.idx]
param.idx <- param.idx + 1
- # outcome_formula likelihood using linear regression
+ # outcome_formula likelihood using linear regression
ll.y.obs <- dnorm(y.obs, outcome.params %*% t(outcome.model.matrix),sd=sigma.y, log=TRUE)
}
if( (proxy_family$family=="binomial") & (proxy_family$link=='logit')){
ll.w.obs <- vector(mode='numeric',length=dim(proxy.model.matrix)[1])
- # proxy_formula likelihood using logistic regression
+ # proxy_formula likelihood using logistic regression
ll.w.obs[proxy.obs==1] <- plogis(proxy.params %*% t(proxy.model.matrix[proxy.obs==1,]),log=TRUE)
ll.w.obs[proxy.obs==0] <- plogis(proxy.params %*% t(proxy.model.matrix[proxy.obs==0,]),log=TRUE, lower.tail=FALSE)
}
if( (truth_family$family=="binomial") & (truth_family$link=='logit')){
ll.x.obs <- vector(mode='numeric',length=dim(truth.model.matrix)[1])
- # truth_formula likelihood using logistic regression
+ # truth_formula likelihood using logistic regression
ll.x.obs[truth.obs==1] <- plogis(truth.params %*% t(truth.model.matrix[truth.obs==1,]),log=TRUE)
ll.x.obs[truth.obs==0] <- plogis(truth.params %*% t(truth.model.matrix[truth.obs==0,]),log=TRUE, lower.tail=FALSE)
}
- # add the three likelihoods
+ # add the three likelihoods
ll.obs <- sum(ll.y.obs + ll.w.obs + ll.x.obs)
## likelihood for the predicted data
outcome.model.matrix.x1 <- model.matrix(outcome_formula, df.unobs.x1)
if(outcome_family$family=="gaussian"){
- # likelihood of outcome
- ll.y.x0 <- dnorm(outcome.unobs, outcome.params %*% t(outcome.model.matrix.x0), sd=sigma.y, log=TRUE)
- ll.y.x1 <- dnorm(outcome.unobs, outcome.params %*% t(outcome.model.matrix.x1), sd=sigma.y, log=TRUE)
+ # likelihood of outcome
+ ll.y.x0 <- dnorm(outcome.unobs, outcome.params %*% t(outcome.model.matrix.x0), sd=sigma.y, log=TRUE)
+ ll.y.x1 <- dnorm(outcome.unobs, outcome.params %*% t(outcome.model.matrix.x1), sd=sigma.y, log=TRUE)
}
if( (proxy_family$family=='binomial') & (proxy_family$link=='logit')){
ll.w.x0 <- vector(mode='numeric', length=dim(df.unobs)[1])
ll.w.x1 <- vector(mode='numeric', length=dim(df.unobs)[1])
- # likelihood of proxy
+ # likelihood of proxy
ll.w.x0[proxy.unobs==1] <- plogis(proxy.params %*% t(proxy.model.matrix.x0[proxy.unobs==1,]), log=TRUE)
ll.w.x1[proxy.unobs==1] <- plogis(proxy.params %*% t(proxy.model.matrix.x1[proxy.unobs==1,]), log=TRUE)
if(truth_family$link=='logit'){
truth.model.matrix <- model.matrix(truth_formula, df.unobs.x0)
- # likelihood of truth
+ # likelihood of truth
ll.x.x1 <- plogis(truth.params %*% t(truth.model.matrix), log=TRUE)
ll.x.x0 <- plogis(truth.params %*% t(truth.model.matrix), log=TRUE, lower.tail=FALSE)
}
parser <- add_argument(parser, "--name", default="", help="The name to safe the data to in the remember file.")
args <- parse_args(parser)
-
-
summarize.estimator <- function(df, suffix='naive', coefname='x'){
part <- df[,c('N',
--- /dev/null
+source("RemembR/R/RemembeR.R")
+library(arrow)
+library(data.table)
+library(ggplot2)
+library(filelock)
+library(argparser)
+
+parser <- arg_parser("Simulate data and fit corrected models.")
+parser <- add_argument(parser, "--infile", default="", help="name of the file to read.")
+parser <- add_argument(parser, "--name", default="", help="The name to safe the data to in the remember file.")
+args <- parse_args(parser)
+source("summarize_estimator.R")
+
+build_plot_dataset <- function(df){
+
+ x.true <- summarize.estimator(df, 'true','x')
+
+ z.true <- summarize.estimator(df, 'true','z')
+
+ x.loa0.feasible <- summarize.estimator(df, 'loa0.feasible','x')
+
+ z.loa0.feasible <- summarize.estimator(df,'loa0.feasible','z')
+
+ x.loa0.mle <- summarize.estimator(df, 'loa0.mle', 'x')
+
+ z.loa0.mle <- summarize.estimator(df, 'loa0.mle', 'z')
+
+ x.loco.feasible <- summarize.estimator(df, 'loco.feasible', 'x')
+
+ z.loco.feasible <- summarize.estimator(df, 'loco.feasible', 'z')
+
+ x.loco.mle <- summarize.estimator(df, 'loco.mle', 'x')
+
+ z.loco.mle <- summarize.estimator(df, 'loco.mle', 'z')
+
+
+ accuracy <- df[,mean(accuracy)]
+ plot.df <- rbindlist(list(x.true,z.true,x.loa0.feasible,z.loa0.feasible,x.loa0.mle,z.loa0.mle,x.loco.feasible, z.loco.feasible, z.loco.mle, x.loco.mle),use.names=T)
+ plot.df[,accuracy := accuracy]
+ plot.df <- plot.df[,":="(sd.est=sqrt(var.est)/N.sims)]
+ return(plot.df)
+}
+
+
+plot.df <- read_feather(args$infile)
+print(unique(plot.df$N))
+
+# df <- df[apply(df,1,function(x) !any(is.na(x)))]
+
+if(!('Bzx' %in% names(plot.df)))
+ plot.df[,Bzx:=NA]
+
+if(!('accuracy_imbalance_difference' %in% names(plot.df)))
+ plot.df[,accuracy_imbalance_difference:=NA]
+
+unique(plot.df[,'accuracy_imbalance_difference'])
+
+#plot.df <- build_plot_dataset(df[accuracy_imbalance_difference==0.1][N==700])
+plot.df <- build_plot_dataset(plot.df)
+
+change.remember.file("remember_irr.RDS",clear=TRUE)
+
+remember(plot.df,args$name)
--- /dev/null
+source("RemembR/R/RemembeR.R")
+library(arrow)
+library(data.table)
+library(ggplot2)
+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, "--name", default="", help="The name to safe the data to in the remember file.")
+args <- parse_args(parser)
+source("summarize_estimator.R")
+
+build_plot_dataset <- function(df){
+
+ x.true <- summarize.estimator(df, 'true','x')
+
+ z.true <- summarize.estimator(df, 'true','z')
+
+ x.loa0.feasible <- summarize.estimator(df, 'loa0.feasible','x')
+
+ z.loa0.feasible <- summarize.estimator(df,'loa0.feasible','z')
+
+ x.loa0.mle <- summarize.estimator(df, 'loa0.mle', 'x')
+
+ z.loa0.mle <- summarize.estimator(df, 'loa0.mle', 'z')
+
+ x.loco.feasible <- summarize.estimator(df, 'loco.feasible', 'x')
+
+ z.loco.feasible <- summarize.estimator(df, 'loco.feasible', 'z')
+
+ x.loco.mle <- summarize.estimator(df, 'loco.mle', 'x')
+
+ z.loco.mle <- summarize.estimator(df, 'loco.mle', 'z')
+
+ ## 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[,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))
+
+# df <- df[apply(df,1,function(x) !any(is.na(x)))]
+
+if(!('Bzx' %in% names(plot.df)))
+ plot.df[,Bzx:=NA]
+
+if(!('accuracy_imbalance_difference' %in% names(plot.df)))
+ plot.df[,accuracy_imbalance_difference:=NA]
+
+unique(plot.df[,'accuracy_imbalance_difference'])
+
+#plot.df <- build_plot_dataset(df[accuracy_imbalance_difference==0.1][N==700])
+plot.df <- build_plot_dataset(plot.df)
+change.remember.file("remember_irr.RDS",clear=TRUE)
+remember(plot.df,args$name)
+
+#ggplot(df,aes(x=Bxy.est.mle)) + geom_histogram() + facet_grid(accuracy_imbalance_difference ~ Bzy)
+
+## ## ## df[gmm.ER_pval<0.05]
+
+## plot.df.test <- plot.df[,':='(method=factor(method,levels=c("Naive","Multiple imputation", "Multiple imputation (Classifier features unobserved)","Regression Calibration","2SLS+gmm","Bespoke MLE", "Feasible"),ordered=T),
+## N=factor(N),
+## m=factor(m))]
+
+## plot.df.test <- plot.df.test[(variable=='x') & (method!="Multiple imputation (Classifier features unobserved)")]
+## p <- ggplot(plot.df.test, aes(y=mean.est, ymax=mean.est + var.est/2, ymin=mean.est-var.est/2, x=method))
+## p <- p + geom_hline(data=plot.df.test, mapping=aes(yintercept=0.1),linetype=2)
+
+## p <- p + geom_pointrange() + facet_grid(N~m,as.table=F,scales='free') + scale_x_discrete(labels=label_wrap_gen(4))
+## print(p)
+
+## plot.df.test <- plot.df[,':='(method=factor(method,levels=c("Naive","Multiple imputation", "Multiple imputation (Classifier features unobserved)","Regression Calibration","2SLS+gmm","Bespoke MLE", "Feasible"),ordered=T),
+## N=factor(N),
+## m=factor(m))]
+
+## plot.df.test <- plot.df.test[(variable=='z') & (method!="Multiple imputation (Classifier features unobserved)")]
+## p <- ggplot(plot.df.test, aes(y=mean.est, ymax=mean.est + var.est/2, ymin=mean.est-var.est/2, x=method))
+## p <- p + geom_hline(data=plot.df.test, mapping=aes(yintercept=-0.1),linetype=2)
+
+## p <- p + geom_pointrange() + facet_grid(m~N,as.table=F,scales='free') + scale_x_discrete(labels=label_wrap_gen(4))
+## print(p)
+
+
+## x.mle <- df[,.(N,m,Bxy.est.mle,Bxy.ci.lower.mle, Bxy.ci.upper.mle, y_explained_variance, Bzx, Bzy, accuracy_imbalance_difference)]
+## x.mle.plot <- x.mle[,.(mean.est = mean(Bxy.est.mle),
+## var.est = var(Bxy.est.mle),
+## N.sims = .N,
+## variable='z',
+## method='Bespoke MLE'
+## ),
+## by=c("N","m",'y_explained_variance', 'Bzx', 'Bzy','accuracy_imbalance_difference')]
+
+## z.mle <- df[,.(N,m,Bzy.est.mle,Bzy.ci.lower.mle, Bzy.ci.upper.mle, y_explained_variance, Bzx, Bzy, accuracy_imbalance_difference)]
+
+## z.mle.plot <- z.mle[,.(mean.est = mean(Bzy.est.mle),
+## var.est = var(Bzy.est.mle),
+## N.sims = .N,
+## variable='z',
+## method='Bespoke MLE'
+## ),
+## by=c("N","m",'y_explained_variance','Bzx')]
+
+## plot.df <- z.mle.plot
+## plot.df.test <- plot.df[,':='(method=factor(method,levels=c("Naive","Multiple imputation", "Multiple imputation (Classifier features unobserved)","Regression Calibration","2SLS+gmm","Bespoke MLE", "Feasible"),ordered=T),
+## N=factor(N),
+## m=factor(m))]
+
+## plot.df.test <- plot.df.test[(variable=='z') & (m != 1000) & (m!=500) & (method!="Multiple imputation (Classifier features unobserved)")]
+## p <- ggplot(plot.df.test, aes(y=mean.est, ymax=mean.est + var.est/2, ymin=mean.est-var.est/2, x=method))
+## p <- p + geom_hline(aes(yintercept=0.2),linetype=2)
+
+## p <- p + geom_pointrange() + facet_grid(m~Bzx, Bzy,as.table=F) + scale_x_discrete(labels=label_wrap_gen(4))
+## print(p)
+
+
+## ## ggplot(plot.df[variable=='x'], aes(y=mean.est, ymax=mean.est + var.est/2, ymin=mean.est-var.est/2, x=method)) + geom_pointrange() + facet_grid(-m~N) + scale_x_discrete(labels=label_wrap_gen(10))
+
+## ## ggplot(plot.df,aes(y=N,x=m,color=p.sign.correct)) + geom_point() + facet_grid(variable ~ method) + scale_color_viridis_c(option='D') + theme_minimal() + xlab("Number of gold standard labels") + ylab("Total sample size")
+
+## ## ggplot(plot.df,aes(y=N,x=m,color=abs(mean.bias))) + geom_point() + facet_grid(variable ~ method) + scale_color_viridis_c(option='D') + theme_minimal() + xlab("Number of gold standard labels") + ylab("Total sample size")
--- /dev/null
+
+summarize.estimator <- function(df, suffix='naive', coefname='x'){
+
+ part <- df[,c('N',
+ 'm',
+ 'Bxy',
+ paste0('B',coefname,'y.est.',suffix),
+ paste0('B',coefname,'y.ci.lower.',suffix),
+ paste0('B',coefname,'y.ci.upper.',suffix),
+ 'y_explained_variance',
+ 'Bzx',
+ 'Bzy',
+ 'accuracy_imbalance_difference'
+ ),
+ with=FALSE]
+
+ true.in.ci <- as.integer((part$Bxy >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (part$Bxy <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]]))
+ zero.in.ci <- as.integer(0 >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (0 <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]])
+ bias <- part$Bxy - part[[paste0('B',coefname,'y.est.',suffix)]]
+ sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))
+
+ part <- part[,':='(true.in.ci = true.in.ci,
+ zero.in.ci = zero.in.ci,
+ bias=bias,
+ sign.correct =sign.correct)]
+
+ part.plot <- part[, .(p.true.in.ci = mean(true.in.ci),
+ mean.bias = mean(bias),
+ mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
+ var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
+ est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.95,na.rm=T),
+ est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.05,na.rm=T),
+ N.sims = .N,
+ p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
+ variable=coefname,
+ method=suffix
+ ),
+ by=c("N","m",'y_explained_variance','Bzx', 'Bzy', 'accuracy_imbalance_difference')
+ ]
+
+ return(part.plot)
+}
projects / ml_measurement_error_public.git / commitdiff