278 lines
16 KiB
R
278 lines
16 KiB
R
source("RemembR/R/RemembeR.R")
|
|
library(arrow)
|
|
library(data.table)
|
|
library(ggplot2)
|
|
library(filelock)
|
|
library(argparse)
|
|
|
|
l <- filelock::lock("example_2_B.feather_lock",exclusive=FALSE)
|
|
df <- data.table(read_feather("example_2_B.feather"))
|
|
filelock::unlock(l)
|
|
|
|
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)
|
|
|
|
build_plot_dataset <- function(df){
|
|
x.naive <- df[,.(N, m, Bxy, Bxy.est.naive, Bxy.ci.lower.naive, Bxy.ci.upper.naive)]
|
|
x.naive <- x.naive[,':='(true.in.ci = as.integer((Bxy >= Bxy.ci.lower.naive) & (Bxy <= Bxy.ci.upper.naive)),
|
|
zero.in.ci = (0 >= Bxy.ci.lower.naive) & (0 <= Bxy.ci.upper.naive),
|
|
bias = Bxy - Bxy.est.naive,
|
|
Bxy.est.naive = Bxy.est.naive,
|
|
sign.correct = as.integer(sign(Bxy) == sign(Bxy.est.naive)))]
|
|
|
|
x.naive.plot <- x.naive[,.(p.true.in.ci = mean(true.in.ci),
|
|
mean.bias = mean(bias),
|
|
mean.est = mean(Bxy.est.naive),
|
|
var.est = var(Bxy.est.naive),
|
|
N.sims = .N,
|
|
p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
|
|
variable='x',
|
|
method='Naive'
|
|
),
|
|
by=c('N','m')]
|
|
|
|
|
|
g.naive <- df[,.(N, m, Bgy, Bgy.est.naive, Bgy.ci.lower.naive, Bgy.ci.upper.naive)]
|
|
g.naive <- g.naive[,':='(true.in.ci = as.integer((Bgy >= Bgy.ci.lower.naive) & (Bgy <= Bgy.ci.upper.naive)),
|
|
zero.in.ci = (0 >= Bgy.ci.lower.naive) & (0 <= Bgy.ci.upper.naive),
|
|
bias = Bgy - Bgy.est.naive,
|
|
Bgy.est.naive = Bgy.est.naive,
|
|
sign.correct = as.integer(sign(Bgy) == sign(Bgy.est.naive)))]
|
|
|
|
g.naive.plot <- g.naive[,.(p.true.in.ci = mean(true.in.ci),
|
|
mean.bias = mean(bias),
|
|
mean.est = mean(Bgy.est.naive),
|
|
var.est = var(Bgy.est.naive),
|
|
N.sims = .N,
|
|
p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
|
|
variable='g',
|
|
method='Naive'
|
|
),
|
|
by=c('N','m')]
|
|
|
|
|
|
x.feasible <- df[,.(N, m, Bxy, Bxy.est.feasible, Bxy.ci.lower.feasible, Bxy.ci.upper.feasible)]
|
|
x.feasible <- x.feasible[,':='(true.in.ci = as.integer((Bxy >= Bxy.ci.lower.feasible) & (Bxy <= Bxy.ci.upper.feasible)),
|
|
zero.in.ci = (0 >= Bxy.ci.lower.feasible) & (0 <= Bxy.ci.upper.feasible),
|
|
bias = Bxy - Bxy.est.feasible,
|
|
Bxy.est.feasible = Bxy.est.feasible,
|
|
sign.correct = as.integer(sign(Bxy) == sign(Bxy.est.feasible)))]
|
|
|
|
x.feasible.plot <- x.feasible[,.(p.true.in.ci = mean(true.in.ci),
|
|
mean.bias = mean(bias),
|
|
mean.est = mean(Bxy.est.feasible),
|
|
var.est = var(Bxy.est.feasible),
|
|
N.sims = .N,
|
|
p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
|
|
variable='x',
|
|
method='Feasible'
|
|
),
|
|
by=c('N','m')]
|
|
|
|
|
|
g.feasible <- df[,.(N, m, Bgy, Bgy.est.feasible, Bgy.ci.lower.feasible, Bgy.ci.upper.feasible)]
|
|
g.feasible <- g.feasible[,':='(true.in.ci = as.integer((Bgy >= Bgy.ci.lower.feasible) & (Bgy <= Bgy.ci.upper.feasible)),
|
|
zero.in.ci = (0 >= Bgy.ci.lower.feasible) & (0 <= Bgy.ci.upper.feasible),
|
|
bias = Bgy - Bgy.est.feasible,
|
|
Bgy.est.feasible = Bgy.est.feasible,
|
|
sign.correct = as.integer(sign(Bgy) == sign(Bgy.est.feasible)))]
|
|
|
|
g.feasible.plot <- g.feasible[,.(p.true.in.ci = mean(true.in.ci),
|
|
mean.bias = mean(bias),
|
|
mean.est = mean(Bgy.est.feasible),
|
|
var.est = var(Bgy.est.feasible),
|
|
N.sims = .N,
|
|
p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
|
|
variable='g',
|
|
method='Feasible'
|
|
),
|
|
by=c('N','m')]
|
|
|
|
|
|
|
|
x.amelia.full <- df[,.(N, m, Bxy, Bxy.est.true, Bxy.ci.lower.amelia.full, Bxy.ci.upper.amelia.full, Bxy.est.amelia.full)]
|
|
|
|
x.amelia.full <- x.amelia.full[,':='(true.in.ci = (Bxy.est.true >= Bxy.ci.lower.amelia.full) & (Bxy.est.true <= Bxy.ci.upper.amelia.full),
|
|
zero.in.ci = (0 >= Bxy.ci.lower.amelia.full) & (0 <= Bxy.ci.upper.amelia.full),
|
|
bias = Bxy.est.true - Bxy.est.amelia.full,
|
|
sign.correct = sign(Bxy.est.true) == sign(Bxy.est.amelia.full))]
|
|
|
|
x.amelia.full.plot <- x.amelia.full[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
|
|
mean.bias = mean(bias),
|
|
p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
|
|
mean.est = mean(Bxy.est.amelia.full),
|
|
var.est = var(Bxy.est.amelia.full),
|
|
N.sims = .N,
|
|
variable='x',
|
|
method='Multiple imputation'
|
|
),
|
|
by=c('N','m')]
|
|
|
|
|
|
g.amelia.full <- df[,.(N, m, Bgy.est.true, Bgy.est.amelia.full, Bgy.ci.lower.amelia.full, Bgy.ci.upper.amelia.full)]
|
|
g.amelia.full <- g.amelia.full[,':='(true.in.ci = (Bgy.est.true >= Bgy.ci.lower.amelia.full) & (Bgy.est.true <= Bgy.ci.upper.amelia.full),
|
|
zero.in.ci = (0 >= Bgy.ci.lower.amelia.full) & (0 <= Bgy.ci.upper.amelia.full),
|
|
bias = Bgy.est.amelia.full - Bgy.est.true,
|
|
sign.correct = sign(Bgy.est.true) == sign(Bgy.est.amelia.full))]
|
|
|
|
g.amelia.full.plot <- g.amelia.full[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
|
|
mean.bias = mean(bias),
|
|
p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
|
|
mean.est = mean(Bgy.est.amelia.full),
|
|
var.est = var(Bgy.est.amelia.full),
|
|
N.sims = .N,
|
|
variable='g',
|
|
method='Multiple imputation'
|
|
),
|
|
by=c('N','m')]
|
|
|
|
x.amelia.nok <- df[,.(N, m, Bxy.est.true, Bxy.est.amelia.nok, Bxy.ci.lower.amelia.nok, Bxy.ci.upper.amelia.nok)]
|
|
x.amelia.nok <- x.amelia.nok[,':='(true.in.ci = (Bxy.est.true >= Bxy.ci.lower.amelia.nok) & (Bxy.est.true <= Bxy.ci.upper.amelia.nok),
|
|
zero.in.ci = (0 >= Bxy.ci.lower.amelia.nok) & (0 <= Bxy.ci.upper.amelia.nok),
|
|
bias = Bxy.est.amelia.nok - Bxy.est.true,
|
|
sign.correct = sign(Bxy.est.true) == sign(Bxy.est.amelia.nok))]
|
|
|
|
x.amelia.nok.plot <- x.amelia.nok[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
|
|
mean.bias = mean(bias),
|
|
p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
|
|
mean.est = mean(Bxy.est.amelia.nok),
|
|
var.est = var(Bxy.est.amelia.nok),
|
|
N.sims = .N,
|
|
variable='x',
|
|
method='Multiple imputation (Classifier features unobserved)'
|
|
),
|
|
by=c('N','m')]
|
|
|
|
g.amelia.nok <- df[,.(N, m, Bgy.est.true, Bgy.est.amelia.nok, Bgy.ci.lower.amelia.nok, Bgy.ci.upper.amelia.nok)]
|
|
g.amelia.nok <- g.amelia.nok[,':='(true.in.ci = (Bgy.est.true >= Bgy.ci.lower.amelia.nok) & (Bgy.est.true <= Bgy.ci.upper.amelia.nok),
|
|
zero.in.ci = (0 >= Bgy.ci.lower.amelia.nok) & (0 <= Bgy.ci.upper.amelia.nok),
|
|
bias = Bgy.est.amelia.nok - Bgy.est.true,
|
|
sign.correct = sign(Bgy.est.true) == sign(Bgy.est.amelia.nok))]
|
|
|
|
g.amelia.nok.plot <- g.amelia.nok[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
|
|
mean.bias = mean(bias),
|
|
p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
|
|
mean.est = mean(Bgy.est.amelia.nok),
|
|
var.est = var(Bgy.est.amelia.nok),
|
|
N.sims = .N,
|
|
variable='g',
|
|
method="Multiple imputation (Classifier features unobserved)"
|
|
),
|
|
by=c('N','m')]
|
|
|
|
|
|
x.mecor <- df[,.(N,m,Bxy.est.true, Bxy.est.mecor,Bxy.lower.mecor, Bxy.upper.mecor)]
|
|
|
|
x.mecor <- x.mecor[,':='(true.in.ci = (Bxy.est.true >= Bxy.lower.mecor) & (Bxy.est.true <= Bxy.upper.mecor),
|
|
zero.in.ci = (0 >= Bxy.lower.mecor) & (0 <= Bxy.upper.mecor),
|
|
bias = Bxy.est.mecor - Bxy.est.true,
|
|
sign.correct = sign(Bxy.est.true) == sign(Bxy.est.mecor))]
|
|
|
|
x.mecor.plot <- x.mecor[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
|
|
mean.bias = mean(bias),
|
|
p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
|
|
mean.est = mean(Bxy.est.mecor),
|
|
var.est = var(Bxy.est.mecor),
|
|
N.sims = .N,
|
|
variable='x',
|
|
method='Regression Calibration'
|
|
),
|
|
by=c('N','m')]
|
|
|
|
g.mecor <- df[,.(N,m,Bgy.est.true, Bgy.est.mecor,Bgy.lower.mecor, Bgy.upper.mecor)]
|
|
|
|
g.mecor <- g.mecor[,':='(true.in.ci = (Bgy.est.true >= Bgy.lower.mecor) & (Bgy.est.true <= Bgy.upper.mecor),
|
|
zero.in.ci = (0 >= Bgy.lower.mecor) & (0 <= Bgy.upper.mecor),
|
|
bias = Bgy.est.mecor - Bgy.est.true,
|
|
sign.correct = sign(Bgy.est.true) == sign(Bgy.est.mecor))]
|
|
|
|
g.mecor.plot <- g.mecor[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
|
|
mean.bias = mean(bias),
|
|
p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
|
|
mean.est = mean(Bgy.est.mecor),
|
|
var.est = var(Bgy.est.mecor),
|
|
N.sims = .N,
|
|
variable='g',
|
|
method='Regression Calibration'
|
|
),
|
|
by=c('N','m')]
|
|
|
|
## x.mecor <- df[,.(N,m,Bgy.est.true, Bgy.est.mecor,Bgy.ci.lower.mecor, Bgy.ci.upper.mecor)]
|
|
|
|
## x.mecor <- x.mecor[,':='(true.in.ci = (Bgy.est.true >= Bgy.ci.lower.mecor) & (Bgy.est.true <= Bgy.ci.upper.mecor),
|
|
## zero.in.ci = (0 >= Bgy.ci.lower.mecor) & (0 <= Bgy.ci.upper.mecor),
|
|
## bias = Bgy.est.mecor - Bgy.est.true,
|
|
## sign.correct = sign(Bgy.est.true) == sign(Bgy.est.mecor))]
|
|
|
|
## x.mecor.plot <- x.mecor[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
|
|
## mean.bias = mean(bias),
|
|
## p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
|
|
## variable='g',
|
|
## method='Regression Calibration'
|
|
## ),
|
|
## by=c('N','m')]
|
|
|
|
|
|
x.gmm <- df[,.(N,m,Bxy.est.true, Bxy.est.gmm,Bxy.ci.lower.gmm, Bxy.ci.upper.gmm)]
|
|
x.gmm <- x.gmm[,':='(true.in.ci = (Bxy.est.true >= Bxy.ci.lower.gmm) & (Bxy.est.true <= Bxy.ci.upper.gmm),
|
|
zero.in.ci = (0 >= Bxy.ci.lower.gmm) & (0 <= Bxy.ci.upper.gmm),
|
|
bias = Bxy.est.gmm - Bxy.est.true,
|
|
sign.correct = sign(Bxy.est.true) == sign(Bxy.est.gmm))]
|
|
|
|
x.gmm.plot <- x.gmm[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
|
|
mean.bias = mean(bias),
|
|
p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
|
|
mean.est = mean(Bxy.est.gmm),
|
|
var.est = var(Bxy.est.gmm),
|
|
N.sims = .N,
|
|
variable='x',
|
|
method='2SLS+gmm'
|
|
),
|
|
by=c('N','m')]
|
|
|
|
g.gmm <- df[,.(N,m,Bgy.est.true, Bgy.est.gmm,Bgy.ci.lower.gmm, Bgy.ci.upper.gmm)]
|
|
g.gmm <- g.gmm[,':='(true.in.ci = (Bgy.est.true >= Bgy.ci.lower.gmm) & (Bgy.est.true <= Bgy.ci.upper.gmm),
|
|
zero.in.ci = (0 >= Bgy.ci.lower.gmm) & (0 <= Bgy.ci.upper.gmm),
|
|
bias = Bgy.est.gmm - Bgy.est.true,
|
|
sign.correct = sign(Bgy.est.true) == sign(Bgy.est.gmm))]
|
|
|
|
g.gmm.plot <- g.gmm[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
|
|
mean.bias = mean(bias),
|
|
p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
|
|
mean.est = mean(Bgy.est.gmm),
|
|
var.est = var(Bgy.est.gmm),
|
|
N.sims = .N,
|
|
variable='g',
|
|
method='2SLS+gmm'
|
|
),
|
|
by=c('N','m')]
|
|
|
|
accuracy <- df[,mean(accuracy)]
|
|
return(plot.df)
|
|
}
|
|
|
|
df <- read_feather(args$infile)
|
|
plot.df <- build_plot_dataset(df)
|
|
remember(plot.df,args$name))
|
|
|
|
|
|
## df[gmm.ER_pval<0.05]
|
|
|
|
|
|
|
|
## plot.df <- rbindlist(list(x.naive.plot,g.naive.plot,x.amelia.full.plot,g.amelia.full.plot,x.amelia.nok.plot,g.amelia.nok.plot, x.mecor.plot, g.mecor.plot, x.gmm.plot, g.gmm.plot, x.feasible.plot, g.feasible.plot),use.names=T)
|
|
|
|
## plot.df[,accuracy := accuracy]
|
|
|
|
## # plot.df <- plot.df[,":="(sd.est=sqrt(var.est)/N.sims)]
|
|
|
|
|
|
|
|
## 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")
|