109 lines
4.8 KiB
R
109 lines
4.8 KiB
R
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)
|
|
|
|
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',
|
|
'Bzy'
|
|
),
|
|
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),
|
|
est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.05),
|
|
N.sims = .N,
|
|
p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
|
|
variable=coefname,
|
|
method=suffix
|
|
),
|
|
by=c("N","m",'Bzy','y_explained_variance')
|
|
]
|
|
|
|
return(part.plot)
|
|
}
|
|
|
|
|
|
build_plot_dataset <- function(df){
|
|
|
|
x.true <- summarize.estimator(df, 'true','x')
|
|
z.true <- summarize.estimator(df, 'true','z')
|
|
|
|
x.naive <- summarize.estimator(df, 'naive','x')
|
|
z.naive <- summarize.estimator(df, 'naive','z')
|
|
|
|
x.feasible <- summarize.estimator(df, 'feasible','x')
|
|
z.feasible <- summarize.estimator(df, 'feasible','z')
|
|
|
|
x.amelia.full <- summarize.estimator(df, 'amelia.full','x')
|
|
z.amelia.full <- summarize.estimator(df, 'amelia.full','z')
|
|
|
|
x.mle <- summarize.estimator(df, 'mle','x')
|
|
z.mle <- summarize.estimator(df, 'mle','z')
|
|
|
|
x.zhang <- summarize.estimator(df, 'zhang','x')
|
|
z.zhang <- summarize.estimator(df, 'zhang','z')
|
|
|
|
accuracy <- df[,mean(accuracy)]
|
|
|
|
plot.df <- rbindlist(list(x.true, z.true, x.naive,z.naive,x.amelia.full,z.amelia.full,x.mle, z.mle, x.zhang, z.zhang, x.feasible, z.feasible),use.names=T)
|
|
|
|
plot.df[,accuracy := accuracy]
|
|
|
|
plot.df <- plot.df[,":="(sd.est=sqrt(var.est)/N.sims)]
|
|
|
|
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.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) & !is.na(p.true.in.ci) & (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=-0.05),linetype=2)
|
|
|
|
## p <- p + geom_pointrange() + facet_grid(m~N,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")
|