236 lines
13 KiB
R
236 lines
13 KiB
R
source("RemembR/R/RemembeR.R")
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library(arrow)
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library(data.table)
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library(ggplot2)
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library(filelock)
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library(argparser)
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parser <- arg_parser("Simulate data and fit corrected models.")
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parser <- add_argument(parser, "--infile", default="", help="name of the file to read.")
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parser <- add_argument(parser, "--name", default="", help="The name to safe the data to in the remember file.")
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args <- parse_args(parser)
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build_plot_dataset <- function(df){
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x.naive <- df[,.(N, m, Bxy, Bxy.est.naive, Bxy.ci.lower.naive, Bxy.ci.upper.naive)]
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x.naive <- x.naive[,':='(true.in.ci = as.integer((Bxy >= Bxy.ci.lower.naive) & (Bxy <= Bxy.ci.upper.naive)),
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zero.in.ci = (0 >= Bxy.ci.lower.naive) & (0 <= Bxy.ci.upper.naive),
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bias = Bxy - Bxy.est.naive,
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Bxy.est.naive = Bxy.est.naive,
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sign.correct = as.integer(sign(Bxy) == sign(Bxy.est.naive)))]
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x.naive.plot <- x.naive[,.(p.true.in.ci = mean(true.in.ci),
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mean.bias = mean(bias),
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mean.est = mean(Bxy.est.naive),
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var.est = var(Bxy.est.naive),
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N.sims = .N,
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p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
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variable='x',
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method='Naive'
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),
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by=c('N','m')]
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g.naive <- df[,.(N, m, Bgy, Bgy.est.naive, Bgy.ci.lower.naive, Bgy.ci.upper.naive)]
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g.naive <- g.naive[,':='(true.in.ci = as.integer((Bgy >= Bgy.ci.lower.naive) & (Bgy <= Bgy.ci.upper.naive)),
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zero.in.ci = (0 >= Bgy.ci.lower.naive) & (0 <= Bgy.ci.upper.naive),
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bias = Bgy - Bgy.est.naive,
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Bgy.est.naive = Bgy.est.naive,
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sign.correct = as.integer(sign(Bgy) == sign(Bgy.est.naive)))]
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g.naive.plot <- g.naive[,.(p.true.in.ci = mean(true.in.ci),
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mean.bias = mean(bias),
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mean.est = mean(Bgy.est.naive),
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var.est = var(Bgy.est.naive),
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N.sims = .N,
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p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
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variable='g',
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method='Naive'
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),
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by=c('N','m')]
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x.feasible <- df[,.(N, m, Bxy, Bxy.est.feasible, Bxy.ci.lower.feasible, Bxy.ci.upper.feasible)]
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x.feasible <- x.feasible[,':='(true.in.ci = as.integer((Bxy >= Bxy.ci.lower.feasible) & (Bxy <= Bxy.ci.upper.feasible)),
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zero.in.ci = (0 >= Bxy.ci.lower.feasible) & (0 <= Bxy.ci.upper.feasible),
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bias = Bxy - Bxy.est.feasible,
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Bxy.est.feasible = Bxy.est.feasible,
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sign.correct = as.integer(sign(Bxy) == sign(Bxy.est.feasible)))]
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x.feasible.plot <- x.feasible[,.(p.true.in.ci = mean(true.in.ci),
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mean.bias = mean(bias),
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mean.est = mean(Bxy.est.feasible),
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var.est = var(Bxy.est.feasible),
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N.sims = .N,
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p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
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variable='x',
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method='Feasible'
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),
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by=c('N','m')]
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g.feasible <- df[,.(N, m, Bgy, Bgy.est.feasible, Bgy.ci.lower.feasible, Bgy.ci.upper.feasible)]
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g.feasible <- g.feasible[,':='(true.in.ci = as.integer((Bgy >= Bgy.ci.lower.feasible) & (Bgy <= Bgy.ci.upper.feasible)),
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zero.in.ci = (0 >= Bgy.ci.lower.feasible) & (0 <= Bgy.ci.upper.feasible),
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bias = Bgy - Bgy.est.feasible,
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Bgy.est.feasible = Bgy.est.feasible,
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sign.correct = as.integer(sign(Bgy) == sign(Bgy.est.feasible)))]
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g.feasible.plot <- g.feasible[,.(p.true.in.ci = mean(true.in.ci),
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mean.bias = mean(bias),
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mean.est = mean(Bgy.est.feasible),
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var.est = var(Bgy.est.feasible),
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N.sims = .N,
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p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
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variable='g',
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method='Feasible'
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),
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by=c('N','m')]
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x.amelia.full <- df[,.(N, m, Bxy, Bxy.est.true, Bxy.ci.lower.amelia.full, Bxy.ci.upper.amelia.full, Bxy.est.amelia.full)]
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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),
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zero.in.ci = (0 >= Bxy.ci.lower.amelia.full) & (0 <= Bxy.ci.upper.amelia.full),
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bias = Bxy.est.true - Bxy.est.amelia.full,
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sign.correct = sign(Bxy.est.true) == sign(Bxy.est.amelia.full))]
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x.amelia.full.plot <- x.amelia.full[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
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mean.bias = mean(bias),
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p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
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mean.est = mean(Bxy.est.amelia.full),
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var.est = var(Bxy.est.amelia.full),
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N.sims = .N,
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variable='x',
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method='Multiple imputation'
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),
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by=c('N','m')]
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g.amelia.full <- df[,.(N, m, Bgy.est.true, Bgy.est.amelia.full, Bgy.ci.lower.amelia.full, Bgy.ci.upper.amelia.full)]
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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),
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zero.in.ci = (0 >= Bgy.ci.lower.amelia.full) & (0 <= Bgy.ci.upper.amelia.full),
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bias = Bgy.est.amelia.full - Bgy.est.true,
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sign.correct = sign(Bgy.est.true) == sign(Bgy.est.amelia.full))]
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g.amelia.full.plot <- g.amelia.full[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
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mean.bias = mean(bias),
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p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
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mean.est = mean(Bgy.est.amelia.full),
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var.est = var(Bgy.est.amelia.full),
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N.sims = .N,
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variable='g',
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method='Multiple imputation'
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),
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by=c('N','m')]
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x.mle <- df[,.(N,m, Bxy.est.true, Bxy.est.mle, Bxy.ci.lower.mle, Bxy.ci.upper.mle)]
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x.mle <- x.mle[,':='(true.in.ci = (Bxy.est.true >= Bxy.ci.lower.mle) & (Bxy.est.true <= Bxy.ci.upper.mle),
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zero.in.ci = (0 >= Bxy.ci.lower.mle) & (0 <= Bxy.ci.upper.mle),
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bias = Bxy.est.mle - Bxy.est.true,
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sign.correct = sign(Bxy.est.true) == sign(Bxy.est.mle))]
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x.mle.plot <- x.mle[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
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mean.bias = mean(bias),
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p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
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mean.est = mean(Bxy.est.mle),
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var.est = var(Bxy.est.mle),
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N.sims = .N,
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variable='x',
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method='Maximum Likelihood'
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),
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by=c('N','m')]
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g.mle <- df[,.(N,m, Bgy.est.true, Bgy.est.mle, Bgy.ci.lower.mle, Bgy.ci.upper.mle)]
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g.mle <- g.mle[,':='(true.in.ci = (Bgy.est.true >= Bgy.ci.lower.mle) & (Bgy.est.true <= Bgy.ci.upper.mle),
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zero.in.ci = (0 >= Bgy.ci.lower.mle) & (0 <= Bgy.ci.upper.mle),
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bias = Bgy.est.mle - Bgy.est.true,
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sign.correct = sign(Bgy.est.true) == sign(Bgy.est.mle))]
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g.mle.plot <- g.mle[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
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mean.bias = mean(bias),
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p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
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mean.est = mean(Bgy.est.mle),
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var.est = var(Bgy.est.mle),
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N.sims = .N,
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variable='g',
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method='Maximum Likelihood'
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),
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by=c('N','m')]
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x.pseudo <- df[,.(N,m, Bxy.est.true, Bxy.est.pseudo, Bxy.ci.lower.pseudo, Bxy.ci.upper.pseudo)]
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x.pseudo <- x.pseudo[,':='(true.in.ci = (Bxy.est.true >= Bxy.ci.lower.pseudo) & (Bxy.est.true <= Bxy.ci.upper.pseudo),
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zero.in.ci = (0 >= Bxy.ci.lower.pseudo) & (0 <= Bxy.ci.upper.pseudo),
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bias = Bxy.est.pseudo - Bxy.est.true,
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sign.correct = sign(Bxy.est.true) == sign(Bxy.est.pseudo))]
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x.pseudo.plot <- x.pseudo[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
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mean.bias = mean(bias),
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p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
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mean.est = mean(Bxy.est.pseudo),
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var.est = var(Bxy.est.pseudo),
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N.sims = .N,
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variable='x',
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method='Pseudo Likelihood'
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),
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by=c('N','m')]
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g.pseudo <- df[,.(N,m, Bgy.est.true, Bgy.est.pseudo, Bgy.ci.lower.pseudo, Bgy.ci.upper.pseudo)]
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g.pseudo <- g.pseudo[,':='(true.in.ci = (Bgy.est.true >= Bgy.ci.lower.pseudo) & (Bgy.est.true <= Bgy.ci.upper.pseudo),
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zero.in.ci = (0 >= Bgy.ci.lower.pseudo) & (0 <= Bgy.ci.upper.pseudo),
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bias = Bgy.est.pseudo - Bgy.est.true,
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sign.correct = sign(Bgy.est.true) == sign(Bgy.est.pseudo))]
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g.pseudo.plot <- g.pseudo[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
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mean.bias = mean(bias),
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p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
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mean.est = mean(Bgy.est.pseudo),
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var.est = var(Bgy.est.pseudo),
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N.sims = .N,
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variable='g',
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method='Pseudo Likelihood'
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),
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by=c('N','m')]
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accuracy <- df[,mean(accuracy)]
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plot.df <- rbindlist(list(x.naive.plot,g.naive.plot,x.amelia.full.plot,g.amelia.full.plot,x.mle.plot, g.mle.plot, x.pseudo.plot, g.pseudo.plot, x.feasible.plot, g.feasible.plot),use.names=T)
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plot.df[,accuracy := accuracy]
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plot.df <- plot.df[,":="(sd.est=sqrt(var.est)/N.sims)]
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return(plot.df)
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}
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df <- read_feather(args$infile)
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plot.df <- build_plot_dataset(df)
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remember(plot.df,args$name)
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## df[gmm.ER_pval<0.05]
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## 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))
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## 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")
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## 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")
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