simulations/plot_dv_example.R

   1 source("RemembR/R/RemembeR.R")
   2 library(arrow)
   3 library(data.table)
   4 library(ggplot2)
   5 library(filelock)
   6 library(argparser)
   7
   8 parser <- arg_parser("Simulate data and fit corrected models.")
   9 parser <- add_argument(parser, "--infile", default="", help="name of the file to read.")
  10 parser <- add_argument(parser, "--name", default="", help="The name to safe the data to in the remember file.")
  11 args <- parse_args(parser)
  12
  13
  14
  15 summarize.estimator <- function(df, suffix='naive', coefname='x'){
  16
  17     part <- df[,c('N',
  18                   'm',
  19                   'Bxy',
  20                   paste0('B',coefname,'y.est.',suffix),
  21                   paste0('B',coefname,'y.ci.lower.',suffix),
  22                   paste0('B',coefname,'y.ci.upper.',suffix),
  23                   'y_explained_variance',
  24                   'Bzy'
  25                   ),
  26                with=FALSE]
  27
  28     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)]]))
  29     zero.in.ci <- as.integer(0 >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (0 <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]])
  30     bias <- part$Bxy - part[[paste0('B',coefname,'y.est.',suffix)]]
  31     sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))
  32
  33     part <- part[,':='(true.in.ci = true.in.ci,
  34                        zero.in.ci = zero.in.ci,
  35                        bias=bias,
  36                        sign.correct =sign.correct)]
  37
  38     part.plot <- part[, .(p.true.in.ci = mean(true.in.ci),
  39                           mean.bias = mean(bias),
  40                           mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
  41                           var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
  42                           est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.95),
  43                           est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.05),
  44                           N.sims = .N,
  45                           p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
  46                           variable=coefname,
  47                           method=suffix
  48                           ),
  49                       by=c("N","m",'Bzy','y_explained_variance')
  50                       ]
  51
  52     return(part.plot)
  53 }
  54
  55
  56 build_plot_dataset <- function(df){
  57
  58     x.true <- summarize.estimator(df, 'true','x')
  59     z.true <- summarize.estimator(df, 'true','z')
  60
  61     x.naive <- summarize.estimator(df, 'naive','x')
  62     z.naive <- summarize.estimator(df, 'naive','z')
  63
  64     x.feasible <- summarize.estimator(df, 'feasible','x')
  65     z.feasible <- summarize.estimator(df, 'feasible','z')
  66
  67     x.amelia.full <- summarize.estimator(df, 'amelia.full','x')
  68     z.amelia.full <- summarize.estimator(df, 'amelia.full','z')
  69
  70     x.mle <- summarize.estimator(df, 'mle','x')
  71     z.mle <- summarize.estimator(df, 'mle','z')
  72
  73     x.zhang <- summarize.estimator(df, 'zhang','x')
  74     z.zhang <- summarize.estimator(df, 'zhang','z')
  75
  76     accuracy <- df[,mean(accuracy)]
  77
  78     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)
  79
  80     plot.df[,accuracy := accuracy]
  81
  82     plot.df <- plot.df[,":="(sd.est=sqrt(var.est)/N.sims)]
  83
  84     return(plot.df)
  85 }
  86
  87
  88 df <- read_feather(args$infile)
  89 plot.df <- build_plot_dataset(df)
  90
  91 remember(plot.df,args$name)
  92
  93
  94 ## df[gmm.ER_pval<0.05]
  95 ## 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),
  96 ##                                    N=factor(N),
  97 ##                                    m=factor(m))]
  98
  99
 100 ## plot.df.test <- plot.df.test[(variable=='z') & (m != 1000) & (m!=500) & !is.na(p.true.in.ci) & (method!="Multiple imputation (Classifier features unobserved)")]
 101 ## p <- ggplot(plot.df.test, aes(y=mean.est, ymax=mean.est + var.est/2, ymin=mean.est-var.est/2, x=method))
 102 ## p <- p + geom_hline(aes(yintercept=-0.05),linetype=2)
 103
 104 ## p <- p + geom_pointrange() + facet_grid(m~N,as.table=F) + scale_x_discrete(labels=label_wrap_gen(4))
 105 ## print(p)
 106 ## 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))
 107
 108 ## 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")
 109
 110 ## 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")