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 build_plot_dataset <- function(df){
  14     x.naive <- df[,.(N, m, Bxy, Bxy.est.naive, Bxy.ci.lower.naive, Bxy.ci.upper.naive)]
  15     x.naive <- x.naive[,':='(true.in.ci = as.integer((Bxy >= Bxy.ci.lower.naive) & (Bxy <= Bxy.ci.upper.naive)),
  16                              zero.in.ci = (0 >= Bxy.ci.lower.naive) & (0 <= Bxy.ci.upper.naive),
  17                              bias = Bxy - Bxy.est.naive,
  18                              Bxy.est.naive = Bxy.est.naive,
  19                              sign.correct = as.integer(sign(Bxy) == sign(Bxy.est.naive)))]
  20
  21     x.naive.plot <- x.naive[,.(p.true.in.ci = mean(true.in.ci),
  22                                mean.bias = mean(bias),
  23                                mean.est = mean(Bxy.est.naive),
  24                                var.est = var(Bxy.est.naive),
  25                                N.sims = .N,
  26                                p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
  27                                variable='x',
  28                                method='Naive'
  29                                ),
  30                             by=c('N','m')]
  31
  32
  33     g.naive <- df[,.(N, m, Bgy, Bgy.est.naive, Bgy.ci.lower.naive, Bgy.ci.upper.naive)]
  34     g.naive <- g.naive[,':='(true.in.ci = as.integer((Bgy >= Bgy.ci.lower.naive) & (Bgy <= Bgy.ci.upper.naive)),
  35                              zero.in.ci = (0 >= Bgy.ci.lower.naive) & (0 <= Bgy.ci.upper.naive),
  36                              bias = Bgy - Bgy.est.naive,
  37                              Bgy.est.naive = Bgy.est.naive,
  38                              sign.correct = as.integer(sign(Bgy) == sign(Bgy.est.naive)))]
  39
  40     g.naive.plot <- g.naive[,.(p.true.in.ci = mean(true.in.ci),
  41                                mean.bias = mean(bias),
  42                                mean.est = mean(Bgy.est.naive),
  43                                var.est = var(Bgy.est.naive),
  44                                N.sims = .N,
  45                                p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
  46                                variable='g',
  47                                method='Naive'
  48                                ),
  49                             by=c('N','m')]
  50
  51
  52     x.feasible <- df[,.(N, m, Bxy, Bxy.est.feasible, Bxy.ci.lower.feasible, Bxy.ci.upper.feasible)]
  53     x.feasible <- x.feasible[,':='(true.in.ci = as.integer((Bxy >= Bxy.ci.lower.feasible) & (Bxy <= Bxy.ci.upper.feasible)),
  54                                    zero.in.ci = (0 >= Bxy.ci.lower.feasible) & (0 <= Bxy.ci.upper.feasible),
  55                                    bias = Bxy - Bxy.est.feasible,
  56                                    Bxy.est.feasible = Bxy.est.feasible,
  57                                    sign.correct = as.integer(sign(Bxy) == sign(Bxy.est.feasible)))]
  58
  59     x.feasible.plot <- x.feasible[,.(p.true.in.ci = mean(true.in.ci),
  60                                      mean.bias = mean(bias),
  61                                      mean.est = mean(Bxy.est.feasible),
  62                                      var.est = var(Bxy.est.feasible),
  63                                      N.sims = .N,
  64                                      p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
  65                                      variable='x',
  66                                      method='Feasible'
  67                                      ),
  68                                   by=c('N','m')]
  69
  70
  71     g.feasible <- df[,.(N, m, Bgy, Bgy.est.feasible, Bgy.ci.lower.feasible, Bgy.ci.upper.feasible)]
  72     g.feasible <- g.feasible[,':='(true.in.ci = as.integer((Bgy >= Bgy.ci.lower.feasible) & (Bgy <= Bgy.ci.upper.feasible)),
  73                                    zero.in.ci = (0 >= Bgy.ci.lower.feasible) & (0 <= Bgy.ci.upper.feasible),
  74                                    bias = Bgy - Bgy.est.feasible,
  75                                    Bgy.est.feasible = Bgy.est.feasible,
  76                                    sign.correct = as.integer(sign(Bgy) == sign(Bgy.est.feasible)))]
  77
  78     g.feasible.plot <- g.feasible[,.(p.true.in.ci = mean(true.in.ci),
  79                                      mean.bias = mean(bias),
  80                                      mean.est = mean(Bgy.est.feasible),
  81                                      var.est = var(Bgy.est.feasible),
  82                                      N.sims = .N,
  83                                      p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
  84                                      variable='g',
  85                                      method='Feasible'
  86                                      ),
  87                                   by=c('N','m')]
  88
  89
  90
  91     x.amelia.full <- df[,.(N, m, Bxy, Bxy.est.true, Bxy.ci.lower.amelia.full, Bxy.ci.upper.amelia.full, Bxy.est.amelia.full)]
  92
  93     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),
  94                                          zero.in.ci = (0 >= Bxy.ci.lower.amelia.full) & (0 <= Bxy.ci.upper.amelia.full),
  95                                          bias = Bxy.est.true - Bxy.est.amelia.full,
  96                                          sign.correct = sign(Bxy.est.true) == sign(Bxy.est.amelia.full))]
  97
  98     x.amelia.full.plot <- x.amelia.full[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
  99                                            mean.bias = mean(bias),
 100                                            p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
 101                                            mean.est = mean(Bxy.est.amelia.full),
 102                                            var.est = var(Bxy.est.amelia.full),
 103                                            N.sims = .N,
 104                                            variable='x',
 105                                            method='Multiple imputation'
 106                                            ),
 107                                         by=c('N','m')]
 108
 109
 110     g.amelia.full <- df[,.(N, m, Bgy.est.true, Bgy.est.amelia.full, Bgy.ci.lower.amelia.full, Bgy.ci.upper.amelia.full)]
 111     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),
 112                                          zero.in.ci = (0 >= Bgy.ci.lower.amelia.full) & (0 <= Bgy.ci.upper.amelia.full),
 113                                          bias =  Bgy.est.amelia.full - Bgy.est.true,
 114                                          sign.correct = sign(Bgy.est.true) == sign(Bgy.est.amelia.full))]
 115
 116     g.amelia.full.plot <- g.amelia.full[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
 117                                            mean.bias = mean(bias),
 118                                            p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
 119                                            mean.est = mean(Bgy.est.amelia.full),
 120                                            var.est = var(Bgy.est.amelia.full),
 121                                            N.sims = .N,
 122                                            variable='g',
 123                                            method='Multiple imputation'
 124                                            ),
 125                                         by=c('N','m')]
 126
 127     x.mle <- df[,.(N,m, Bxy.est.true, Bxy.est.mle, Bxy.ci.lower.mle, Bxy.ci.upper.mle)]
 128
 129     x.mle <- x.mle[,':='(true.in.ci = (Bxy.est.true >= Bxy.ci.lower.mle) & (Bxy.est.true <= Bxy.ci.upper.mle),
 130                                          zero.in.ci = (0 >= Bxy.ci.lower.mle) & (0 <= Bxy.ci.upper.mle),
 131                                          bias =  Bxy.est.mle - Bxy.est.true,
 132                                          sign.correct = sign(Bxy.est.true) == sign(Bxy.est.mle))]
 133
 134     x.mle.plot <- x.mle[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
 135                                    mean.bias = mean(bias),
 136                                    p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
 137                                    mean.est = mean(Bxy.est.mle),
 138                                    var.est = var(Bxy.est.mle),
 139                                    N.sims = .N,
 140                                    variable='x',
 141                                    method='Maximum Likelihood'
 142                                    ),
 143                                 by=c('N','m')]
 144
 145
 146
 147     g.mle <- df[,.(N,m, Bgy.est.true, Bgy.est.mle, Bgy.ci.lower.mle, Bgy.ci.upper.mle)]
 148
 149     g.mle <- g.mle[,':='(true.in.ci = (Bgy.est.true >= Bgy.ci.lower.mle) & (Bgy.est.true <= Bgy.ci.upper.mle),
 150                                          zero.in.ci = (0 >= Bgy.ci.lower.mle) & (0 <= Bgy.ci.upper.mle),
 151                                          bias =  Bgy.est.mle - Bgy.est.true,
 152                                          sign.correct = sign(Bgy.est.true) == sign(Bgy.est.mle))]
 153
 154     g.mle.plot <- g.mle[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
 155                                    mean.bias = mean(bias),
 156                                    p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
 157                                    mean.est = mean(Bgy.est.mle),
 158                                    var.est = var(Bgy.est.mle),
 159                                    N.sims = .N,
 160                                    variable='g',
 161                                    method='Maximum Likelihood'
 162                                    ),
 163                                 by=c('N','m')]
 164
 165
 166
 167
 168     x.pseudo <- df[,.(N,m, Bxy.est.true, Bxy.est.pseudo, Bxy.ci.lower.pseudo, Bxy.ci.upper.pseudo)]
 169
 170     x.pseudo <- x.pseudo[,':='(true.in.ci = (Bxy.est.true >= Bxy.ci.lower.pseudo) & (Bxy.est.true <= Bxy.ci.upper.pseudo),
 171                                          zero.in.ci = (0 >= Bxy.ci.lower.pseudo) & (0 <= Bxy.ci.upper.pseudo),
 172                                          bias =  Bxy.est.pseudo - Bxy.est.true,
 173                                          sign.correct = sign(Bxy.est.true) == sign(Bxy.est.pseudo))]
 174
 175     x.pseudo.plot <- x.pseudo[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
 176                                    mean.bias = mean(bias),
 177                                    p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
 178                                    mean.est = mean(Bxy.est.pseudo),
 179                                    var.est = var(Bxy.est.pseudo),
 180                                    N.sims = .N,
 181                                    variable='x',
 182                                    method='Pseudo Likelihood'
 183                                    ),
 184                                 by=c('N','m')]
 185
 186
 187
 188     g.pseudo <- df[,.(N,m, Bgy.est.true, Bgy.est.pseudo, Bgy.ci.lower.pseudo, Bgy.ci.upper.pseudo)]
 189
 190     g.pseudo <- g.pseudo[,':='(true.in.ci = (Bgy.est.true >= Bgy.ci.lower.pseudo) & (Bgy.est.true <= Bgy.ci.upper.pseudo),
 191                                          zero.in.ci = (0 >= Bgy.ci.lower.pseudo) & (0 <= Bgy.ci.upper.pseudo),
 192                                          bias =  Bgy.est.pseudo - Bgy.est.true,
 193                                          sign.correct = sign(Bgy.est.true) == sign(Bgy.est.pseudo))]
 194
 195     g.pseudo.plot <- g.pseudo[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
 196                                    mean.bias = mean(bias),
 197                                    p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
 198                                    mean.est = mean(Bgy.est.pseudo),
 199                                    var.est = var(Bgy.est.pseudo),
 200                                    N.sims = .N,
 201                                    variable='g',
 202                                    method='Pseudo Likelihood'
 203                                    ),
 204                                 by=c('N','m')]
 205
 206
 207
 208     accuracy <- df[,mean(accuracy)]
 209
 210     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)
 211
 212     plot.df[,accuracy := accuracy]
 213
 214     plot.df <- plot.df[,":="(sd.est=sqrt(var.est)/N.sims)]
 215
 216     return(plot.df)
 217 }
 218
 219
 220 df <- read_feather(args$infile)
 221 plot.df <- build_plot_dataset(df)
 222 remember(plot.df,args$name)
 223
 224
 225 ## df[gmm.ER_pval<0.05]
 226
 227
 228
 229
 230
 231 ## 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))
 232
 233 ## 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")
 234
 235 ## 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")