update plotting code

[ml_measurement_error_public.git] / simulations / 03_depvar.R

diff --git a/simulations/03_depvar.R b/simulations/03_depvar.R
index dde1beec9ec7800d3850947a77000b3ec8187f4f..461c01a26f9251d446e3b41a8f06b6a60624e023 100644 (file)
--- a/simulations/03_depvar.R
+++ b/simulations/03_depvar.R
@@ -76,12 +76,14 @@ parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is th
 parser <- add_argument(parser, "--Bxy", help='coefficient of x on y', default=0.01)
 parser <- add_argument(parser, "--Bzy", help='coeffficient of z on y', default=-0.01)
 parser <- add_argument(parser, "--Bzx", help='coeffficient of z on x', default=-0.5)
 parser <- add_argument(parser, "--Bxy", help='coefficient of x on y', default=0.01)
 parser <- add_argument(parser, "--Bzy", help='coeffficient of z on y', default=-0.01)
 parser <- add_argument(parser, "--Bzx", help='coeffficient of z on x', default=-0.5)

+parser <- add_argument(parser, "--B0", help='Base rate of y', default=0.5)

 parser <- add_argument(parser, "--outcome_formula", help='formula for the outcome variable', default="y~x+z")
 parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~y")
 parser <- add_argument(parser, "--outcome_formula", help='formula for the outcome variable', default="y~x+z")
 parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~y")

+parser <- add_argument(parser, "--confint_method", help='method for getting confidence intervals', default="quad")

 
 args <- parse_args(parser)
 
 
 args <- parse_args(parser)
 

-B0 <- 0
+B0 <- args$B0

 Bxy <- args$Bxy
 Bzy <- args$Bzy
 Bzx <- args$Bzx
 Bxy <- args$Bxy
 Bzy <- args$Bzy
 Bzx <- args$Bzx


@@ -90,9 +92,9 @@ if(args$m < args$N){
     df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, Bzx, args$seed, args$prediction_accuracy)
 
 #    result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'x_bias_y0'=args$x_bias_y0,'x_bias_y1'=args$x_bias_y1,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)
     df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, Bzx, args$seed, args$prediction_accuracy)
 
 #    result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'x_bias_y0'=args$x_bias_y0,'x_bias_y1'=args$x_bias_y1,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)

-    result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'Bzx'=Bzx,'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)
+    result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'Bzx'=Bzx,'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula, 'confint_method'=args$confint_method)

 
 

-    outline <- run_simulation_depvar(df, result, outcome_formula = as.formula(args$outcome_formula), proxy_formula = as.formula(args$proxy_formula))
+    outline <- run_simulation_depvar(df, result, outcome_formula = as.formula(args$outcome_formula), proxy_formula = as.formula(args$proxy_formula), confint_method=args$confint_method)

 
     outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE)
 
 
     outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE)