X-Git-Url: https://code.communitydata.science/ml_measurement_error_public.git/blobdiff_plain/5c931a7198452ff3ce0ace5b1f68046bfb33d352..69948cae1e691191fc86e6abdaa485bc98f38f1f:/simulations/03_depvar.R diff --git a/simulations/03_depvar.R b/simulations/03_depvar.R index dde1bee..461c01a 100644 --- 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, "--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, "--confint_method", help='method for getting confidence intervals', default="quad") args <- parse_args(parser) -B0 <- 0 +B0 <- args$B0 Bxy <- args$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$proxy_formula) - result <- list('N'=args$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)