X-Git-Url: https://code.communitydata.science/ml_measurement_error_public.git/blobdiff_plain/46e2d1fe4876a9ed906b723f9e5f74fcc949e339..d9d3e47a44ddead1cdf7a649bc0e9849c2219498:/simulations/03_depvar_differential.R?ds=sidebyside diff --git a/simulations/03_depvar_differential.R b/simulations/03_depvar_differential.R index 872931f..02944a5 100644 --- a/simulations/03_depvar_differential.R +++ b/simulations/03_depvar_differential.R @@ -31,13 +31,14 @@ source("simulation_base.R") ## one way to do it is by adding correlation to x.obs and y that isn't in w. ## in other words, the model is missing an important feature of x.obs that's related to y. -simulate_data <- function(N, m, B0, Bxy, Bzy, seed, prediction_accuracy=0.73, accuracy_imbalance_difference=0.3){ +simulate_data <- function(N, m, B0, Bxy, Bzy, Py, seed, prediction_accuracy=0.73, x_bias=-0.75){ set.seed(seed) + # make w and y dependent z <- rbinom(N, 1, 0.5) x <- rbinom(N, 1, 0.5) - ystar <- Bzy * z + Bxy * x + ystar <- Bzy * z + Bxy * x + B0 + qlogix(Py) y <- rbinom(N,1,plogis(ystar)) # glm(y ~ x + z, family="binomial") @@ -49,40 +50,18 @@ simulate_data <- function(N, m, B0, Bxy, Bzy, seed, prediction_accuracy=0.73, ac } else { df <- df[, y.obs := y] } - - df <- df[,w_pred:=y] - - pz <- mean(z) - - accuracy_imbalance_ratio <- (prediction_accuracy + accuracy_imbalance_difference/2) / (prediction_accuracy - accuracy_imbalance_difference/2) - - # this works because of conditional probability - accuracy_z0 <- prediction_accuracy / (pz*(accuracy_imbalance_ratio) + (1-pz)) - accuracy_z1 <- accuracy_imbalance_ratio * accuracy_z0 - - - yz0 <- df[z==0]$y - yz1 <- df[z==1]$y - nz1 <- nrow(df[z==1]) - nz0 <- nrow(df[z==0]) - - acc_z0 <- plogis(0.7*scale(yz0) + qlogis(accuracy_z0)) - acc_z1 <- plogis(1.3*scale(yz1) + qlogis(accuracy_z1)) - - w0z0 <- (1-yz0)**2 + (-1)**(1-yz0) * acc_z0 - w0z1 <- (1-yz1)**2 + (-1)**(1-yz1) * acc_z1 - w0z0.noisy.odds <- rlogis(nz0,qlogis(w0z0)) - w0z1.noisy.odds <- rlogis(nz1,qlogis(w0z1)) - df[z==0,w:=plogis(w0z0.noisy.odds)] - df[z==1,w:=plogis(w0z1.noisy.odds)] + odds.y1 <- qlogis(prediction_accuracy) + x_bias*df[y==1]$x + odds.y0 <- qlogis(prediction_accuracy,lower.tail=F) + x_bias*df[y==0]$x - df[,w_pred:=as.integer(w > 0.5)] + df[y==0,w:=plogis(rlogis(.N,odds.y0))] + df[y==1,w:=plogis(rlogis(.N,odds.y1))] - print(mean(df[y==0]$y == df[y==0]$w_pred)) - print(mean(df[y==1]$y == df[y==1]$w_pred)) - print(mean(df$w_pred == df$y)) + df[,w_pred := as.integer(w > 0.5)] + print(mean(df[x==0]$y == df[x==0]$w_pred)) + print(mean(df[x==1]$y == df[x==1]$w_pred)) + print(mean(df$w_pred == df$y)) return(df) } @@ -92,21 +71,30 @@ parser <- add_argument(parser, "--m", default=500, help="m the number of ground parser <- add_argument(parser, "--seed", default=17, help='seed for the rng') parser <- add_argument(parser, "--outfile", help='output file', default='example_2.feather') parser <- add_argument(parser, "--y_explained_variance", help='what proportion of the variance of y can be explained?', default=0.005) -parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.73) -parser <- add_argument(parser, "--accuracy_imbalance_difference", help='how much more accurate is the predictive model for one class than the other?', default=0.3) +parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.8) +## parser <- add_argument(parser, "--x_bias_y1", help='how is the classifier biased when y = 1?', default=-0.75) +## parser <- add_argument(parser, "--x_bias_y0", help='how is the classifier biased when y = 0 ?', default=0.75) +parser <- add_argument(parser, "--x_bias", help='how is the classifier biased?', default=0.75) +parser <- add_argument(parser, "--Bxy", help='coefficient of x on y', default=0.3) +parser <- add_argument(parser, "--Bzy", help='coeffficient of z on y', default=-0.3) +parser <- add_argument(parser, "--Py", 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*x") args <- parse_args(parser) B0 <- 0 -Bxy <- 0.7 -Bzy <- -0.7 +Bxy <- args$Bxy +Bzy <- args$Bzy + if(args$m < args$N){ - df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, args$seed, args$prediction_accuracy, args$accuracy_imbalance_difference) + df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, args$seed, args$prediction_accuracy, args$x_bias_y0, args$x_bias_y1) - result <- list('N'=args$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, 'accuracy_imbalance_difference'=args$accuracy_imbalance_difference) +# result <- list('N'=args$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, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'x_bias'=args$x_bias,'x_bias'=args$x_bias,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula) - outline <- run_simulation_depvar(df, result, outcome_formula = y ~ x + z, proxy_formula = w_pred ~ y*x + y*z + z*x) + outline <- run_simulation_depvar(df, result, outcome_formula = as.formula(args$outcome_formula), proxy_formula = as.formula(args$proxy_formula)) outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE)