X-Git-Url: https://code.communitydata.science/ml_measurement_error_public.git/blobdiff_plain/588bdd7ed74cf8fe8fd0f15df58a6a40c26ebae5..d8bc08f18f8c2128369ee959196e0e6080a11689:/simulations/03_depvar_differential.R?ds=sidebyside diff --git a/simulations/03_depvar_differential.R b/simulations/03_depvar_differential.R index d52afe7..02944a5 100644 --- a/simulations/03_depvar_differential.R +++ b/simulations/03_depvar_differential.R @@ -31,83 +31,81 @@ 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, Bgy, 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 - g <- rbinom(N, 1, 0.5) + z <- rbinom(N, 1, 0.5) x <- rbinom(N, 1, 0.5) - ystar <- Bgy * g + Bxy * x - y <- rbinom(N,1,logistic(ystar)) + ystar <- Bzy * z + Bxy * x + B0 + qlogix(Py) + y <- rbinom(N,1,plogis(ystar)) - # glm(y ~ x + g, family="binomial") + # glm(y ~ x + z, family="binomial") - df <- data.table(x=x,y=y,ystar=ystar,g=g) + df <- data.table(x=x,y=y,ystar=ystar,z=z) if(m < N){ df <- df[sample(nrow(df), m), y.obs := y] } else { df <- df[, y.obs := y] } + + 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 <- df[,w_pred:=y] - - pg <- mean(g) - - accuracy_imbalance_ratio <- (prediction_accuracy + accuracy_imbalance_difference/2) / (prediction_accuracy - accuracy_imbalance_difference/2) - - # this works because of conditional probability - accuracy_g0 <- prediction_accuracy / (pg*(accuracy_imbalance_ratio) + (1-pg)) - accuracy_g1 <- accuracy_imbalance_ratio * accuracy_g0 - - dfg0 <- df[g==0] - ng0 <- nrow(dfg0) - dfg1 <- df[g==1] - ng1 <- nrow(dfg1) - - dfg0 <- dfg0[sample(ng0, (1-accuracy_g0)*ng0), w_pred := (w_pred-1)**2] - dfg1 <- dfg1[sample(ng1, (1-accuracy_g1)*ng1), w_pred := (w_pred-1)**2] - - df <- rbind(dfg0,dfg1) + df[y==0,w:=plogis(rlogis(.N,odds.y0))] + df[y==1,w:=plogis(rlogis(.N,odds.y1))] - wmod <- glm(y.obs ~ w_pred,data=df[!is.null(y.obs)],family=binomial(link='logit')) - w <- predict(wmod,df,type='response') - - df <- df[,':='(w=w)] + 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) } parser <- arg_parser("Simulate data and fit corrected models") -parser <- add_argument(parser, "--N", default=5000, help="number of observations of w") -parser <- add_argument(parser, "--m", default=200, help="m the number of ground truth observations") -parser <- add_argument(parser, "--seed", default=4321, help='seed for the rng') +parser <- add_argument(parser, "--N", default=1000, help="number of observations of w") +parser <- add_argument(parser, "--m", default=500, help="m the number of ground truth observations") +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 <- 0.2 -Bgy <- -0.2 +Bxy <- args$Bxy +Bzy <- args$Bzy -df <- simulate_data(args$N, args$m, B0, Bxy, Bgy, args$seed, args$prediction_accuracy, args$accuracy_imbalance_difference) -result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bgy'=Bgy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'accuracy_imbalance_difference'=args$accuracy_imbalance_difference) +if(args$m < args$N){ + df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, args$seed, args$prediction_accuracy, args$x_bias_y0, args$x_bias_y1) -outline <- run_simulation_depvar(df=df, result) +# result <- list('N'=args$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$proxy_formula) + outline <- run_simulation_depvar(df, result, outcome_formula = as.formula(args$outcome_formula), proxy_formula = as.formula(args$proxy_formula)) -outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE) -if(file.exists(args$outfile)){ - logdata <- read_feather(args$outfile) - logdata <- rbind(logdata,as.data.table(outline)) -} else { - logdata <- as.data.table(outline) -} + outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE) + + if(file.exists(args$outfile)){ + logdata <- read_feather(args$outfile) + logdata <- rbind(logdata,as.data.table(outline),fill=TRUE) + } else { + logdata <- as.data.table(outline) + } -print(outline) -write_feather(logdata, args$outfile) -unlock(outfile_lock) + print(outline) + write_feather(logdata, args$outfile) + unlock(outfile_lock) +}