changes from klone
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@ -73,8 +73,8 @@ parser <- add_argument(parser, "--y_explained_variance", help='what proportion o
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parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.72)
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## parser <- add_argument(parser, "--x_bias_y1", help='how is the classifier biased when y = 1?', default=-0.75)
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## parser <- add_argument(parser, "--x_bias_y0", help='how is the classifier biased when y = 0 ?', default=0.75)
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parser <- add_argument(parser, "--Bxy", help='coefficient of x on y', default=0.01)
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parser <- add_argument(parser, "--Bzy", help='coeffficient of z on y', default=-0.01)
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parser <- add_argument(parser, "--Bxy", help='coefficient of x on y', default=0.3)
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parser <- add_argument(parser, "--Bzy", help='coeffficient of z on y', default=-0.3)
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parser <- add_argument(parser, "--Bzx", help='coeffficient of z on x', default=-0.5)
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parser <- add_argument(parser, "--B0", help='Base rate of y', default=0.5)
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parser <- add_argument(parser, "--outcome_formula", help='formula for the outcome variable', default="y~x+z")
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185
simulations/03_indep_differential_nonnorm.R
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185
simulations/03_indep_differential_nonnorm.R
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@ -0,0 +1,185 @@
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### EXAMPLE 1: demonstrates how measurement error can lead to a type sign error in a covariate
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### What kind of data invalidates fong + tyler?
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### Even when you have a good predictor, if it's biased against a covariate you can get the wrong sign.
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### Even when you include the proxy variable in the regression.
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### But with some ground truth and multiple imputation, you can fix it.
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library(argparser)
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library(mecor)
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library(ggplot2)
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library(data.table)
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library(filelock)
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library(arrow)
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library(Amelia)
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library(Zelig)
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library(predictionError)
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options(amelia.parallel="no",
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amelia.ncpus=1)
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setDTthreads(40)
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source("simulation_base.R")
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## SETUP:
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### we want to estimate x -> y; x is MAR
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### we have x -> k; k -> w; x -> w is used to predict x via the model w.
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### A realistic scenario is that we have an NLP model predicting something like "racial harassment" in social media comments
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### The labels x are binary, but the model provides a continuous predictor
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### simulation:
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#### how much power do we get from the model in the first place? (sweeping N and m)
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####
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## one way to do it is by adding correlation to x.obs and y that isn't in w.
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## in other words, the model is missing an important feature of x.obs that's related to y.
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simulate_data <- function(N, m, B0, Bxy, Bzx, Bzy, seed, y_explained_variance=0.025, prediction_accuracy=0.73, y_bias=-0.8,z_bias=0,Px=0.5,accuracy_imbalance_difference=0.3,sd_y_mixin=1){
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set.seed(seed)
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# make w and y dependent
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z <- rnorm(N,sd=0.5)
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x <- rbinom(N, 1, plogis(Bzx * z + qlogis(Px)))
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## following Fong + Tyler: mix y with a Bernoulli(0.15) × |N (0, 20)| to make a skewed non-normal distribution
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y.var.epsilon <- (var(Bzy * z) + var(Bxy *x) + 2*cov(Bzy*z,Bxy*x)) * ((1-y_explained_variance)/y_explained_variance)
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y.epsilon <- rnorm(N, sd = sqrt(y.var.epsilon))
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y <- Bzy * z + Bxy * x + y.epsilon + rbinom(N,1,0.15) * rnorm(N,0,sd_y_mixin)
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df <- data.table(x=x,y=y,z=z)
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if(m < N){
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df <- df[sample(nrow(df), m), x.obs := x]
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} else {
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df <- df[, x.obs := x]
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}
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## probablity of an error is correlated with y
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## pz <- mean(z)
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## accuracy_imbalance_ratio <- (prediction_accuracy + accuracy_imbalance_difference/2) / (prediction_accuracy - accuracy_imbalance_difference/2)
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## # this works because of conditional probability
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## accuracy_z0 <- prediction_accuracy / (pz*(accuracy_imbalance_ratio) + (1-pz))
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## accuracy_z1 <- accuracy_imbalance_ratio * accuracy_z0
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## z0x0 <- df[(z==0) & (x==0)]$x
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## z0x1 <- df[(z==0) & (x==1)]$x
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## z1x0 <- df[(z==1) & (x==0)]$x
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## z1x1 <- df[(z==1) & (x==1)]$x
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## yz0x0 <- df[(z==0) & (x==0)]$y
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## yz0x1 <- df[(z==0) & (x==1)]$y
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## yz1x0 <- df[(z==1) & (x==0)]$y
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## yz1x1 <- df[(z==1) & (x==1)]$y
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## nz0x0 <- nrow(df[(z==0) & (x==0)])
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## nz0x1 <- nrow(df[(z==0) & (x==1)])
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## nz1x0 <- nrow(df[(z==1) & (x==0)])
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## nz1x1 <- nrow(df[(z==1) & (x==1)])
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## yz1 <- df[z==1]$y
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## yz1 <- df[z==1]$y
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## # tranform yz0.1 into a logistic distribution with mean accuracy_z0
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## acc.z0x0 <- plogis(0.5*scale(yz0x0) + qlogis(accuracy_z0))
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## acc.z0x1 <- plogis(0.5*scale(yz0x1) + qlogis(accuracy_z0))
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## acc.z1x0 <- plogis(1.5*scale(yz1x0) + qlogis(accuracy_z1))
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## acc.z1x1 <- plogis(1.5*scale(yz1x1) + qlogis(accuracy_z1))
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## w0z0x0 <- (1-z0x0)**2 + (-1)**(1-z0x0) * acc.z0x0
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## w0z0x1 <- (1-z0x1)**2 + (-1)**(1-z0x1) * acc.z0x1
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## w0z1x0 <- (1-z1x0)**2 + (-1)**(1-z1x0) * acc.z1x0
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## w0z1x1 <- (1-z1x1)**2 + (-1)**(1-z1x1) * acc.z1x1
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## ##perrorz0 <- w0z0*(pyz0)
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## ##perrorz1 <- w0z1*(pyz1)
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## w0z0x0.noisy.odds <- rlogis(nz0x0,qlogis(w0z0x0))
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## w0z0x1.noisy.odds <- rlogis(nz0x1,qlogis(w0z0x1))
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## w0z1x0.noisy.odds <- rlogis(nz1x0,qlogis(w0z1x0))
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## w0z1x1.noisy.odds <- rlogis(nz1x1,qlogis(w0z1x1))
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## df[(z==0)&(x==0),w:=plogis(w0z0x0.noisy.odds)]
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## df[(z==0)&(x==1),w:=plogis(w0z0x1.noisy.odds)]
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## df[(z==1)&(x==0),w:=plogis(w0z1x0.noisy.odds)]
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## df[(z==1)&(x==1),w:=plogis(w0z1x1.noisy.odds)]
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## df[,w_pred:=as.integer(w > 0.5)]
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## print(mean(df[z==0]$x == df[z==0]$w_pred))
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## print(mean(df[z==1]$x == df[z==1]$w_pred))
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## print(mean(df$w_pred == df$x))
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resids <- resid(lm(y~x + z))
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odds.x1 <- qlogis(prediction_accuracy) + y_bias*qlogis(pnorm(resids[x==1],log.p=T),log.p=T) + z_bias * qlogis(pnorm(z[x==1],sd(z),log.p=T),log.p=T)
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odds.x0 <- qlogis(prediction_accuracy,lower.tail=F) + y_bias*qlogis(pnorm(resids[x==0],log.p=T),log.p=T) + z_bias * qlogis(pnorm(z[x==0],sd(z),log.p=T),log.p=T)
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## acc.x0 <- p.correct[df[,x==0]]
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## acc.x1 <- p.correct[df[,x==1]]
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df[x==0,w:=plogis(rlogis(.N,odds.x0))]
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df[x==1,w:=plogis(rlogis(.N,odds.x1))]
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print(prediction_accuracy)
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print(resids[is.na(df$w)])
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print(odds.x0[is.na(df$w)])
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print(odds.x1[is.na(df$w)])
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df[,w_pred := as.integer(w > 0.5)]
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print(mean(df$w_pred == df$x))
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print(mean(df[y>=0]$w_pred == df[y>=0]$x))
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print(mean(df[y<=0]$w_pred == df[y<=0]$x))
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return(df)
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}
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parser <- arg_parser("Simulate data and fit corrected models")
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parser <- add_argument(parser, "--N", default=5000, help="number of observations of w")
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parser <- add_argument(parser, "--m", default=500, help="m the number of ground truth observations")
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parser <- add_argument(parser, "--seed", default=51, help='seed for the rng')
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parser <- add_argument(parser, "--outfile", help='output file', default='example_2.feather')
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parser <- add_argument(parser, "--y_explained_variance", help='what proportion of the variance of y can be explained?', default=0.1)
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parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.75)
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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)
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parser <- add_argument(parser, "--Bzx", help='Effect of z on x', default=0.3)
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parser <- add_argument(parser, "--Bzy", help='Effect of z on y', default=-0.3)
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parser <- add_argument(parser, "--Bxy", help='Effect of z on y', default=0.3)
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parser <- add_argument(parser, "--outcome_formula", help='formula for the outcome variable', default="y~x+z")
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parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~y*z*x")
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parser <- add_argument(parser, "--y_bias", help='coefficient of y on the probability a classification is correct', default=-0.5)
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parser <- add_argument(parser, "--z_bias", help='coefficient of z on the probability a classification is correct', default=0)
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parser <- add_argument(parser, "--truth_formula", help='formula for the true variable', default="x~z")
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parser <- add_argument(parser, "--Px", help='base rate of x', default=0.5)
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parser <- add_argument(parser, "--confint_method", help='method for approximating confidence intervals', default='quad')
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parser <- add_argument(parser, "--sd_y_mixin", help='varience of the non-normal part of Y', default=10)
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args <- parse_args(parser)
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B0 <- 0
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Px <- args$Px
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Bxy <- args$Bxy
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Bzy <- args$Bzy
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Bzx <- args$Bzx
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if(args$m < args$N){
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df <- simulate_data(args$N, args$m, B0, Bxy, Bzx, Bzy, args$seed, args$y_explained_variance, args$prediction_accuracy, y_bias=args$y_bias, sd_y_mixin=args$sd_y_mixin)
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## df.pc <- df[,.(x,y,z,w_pred,w)]
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## # df.pc <- df.pc[,err:=x-w_pred]
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## pc.df <- pc(suffStat=list(C=cor(df.pc),n=nrow(df.pc)),indepTest=gaussCItest,labels=names(df.pc),alpha=0.05)
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## plot(pc.df)
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result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy, 'Bzx'=args$Bzx, 'Bzy'=Bzy, 'Px'=Px, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'accuracy_imbalance_difference'=args$accuracy_imbalance_difference, 'y_bias'=args$y_bias,'outcome_formula'=args$outcome_formula, 'proxy_formula'=args$proxy_formula,truth_formula=args$truth_formula, confint_method=args$confint_method, error='', 'sd_y_mixin'=args$sd_y_mixin)
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outline <- run_simulation(df, result, outcome_formula=as.formula(args$outcome_formula), proxy_formula=as.formula(args$proxy_formula), truth_formula=as.formula(args$truth_formula),confint_method=args$confint_method)
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outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE)
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if(file.exists(args$outfile)){
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logdata <- read_feather(args$outfile)
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logdata <- rbind(logdata,as.data.table(outline), fill=TRUE)
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} else {
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logdata <- as.data.table(outline)
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}
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print(outline)
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write_feather(logdata, args$outfile)
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unlock(outfile_lock)
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}
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@ -8,7 +8,7 @@ explained_variances=[0.1]
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all:main supplement
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main:remembr.RDS
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supplement:robustness_1.RDS robustness_1_dv.RDS robustness_2.RDS robustness_2_dv.RDS robustness_3.RDS robustness_3_dv.RDS robustness_3_proflik.RDS robustness_3_dv_proflik.RDS robustness_4.RDS robustness_4_dv.RDS
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supplement:robustness_1.RDS robustness_1_dv.RDS robustness_2.RDS robustness_2_dv.RDS robustness_3.RDS robustness_3_dv.RDS robustness_3_proflik.RDS robustness_3_dv_proflik.RDS robustness_4.RDS robustness_4_dv.RDS robustness_5.RDS robustness_5_dv.RDS robustness_6.feather
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srun=sbatch --wait --verbose run_job.sbatch
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@ -16,7 +16,7 @@ srun=sbatch --wait --verbose run_job.sbatch
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joblists:example_1_jobs example_2_jobs example_3_jobs
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# test_true_z_jobs: test_true_z.R simulation_base.R
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# sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript test_true_z.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["test_true_z.feather"], "y_explained_variancevari":${explained_variances}, "Bzx":${Bzx}}' --outfile test_true_z_jobsb
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# sbatch --wait --verbose run_job.sbatch ./grid_sweep.py --command "Rscript test_true_z.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["test_true_z.feather"], "y_explained_variancevari":${explained_variances}, "Bzx":${Bzx}}' --outfile test_true_z_jobsb
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# test_true_z.feather: test_true_z_jobs
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# rm -f test_true_z.feather
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@ -25,48 +25,46 @@ joblists:example_1_jobs example_2_jobs example_3_jobs
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example_1_jobs: 01_two_covariates.R simulation_base.R grid_sweep.py pl_methods.R
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${srun} grid_sweep.py --command "Rscript 01_two_covariates.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_1.feather"], "y_explained_variance":${explained_variances}, "Bzx":[1]}' --outfile example_1_jobs
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${srun} ./grid_sweep.py --command "Rscript 01_two_covariates.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_1.feather"], "y_explained_variance":${explained_variances}, "Bzx":[1]}' --outfile example_1_jobs
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example_1.feather: example_1_jobs
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rm -f example_1.feather
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sbatch --wait --verbose --array=1-1000 run_simulation.sbatch 0 example_1_jobs
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sbatch --wait --verbose --array=1001-2000 run_simulation.sbatch 0 example_1_jobs
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sbatch --wait --verbose --array=2001-3000 run_simulation.sbatch 0 example_1_jobs
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sbatch --wait --verbose --array=3001-4000 run_simulation.sbatch 0 example_1_jobs
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sbatch --wait --verbose --array=4001-$(shell cat example_1_jobs | wc -l) run_simulation.sbatch 0 example_1_jobs
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sbatch --wait --verbose --array=3001-$(shell cat example_1_jobs | wc -l) run_simulation.sbatch 0 example_1_jobs
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example_2_jobs: 02_indep_differential.R simulation_base.R grid_sweep.py pl_methods.R
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sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 02_indep_differential.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_2.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+z+x"]}' --outfile example_2_jobs
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sbatch --wait --verbose run_job.sbatch ./grid_sweep.py --command "Rscript 02_indep_differential.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_2.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+z+x"]}' --outfile example_2_jobs
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example_2.feather: example_2_jobs
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rm -f example_2.feather
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sbatch --wait --verbose --array=1-1000 run_simulation.sbatch 0 example_2_jobs
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sbatch --wait --verbose --array=1001-2000 run_simulation.sbatch 0 example_2_jobs
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sbatch --wait --verbose --array=2001-3000 run_simulation.sbatch 0 example_2_jobs
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sbatch --wait --verbose --array=3001-4000 run_simulation.sbatch 0 example_2_jobs
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sbatch --wait --verbose --array=4001-$(shell cat example_2_jobs | wc -l)
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sbatch --wait --verbose --array=3001-$(shell cat example_2_jobs | wc -l) run_simulation.sbatch 0 example_2_jobs
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# example_2_B_jobs: example_2_B.R
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# sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript example_2_B.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_2_B.feather"]}' --outfile example_2_B_jobs
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# sbatch --wait --verbose run_job.sbatch python3 ./grid_sweep.py --command "Rscript example_2_B.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_2_B.feather"]}' --outfile example_2_B_jobs
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# example_2_B.feather: example_2_B_jobs
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# rm -f example_2_B.feather
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# sbatch --wait --verbose --array=1-3000 run_simulation.sbatch 0 example_2_B_jobs
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example_3_jobs: 03_depvar.R simulation_base.R grid_sweep.py pl_methods.R
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sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 03_depvar.R" --arg_dict '{"N":${Ns},"m":${ms}, "Bxy":[0.7],"Bzy":[-0.7],"Bzx":[1],"seed":${seeds}, "outfile":["example_3.feather"], "y_explained_variance":${explained_variances}}' --outfile example_3_jobs
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sbatch --wait --verbose run_job.sbatch ./grid_sweep.py --command "Rscript 03_depvar.R" --arg_dict '{"N":${Ns},"m":${ms}, "Bxy":[0.7],"Bzy":[-0.7],"Bzx":[1],"seed":${seeds}, "outfile":["example_3.feather"], "y_explained_variance":${explained_variances}}' --outfile example_3_jobs
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example_3.feather: example_3_jobs
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rm -f example_3.feather
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sbatch --wait --verbose --array=1-1000 run_simulation.sbatch 0 example_3_jobs
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sbatch --wait --verbose --array=1001-2000 run_simulation.sbatch 0 example_3_jobs
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sbatch --wait --verbose --array=2001-3000 run_simulation.sbatch 0 example_3_jobs
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sbatch --wait --verbose --array=3001-4000 run_simulation.sbatch 0 example_3_jobs
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sbatch --wait --verbose --array=4001-$(shell cat example_3_jobs | wc -l) run_simulation.sbatch 0 example_3_jobs
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sbatch --wait --verbose --array=3001-$(shell cat example_3_jobs | wc -l) run_simulation.sbatch 0 example_3_jobs
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example_4_jobs: 04_depvar_differential.R simulation_base.R grid_sweep.py pl_methods.R
|
||||
sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 04_depvar_differential.R" --arg_dict '{"N":${Ns},"Bxy":[0.7],"Bzy":[-0.7],"Bzx":[1], "m":${ms}, "seed":${seeds}, "outfile":["example_4.feather"], "z_bias":[-0.5], "prediction_accuracy":[0.73]}' --outfile example_4_jobs
|
||||
sbatch --wait --verbose run_job.sbatch ./grid_sweep.py --command "Rscript 04_depvar_differential.R" --arg_dict '{"N":${Ns},"Bxy":[0.7],"Bzy":[-0.7],"Bzx":[1], "m":${ms}, "seed":${seeds}, "outfile":["example_4.feather"], "z_bias":[-0.5], "prediction_accuracy":[0.73]}' --outfile example_4_jobs
|
||||
|
||||
example_4.feather: example_4_jobs
|
||||
rm -f example_4.feather
|
||||
@ -74,9 +72,7 @@ example_4.feather: example_4_jobs
|
||||
sbatch --wait --verbose --array=1001-2000 run_simulation.sbatch 0 example_4_jobs
|
||||
sbatch --wait --verbose --array=2001-3001 run_simulation.sbatch 0 example_4_jobs
|
||||
sbatch --wait --verbose --array=2001-3000 run_simulation.sbatch 0 example_4_jobs
|
||||
sbatch --wait --verbose --array=3001-4000 run_simulation.sbatch 0 example_4_jobs
|
||||
sbatch --wait --verbose --array=4001-$(shell cat example_4_jobs | wc -l) run_simulation.sbatch 0 example_4_jobs
|
||||
|
||||
sbatch --wait --verbose --array=3001-$(shell cat example_4_jobs | wc -l) run_simulation.sbatch 0 example_4_jobs
|
||||
|
||||
|
||||
remembr.RDS:example_1.feather example_2.feather example_3.feather example_4.feather plot_example.R plot_dv_example.R summarize_estimator.R
|
||||
@ -92,22 +88,21 @@ STEP=1000
|
||||
ONE=1
|
||||
|
||||
robustness_Ns=[1000,5000]
|
||||
robustness_robustness_ms=[100,200]
|
||||
robustness_ms=[100,200]
|
||||
|
||||
#in robustness 1 / example 2 misclassification is correlated with Y.
|
||||
robustness_1_jobs_p1: 02_indep_differential.R simulation_base.R grid_sweep.py
|
||||
sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 02_indep_differential.R" --arg_dict '{"N":[1000],"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_1.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3,0],"Bxy":[0.3],"Bzx":[1,0], "outcome_formula":["y~x+z"], "z_bias":[0, 0.5], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~1"]}' --outfile robustness_1_jobs_p1
|
||||
sbatch --wait --verbose run_job.sbatch ./grid_sweep.py --command "Rscript 02_indep_differential.R" --arg_dict '{"N":[1000],"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_1.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3,0],"Bxy":[0.3],"Bzx":[1,0], "outcome_formula":["y~x+z"], "z_bias":[0, 0.5], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~1"]}' --outfile robustness_1_jobs_p1
|
||||
|
||||
robustness_1_jobs_p2: 02_indep_differential.R simulation_base.R grid_sweep.py
|
||||
sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 02_indep_differential.R" --arg_dict '{"N":[5000],"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_1.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3,0],"Bxy":[0.3],"Bzx":[1,0], "outcome_formula":["y~x+z"], "z_bias":[0, 0.5], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~1"]}' --outfile robustness_1_jobs_p2
|
||||
sbatch --wait --verbose run_job.sbatch ./grid_sweep.py --command "Rscript 02_indep_differential.R" --arg_dict '{"N":[5000],"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_1.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3,0],"Bxy":[0.3],"Bzx":[1,0], "outcome_formula":["y~x+z"], "z_bias":[0, 0.5], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~1"]}' --outfile robustness_1_jobs_p2
|
||||
|
||||
robustness_1.feather: robustness_1_jobs_p1 robustness_1_jobs_p2
|
||||
rm -f $@
|
||||
$(eval END_1!=cat robustness_1_jobs_p1 | wc -l)
|
||||
$(eval ITEROBUSTNESS_MS_1!=seq $(START) $(STEP) $(END_1))
|
||||
$(eval ITEMS_1!=seq $(START) $(STEP) $(END_1))
|
||||
$(eval END_2!=cat robustness_1_jobs_p2 | wc -l)
|
||||
$(eval ITEROBUSTNESS_MS_2!=seq $(START) $(STEP) $(END_2))
|
||||
|
||||
$(eval ITEMS_2!=seq $(START) $(STEP) $(END_2))
|
||||
$(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_1_jobs_p1;)
|
||||
$(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_1_jobs_p2;)
|
||||
|
||||
@ -117,10 +112,10 @@ robustness_1.RDS: robustness_1.feather summarize_estimator.R
|
||||
|
||||
# when Bzy is 0 and zbias is not zero, we have the case where P(W|Y,X,Z) has an omitted variable that is conditionanlly independent from Y. Note that X and Z are independent in this scenario.
|
||||
robustness_1_dv_jobs_p1: simulation_base.R 04_depvar_differential.R grid_sweep.py
|
||||
${srun} grid_sweep.py --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":[1000],"Bzx":[1], "Bxy":[0.7,0],"Bzy":[-0.7,0],"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_1_dv.feather"], "proxy_formula":["w_pred~y"],"z_bias":[-0.5]}' --outfile robustness_1_dv_jobs_p1
|
||||
${srun} ./grid_sweep.py --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":[1000],"Bzx":[1], "Bxy":[0.7,0],"Bzy":[-0.7,0],"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_1_dv.feather"], "proxy_formula":["w_pred~y"],"z_bias":[-0.5]}' --outfile robustness_1_dv_jobs_p1
|
||||
|
||||
robustness_1_dv_jobs_p2: simulation_base.R 04_depvar_differential.R grid_sweep.py
|
||||
${srun} grid_sweep.py --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":[5000],"Bzx":[1], "Bxy":[0.7,0],"Bzy":[-0.7,0],"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_1_dv.feather"], "proxy_formula":["w_pred~y"],"z_bias":[-0.5]}' --outfile robustness_1_dv_jobs_p2
|
||||
${srun} ./grid_sweep.py --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":[5000],"Bzx":[1], "Bxy":[0.7,0],"Bzy":[-0.7,0],"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_1_dv.feather"], "proxy_formula":["w_pred~y"],"z_bias":[-0.5]}' --outfile robustness_1_dv_jobs_p2
|
||||
|
||||
robustness_1_dv.feather: robustness_1_dv_jobs_p1 robustness_1_dv_jobs_p2
|
||||
rm -f $@
|
||||
@ -136,21 +131,21 @@ robustness_1_dv.RDS: robustness_1_dv.feather summarize_estimator.R
|
||||
${srun} Rscript plot_dv_example.R --infile $< --name "robustness_1_dv" --remember-file $@
|
||||
|
||||
|
||||
robustness_2_jobs_p1: grid_sweep.py 01_two_covariates.R simulation_base.R grid_sweep.py
|
||||
robustness_2_jobs_p1: grid_sweep.py 01_two_covariates.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.60,0.65]}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.60,0.65]}' --outfile $@
|
||||
|
||||
robustness_2_jobs_p2: grid_sweep.py 01_two_covariates.R simulation_base.R grid_sweep.py
|
||||
robustness_2_jobs_p2: grid_sweep.py 01_two_covariates.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.70,0.75]}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.70,0.75]}' --outfile $@
|
||||
|
||||
robustness_2_jobs_p3: grid_sweep.py 01_two_covariates.R simulation_base.R grid_sweep.py
|
||||
robustness_2_jobs_p3: grid_sweep.py 01_two_covariates.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.80,0.85]}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.80,0.85]}' --outfile $@
|
||||
|
||||
robustness_2_jobs_p4: grid_sweep.py 01_two_covariates.R simulation_base.R grid_sweep.py
|
||||
robustness_2_jobs_p4: grid_sweep.py 01_two_covariates.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.90,0.95]}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.90,0.95]}' --outfile $@
|
||||
|
||||
robustness_2.feather: robustness_2_jobs_p1 robustness_2_jobs_p2 robustness_2_jobs_p3 robustness_2_jobs_p4
|
||||
rm $@
|
||||
@ -172,21 +167,21 @@ robustness_2.RDS: plot_example.R robustness_2.feather summarize_estimator.R
|
||||
rm -f $@
|
||||
${srun} Rscript $< --infile $(word 2, $^) --name "robustness_2" --remember-file $@
|
||||
|
||||
robustness_2_dv_jobs_p1: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py
|
||||
robustness_2_dv_jobs_p1: grid_sweep.py 03_depvar.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.60,0.65]}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.60,0.65]}' --outfile $@
|
||||
|
||||
robustness_2_dv_jobs_p2: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py
|
||||
robustness_2_dv_jobs_p2: grid_sweep.py 03_depvar.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.70,0.75]}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.70,0.75]}' --outfile $@
|
||||
|
||||
robustness_2_dv_jobs_p3: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py
|
||||
robustness_2_dv_jobs_p3: grid_sweep.py 03_depvar.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.80,0.85]}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.80,0.85]}' --outfile $@
|
||||
|
||||
robustness_2_dv_jobs_p4: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py
|
||||
robustness_2_dv_jobs_p4: grid_sweep.py 03_depvar.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.90,0.95]}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.90,0.95]}' --outfile $@
|
||||
|
||||
robustness_2_dv.feather: robustness_2_dv_jobs_p1 robustness_2_dv_jobs_p2 robustness_2_dv_jobs_p3 robustness_2_dv_jobs_p4
|
||||
rm -f $@
|
||||
@ -209,9 +204,9 @@ robustness_2_dv.RDS: plot_dv_example.R robustness_2_dv.feather summarize_estimat
|
||||
${srun} Rscript $< --infile $(word 2, $^) --name "robustness_2_dv" --remember-file $@
|
||||
|
||||
|
||||
robustness_3_proflik_jobs: grid_sweep.py 01_two_covariates.R simulation_base.R grid_sweep.py
|
||||
robustness_3_proflik_jobs: grid_sweep.py 01_two_covariates.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":[1000],"m":[100], "seed":${seeds}, "outfile":["robustness_3_proflik.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"Px":[0.5,0.6,0.7,0.8,0.9,0.95], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85], "confint_method":['spline']}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":[1000],"m":[100], "seed":${seeds}, "outfile":["robustness_3_proflik.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"Px":[0.5,0.6,0.7,0.8,0.9,0.95], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85], "confint_method":['spline']}' --outfile $@
|
||||
|
||||
robustness_3_proflik.feather: robustness_3_proflik_jobs
|
||||
rm -f $@
|
||||
@ -224,17 +219,17 @@ robustness_3_proflik.RDS: plot_example.R robustness_3_proflik.feather summarize_
|
||||
${srun} Rscript $< --infile $(word 2, $^) --name "robustness_3_proflik" --remember-file $@
|
||||
|
||||
|
||||
robustness_3_jobs_p1: grid_sweep.py 01_two_covariates.R simulation_base.R grid_sweep.py
|
||||
robustness_3_jobs_p1: grid_sweep.py 01_two_covariates.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_3.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"Px":[0.5,0.6], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85]}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_3.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"Px":[0.5,0.6], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85]}' --outfile $@
|
||||
|
||||
robustness_3_jobs_p2: grid_sweep.py 01_two_covariates.R simulation_base.R grid_sweep.py
|
||||
robustness_3_jobs_p2: grid_sweep.py 01_two_covariates.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_3.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"Px":[0.7,0.8], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85]}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_3.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"Px":[0.7,0.8], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85]}' --outfile $@
|
||||
|
||||
robustness_3_jobs_p3: grid_sweep.py 01_two_covariates.R simulation_base.R grid_sweep.py
|
||||
robustness_3_jobs_p3: grid_sweep.py 01_two_covariates.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_3.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"Px":[0.9,0.95], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85]}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_3.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"Px":[0.9,0.95], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85]}' --outfile $@
|
||||
|
||||
robustness_3.feather: robustness_3_jobs_p1 robustness_3_jobs_p2 robustness_3_jobs_p3
|
||||
rm -f $@
|
||||
@ -253,9 +248,9 @@ robustness_3.RDS: plot_example.R robustness_3.feather summarize_estimator.R
|
||||
rm -f $@
|
||||
${srun} Rscript $< --infile $(word 2, $^) --name "robustness_3" --remember-file $@
|
||||
|
||||
robustness_3_dv_proflik_jobs: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py
|
||||
robustness_3_dv_proflik_jobs: grid_sweep.py 03_depvar.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":[1000],"m":[100], "seed":${seeds}, "outfile":["robustness_3_dv_proflik.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"B0":[0,0.405,0.846,1.386,2.197,2.944], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],"confint_method":['spline']}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 03_depvar.R' --arg_dict '{"N":[1000],"m":[100], "seed":${seeds}, "outfile":["robustness_3_dv_proflik.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"B0":[0,0.405,0.846,1.386,2.197,2.944], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],"confint_method":['spline']}' --outfile $@
|
||||
|
||||
robustness_3_dv_proflik.feather: robustness_3_dv_proflik_jobs
|
||||
rm -f $@
|
||||
@ -268,20 +263,18 @@ robustness_3_dv_proflik.RDS: plot_dv_example.R robustness_3_dv_proflik.feather s
|
||||
${srun} Rscript $< --infile $(word 2, $^) --name "robustness_3_dv_proflik" --remember-file $@
|
||||
|
||||
|
||||
robustness_3_dv_jobs_p1: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py
|
||||
robustness_3_dv_jobs_p1: grid_sweep.py 03_depvar.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_3_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"B0":[0,0.405], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85]}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_3_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"B0":[0,0.405], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85]}' --outfile $@
|
||||
|
||||
|
||||
|
||||
robustness_3_dv_jobs_p2: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py
|
||||
robustness_3_dv_jobs_p2: grid_sweep.py 03_depvar.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_3_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"B0":[0.847,1.386], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85]}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_3_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"B0":[0.847,1.386], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85]}' --outfile $@
|
||||
|
||||
|
||||
robustness_3_dv_jobs_p3: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py
|
||||
robustness_3_dv_jobs_p3: grid_sweep.py 03_depvar.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_3_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "B0":[2.197,2.944], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85]}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_3_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "B0":[2.197,2.944], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85]}' --outfile $@
|
||||
|
||||
robustness_3_dv.feather: robustness_3_dv_jobs_p1 robustness_3_dv_jobs_p2 robustness_3_dv_jobs_p3
|
||||
rm -f $@
|
||||
@ -303,17 +296,22 @@ robustness_3_dv.RDS: plot_dv_example.R robustness_3_dv.feather summarize_estimat
|
||||
|
||||
|
||||
|
||||
robustness_4_jobs_p1: grid_sweep.py 02_indep_differential.R simulation_base.R grid_sweep.py
|
||||
robustness_4_jobs_p1: grid_sweep.py 02_indep_differential.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85],"y_bias":[-2.944,-2.197]}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"], "y_bias":[-2.944,-2.197]}' --outfile $@
|
||||
|
||||
robustness_4_jobs_p2: grid_sweep.py 02_indep_differential.R simulation_base.R grid_sweep.py
|
||||
robustness_4_jobs_p2: grid_sweep.py 02_indep_differential.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85], "y_bias":[-1.386,-0.846]}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"], "y_bias":[-1.386,-0.846]}' --outfile $@
|
||||
|
||||
robustness_4_jobs_p3: grid_sweep.py 02_indep_differential.R simulation_base.R
|
||||
rm -f ./$@
|
||||
${srun} ./$< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"], "y_bias":[-0.405,-0.25]}' --outfile $@
|
||||
|
||||
robustness_4_jobs_p4: grid_sweep.py 02_indep_differential.R simulation_base.R
|
||||
rm -f ./$@
|
||||
${srun} ./$< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"], "y_bias":[0,-0.1]}' --outfile $@
|
||||
|
||||
robustness_4_jobs_p3: grid_sweep.py 02_indep_differential.R simulation_base.R grid_sweep.py
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85],"y_bias":[-0.405,-0.25]}' --outfile $@
|
||||
|
||||
robustness_4.feather: robustness_4_jobs_p1 robustness_4_jobs_p2 robustness_4_jobs_p3
|
||||
rm -f $@
|
||||
@ -323,10 +321,15 @@ robustness_4.feather: robustness_4_jobs_p1 robustness_4_jobs_p2 robustness_4_job
|
||||
$(eval ITEMS_2!=seq $(START) $(STEP) $(END_2))
|
||||
$(eval END_3!=cat robustness_4_jobs_p3 | wc -l)
|
||||
$(eval ITEMS_3!=seq $(START) $(STEP) $(END_3))
|
||||
$(eval END_4!=cat robustness_4_jobs_p3 | wc -l)
|
||||
$(eval ITEMS_3!=seq $(START) $(STEP) $(END_4))
|
||||
|
||||
|
||||
$(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_jobs_p1;)
|
||||
$(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_jobs_p2;)
|
||||
$(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_jobs_p3;)
|
||||
$(foreach item,$(ITEMS_4),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_jobs_p3;)
|
||||
|
||||
|
||||
robustness_4.RDS: plot_example.R robustness_4.feather summarize_estimator.R
|
||||
rm -f $@
|
||||
@ -335,34 +338,32 @@ robustness_4.RDS: plot_example.R robustness_4.feather summarize_estimator.R
|
||||
|
||||
# '{"N":${robustness_Ns},"Bxy":[0.7],"Bzy":[-0.7],"m":${ms}, "seed":${seeds}, "outfile":["example_4.feather"], "z_bias":[0.5]}' --example_4_jobs
|
||||
|
||||
robustness_4_dv_jobs_p1: grid_sweep.py 04_depvar_differential.R simulation_base.R grid_sweep.py
|
||||
robustness_4_dv_jobs_p1: grid_sweep.py 04_depvar_differential.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7],"Bzx":[1], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],"z_bias":[0,0.1]}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7],"Bzx":[1], "outcome_formula":["y~x+z"], "z_bias":[0,0.1]}' --outfile $@
|
||||
|
||||
robustness_4_dv_jobs_p2: grid_sweep.py 04_depvar_differential.R simulation_base.R grid_sweep.py
|
||||
robustness_4_dv_jobs_p2: grid_sweep.py 04_depvar_differential.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7],"Bzx":[1], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],"z_bias":[0.25,0.405]}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7],"Bzx":[1], "outcome_formula":["y~x+z"], "z_bias":[0.25,0.405]}' --outfile $@
|
||||
|
||||
robustness_4_dv_jobs_p3: grid_sweep.py 04_depvar_differential.R simulation_base.R grid_sweep.py
|
||||
robustness_4_dv_jobs_p3: grid_sweep.py 04_depvar_differential.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7],"Bzx":[1],"outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],"z_bias":[0.846,1.386]}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7],"Bzx":[1],"outcome_formula":["y~x+z"],"z_bias":[0.846,1.386]}' --outfile $@
|
||||
|
||||
robustness_4_dv_jobs_p4: grid_sweep.py 04_depvar_differential.R simulation_base.R grid_sweep.py
|
||||
robustness_4_dv_jobs_p4: grid_sweep.py 04_depvar_differential.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4_dv.feather"],"Bzy":[-0.7],"Bxy":[0.7],"Bzx":[1], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],"z_bias":[2.197,2.944]}' --outfile $@
|
||||
${srun} ./$< --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4_dv.feather"],"Bzy":[-0.7],"Bxy":[0.7],"Bzx":[1], "outcome_formula":["y~x+z"], "z_bias":[2.197,2.944]}' --outfile $@
|
||||
|
||||
robustness_4_dv.feather: robustness_4_dv_jobs_p1 robustness_4_dv_jobs_p2 robustness_4_dv_jobs_p3 robustness_4_dv_jobs_p4
|
||||
rm -f $@
|
||||
$(eval END_1!=cat robustness_4_dv_jobs_p1 | wc -l)
|
||||
$(eval ITEMS_1!=seq $(START) $(STEP) $(END_1))
|
||||
$(eval END_2!=cat robustness_4_dv_p2 | wc -l)
|
||||
$(eval END_2!=cat robustness_4_dv_jobs_p2 | wc -l)
|
||||
$(eval ITEMS_2!=seq $(START) $(STEP) $(END_2))
|
||||
$(eval END_3!=cat robustness_4_dv_p3 | wc -l)
|
||||
$(eval END_3!=cat robustness_4_dv_jobs_p3 | wc -l)
|
||||
$(eval ITEMS_3!=seq $(START) $(STEP) $(END_3))
|
||||
$(eval END_3!=cat robustness_4_dv_p4 | wc -l)
|
||||
$(eval ITEMS_3!=seq $(START) $(STEP) $(END_3))
|
||||
|
||||
|
||||
$(eval END_4!=cat robustness_4_dv_jobs_p4 | wc -l)
|
||||
$(eval ITEMS_4!=seq $(START) $(STEP) $(END_4))
|
||||
$(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_dv_jobs_p1;)
|
||||
$(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_dv_jobs_p2;)
|
||||
$(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_dv_jobs_p3;)
|
||||
@ -371,7 +372,86 @@ robustness_4_dv.feather: robustness_4_dv_jobs_p1 robustness_4_dv_jobs_p2 robustn
|
||||
|
||||
robustness_4_dv.RDS: plot_dv_example.R robustness_4_dv.feather summarize_estimator.R
|
||||
rm -f $@
|
||||
${srun} Rscript $< --infile $(word 2, $^) --name "robustness_4" --remember-file $@
|
||||
${srun} Rscript $< --infile $(word 2, $^) --name "robustness_4_dv" --remember-file $@
|
||||
|
||||
|
||||
robustness_5_jobs_p1: grid_sweep.py 02_indep_differential.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} ./$< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_5.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1.386,2.197], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"]}' --outfile $@
|
||||
|
||||
robustness_5_jobs_p2: grid_sweep.py 02_indep_differential.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} ./$< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_5.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.405,0.846], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"]}' --outfile $@
|
||||
|
||||
robustness_5_jobs_p3: grid_sweep.py 02_indep_differential.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} ./$< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_5.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0,0.25], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"]}' --outfile $@
|
||||
|
||||
robustness_5_jobs_p4: grid_sweep.py 02_indep_differential.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} ./$< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_5.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[2.944], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"]}' --outfile $@
|
||||
|
||||
|
||||
robustness_5.feather: robustness_5_jobs_p1 robustness_5_jobs_p2 robustness_5_jobs_p3
|
||||
rm -f $@
|
||||
$(eval END_1!=cat robustness_5_jobs_p1 | wc -l)
|
||||
$(eval ITEMS_1!=seq $(START) $(STEP) $(END_1))
|
||||
$(eval END_2!=cat robustness_5_jobs_p2 | wc -l)
|
||||
$(eval ITEMS_2!=seq $(START) $(STEP) $(END_2))
|
||||
$(eval END_3!=cat robustness_5_jobs_p3 | wc -l)
|
||||
$(eval ITEMS_3!=seq $(START) $(STEP) $(END_3))
|
||||
$(eval END_4!=cat robustness_5_jobs_p4 | wc -l)
|
||||
$(eval ITEMS_4!=seq $(START) $(STEP) $(END_4))
|
||||
|
||||
$(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_5_jobs_p1;)
|
||||
$(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_5_jobs_p2;)
|
||||
$(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_5_jobs_p3;)
|
||||
$(foreach item,$(ITEMS_4),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_5_jobs_p4;)
|
||||
|
||||
robustness_5.RDS: plot_example.R robustness_5.feather summarize_estimator.R
|
||||
rm -f $@
|
||||
${srun} Rscript $< --infile $(word 2, $^) --name "robustness_5" --remember-file $@
|
||||
|
||||
|
||||
# '{"N":${robustness_Ns},"Bxy":[0.7],"Bzy":[-0.7],"m":${ms}, "seed":${seeds}, "outfile":["example_4.feather"], "z_bias":[0.5]}' --example_4_jobs
|
||||
|
||||
robustness_5_dv_jobs_p1: grid_sweep.py 04_depvar_differential.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} ./$< --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_5_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7],"Bzx":[0,0.25], "outcome_formula":["y~x+z"], "z_bias":[-0.5]}' --outfile $@
|
||||
|
||||
robustness_5_dv_jobs_p2: grid_sweep.py 04_depvar_differential.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} ./$< --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_5_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7],"Bzx":[0.405,0.846], "outcome_formula":["y~x+z"], "z_bias":[-0.5]}' --outfile $@
|
||||
|
||||
robustness_5_dv_jobs_p3: grid_sweep.py 04_depvar_differential.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} ./$< --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_5_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7],"Bzx":[1.386,2.197],"outcome_formula":["y~x+z"], "z_bias":[-0.5]}' --outfile $@
|
||||
|
||||
robustness_5_dv_jobs_p4: grid_sweep.py 04_depvar_differential.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} ./$< --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_5_dv.feather"],"Bzy":[-0.7],"Bxy":[0.7],"Bzx":[2.944], "outcome_formula":["y~x+z"], "z_bias":[-0.5]}' --outfile $@
|
||||
|
||||
robustness_5_dv.feather: robustness_5_dv_jobs_p1 robustness_5_dv_jobs_p2 robustness_5_dv_jobs_p3 robustness_5_dv_jobs_p4
|
||||
rm -f $@
|
||||
$(eval END_1!=cat robustness_5_dv_jobs_p1 | wc -l)
|
||||
$(eval ITEMS_1!=seq $(START) $(STEP) $(END_1))
|
||||
$(eval END_2!=cat robustness_5_dv_jobs_p2 | wc -l)
|
||||
$(eval ITEMS_2!=seq $(START) $(STEP) $(END_2))
|
||||
$(eval END_3!=cat robustness_5_dv_jobs_p3 | wc -l)
|
||||
$(eval ITEMS_3!=seq $(START) $(STEP) $(END_3))
|
||||
$(eval END_4!=cat robustness_5_dv_jobs_p4 | wc -l)
|
||||
$(eval ITEMS_4!=seq $(START) $(STEP) $(END_4))
|
||||
|
||||
|
||||
$(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_5_dv_jobs_p1;)
|
||||
$(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_5_dv_jobs_p2;)
|
||||
$(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_5_dv_jobs_p3;)
|
||||
$(foreach item,$(ITEMS_4),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_5_dv_jobs_p4;)
|
||||
|
||||
|
||||
robustness_5_dv.RDS: plot_dv_example.R robustness_5_dv.feather summarize_estimator.R
|
||||
rm -f $@
|
||||
${srun} Rscript $< --infile $(word 2, $^) --name "robustness_5_dv" --remember-file $@
|
||||
|
||||
|
||||
clean_main:
|
||||
@ -404,5 +484,44 @@ clean_all:
|
||||
# sbatch --wait --verbose --array=1-3000 run_simulation.sbatch 0 example_2_B_mecor_jobs
|
||||
# sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 example_2_B_mecor_jobs
|
||||
|
||||
robustness_6_jobs_p1: grid_sweep.py 03_indep_differential_nonnorm.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} ./$< --command 'Rscript 03_indep_differential_nonnorm.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_6.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"],"sd_y_mixin":[0,1,2.5]}' --outfile $@
|
||||
|
||||
robustness_6_jobs_p2: grid_sweep.py 03_indep_differential_nonnorm.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} ./$< --command 'Rscript 03_indep_differential_nonnorm.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_6.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"],"sd_y_mixin":[5,10]}' --outfile $@
|
||||
|
||||
robustness_6_jobs_p3: grid_sweep.py 03_indep_differential_nonnorm.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} ./$< --command 'Rscript 03_indep_differential_nonnorm.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_6.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"],"sd_y_mixin":[0,1,2.5],"y_bias":[0]}' --outfile $@
|
||||
|
||||
robustness_6_jobs_p4: grid_sweep.py 03_indep_differential_nonnorm.R simulation_base.R
|
||||
rm -f $@
|
||||
${srun} ./$< --command 'Rscript 03_indep_differential_nonnorm.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_6.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"],"sd_y_mixin":[5,10],"y_bias":[0]}' --outfile $@
|
||||
|
||||
|
||||
robustness_6.feather: robustness_6_jobs_p1 robustness_6_jobs_p2 robustness_6_jobs_p3 robustness_6_jobs_p4
|
||||
rm -f $@
|
||||
$(eval END_1!=cat robustness_6_jobs_p1 | wc -l)
|
||||
$(eval ITEMS_1!=seq $(START) $(STEP) $(END_1))
|
||||
$(eval END_2!=cat robustness_6_jobs_p2 | wc -l)
|
||||
$(eval ITEMS_2!=seq $(START) $(STEP) $(END_2))
|
||||
$(eval END_3!=cat robustness_6_jobs_p3 | wc -l)
|
||||
$(eval ITEMS_3!=seq $(START) $(STEP) $(END_3))
|
||||
$(eval END_4!=cat robustness_6_jobs_p4 | wc -l)
|
||||
$(eval ITEMS_4!=seq $(START) $(STEP) $(END_4))
|
||||
|
||||
|
||||
# $(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_6_jobs_p1;)
|
||||
# $(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_6_jobs_p2;)
|
||||
# $(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_6_jobs_p3;)
|
||||
$(foreach item,$(ITEMS_4),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_6_jobs_p4;)
|
||||
|
||||
|
||||
robustness_6.RDS: plot_example.R robustness_6.feather summarize_estimator.R
|
||||
rm -f $@
|
||||
${srun} Rscript $< --infile $(word 2, $^) --name "robustness_6" --remember-file $@
|
||||
|
||||
|
||||
.PHONY: supplement
|
||||
|
||||
@ -23,7 +23,7 @@ likelihood.logistic <- function(model.params, outcome, model.matrix){
|
||||
}
|
||||
|
||||
## outcome_formula <- y ~ x + z; proxy_formula <- w_pred ~ y + x + z + x:z + x:y + z:y
|
||||
measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='logit'), proxy_formula, proxy_family=binomial(link='logit'),maxit=1e6, method='optim'){
|
||||
measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='logit'), proxy_formula, proxy_family=binomial(link='logit'),maxit=1e6, method='optim',optim_method='L-BFGS-B'){
|
||||
df.obs <- model.frame(outcome_formula, df)
|
||||
proxy.model.matrix <- model.matrix(proxy_formula, df)
|
||||
proxy.variable <- all.vars(proxy_formula)[1]
|
||||
|
||||
@ -51,19 +51,20 @@ zhang.mle.iv <- function(df){
|
||||
fn <- df.obs[(w_pred==0) & (x.obs==1), .N]
|
||||
npv <- tn / (tn + fn)
|
||||
|
||||
|
||||
tp <- df.obs[(w_pred==1) & (x.obs == w_pred),.N]
|
||||
fp <- df.obs[(w_pred==1) & (x.obs == 0),.N]
|
||||
ppv <- tp / (tp + fp)
|
||||
|
||||
nll <- function(B0=0, Bxy=0, Bzy=0, sigma_y=0.1){
|
||||
nll <- function(B0=0, Bxy=0, Bzy=0, sigma_y=9){
|
||||
|
||||
## fpr = 1 - TNR
|
||||
### Problem: accounting for uncertainty in ppv / npv
|
||||
|
||||
## fnr = 1 - TPR
|
||||
ll.y.obs <- with(df.obs, dnorm(y, B0 + Bxy * x + Bzy * z, sd=sigma_y,log=T))
|
||||
ll <- sum(ll.y.obs)
|
||||
|
||||
ll <- sum(ll.y.obs)
|
||||
# unobserved case; integrate out x
|
||||
ll.x.1 <- with(df.unobs, dnorm(y, B0 + Bxy + Bzy * z, sd = sigma_y, log=T))
|
||||
ll.x.0 <- with(df.unobs, dnorm(y, B0 + Bzy * z, sd = sigma_y,log=T))
|
||||
@ -75,10 +76,11 @@ zhang.mle.iv <- function(df){
|
||||
lls.x.0 <- colLogSumExps(rbind(log(1-npv) + ll.x.1, log(npv) + ll.x.0))
|
||||
|
||||
lls <- colLogSumExps(rbind(df.unobs$w_pred * lls.x.1, (1-df.unobs$w_pred) * lls.x.0))
|
||||
|
||||
ll <- ll + sum(lls)
|
||||
return(-ll)
|
||||
|
||||
}
|
||||
mlefit <- mle2(minuslogl = nll, control=list(maxit=1e6), lower=list(sigma_y=0.0001, B0=-Inf, Bxy=-Inf, Bzy=-Inf),
|
||||
mlefit <- mle2(minuslogl = nll, control=list(maxit=1e6), lower=list(sigma_y=0.00001, B0=-Inf, Bxy=-Inf, Bzy=-Inf),
|
||||
upper=list(sigma_y=Inf, B0=Inf, Bxy=Inf, Bzy=Inf),method='L-BFGS-B')
|
||||
return(mlefit)
|
||||
}
|
||||
|
||||
@ -151,21 +151,11 @@ run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formu
|
||||
temp.df <- copy(df)
|
||||
temp.df[,y:=y.obs]
|
||||
|
||||
if(confint_method=='quad'){
|
||||
mod.caroll.lik <- measerr_mle_dv(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula)
|
||||
fischer.info <- solve(mod.caroll.lik$hessian)
|
||||
coef <- mod.caroll.lik$par
|
||||
ci.upper <- coef + sqrt(diag(fischer.info)) * 1.96
|
||||
ci.lower <- coef - sqrt(diag(fischer.info)) * 1.96
|
||||
}
|
||||
else{ ## confint_method is 'profile'
|
||||
|
||||
mod.caroll.lik <- measerr_mle_dv(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula, method='bbmle')
|
||||
coef <- coef(mod.caroll.lik)
|
||||
ci <- confint(mod.caroll.lik, method='spline')
|
||||
ci.lower <- ci[,'2.5 %']
|
||||
ci.upper <- ci[,'97.5 %']
|
||||
}
|
||||
mod.caroll.lik <- measerr_mle_dv(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula)
|
||||
fischer.info <- solve(mod.caroll.lik$hessian)
|
||||
coef <- mod.caroll.lik$par
|
||||
ci.upper <- coef + sqrt(diag(fischer.info)) * 1.96
|
||||
ci.lower <- coef - sqrt(diag(fischer.info)) * 1.96
|
||||
|
||||
result <- append(result,
|
||||
list(Bxy.est.mle = coef['x'],
|
||||
@ -175,6 +165,19 @@ run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formu
|
||||
Bzy.ci.upper.mle = ci.upper['z'],
|
||||
Bzy.ci.lower.mle = ci.lower['z']))
|
||||
|
||||
mod.caroll.profile.lik <- measerr_mle_dv(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula, method='bbmle')
|
||||
coef <- coef(mod.caroll.profile.lik)
|
||||
ci <- confint(mod.caroll.profile.lik, method='spline')
|
||||
ci.lower <- ci[,'2.5 %']
|
||||
ci.upper <- ci[,'97.5 %']
|
||||
|
||||
result <- append(result,
|
||||
list(Bxy.est.mle.profile = coef['x'],
|
||||
Bxy.ci.upper.mle.profile = ci.upper['x'],
|
||||
Bxy.ci.lower.mle.profile = ci.lower['x'],
|
||||
Bzy.est.mle.profile = coef['z'],
|
||||
Bzy.ci.upper.mle.profile = ci.upper['z'],
|
||||
Bzy.ci.lower.mle.profile = ci.lower['z']))
|
||||
|
||||
## my implementatoin of liklihood based correction
|
||||
mod.zhang <- zhang.mle.dv(df)
|
||||
@ -201,8 +204,8 @@ run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formu
|
||||
)
|
||||
|
||||
tryCatch({
|
||||
amelia.out.k <- amelia(df, m=200, p2s=0, idvars=c('y','ystar','w'))
|
||||
mod.amelia.k <- zelig(y.obs~x+z, model='ls', data=amelia.out.k$imputations, cite=FALSE)
|
||||
amelia.out.k <- amelia(df, m=200, p2s=0, idvars=c('y','ystar','w'),ords="y.obs")
|
||||
mod.amelia.k <- zelig(y.obs~x+z, model='logit', data=amelia.out.k$imputations, cite=FALSE)
|
||||
(coefse <- combine_coef_se(mod.amelia.k, messages=FALSE))
|
||||
est.x.mi <- coefse['x','Estimate']
|
||||
est.x.se <- coefse['x','Std.Error']
|
||||
@ -340,44 +343,72 @@ run_simulation <- function(df, result, outcome_formula=y~x+z, proxy_formula=NUL
|
||||
tryCatch({
|
||||
temp.df <- copy(df)
|
||||
temp.df <- temp.df[,x:=x.obs]
|
||||
if(confint_method=='quad'){
|
||||
mod.caroll.lik <- measerr_mle(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula, truth_formula=truth_formula, method='optim')
|
||||
fischer.info <- solve(mod.caroll.lik$hessian)
|
||||
coef <- mod.caroll.lik$par
|
||||
ci.upper <- coef + sqrt(diag(fischer.info)) * 1.96
|
||||
ci.lower <- coef - sqrt(diag(fischer.info)) * 1.96
|
||||
} else { # confint_method == 'bbmle'
|
||||
mod.caroll.lik <- measerr_mle(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula, truth_formula=truth_formula, method='optim')
|
||||
fischer.info <- solve(mod.caroll.lik$hessian)
|
||||
coef <- mod.caroll.lik$par
|
||||
ci.upper <- coef + sqrt(diag(fischer.info)) * 1.96
|
||||
ci.lower <- coef - sqrt(diag(fischer.info)) * 1.96
|
||||
|
||||
mod.caroll.lik <- measerr_mle(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula, truth_formula=truth_formula, method='bbmle')
|
||||
coef <- coef(mod.caroll.lik)
|
||||
ci <- confint(mod.caroll.lik, method='spline')
|
||||
ci.lower <- ci[,'2.5 %']
|
||||
ci.upper <- ci[,'97.5 %']
|
||||
}
|
||||
mle_result <- list(Bxy.est.mle = coef['x'],
|
||||
Bxy.ci.upper.mle = ci.upper['x'],
|
||||
Bxy.ci.lower.mle = ci.lower['x'],
|
||||
Bzy.est.mle = coef['z'],
|
||||
Bzy.ci.upper.mle = ci.upper['z'],
|
||||
Bzy.ci.lower.mle = ci.lower['z'])
|
||||
},
|
||||
error=function(e) {result[['error']] <- as.character(e)
|
||||
})
|
||||
|
||||
result <- append(result, mle_result)
|
||||
mle_result_proflik <- list(Bxy.est.mle.profile = NULL,
|
||||
Bxy.ci.upper.mle.profile = NULL,
|
||||
Bxy.ci.lower.mle.profile = NULL,
|
||||
Bzy.est.mle.profile = NULL,
|
||||
Bzy.ci.upper.mle.profile = NULL,
|
||||
Bzy.ci.lower.mle.profile = NULL)
|
||||
|
||||
tryCatch({
|
||||
## confint_method == 'bbmle'
|
||||
mod.caroll.lik <- measerr_mle(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula, truth_formula=truth_formula, method='bbmle')
|
||||
coef <- coef(mod.caroll.lik)
|
||||
ci <- confint(mod.caroll.lik, method='spline')
|
||||
ci.lower <- ci[,'2.5 %']
|
||||
ci.upper <- ci[,'97.5 %']
|
||||
|
||||
mle_result_proflik <- list(Bxy.est.mle.profile = coef['x'],
|
||||
Bxy.ci.upper.mle.profile = ci.upper['x'],
|
||||
Bxy.ci.lower.mle.profile = ci.lower['x'],
|
||||
Bzy.est.mle.profile = coef['z'],
|
||||
Bzy.ci.upper.mle.profile = ci.upper['z'],
|
||||
Bzy.ci.lower.mle.profile = ci.lower['z'])
|
||||
},
|
||||
|
||||
error=function(e) {result[['error']] <- as.character(e)
|
||||
})
|
||||
|
||||
result <- append(result, mle_result_proflik)
|
||||
|
||||
result <- append(result, mle_result)
|
||||
zhang_result <- list(Bxy.est.mle.zhang = NULL,
|
||||
Bxy.ci.upper.mle.zhang = NULL,
|
||||
Bxy.ci.lower.mle.zhang = NULL,
|
||||
Bzy.est.mle.zhang = NULL,
|
||||
Bzy.ci.upper.mle.zhang = NULL,
|
||||
Bzy.ci.lower.mle.zhang = NULL)
|
||||
|
||||
tryCatch({
|
||||
mod.zhang.lik <- zhang.mle.iv(df)
|
||||
coef <- coef(mod.zhang.lik)
|
||||
ci <- confint(mod.zhang.lik,method='quad')
|
||||
result <- append(result,
|
||||
list(Bxy.est.zhang = coef['Bxy'],
|
||||
Bxy.ci.upper.zhang = ci['Bxy','97.5 %'],
|
||||
Bxy.ci.lower.zhang = ci['Bxy','2.5 %'],
|
||||
Bzy.est.zhang = coef['Bzy'],
|
||||
Bzy.ci.upper.zhang = ci['Bzy','97.5 %'],
|
||||
Bzy.ci.lower.zhang = ci['Bzy','2.5 %']))
|
||||
zhang_result <- list(Bxy.est.zhang = coef['Bxy'],
|
||||
Bxy.ci.upper.zhang = ci['Bxy','97.5 %'],
|
||||
Bxy.ci.lower.zhang = ci['Bxy','2.5 %'],
|
||||
Bzy.est.zhang = coef['Bzy'],
|
||||
Bzy.ci.upper.zhang = ci['Bzy','97.5 %'],
|
||||
Bzy.ci.lower.zhang = ci['Bzy','2.5 %'])
|
||||
},
|
||||
error=function(e) {result[['error']] <- as.character(e)
|
||||
})
|
||||
result <- append(result, zhang_result)
|
||||
|
||||
## What if we can't observe k -- most realistic scenario. We can't include all the ML features in a model.
|
||||
## amelia.out.nok <- amelia(df, m=200, p2s=0, idvars=c("x","w_pred"), noms=noms)
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
library(ggdist)
|
||||
|
||||
summarize.estimator <- function(df, suffix='naive', coefname='x'){
|
||||
summarize.estimator <- function(sims.df, suffix='naive', coefname='x'){
|
||||
|
||||
reported_vars <- c(
|
||||
'Bxy',
|
||||
@ -13,10 +14,10 @@ summarize.estimator <- function(df, suffix='naive', coefname='x'){
|
||||
|
||||
grouping_vars <- grouping_vars[grouping_vars %in% names(df)]
|
||||
|
||||
part <- df[,
|
||||
c(reported_vars,
|
||||
grouping_vars),
|
||||
with=FALSE]
|
||||
part <- sims.df[,
|
||||
unique(c(reported_vars,
|
||||
grouping_vars)),
|
||||
with=FALSE]
|
||||
|
||||
|
||||
true.in.ci <- as.integer((part$Bxy >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (part$Bxy <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]]))
|
||||
@ -29,6 +30,7 @@ summarize.estimator <- function(df, suffix='naive', coefname='x'){
|
||||
bias=bias,
|
||||
sign.correct =sign.correct)]
|
||||
|
||||
|
||||
part.plot <- part[, .(p.true.in.ci = mean(true.in.ci),
|
||||
mean.bias = mean(bias),
|
||||
mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]],na.rm=T),
|
||||
|
||||
Loading…
Reference in New Issue
Block a user