## 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, Bkx, Bgx, seed, xy.explained.variance=0.01, u.explained.variance=0.1){
+simulate_data <- function(N, m, B0, Bxy, Bzx, Bzy, seed, y_explained_variance=0.025, prediction_accuracy=0.73, y_bias=-0.8){
set.seed(seed)
-
- ## the true value of x
-
- g <- rbinom(N, 1, 0.5)
-
# make w and y dependent
- u <- rnorm(N,0,)
-
- xprime <- Bgx * g + rnorm(N,0,1)
-
- k <- Bkx*xprime + rnorm(N,0,1.5) + 1.1*Bkx*u
+ z <- rbinom(N, 1, 0.5)
+ x <- rbinom(N, 1, Bzx * z + 0.5)
- x <- as.integer(logistic(scale(xprime)) > 0.5)
+ y.var.epsilon <- (var(Bzy * z) + var(Bxy *x) + 2*cov(Bzy*z,Bxy*x)) * ((1-y_explained_variance)/y_explained_variance)
+ y.epsilon <- rnorm(N, sd = sqrt(y.var.epsilon))
+ y <- Bzy * z + Bxy * x + y.epsilon
+
+ df <- data.table(x=x,y=y,z=z)
- y <- Bxy * x + Bgy * g + B0 + u + rnorm(N, 0, 1)
-
- df <- data.table(x=x,k=k,y=y,g=g)
-
- w.model <- glm(x ~ k,df, family=binomial(link='logit'))
-
- if( m < N){
+ if(m < N){
df <- df[sample(nrow(df), m), x.obs := x]
} else {
df <- df[, x.obs := x]
}
- df[, x.obs := x.obs]
+ ## probablity of an error is correlated with y
+ p.correct <- plogis(y_bias*scale(y) + qlogis(prediction_accuracy))
- w <- predict(w.model, df) + rnorm(N, 0, 1)
- ## y = B0 + B1x + e
+ acc.x0 <- p.correct[df[,x==0]]
+ acc.x1 <- p.correct[df[,x==1]]
- df[,':='(w=w, w_pred = as.integer(w>0.5),u=u)]
- return(df)
-}
+ df[x==0,w:=rlogis(.N,qlogis(1-acc.x0))]
+ df[x==1,w:=rlogis(.N,qlogis(acc.x1))]
-schennach <- function(df){
+ df[,w_pred := as.integer(w>0.5)]
- fwx <- glm(x.obs~w, df, family=binomial(link='logit'))
- df[,xstar_pred := predict(fwx, df)]
- gxt <- lm(y ~ xstar_pred+g, df)
+ print(mean(df[z==0]$x == df[z==0]$w_pred))
+ print(mean(df[z==1]$x == df[z==1]$w_pred))
+ print(mean(df$w_pred == df$x))
+ print(mean(df[y>=0]$w_pred == df[y>=0]$x))
+ print(mean(df[y<=0]$w_pred == df[y<=0]$x))
+ 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=432, 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=51, 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=0.01)
+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, "--Bzx", help='Effect of z on x', default=0.3)
+parser <- add_argument(parser, "--Bzy", help='Effect of z on y', default=-0.3)
+parser <- add_argument(parser, "--Bxy", help='Effect of z on y', default=0.3)
+parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~x*y")
+parser <- add_argument(parser, "--y_bias", help='coefficient of y on the probability a classification is correct', default=-0.75)
+
args <- parse_args(parser)
B0 <- 0
-Bxy <- 0.2
-Bgy <- 0
-Bkx <- 2
-Bgx <- 0
+Bxy <- args$Bxy
+Bzy <- args$Bzy
+Bzx <- args$Bzx
+if(args$m < args$N){
-outline <- run_simulation(simulate_data(args$N, args$m, B0, Bxy, Bgy, Bkx, Bgx, args$seed)
- ,list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bgy'=Bgy, 'Bkx'=Bkx, 'Bgx'=Bgx, 'seed'=args$seed))
+ df <- simulate_data(args$N, args$m, B0, Bxy, Bzx, Bzy, args$seed, args$y_explained_variance, args$prediction_accuracy, y_bias=args$y_bias)
-outfile_lock <- lock(paste0(args$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)
-}
+ ## df.pc <- df[,.(x,y,z,w_pred)]
+ ## # df.pc <- df.pc[,err:=x-w_pred]
+ ## pc.df <- pc(suffStat=list(C=cor(df.pc),n=nrow(df.pc)),indepTest=gaussCItest,labels=names(df.pc),alpha=0.05)
+ ## plot(pc.df)
+
+ result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy, Bzx=args$Bzx, 'Bzy'=Bzy, 'seed'=args$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,error='')
-print(outline)
-write_feather(logdata, args$outfile)
-unlock(outfile_lock)
+ outline <- run_simulation(df, result, outcome_formula=y~x+z, proxy_formula=as.formula(args$proxy_formula), truth_formula=x~z)
+
+ 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)
+}
projects / ml_measurement_error_public.git / blobdiff