100 lines
4.1 KiB
R
100 lines
4.1 KiB
R
|
|
library(argparser)
|
|
library(mecor)
|
|
library(ggplot2)
|
|
library(data.table)
|
|
library(filelock)
|
|
library(arrow)
|
|
library(Amelia)
|
|
library(Zelig)
|
|
library(predictionError)
|
|
options(amelia.parallel="no",
|
|
amelia.ncpus=1)
|
|
setDTthreads(40)
|
|
|
|
source("irr_dv_simulation_base.R")
|
|
|
|
|
|
## one way to do it is by adding correlation to x.obs and y that isn't in w.
|
|
## in other words, the model is missing an important feature of x.obs that's related to y.
|
|
simulate_data <- function(N, m, B0, Bxy, Bzy, seed, prediction_accuracy=0.73, coder_accuracy=0.8){
|
|
set.seed(seed)
|
|
|
|
# make w and y dependent
|
|
z <- rbinom(N, 1, 0.5)
|
|
x <- rbinom(N, 1, 0.5)
|
|
|
|
ystar <- Bzy * z + Bxy * x + B0
|
|
y <- rbinom(N,1,plogis(ystar))
|
|
|
|
# glm(y ~ x + z, family="binomial")
|
|
|
|
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]
|
|
}
|
|
|
|
df[ (!is.na(y.obs)) ,y.obs.0 := abs(y.obs - rbinom(.N, 1, 1-coder_accuracy))]
|
|
df[ (!is.na(y.obs)) ,y.obs.1 := abs(y.obs - rbinom(.N, 1, 1-coder_accuracy))]
|
|
|
|
odds.y1 <- qlogis(prediction_accuracy)
|
|
odds.y0 <- qlogis(prediction_accuracy,lower.tail=F)
|
|
|
|
df[y==0,w:=plogis(rlogis(.N,odds.y0))]
|
|
df[y==1,w:=plogis(rlogis(.N,odds.y1))]
|
|
|
|
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=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=0.72)
|
|
## 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, "--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, "--outcome_formula", help='formula for the outcome variable', default="y~x+z")
|
|
parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~y")
|
|
parser <- add_argument(parser, "--coder_accuracy", help='How accurate are the coders?', default=0.8)
|
|
|
|
args <- parse_args(parser)
|
|
|
|
B0 <- 0
|
|
Bxy <- args$Bxy
|
|
Bzy <- args$Bzy
|
|
|
|
|
|
if(args$m < args$N){
|
|
df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, args$seed, args$prediction_accuracy, args$coder_accuracy)
|
|
|
|
# result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'x_bias_y0'=args$x_bias_y0,'x_bias_y1'=args$x_bias_y1,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)
|
|
result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)
|
|
|
|
outline <- run_simulation_depvar(df, result, outcome_formula = as.formula(args$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),fill=TRUE)
|
|
} else {
|
|
logdata <- as.data.table(outline)
|
|
}
|
|
|
|
print(outline)
|
|
write_feather(logdata, args$outfile)
|
|
unlock(outfile_lock)
|
|
}
|