add more simulations.
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@ -1,16 +1,9 @@
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#!/usr/bin/env Rscript
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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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@ -31,8 +24,9 @@ simulate_data <- function(N,
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accuracy_imbalance_difference = 0.3) {
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set.seed(seed)
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# make w and y dependent
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z <- rbinom(N, 1, plogis(qlogis(Pz)))
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x <- rbinom(N, 1, plogis(Bzx * z + qlogis(Px)))
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l <- rnorm(N)
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z <- rbinom(N, 1, plogis(qlogis(Pz)+Bzx*l))
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x <- rbinom(N, 1, plogis(-1*Bzx*l + qlogis(Px)))
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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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@ -2,7 +2,7 @@
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SHELL=bash
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Ns=[1000,5000,10000]
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ms=[100,200,400]
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ms=[100,200,400,1000]
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seeds=[$(shell seq -s, 1 500)]
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explained_variances=[0.1]
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@ -17,7 +17,7 @@ joblists:example_1_jobs example_2_jobs example_3_jobs
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grid_sweep_script=../simulations/grid_sweep.py
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example_1_jobs: 01_indep_differential.R simulation_base.R ${grid_sweep_script} skx_dev.sbatch ../misclassificationmodels
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${srun} uv run ${grid_sweep_script} --command 01_indep_differential.R --arg_dict '{"N":${Ns},"m":${ms},"seed":${seeds},"outfile":["example_1.feather"],"y_explained_variance":${explained_variances},"Bzx":[1]}' --outfile example_1_jobs --remember_file=remember_grid_sweep.rds
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${srun} uv run ${grid_sweep_script} --command 01_indep_differential.R --arg_dict '{"N":${Ns},"m":${ms},"seed":${seeds},"outfile":["example_1.feather"],"y_explained_variance":${explained_variances},"Bzx":[1],"prediction_accuracy":0.825}' --outfile example_1_jobs --remember_file=remember_grid_sweep.rds
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example_1.feather: example_1_jobs my_pylauncher.py
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sbatch -J "multiple iv measerr correction" spr_2.sbatch my_pylauncher.py $< --cores 1
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