Update stuff.
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@ -104,8 +104,9 @@ simulate_data <- function(N, m, B0, Bxy, Bzx, Bzy, seed, y_explained_variance=0.
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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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odds.x1 <- qlogis(prediction_accuracy) + y_bias*qlogis(pnorm(scale(df[x==1]$y)))
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odds.x0 <- qlogis(prediction_accuracy,lower.tail=F) + y_bias*qlogis(pnorm(scale(df[x==0]$y)))
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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]))
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odds.x0 <- qlogis(prediction_accuracy,lower.tail=F) + y_bias*qlogis(pnorm(resids[x==0]))
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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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@ -115,8 +116,7 @@ simulate_data <- function(N, m, B0, Bxy, Bzx, Bzy, seed, y_explained_variance=0.
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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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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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@ -124,7 +124,7 @@ simulate_data <- function(N, m, B0, Bxy, Bzx, Bzy, seed, y_explained_variance=0.
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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=1000, help="number of observations of w")
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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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@ -136,7 +136,7 @@ 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=-0.75)
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parser <- add_argument(parser, "--y_bias", help='coefficient of y on the probability a classification is correct', default=-1)
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parser <- add_argument(parser, "--truth_formula", help='formula for the true variable', default="x~z")
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args <- parse_args(parser)
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@ -31,7 +31,8 @@ source("simulation_base.R")
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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, Bzy, seed, prediction_accuracy=0.73){
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simulate_data <- function(N, m, B0, Bxy, Bzy, seed, prediction_accuracy=0.73, log.likelihood.gain = 0.1){
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set.seed(seed)
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set.seed(seed)
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# make w and y dependent
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@ -41,8 +42,6 @@ simulate_data <- function(N, m, B0, Bxy, Bzy, seed, prediction_accuracy=0.73){
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ystar <- Bzy * z + Bxy * x + B0
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y <- rbinom(N,1,plogis(ystar))
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# glm(y ~ x + z, family="binomial")
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df <- data.table(x=x,y=y,ystar=ystar,z=z)
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if(m < N){
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@ -66,7 +65,7 @@ simulate_data <- function(N, m, B0, Bxy, Bzy, seed, prediction_accuracy=0.73){
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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=1000, help="number of observations of w")
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parser <- add_argument(parser, "--N", default=10000, 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=17, 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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@ -74,8 +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=0.3)
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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, "--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, "--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")
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@ -2,19 +2,20 @@
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SHELL=bash
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Ns=[1000, 2000, 4000]
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ms=[200, 400, 800]
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ms=[100, 200, 400, 800]
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seeds=[$(shell seq -s, 1 250)]
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explained_variances=[0.1]
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all:remembr.RDS remember_irr.RDS
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supplement: remember_robustness_misspec.RDS
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srun=srun -A comdata -p compute-bigmem --time=6:00:00 --mem 4G -c 1
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srun=sbatch --wait --verbose run_job.sbatch
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joblists:example_1_jobs example_2_jobs example_3_jobs
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# test_true_z_jobs: test_true_z.R simulation_base.R
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# grid_sweep.py --command "Rscript test_true_z.R" --arg_dict '{"N":${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":${Bzx}}' --outfile test_true_z_jobsb
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# test_true_z.feather: test_true_z_jobs
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# rm -f test_true_z.feather
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@ -22,45 +23,45 @@ joblists:example_1_jobs example_2_jobs example_3_jobs
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# sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 test_true_z_jobs
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example_1_jobs: 01_two_covariates.R simulation_base.R
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grid_sweep.py --command "Rscript 01_two_covariates.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_1.feather"], "y_explained_variance":${explained_variances}, "Bzx":[0.1]}' --outfile example_1_jobs
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example_1_jobs: 01_two_covariates.R simulation_base.R grid_sweep.py
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sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 01_two_covariates.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_1.feather"], "y_explained_variance":${explained_variances}, "Bzx":[0.3]}' --outfile example_1_jobs
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example_1.feather: example_1_jobs
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rm -f example_1.feather
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sbatch --wait --verbose --array=1-$(shell cat example_1_jobs | wc -l) run_simulation.sbatch 0 example_1_jobs
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# sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 example_1_jobs
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example_2_jobs: 02_indep_differential.R simulation_base.R
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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":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y*z*x"]}' --outfile example_2_jobs
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example_2_jobs: 02_indep_differential.R simulation_base.R grid_sweep.py
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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":[0.3], "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-$(shell cat example_2_jobs | wc -l) run_simulation.sbatch 0 example_2_jobs
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sbatch --wait --verbose --array=1-$(shell cat example_1_jobs | wc -l) run_simulation.sbatch 0 example_2_jobs
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# sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 example_2_jobs
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# example_2_B_jobs: example_2_B.R
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# grid_sweep.py --command "Rscript example_2_B.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_2_B.feather"]}' --outfile example_2_B_jobs
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# sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript example_2_B.R" --arg_dict '{"N":${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
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grid_sweep.py --command "Rscript 03_depvar.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_3.feather"], "y_explained_variance":${explained_variances}}' --outfile example_3_jobs
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example_3_jobs: 03_depvar.R simulation_base.R grid_sweep.py
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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":[0.01],"Bzy":[-0.01],"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-$(shell cat example_3_jobs | wc -l) run_simulation.sbatch 0 example_3_jobs
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example_4_jobs: 04_depvar_differential.R simulation_base.R
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grid_sweep.py --command "Rscript 04_depvar_differential.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_4.feather"], "y_explained_variance":${explained_variances}}' --outfile example_4_jobs
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example_4_jobs: 04_depvar_differential.R simulation_base.R grid_sweep.py
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sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 04_depvar_differential.R" --arg_dict '{"N":${Ns},"Bxy":[0.01],"Bzy":[-0.01],"m":${ms}, "seed":${seeds}, "outfile":["example_4.feather"], "y_explained_variance":${explained_variances}}' --outfile example_4_jobs
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example_4.feather: example_4_jobs
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rm -f example_4.feather
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sbatch --wait --verbose --array=1-$(shell cat example_4_jobs | wc -l) run_simulation.sbatch 0 example_4_jobs
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remembr.RDS:example_1.feather example_2.feather example_3.feather example_4.feather plot_example.R plot_dv_example.R
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remembr.RDS:example_1.feather example_2.feather example_3.feather example_4.feather plot_example.R plot_dv_example.R summarize_estimator.R
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rm -f remembr.RDS
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${srun} Rscript plot_example.R --infile example_1.feather --name "plot.df.example.1"
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${srun} Rscript plot_example.R --infile example_2.feather --name "plot.df.example.2"
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@ -73,25 +74,51 @@ irr_ms = ${ms}
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irr_seeds=${seeds}
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irr_explained_variances=${explained_variances}
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example_5_jobs: 05_irr_indep.R irr_simulation_base.R
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grid_sweep.py --command "Rscript 05_irr_indep.R" --arg_dict '{"N":${irr_Ns},"m":${irr_ms}, "seed":${irr_seeds}, "outfile":["example_5.feather"], "y_explained_variance":${irr_explained_variances}}' --outfile example_5_jobs
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example_5_jobs: 05_irr_indep.R irr_simulation_base.R grid_sweep.py
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sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 05_irr_indep.R" --arg_dict '{"N":${irr_Ns},"m":${irr_ms}, "seed":${irr_seeds}, "outfile":["example_5.feather"], "y_explained_variance":${irr_explained_variances}}' --outfile example_5_jobs
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example_5.feather:example_5_jobs
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rm -f example_5.feather
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sbatch --wait --verbose --array=1-$(shell cat example_5_jobs | wc -l) run_simulation.sbatch 0 example_5_jobs
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example_6_jobs: 06_irr_dv.R irr_dv_simulation_base.R
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grid_sweep.py --command "Rscript 06_irr_dv.R" --arg_dict '{"N":${irr_Ns},"m":${irr_ms}, "seed":${irr_seeds}, "outfile":["example_6.feather"], "y_explained_variance":${irr_explained_variances}}' --outfile example_6_jobs
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example_6_jobs: 06_irr_dv.R irr_dv_simulation_base.R grid_sweep.py
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sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 06_irr_dv.R" --arg_dict '{"N":${irr_Ns},"m":${irr_ms}, "seed":${irr_seeds}, "outfile":["example_6.feather"], "y_explained_variance":${irr_explained_variances}}' --outfile example_6_jobs
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example_6.feather:example_6_jobs
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rm -f example_6.feather
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sbatch --wait --verbose --array=1-$(shell cat example_6_jobs | wc -l) run_simulation.sbatch 0 example_6_jobs
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remember_irr.RDS: example_5.feather example_6.feather plot_irr_example.R plot_irr_dv_example.R
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remember_irr.RDS: example_5.feather example_6.feather plot_irr_example.R plot_irr_dv_example.R summarize_estimator.R
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rm -f remember_irr.RDS
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${srun} Rscript plot_irr_example.R --infile example_5.feather --name "plot.df.example.5"
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${srun} Rscript plot_irr_dv_example.R --infile example_6.feather --name "plot.df.example.6"
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sbatch --wait --verbose run_job.sbatch Rscript plot_irr_example.R --infile example_5.feather --name "plot.df.example.5"
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sbatch --wait --verbose run_job.sbatch Rscript plot_irr_dv_example.R --infile example_6.feather --name "plot.df.example.6"
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robustness_1_jobs: 02_indep_differential.R simulation_base.R grid_sweep.py
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sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 02_indep_differential.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_1.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~1"]}' --outfile robustness_1_jobs
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robustness_1.feather: robustness_1_jobs
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rm -f robustness_1.feather
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sbatch --wait --verbose --array=1-$(shell cat robustness_1_jobs | wc -l) run_simulation.sbatch 0 robustness_1_jobs
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robustness_1_dv_jobs: simulation_base.R 04_depvar_differential.R grid_sweep.py
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${srun} bash -c "source ~/.bashrc && grid_sweep.py --command 'Rscript 04_depvar_differential.R' --arg_dict \"{'N':${Ns},'m':${ms}, 'seed':${seeds}, 'outfile':['robustness_1_dv.feather'], 'y_explained_variance':${explained_variances}, 'proxy_formula':['w_pred~y']}\" --outfile robustness_1_dv_jobs"
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robustness_1_dv.feather: robustness_1_dv_jobs
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rm -f robustness_1_dv.feather
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sbatch --wait --verbose --array=1-$(shell cat example_3_jobs | wc -l) run_simulation.sbatch 0 robustness_1_dv_jobs
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remember_robustness_misspec.RDS: robustness_1.feather robustness_1_dv.feather
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rm -f remember_robustness_misspec.RDS
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sbatch --wait --verbose run_job.sbatch Rscript plot_example.R --infile robustness_1.feather --name "plot.df.robustness.1" --remember-file "remember_robustness_misspec.RDS"
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sbatch --wait --verbose run_job.sbatch Rscript plot_dv_example.R --infile robustness_1_dv.feather --name "plot.df.robustness.1.dv" --remember-file "remember_robustness_mispec.RDS"
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clean:
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rm *.feather
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@ -100,7 +127,7 @@ clean:
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# sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 example_2_B_jobs
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# example_2_B_mecor_jobs:
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# grid_sweep.py --command "Rscript example_2_B_mecor.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_2_B_mecor.feather"]}' --outfile example_2_B_mecor_jobs
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# sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript example_2_B_mecor.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_2_B_mecor.feather"]}' --outfile example_2_B_mecor_jobs
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# example_2_B_mecor.feather:example_2_B_mecor.R example_2_B_mecor_jobs
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# rm -f example_2_B_mecor.feather
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@ -109,3 +136,4 @@ clean:
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.PHONY: supplement
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@ -2,8 +2,13 @@
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import fire
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from itertools import product
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import pyRemembeR
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def main(command, arg_dict, outfile):
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def main(command, arg_dict, outfile, remember_file='remember_grid_sweep.RDS'):
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remember = pyRemembeR.remember.Remember()
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remember.set_file(remember_file)
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remember[outfile] = arg_dict
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remember.save_to_r()
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keys = []
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values = []
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@ -82,7 +82,7 @@ run_simulation_depvar <- function(df, result, outcome_formula = y ~ x + z, rater
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Bxy.ci.upper.loco.mle = ci.upper['x'],
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Bxy.ci.lower.loco.mle = ci.lower['x'],
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Bzy.ci.upper.loco.mle = ci.upper['z'],
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Bzy.ci.lower.loco.mle = ci.upper['z']))
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Bzy.ci.lower.loco.mle = ci.lower['z']))
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print(rater_formula)
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print(proxy_formula)
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@ -82,7 +82,7 @@ run_simulation <- function(df, result, outcome_formula = y ~ x + z, proxy_formul
|
||||
Bxy.ci.upper.loco.mle = ci.upper['x'],
|
||||
Bxy.ci.lower.loco.mle = ci.lower['x'],
|
||||
Bzy.ci.upper.loco.mle = ci.upper['z'],
|
||||
Bzy.ci.lower.loco.mle = ci.upper['z']))
|
||||
Bzy.ci.lower.loco.mle = ci.lower['z']))
|
||||
|
||||
## print(rater_formula)
|
||||
## print(proxy_formula)
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
library(formula.tools)
|
||||
library(matrixStats)
|
||||
|
||||
library(bbmle)
|
||||
## df: dataframe to model
|
||||
## outcome_formula: formula for y | x, z
|
||||
## outcome_family: family for y | x, z
|
||||
@ -17,7 +17,7 @@ library(matrixStats)
|
||||
|
||||
|
||||
## 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')){
|
||||
measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='logit'), proxy_formula, proxy_family=binomial(link='logit'),method='optim'){
|
||||
|
||||
nll <- function(params){
|
||||
df.obs <- model.frame(outcome_formula, df)
|
||||
@ -98,12 +98,23 @@ measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='lo
|
||||
start <- rep(0.1,length(params))
|
||||
names(start) <- params
|
||||
|
||||
if(method=='optim'){
|
||||
fit <- optim(start, fn = nll, lower=lower, method='L-BFGS-B', hessian=TRUE, control=list(maxit=1e6))
|
||||
} else {
|
||||
quoted.names <- gsub("[\\(\\)]",'',names(start))
|
||||
print(quoted.names)
|
||||
text <- paste("function(", paste0(quoted.names,'=',start,collapse=','),"){params<-c(",paste0(quoted.names,collapse=','),");return(nll(params))}")
|
||||
|
||||
measerr_mle_nll <- eval(parse(text=text))
|
||||
names(start) <- quoted.names
|
||||
names(lower) <- quoted.names
|
||||
fit <- mle2(minuslogl=measerr_mle_nll, start=start, lower=lower, parnames=params,control=list(maxit=1e6),method='L-BFGS-B')
|
||||
}
|
||||
return(fit)
|
||||
}
|
||||
|
||||
## Experimental, and not necessary if errors are independent.
|
||||
measerr_irr_mle <- function(df, outcome_formula, outcome_family=gaussian(), rater_formula, proxy_formula, proxy_family=binomial(link='logit'), truth_formula, truth_family=binomial(link='logit')){
|
||||
measerr_irr_mle <- function(df, outcome_formula, outcome_family=gaussian(), rater_formula, proxy_formula, proxy_family=binomial(link='logit'), truth_formula, truth_family=binomial(link='logit'),method='optim'){
|
||||
|
||||
### in this scenario, the ground truth also has measurement error, but we have repeated measures for it.
|
||||
|
||||
@ -293,14 +304,28 @@ measerr_irr_mle <- function(df, outcome_formula, outcome_family=gaussian(), rate
|
||||
start <- rep(0.1,length(params))
|
||||
names(start) <- params
|
||||
|
||||
|
||||
if(method=='optim'){
|
||||
fit <- optim(start, fn = nll, lower=lower, method='L-BFGS-B', hessian=TRUE, control=list(maxit=1e6))
|
||||
} else {
|
||||
|
||||
quoted.names <- gsub("[\\(\\)]",'',names(start))
|
||||
print(quoted.names)
|
||||
text <- paste("function(", paste0(quoted.names,'=',start,collapse=','),"){params<-c(",paste0(quoted.names,collapse=','),");return(nll(params))}")
|
||||
|
||||
measerr_mle_nll <- eval(parse(text=text))
|
||||
names(start) <- quoted.names
|
||||
names(lower) <- quoted.names
|
||||
fit <- mle2(minuslogl=measerr_mle_nll, start=start, lower=lower, parnames=params,control=list(maxit=1e6),method='L-BFGS-B')
|
||||
}
|
||||
|
||||
return(fit)
|
||||
}
|
||||
|
||||
|
||||
measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_formula, proxy_family=binomial(link='logit'), truth_formula, truth_family=binomial(link='logit')){
|
||||
measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_formula, proxy_family=binomial(link='logit'), truth_formula, truth_family=binomial(link='logit'),method='optim'){
|
||||
|
||||
measrr_mle_nll <- function(params){
|
||||
measerr_mle_nll <- function(params){
|
||||
df.obs <- model.frame(outcome_formula, df)
|
||||
proxy.variable <- all.vars(proxy_formula)[1]
|
||||
proxy.model.matrix <- model.matrix(proxy_formula, df)
|
||||
@ -426,7 +451,20 @@ measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_fo
|
||||
start <- rep(0.1,length(params))
|
||||
names(start) <- params
|
||||
|
||||
fit <- optim(start, fn = measrr_mle_nll, lower=lower, method='L-BFGS-B', hessian=TRUE, control=list(maxit=1e6))
|
||||
if(method=='optim'){
|
||||
fit <- optim(start, fn = measerr_mle_nll, lower=lower, method='L-BFGS-B', hessian=TRUE, control=list(maxit=1e6))
|
||||
} else { # method='mle2'
|
||||
|
||||
quoted.names <- gsub("[\\(\\)]",'',names(start))
|
||||
|
||||
text <- paste("function(", paste0(quoted.names,'=',start,collapse=','),"){params<-c(",paste0(quoted.names,collapse=','),");return(measerr_mle_nll(params))}")
|
||||
|
||||
measerr_mle_nll_mle <- eval(parse(text=text))
|
||||
names(start) <- quoted.names
|
||||
names(lower) <- quoted.names
|
||||
fit <- mle2(minuslogl=measerr_mle_nll_mle, start=start, lower=lower, parnames=params,control=list(maxit=1e6),method='L-BFGS-B')
|
||||
}
|
||||
|
||||
return(fit)
|
||||
}
|
||||
|
||||
|
||||
@ -7,49 +7,51 @@ library(argparser)
|
||||
|
||||
parser <- arg_parser("Simulate data and fit corrected models.")
|
||||
parser <- add_argument(parser, "--infile", default="", help="name of the file to read.")
|
||||
parser <- add_argument(parser, "--remember-file", default="remembr.RDS", help="name of the remember file.")
|
||||
parser <- add_argument(parser, "--name", default="", help="The name to safe the data to in the remember file.")
|
||||
args <- parse_args(parser)
|
||||
|
||||
summarize.estimator <- function(df, suffix='naive', coefname='x'){
|
||||
## summarize.estimator <- function(df, suffix='naive', coefname='x'){
|
||||
|
||||
part <- df[,c('N',
|
||||
'm',
|
||||
'Bxy',
|
||||
paste0('B',coefname,'y.est.',suffix),
|
||||
paste0('B',coefname,'y.ci.lower.',suffix),
|
||||
paste0('B',coefname,'y.ci.upper.',suffix),
|
||||
'y_explained_variance',
|
||||
'Bzy'
|
||||
),
|
||||
with=FALSE]
|
||||
## part <- df[,c('N',
|
||||
## 'm',
|
||||
## 'Bxy',
|
||||
## paste0('B',coefname,'y.est.',suffix),
|
||||
## paste0('B',coefname,'y.ci.lower.',suffix),
|
||||
## paste0('B',coefname,'y.ci.upper.',suffix),
|
||||
## 'y_explained_variance',
|
||||
## 'Bzy'
|
||||
## ),
|
||||
## 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)]]))
|
||||
zero.in.ci <- as.integer(0 >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (0 <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]])
|
||||
bias <- part$Bxy - part[[paste0('B',coefname,'y.est.',suffix)]]
|
||||
sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))
|
||||
## 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)]]))
|
||||
## zero.in.ci <- as.integer(0 >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (0 <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]])
|
||||
## bias <- part$Bxy - part[[paste0('B',coefname,'y.est.',suffix)]]
|
||||
## sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))
|
||||
|
||||
part <- part[,':='(true.in.ci = true.in.ci,
|
||||
zero.in.ci = zero.in.ci,
|
||||
bias=bias,
|
||||
sign.correct =sign.correct)]
|
||||
## part <- part[,':='(true.in.ci = true.in.ci,
|
||||
## zero.in.ci = zero.in.ci,
|
||||
## 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)]]),
|
||||
var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
|
||||
est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.95),
|
||||
est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.05),
|
||||
N.sims = .N,
|
||||
p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
|
||||
variable=coefname,
|
||||
method=suffix
|
||||
),
|
||||
by=c("N","m",'Bzy','y_explained_variance')
|
||||
]
|
||||
## 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)]]),
|
||||
## var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
|
||||
## est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.95),
|
||||
## est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.05),
|
||||
## N.sims = .N,
|
||||
## p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
|
||||
## variable=coefname,
|
||||
## method=suffix
|
||||
## ),
|
||||
## by=c("N","m",'Bzy','y_explained_variance')
|
||||
## ]
|
||||
|
||||
return(part.plot)
|
||||
}
|
||||
## return(part.plot)
|
||||
## }
|
||||
|
||||
source("summarize_estimator.R")
|
||||
|
||||
build_plot_dataset <- function(df){
|
||||
|
||||
@ -82,12 +84,23 @@ build_plot_dataset <- function(df){
|
||||
return(plot.df)
|
||||
}
|
||||
|
||||
|
||||
df <- read_feather(args$infile)
|
||||
plot.df <- build_plot_dataset(df)
|
||||
change.remember.file(args$remember_file, clear=TRUE)
|
||||
sims.df <- read_feather(args$infile)
|
||||
sims.df[,Bzx:=NA]
|
||||
sims.df[,accuracy_imbalance_difference:=NA]
|
||||
plot.df <- build_plot_dataset(sims.df)
|
||||
|
||||
remember(plot.df,args$name)
|
||||
|
||||
set.remember.prefix(gsub("plot.df.","",args$name))
|
||||
|
||||
remember(median(sims.df$cor.xz),'med.cor.xz')
|
||||
remember(median(sims.df$accuracy),'med.accuracy')
|
||||
remember(median(sims.df$error.cor.x),'med.error.cor.x')
|
||||
remember(median(sims.df$lik.ratio),'med.lik.ratio')
|
||||
|
||||
|
||||
|
||||
|
||||
## df[gmm.ER_pval<0.05]
|
||||
## plot.df.test <- plot.df[,':='(method=factor(method,levels=c("Naive","Multiple imputation", "Multiple imputation (Classifier features unobserved)","Regression Calibration","2SLS+gmm","Bespoke MLE", "Feasible"),ordered=T),
|
||||
|
||||
@ -5,52 +5,58 @@ library(ggplot2)
|
||||
library(filelock)
|
||||
library(argparser)
|
||||
|
||||
source("summarize_estimator.R")
|
||||
|
||||
|
||||
parser <- arg_parser("Simulate data and fit corrected models.")
|
||||
parser <- add_argument(parser, "--infile", default="", help="name of the file to read.")
|
||||
parser <- add_argument(parser, "--remember-file", default="remembr.RDS", help="name of the remember file.")
|
||||
parser <- add_argument(parser, "--name", default="", help="The name to safe the data to in the remember file.")
|
||||
args <- parse_args(parser)
|
||||
|
||||
summarize.estimator <- function(df, suffix='naive', coefname='x'){
|
||||
|
||||
part <- df[,c('N',
|
||||
'm',
|
||||
'Bxy',
|
||||
paste0('B',coefname,'y.est.',suffix),
|
||||
paste0('B',coefname,'y.ci.lower.',suffix),
|
||||
paste0('B',coefname,'y.ci.upper.',suffix),
|
||||
'y_explained_variance',
|
||||
'Bzx',
|
||||
'Bzy',
|
||||
'accuracy_imbalance_difference'
|
||||
),
|
||||
with=FALSE]
|
||||
|
||||
true.in.ci <- as.integer((part$Bxy >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (part$Bxy <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]]))
|
||||
zero.in.ci <- as.integer(0 >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (0 <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]])
|
||||
bias <- part$Bxy - part[[paste0('B',coefname,'y.est.',suffix)]]
|
||||
sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))
|
||||
## summarize.estimator <- function(df, suffix='naive', coefname='x'){
|
||||
|
||||
part <- part[,':='(true.in.ci = true.in.ci,
|
||||
zero.in.ci = zero.in.ci,
|
||||
bias=bias,
|
||||
sign.correct =sign.correct)]
|
||||
## part <- df[,c('N',
|
||||
## 'm',
|
||||
## 'Bxy',
|
||||
## paste0('B',coefname,'y.est.',suffix),
|
||||
## paste0('B',coefname,'y.ci.lower.',suffix),
|
||||
## paste0('B',coefname,'y.ci.upper.',suffix),
|
||||
## 'y_explained_variance',
|
||||
## 'Bzx',
|
||||
## 'Bzy',
|
||||
## 'accuracy_imbalance_difference'
|
||||
## ),
|
||||
## with=FALSE]
|
||||
|
||||
part.plot <- part[, .(p.true.in.ci = mean(true.in.ci),
|
||||
mean.bias = mean(bias),
|
||||
mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
|
||||
var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
|
||||
est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.95,na.rm=T),
|
||||
est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.05,na.rm=T),
|
||||
N.sims = .N,
|
||||
p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
|
||||
variable=coefname,
|
||||
method=suffix
|
||||
),
|
||||
by=c("N","m",'y_explained_variance','Bzx', 'Bzy', 'accuracy_imbalance_difference')
|
||||
]
|
||||
## true.in.ci <- as.integer((part$Bxy >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (part$Bxy <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]]))
|
||||
## zero.in.ci <- as.integer(0 >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (0 <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]])
|
||||
## bias <- part$Bxy - part[[paste0('B',coefname,'y.est.',suffix)]]
|
||||
## sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))
|
||||
|
||||
return(part.plot)
|
||||
}
|
||||
## part <- part[,':='(true.in.ci = true.in.ci,
|
||||
## zero.in.ci = zero.in.ci,
|
||||
## bias=bias,
|
||||
## sign.correct =sign.correct)]
|
||||
|
||||
## part.plot <- part[, .(p.true.in.ci = mean(true.in.ci),
|
||||
## mean.bias = mean(bias),
|
||||
## mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
|
||||
## var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
|
||||
## est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.95,na.rm=T),
|
||||
## est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.05,na.rm=T),
|
||||
## N.sims = .N,
|
||||
## p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
|
||||
## variable=coefname,
|
||||
## method=suffix
|
||||
## ),
|
||||
## by=c("N","m",'y_explained_variance','Bzx', 'Bzy', 'accuracy_imbalance_difference')
|
||||
## ]
|
||||
|
||||
## return(part.plot)
|
||||
## }
|
||||
|
||||
build_plot_dataset <- function(df){
|
||||
|
||||
@ -98,24 +104,40 @@ build_plot_dataset <- function(df){
|
||||
}
|
||||
|
||||
|
||||
plot.df <- read_feather(args$infile)
|
||||
print(unique(plot.df$N))
|
||||
sims.df <- read_feather(args$infile)
|
||||
print(unique(sims.df$N))
|
||||
|
||||
# df <- df[apply(df,1,function(x) !any(is.na(x)))]
|
||||
|
||||
if(!('Bzx' %in% names(plot.df)))
|
||||
plot.df[,Bzx:=NA]
|
||||
if(!('Bzx' %in% names(sims.df)))
|
||||
sims.df[,Bzx:=NA]
|
||||
|
||||
if(!('accuracy_imbalance_difference' %in% names(plot.df)))
|
||||
plot.df[,accuracy_imbalance_difference:=NA]
|
||||
if(!('accuracy_imbalance_difference' %in% names(sims.df)))
|
||||
sims.df[,accuracy_imbalance_difference:=NA]
|
||||
|
||||
unique(plot.df[,'accuracy_imbalance_difference'])
|
||||
unique(sims.df[,'accuracy_imbalance_difference'])
|
||||
|
||||
change.remember.file(args$remember_file, clear=TRUE)
|
||||
#plot.df <- build_plot_dataset(df[accuracy_imbalance_difference==0.1][N==700])
|
||||
plot.df <- build_plot_dataset(plot.df)
|
||||
plot.df <- build_plot_dataset(sims.df)
|
||||
|
||||
remember(plot.df,args$name)
|
||||
|
||||
set.remember.prefix(gsub("plot.df.","",args$name))
|
||||
|
||||
remember(median(sims.df$cor.xz),'med.cor.xz')
|
||||
remember(median(sims.df$accuracy),'med.accuracy')
|
||||
remember(median(sims.df$accuracy.y0),'med.accuracy.y0')
|
||||
remember(median(sims.df$accuracy.y1),'med.accuracy.y1')
|
||||
remember(median(sims.df$fpr),'med.fpr')
|
||||
remember(median(sims.df$fpr.y0),'med.fpr.y0')
|
||||
remember(median(sims.df$fpr.y1),'med.fpr.y1')
|
||||
remember(median(sims.df$fnr),'med.fnr')
|
||||
remember(median(sims.df$fnr.y0),'med.fnr.y0')
|
||||
remember(median(sims.df$fnr.y1),'med.fnr.y1')
|
||||
|
||||
remember(median(sims.df$cor.resid.w_pred),'cor.resid.w_pred')
|
||||
|
||||
#ggplot(df,aes(x=Bxy.est.mle)) + geom_histogram() + facet_grid(accuracy_imbalance_difference ~ Bzy)
|
||||
|
||||
## ## ## df[gmm.ER_pval<0.05]
|
||||
|
||||
@ -5,15 +5,16 @@
|
||||
#SBATCH --partition=compute-bigmem
|
||||
## Resources
|
||||
#SBATCH --nodes=1
|
||||
## Walltime (12 hours)
|
||||
#SBATCH --time=1:00:00
|
||||
## Walltime (4 hours)
|
||||
#SBATCH --time=4:00:00
|
||||
## Memory per node
|
||||
#SBATCH --mem=8G
|
||||
#SBATCH --mem=4G
|
||||
#SBATCH --cpus-per-task=1
|
||||
#SBATCH --ntasks-per-node=1
|
||||
#SBATCH --chdir /gscratch/comdata/users/nathante/ml_measurement_error_public/simulations
|
||||
#SBATCH --output=simulation_jobs/%A_%a.out
|
||||
#SBATCH --error=simulation_jobs/%A_%a.err
|
||||
source ~/.bashrc
|
||||
|
||||
TASK_NUM=$(($SLURM_ARRAY_TASK_ID + $1))
|
||||
TASK_CALL=$(sed -n ${TASK_NUM}p $2)
|
||||
|
||||
@ -210,11 +210,19 @@ run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formu
|
||||
|
||||
accuracy <- df[,mean(w_pred==y)]
|
||||
result <- append(result, list(accuracy=accuracy))
|
||||
error.cor.x <- cor(df$x, df$w - df$x)
|
||||
result <- append(result, list(error.cor.x = error.cor.x))
|
||||
|
||||
model.null <- glm(y~1, data=df,family=binomial(link='logit'))
|
||||
(model.true <- glm(y ~ x + z, data=df,family=binomial(link='logit')))
|
||||
(lik.ratio <- exp(logLik(model.true) - logLik(model.null)))
|
||||
|
||||
true.ci.Bxy <- confint(model.true)['x',]
|
||||
true.ci.Bzy <- confint(model.true)['z',]
|
||||
|
||||
|
||||
result <- append(result, list(lik.ratio=lik.ratio))
|
||||
|
||||
result <- append(result, list(Bxy.est.true=coef(model.true)['x'],
|
||||
Bzy.est.true=coef(model.true)['z'],
|
||||
Bxy.ci.upper.true = true.ci.Bxy[2],
|
||||
@ -322,8 +330,33 @@ run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formu
|
||||
run_simulation <- function(df, result, outcome_formula=y~x+z, proxy_formula=NULL, truth_formula=NULL){
|
||||
|
||||
accuracy <- df[,mean(w_pred==x)]
|
||||
result <- append(result, list(accuracy=accuracy))
|
||||
accuracy.y0 <- df[y<=0,mean(w_pred==x)]
|
||||
accuracy.y1 <- df[y>=0,mean(w_pred==x)]
|
||||
cor.y.xi <- cor(df$x - df$w_pred, df$y)
|
||||
|
||||
fnr <- df[w_pred==0,mean(w_pred!=x)]
|
||||
fnr.y0 <- df[(w_pred==0) & (y<=0),mean(w_pred!=x)]
|
||||
fnr.y1 <- df[(w_pred==0) & (y>=0),mean(w_pred!=x)]
|
||||
|
||||
fpr <- df[w_pred==1,mean(w_pred!=x)]
|
||||
fpr.y0 <- df[(w_pred==1) & (y<=0),mean(w_pred!=x)]
|
||||
fpr.y1 <- df[(w_pred==1) & (y>=0),mean(w_pred!=x)]
|
||||
cor.resid.w_pred <- cor(resid(lm(y~x+z,df)),df$w_pred)
|
||||
|
||||
result <- append(result, list(accuracy=accuracy,
|
||||
accuracy.y0=accuracy.y0,
|
||||
accuracy.y1=accuracy.y1,
|
||||
cor.y.xi=cor.y.xi,
|
||||
fnr=fnr,
|
||||
fnr.y0=fnr.y0,
|
||||
fnr.y1=fnr.y1,
|
||||
fpr=fpr,
|
||||
fpr.y0=fpr.y0,
|
||||
fpr.y1=fpr.y1,
|
||||
cor.resid.w_pred=cor.resid.w_pred
|
||||
))
|
||||
|
||||
result <- append(result, list(cor.xz=cor(df$x,df$z)))
|
||||
(model.true <- lm(y ~ x + z, data=df))
|
||||
true.ci.Bxy <- confint(model.true)['x',]
|
||||
true.ci.Bzy <- confint(model.true)['z',]
|
||||
|
||||
@ -14,9 +14,10 @@ summarize.estimator <- function(df, suffix='naive', coefname='x'){
|
||||
),
|
||||
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)]]))
|
||||
zero.in.ci <- as.integer(0 >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (0 <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]])
|
||||
bias <- part$Bxy - part[[paste0('B',coefname,'y.est.',suffix)]]
|
||||
bias <- part[[paste0('B',coefname,'y')]] - part[[paste0('B',coefname,'y.est.',suffix)]]
|
||||
sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))
|
||||
|
||||
part <- part[,':='(true.in.ci = true.in.ci,
|
||||
@ -28,8 +29,15 @@ summarize.estimator <- function(df, suffix='naive', coefname='x'){
|
||||
mean.bias = mean(bias),
|
||||
mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
|
||||
var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
|
||||
est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.95,na.rm=T),
|
||||
est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.05,na.rm=T),
|
||||
est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.975,na.rm=T),
|
||||
est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.025,na.rm=T),
|
||||
mean.ci.upper = mean(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]]),
|
||||
mean.ci.lower = mean(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]]),
|
||||
ci.upper.975 = quantile(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]],0.975,na.rm=T),
|
||||
ci.upper.025 = quantile(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]],0.025,na.rm=T),
|
||||
ci.lower.975 = quantile(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]],0.975,na.rm=T),
|
||||
ci.lower.025 = quantile(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]],0.025,na.rm=T),
|
||||
N.ci.is.NA = sum(is.na(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]])),
|
||||
N.sims = .N,
|
||||
p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
|
||||
variable=coefname,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user