groceryheist/ml_measurement_error_public
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Update stuff.

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This commit is contained in:
Nathan TeBlunthuis 2022-10-07 10:42:50 -07:00
parent 979dc14b68
commit b52b4f7daa
12 changed files with 282 additions and 135 deletions
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12
simulations/02_indep_differential.R
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@ -104,8 +104,9 @@ simulate_data <- function(N, m, B0, Bxy, Bzx, Bzy, seed, y_explained_variance=0.
## print(mean(df[z==1]$x == df[z==1]$w_pred)) ## print(mean(df[z==1]$x == df[z==1]$w_pred))
## print(mean(df$w_pred == df$x)) ## print(mean(df$w_pred == df$x))
odds.x1 <- qlogis(prediction_accuracy) + y_bias*qlogis(pnorm(scale(df[x==1]$y))) resids <- resid(lm(y~x + z))
odds.x0 <- qlogis(prediction_accuracy,lower.tail=F) + y_bias*qlogis(pnorm(scale(df[x==0]$y))) odds.x1 <- qlogis(prediction_accuracy) + y_bias*qlogis(pnorm(resids[x==1]))
odds.x0 <- qlogis(prediction_accuracy,lower.tail=F) + y_bias*qlogis(pnorm(resids[x==0]))
## acc.x0 <- p.correct[df[,x==0]] ## acc.x0 <- p.correct[df[,x==0]]
## acc.x1 <- p.correct[df[,x==1]] ## acc.x1 <- p.correct[df[,x==1]]
@ -115,8 +116,7 @@ simulate_data <- function(N, m, B0, Bxy, Bzx, Bzy, seed, y_explained_variance=0.
df[,w_pred := as.integer(w > 0.5)] df[,w_pred := as.integer(w > 0.5)]
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$w_pred == df$x))
print(mean(df[y>=0]$w_pred == df[y>=0]$x)) print(mean(df[y>=0]$w_pred == df[y>=0]$x))
print(mean(df[y<=0]$w_pred == df[y<=0]$x)) print(mean(df[y<=0]$w_pred == df[y<=0]$x))
@ -124,7 +124,7 @@ simulate_data <- function(N, m, B0, Bxy, Bzx, Bzy, seed, y_explained_variance=0.
} }
parser <- arg_parser("Simulate data and fit corrected models") 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, "--N", default=5000, 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, "--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, "--seed", default=51, help='seed for the rng')
parser <- add_argument(parser, "--outfile", help='output file', default='example_2.feather') parser <- add_argument(parser, "--outfile", help='output file', default='example_2.feather')
@ -136,7 +136,7 @@ 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, "--Bxy", help='Effect 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, "--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*z*x") parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~y*z*x")
parser <- add_argument(parser, "--y_bias", help='coefficient of y on the probability a classification is correct', default=-0.75) parser <- add_argument(parser, "--y_bias", help='coefficient of y on the probability a classification is correct', default=-1)
parser <- add_argument(parser, "--truth_formula", help='formula for the true variable', default="x~z") parser <- add_argument(parser, "--truth_formula", help='formula for the true variable', default="x~z")
args <- parse_args(parser) args <- parse_args(parser)

11
simulations/03_depvar.R
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@ -31,7 +31,8 @@ source("simulation_base.R")
## one way to do it is by adding correlation to x.obs and y that isn't in w. ## 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. ## 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){ simulate_data <- function(N, m, B0, Bxy, Bzy, seed, prediction_accuracy=0.73, log.likelihood.gain = 0.1){
set.seed(seed)
set.seed(seed) set.seed(seed)
# make w and y dependent # make w and y dependent
@ -41,8 +42,6 @@ simulate_data <- function(N, m, B0, Bxy, Bzy, seed, prediction_accuracy=0.73){
ystar <- Bzy * z + Bxy * x + B0 ystar <- Bzy * z + Bxy * x + B0
y <- rbinom(N,1,plogis(ystar)) y <- rbinom(N,1,plogis(ystar))
# glm(y ~ x + z, family="binomial")
df <- data.table(x=x,y=y,ystar=ystar,z=z) df <- data.table(x=x,y=y,ystar=ystar,z=z)
if(m < N){ if(m < N){
@ -66,7 +65,7 @@ simulate_data <- function(N, m, B0, Bxy, Bzy, seed, prediction_accuracy=0.73){
} }
parser <- arg_parser("Simulate data and fit corrected models") 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, "--N", default=10000, 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, "--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, "--seed", default=17, help='seed for the rng')
parser <- add_argument(parser, "--outfile", help='output file', default='example_2.feather') parser <- add_argument(parser, "--outfile", help='output file', default='example_2.feather')
@ -74,8 +73,8 @@ parser <- add_argument(parser, "--y_explained_variance", help='what proportion o
parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.72) 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_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, "--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, "--Bxy", help='coefficient of x on y', default=0.01)
parser <- add_argument(parser, "--Bzy", help='coeffficient of z on y', default=-0.3) parser <- add_argument(parser, "--Bzy", help='coeffficient of z on y', default=-0.01)
parser <- add_argument(parser, "--outcome_formula", help='formula for the outcome variable', default="y~x+z") 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, "--proxy_formula", help='formula for the proxy variable', default="w_pred~y")

72
simulations/Makefile
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@ -2,19 +2,20 @@
SHELL=bash SHELL=bash
Ns=[1000, 2000, 4000] Ns=[1000, 2000, 4000]
ms=[200, 400, 800] ms=[100, 200, 400, 800]
seeds=[$(shell seq -s, 1 250)] seeds=[$(shell seq -s, 1 250)]
explained_variances=[0.1] explained_variances=[0.1]
all:remembr.RDS remember_irr.RDS all:remembr.RDS remember_irr.RDS
supplement: remember_robustness_misspec.RDS
srun=srun -A comdata -p compute-bigmem --time=6:00:00 --mem 4G -c 1 srun=sbatch --wait --verbose run_job.sbatch
joblists:example_1_jobs example_2_jobs example_3_jobs joblists:example_1_jobs example_2_jobs example_3_jobs
# test_true_z_jobs: test_true_z.R simulation_base.R # test_true_z_jobs: test_true_z.R simulation_base.R
# 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":${Bzx}}' --outfile test_true_z_jobsb # sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript test_true_z.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["test_true_z.feather"], "y_explained_variancevari":${explained_variances}, "Bzx":${Bzx}}' --outfile test_true_z_jobsb
# test_true_z.feather: test_true_z_jobs # test_true_z.feather: test_true_z_jobs
# rm -f test_true_z.feather # rm -f test_true_z.feather
@ -22,45 +23,45 @@ joblists:example_1_jobs example_2_jobs example_3_jobs
# sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 test_true_z_jobs # sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 test_true_z_jobs
example_1_jobs: 01_two_covariates.R simulation_base.R example_1_jobs: 01_two_covariates.R simulation_base.R grid_sweep.py
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 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
example_1.feather: example_1_jobs example_1.feather: example_1_jobs
rm -f example_1.feather rm -f example_1.feather
sbatch --wait --verbose --array=1-$(shell cat example_1_jobs | wc -l) run_simulation.sbatch 0 example_1_jobs sbatch --wait --verbose --array=1-$(shell cat example_1_jobs | wc -l) run_simulation.sbatch 0 example_1_jobs
# sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 example_1_jobs # sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 example_1_jobs
example_2_jobs: 02_indep_differential.R simulation_base.R example_2_jobs: 02_indep_differential.R simulation_base.R grid_sweep.py
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 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
example_2.feather: example_2_jobs example_2.feather: example_2_jobs
rm -f example_2.feather rm -f example_2.feather
sbatch --wait --verbose --array=1-$(shell cat example_2_jobs | wc -l) run_simulation.sbatch 0 example_2_jobs sbatch --wait --verbose --array=1-$(shell cat example_1_jobs | wc -l) run_simulation.sbatch 0 example_2_jobs
# sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 example_2_jobs # sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 example_2_jobs
# example_2_B_jobs: example_2_B.R # example_2_B_jobs: example_2_B.R
# grid_sweep.py --command "Rscript example_2_B.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_2_B.feather"]}' --outfile example_2_B_jobs # sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript example_2_B.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_2_B.feather"]}' --outfile example_2_B_jobs
# example_2_B.feather: example_2_B_jobs # example_2_B.feather: example_2_B_jobs
# rm -f example_2_B.feather # rm -f example_2_B.feather
# sbatch --wait --verbose --array=1-3000 run_simulation.sbatch 0 example_2_B_jobs # sbatch --wait --verbose --array=1-3000 run_simulation.sbatch 0 example_2_B_jobs
example_3_jobs: 03_depvar.R simulation_base.R example_3_jobs: 03_depvar.R simulation_base.R grid_sweep.py
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 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
example_3.feather: example_3_jobs example_3.feather: example_3_jobs
rm -f example_3.feather rm -f example_3.feather
sbatch --wait --verbose --array=1-$(shell cat example_3_jobs | wc -l) run_simulation.sbatch 0 example_3_jobs sbatch --wait --verbose --array=1-$(shell cat example_3_jobs | wc -l) run_simulation.sbatch 0 example_3_jobs
example_4_jobs: 04_depvar_differential.R simulation_base.R example_4_jobs: 04_depvar_differential.R simulation_base.R grid_sweep.py
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 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
example_4.feather: example_4_jobs example_4.feather: example_4_jobs
rm -f example_4.feather rm -f example_4.feather
sbatch --wait --verbose --array=1-$(shell cat example_4_jobs | wc -l) run_simulation.sbatch 0 example_4_jobs sbatch --wait --verbose --array=1-$(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 remembr.RDS:example_1.feather example_2.feather example_3.feather example_4.feather plot_example.R plot_dv_example.R summarize_estimator.R
rm -f remembr.RDS rm -f remembr.RDS
${srun} Rscript plot_example.R --infile example_1.feather --name "plot.df.example.1" ${srun} Rscript plot_example.R --infile example_1.feather --name "plot.df.example.1"
${srun} Rscript plot_example.R --infile example_2.feather --name "plot.df.example.2" ${srun} Rscript plot_example.R --infile example_2.feather --name "plot.df.example.2"
@ -73,25 +74,51 @@ irr_ms = ${ms}
irr_seeds=${seeds} irr_seeds=${seeds}
irr_explained_variances=${explained_variances} irr_explained_variances=${explained_variances}
example_5_jobs: 05_irr_indep.R irr_simulation_base.R example_5_jobs: 05_irr_indep.R irr_simulation_base.R grid_sweep.py
grid_sweep.py --command "Rscript 05_irr_indep.R" --arg_dict '{"N":${irr_Ns},"m":${irr_ms}, "seed":${irr_seeds}, "outfile":["example_5.feather"], "y_explained_variance":${irr_explained_variances}}' --outfile example_5_jobs sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 05_irr_indep.R" --arg_dict '{"N":${irr_Ns},"m":${irr_ms}, "seed":${irr_seeds}, "outfile":["example_5.feather"], "y_explained_variance":${irr_explained_variances}}' --outfile example_5_jobs
example_5.feather:example_5_jobs example_5.feather:example_5_jobs
rm -f example_5.feather rm -f example_5.feather
sbatch --wait --verbose --array=1-$(shell cat example_5_jobs | wc -l) run_simulation.sbatch 0 example_5_jobs sbatch --wait --verbose --array=1-$(shell cat example_5_jobs | wc -l) run_simulation.sbatch 0 example_5_jobs
example_6_jobs: 06_irr_dv.R irr_dv_simulation_base.R example_6_jobs: 06_irr_dv.R irr_dv_simulation_base.R grid_sweep.py
grid_sweep.py --command "Rscript 06_irr_dv.R" --arg_dict '{"N":${irr_Ns},"m":${irr_ms}, "seed":${irr_seeds}, "outfile":["example_6.feather"], "y_explained_variance":${irr_explained_variances}}' --outfile example_6_jobs sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 06_irr_dv.R" --arg_dict '{"N":${irr_Ns},"m":${irr_ms}, "seed":${irr_seeds}, "outfile":["example_6.feather"], "y_explained_variance":${irr_explained_variances}}' --outfile example_6_jobs
example_6.feather:example_6_jobs example_6.feather:example_6_jobs
rm -f example_6.feather rm -f example_6.feather
sbatch --wait --verbose --array=1-$(shell cat example_6_jobs | wc -l) run_simulation.sbatch 0 example_6_jobs sbatch --wait --verbose --array=1-$(shell cat example_6_jobs | wc -l) run_simulation.sbatch 0 example_6_jobs
remember_irr.RDS: example_5.feather example_6.feather plot_irr_example.R plot_irr_dv_example.R remember_irr.RDS: example_5.feather example_6.feather plot_irr_example.R plot_irr_dv_example.R summarize_estimator.R
rm -f remember_irr.RDS rm -f remember_irr.RDS
${srun} Rscript plot_irr_example.R --infile example_5.feather --name "plot.df.example.5" sbatch --wait --verbose run_job.sbatch Rscript plot_irr_example.R --infile example_5.feather --name "plot.df.example.5"
${srun} Rscript plot_irr_dv_example.R --infile example_6.feather --name "plot.df.example.6" sbatch --wait --verbose run_job.sbatch Rscript plot_irr_dv_example.R --infile example_6.feather --name "plot.df.example.6"
robustness_1_jobs: 02_indep_differential.R simulation_base.R grid_sweep.py
sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 02_indep_differential.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_1.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~1"]}' --outfile robustness_1_jobs
robustness_1.feather: robustness_1_jobs
rm -f robustness_1.feather
sbatch --wait --verbose --array=1-$(shell cat robustness_1_jobs | wc -l) run_simulation.sbatch 0 robustness_1_jobs
robustness_1_dv_jobs: simulation_base.R 04_depvar_differential.R grid_sweep.py
${srun} bash -c "source ~/.bashrc && grid_sweep.py --command 'Rscript 04_depvar_differential.R' --arg_dict \"{'N':${Ns},'m':${ms}, 'seed':${seeds}, 'outfile':['robustness_1_dv.feather'], 'y_explained_variance':${explained_variances}, 'proxy_formula':['w_pred~y']}\" --outfile robustness_1_dv_jobs"
robustness_1_dv.feather: robustness_1_dv_jobs
rm -f robustness_1_dv.feather
sbatch --wait --verbose --array=1-$(shell cat example_3_jobs | wc -l) run_simulation.sbatch 0 robustness_1_dv_jobs
remember_robustness_misspec.RDS: robustness_1.feather robustness_1_dv.feather
rm -f remember_robustness_misspec.RDS
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"
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"
clean: clean:
rm *.feather rm *.feather
@ -100,7 +127,7 @@ clean:
# sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 example_2_B_jobs # sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 example_2_B_jobs
# example_2_B_mecor_jobs: # example_2_B_mecor_jobs:
# grid_sweep.py --command "Rscript example_2_B_mecor.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_2_B_mecor.feather"]}' --outfile example_2_B_mecor_jobs # sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript example_2_B_mecor.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_2_B_mecor.feather"]}' --outfile example_2_B_mecor_jobs
# example_2_B_mecor.feather:example_2_B_mecor.R example_2_B_mecor_jobs # example_2_B_mecor.feather:example_2_B_mecor.R example_2_B_mecor_jobs
# rm -f example_2_B_mecor.feather # rm -f example_2_B_mecor.feather
@ -109,3 +136,4 @@ clean:
.PHONY: supplement

7
simulations/grid_sweep.py
View File

@ -2,8 +2,13 @@
import fire import fire
from itertools import product from itertools import product
import pyRemembeR
def main(command, arg_dict, outfile): def main(command, arg_dict, outfile, remember_file='remember_grid_sweep.RDS'):
remember = pyRemembeR.remember.Remember()
remember.set_file(remember_file)
remember[outfile] = arg_dict
remember.save_to_r()
keys = [] keys = []
values = [] values = []

2
simulations/irr_dv_simulation_base.R
View File

@ -82,7 +82,7 @@ run_simulation_depvar <- function(df, result, outcome_formula = y ~ x + z, rater
Bxy.ci.upper.loco.mle = ci.upper['x'], Bxy.ci.upper.loco.mle = ci.upper['x'],
Bxy.ci.lower.loco.mle = ci.lower['x'], Bxy.ci.lower.loco.mle = ci.lower['x'],
Bzy.ci.upper.loco.mle = ci.upper['z'], 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(rater_formula)
print(proxy_formula) print(proxy_formula)

2
simulations/irr_simulation_base.R
View File

@ -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.upper.loco.mle = ci.upper['x'],
Bxy.ci.lower.loco.mle = ci.lower['x'], Bxy.ci.lower.loco.mle = ci.lower['x'],
Bzy.ci.upper.loco.mle = ci.upper['z'], 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(rater_formula)
## print(proxy_formula) ## print(proxy_formula)

56
simulations/measerr_methods.R
View File

@ -1,6 +1,6 @@
library(formula.tools) library(formula.tools)
library(matrixStats) library(matrixStats)
library(bbmle)
## df: dataframe to model ## df: dataframe to model
## outcome_formula: formula for y | x, z ## outcome_formula: formula for y | x, z
## outcome_family: family 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 ## 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){ nll <- function(params){
df.obs <- model.frame(outcome_formula, df) 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)) start <- rep(0.1,length(params))
names(start) <- params names(start) <- params
fit <- optim(start, fn = nll, lower=lower, method='L-BFGS-B', hessian=TRUE, control=list(maxit=1e6)) 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) return(fit)
} }
## Experimental, and not necessary if errors are independent. ## 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. ### 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)) start <- rep(0.1,length(params))
names(start) <- params names(start) <- params
fit <- optim(start, fn = nll, lower=lower, method='L-BFGS-B', hessian=TRUE, control=list(maxit=1e6))
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) 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) df.obs <- model.frame(outcome_formula, df)
proxy.variable <- all.vars(proxy_formula)[1] proxy.variable <- all.vars(proxy_formula)[1]
proxy.model.matrix <- model.matrix(proxy_formula, df) proxy.model.matrix <- model.matrix(proxy_formula, df)
@ -425,8 +450,21 @@ measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_fo
lower <- c(lower, rep(-Inf, length(truth.params))) lower <- c(lower, rep(-Inf, length(truth.params)))
start <- rep(0.1,length(params)) start <- rep(0.1,length(params))
names(start) <- 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) return(fit)
} }

87
simulations/plot_dv_example.R
View File

@ -7,49 +7,51 @@ library(argparser)
parser <- arg_parser("Simulate data and fit corrected models.") 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, "--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.") parser <- add_argument(parser, "--name", default="", help="The name to safe the data to in the remember file.")
args <- parse_args(parser) args <- parse_args(parser)
summarize.estimator <- function(df, suffix='naive', coefname='x'){ ## summarize.estimator <- function(df, suffix='naive', coefname='x'){
part <- df[,c('N', ## part <- df[,c('N',
'm', ## 'm',
'Bxy', ## 'Bxy',
paste0('B',coefname,'y.est.',suffix), ## paste0('B',coefname,'y.est.',suffix),
paste0('B',coefname,'y.ci.lower.',suffix), ## paste0('B',coefname,'y.ci.lower.',suffix),
paste0('B',coefname,'y.ci.upper.',suffix), ## paste0('B',coefname,'y.ci.upper.',suffix),
'y_explained_variance', ## 'y_explained_variance',
'Bzy' ## 'Bzy'
), ## ),
with=FALSE] ## 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)]])) ## 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)]]) ## 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$Bxy - part[[paste0('B',coefname,'y.est.',suffix)]] ## bias <- part$Bxy - part[[paste0('B',coefname,'y.est.',suffix)]]
sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]])) ## sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))
part <- part[,':='(true.in.ci = true.in.ci, ## part <- part[,':='(true.in.ci = true.in.ci,
zero.in.ci = zero.in.ci, ## zero.in.ci = zero.in.ci,
bias=bias, ## bias=bias,
sign.correct =sign.correct)] ## sign.correct =sign.correct)]
part.plot <- part[, .(p.true.in.ci = mean(true.in.ci), ## part.plot <- part[, .(p.true.in.ci = mean(true.in.ci),
mean.bias = mean(bias), ## mean.bias = mean(bias),
mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]), ## mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
var.est = var(.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.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), ## est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.05),
N.sims = .N, ## N.sims = .N,
p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))), ## p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
variable=coefname, ## variable=coefname,
method=suffix ## method=suffix
), ## ),
by=c("N","m",'Bzy','y_explained_variance') ## by=c("N","m",'Bzy','y_explained_variance')
] ## ]
return(part.plot) ## return(part.plot)
} ## }
source("summarize_estimator.R")
build_plot_dataset <- function(df){ build_plot_dataset <- function(df){
@ -82,12 +84,23 @@ build_plot_dataset <- function(df){
return(plot.df) return(plot.df)
} }
change.remember.file(args$remember_file, clear=TRUE)
df <- read_feather(args$infile) sims.df <- read_feather(args$infile)
plot.df <- build_plot_dataset(df) sims.df[,Bzx:=NA]
sims.df[,accuracy_imbalance_difference:=NA]
plot.df <- build_plot_dataset(sims.df)
remember(plot.df,args$name) 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] ## 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), ## 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),

110
simulations/plot_example.R
View File

@ -5,52 +5,58 @@ library(ggplot2)
library(filelock) library(filelock)
library(argparser) library(argparser)
source("summarize_estimator.R")
parser <- arg_parser("Simulate data and fit corrected models.") 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, "--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.") parser <- add_argument(parser, "--name", default="", help="The name to safe the data to in the remember file.")
args <- parse_args(parser) args <- parse_args(parser)
summarize.estimator <- function(df, suffix='naive', coefname='x'){
part <- df[,c('N',
'm', ## summarize.estimator <- function(df, suffix='naive', coefname='x'){
'Bxy',
paste0('B',coefname,'y.est.',suffix), ## part <- df[,c('N',
paste0('B',coefname,'y.ci.lower.',suffix), ## 'm',
paste0('B',coefname,'y.ci.upper.',suffix), ## 'Bxy',
'y_explained_variance', ## paste0('B',coefname,'y.est.',suffix),
'Bzx', ## paste0('B',coefname,'y.ci.lower.',suffix),
'Bzy', ## paste0('B',coefname,'y.ci.upper.',suffix),
'accuracy_imbalance_difference' ## 'y_explained_variance',
), ## 'Bzx',
with=FALSE] ## '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)]])) ## 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)]]) ## 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$Bxy - part[[paste0('B',coefname,'y.est.',suffix)]] ## bias <- part$Bxy - part[[paste0('B',coefname,'y.est.',suffix)]]
sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]])) ## sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))
part <- part[,':='(true.in.ci = true.in.ci, ## part <- part[,':='(true.in.ci = true.in.ci,
zero.in.ci = zero.in.ci, ## zero.in.ci = zero.in.ci,
bias=bias, ## bias=bias,
sign.correct =sign.correct)] ## sign.correct =sign.correct)]
part.plot <- part[, .(p.true.in.ci = mean(true.in.ci), ## part.plot <- part[, .(p.true.in.ci = mean(true.in.ci),
mean.bias = mean(bias), ## mean.bias = mean(bias),
mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]), ## mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
var.est = var(.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.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.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.05,na.rm=T),
N.sims = .N, ## N.sims = .N,
p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))), ## p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
variable=coefname, ## variable=coefname,
method=suffix ## method=suffix
), ## ),
by=c("N","m",'y_explained_variance','Bzx', 'Bzy', 'accuracy_imbalance_difference') ## by=c("N","m",'y_explained_variance','Bzx', 'Bzy', 'accuracy_imbalance_difference')
] ## ]
return(part.plot) ## return(part.plot)
} ## }
build_plot_dataset <- function(df){ build_plot_dataset <- function(df){
@ -98,24 +104,40 @@ build_plot_dataset <- function(df){
} }
plot.df <- read_feather(args$infile) sims.df <- read_feather(args$infile)
print(unique(plot.df$N)) print(unique(sims.df$N))
# df <- df[apply(df,1,function(x) !any(is.na(x)))] # df <- df[apply(df,1,function(x) !any(is.na(x)))]
if(!('Bzx' %in% names(plot.df))) if(!('Bzx' %in% names(sims.df)))
plot.df[,Bzx:=NA] sims.df[,Bzx:=NA]
if(!('accuracy_imbalance_difference' %in% names(plot.df))) if(!('accuracy_imbalance_difference' %in% names(sims.df)))
plot.df[,accuracy_imbalance_difference:=NA] sims.df[,accuracy_imbalance_difference:=NA]
unique(plot.df[,'accuracy_imbalance_difference']) unique(sims.df[,'accuracy_imbalance_difference'])
change.remember.file(args$remember_file, clear=TRUE)
#plot.df <- build_plot_dataset(df[accuracy_imbalance_difference==0.1][N==700]) #plot.df <- build_plot_dataset(df[accuracy_imbalance_difference==0.1][N==700])
plot.df <- build_plot_dataset(plot.df) plot.df <- build_plot_dataset(sims.df)
remember(plot.df,args$name) 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) #ggplot(df,aes(x=Bxy.est.mle)) + geom_histogram() + facet_grid(accuracy_imbalance_difference ~ Bzy)
## ## ## df[gmm.ER_pval<0.05] ## ## ## df[gmm.ER_pval<0.05]

7
simulations/run_simulation.sbatch
View File

@ -5,15 +5,16 @@
#SBATCH --partition=compute-bigmem #SBATCH --partition=compute-bigmem
## Resources ## Resources
#SBATCH --nodes=1 #SBATCH --nodes=1
## Walltime (12 hours) ## Walltime (4 hours)
#SBATCH --time=1:00:00 #SBATCH --time=4:00:00
## Memory per node ## Memory per node
#SBATCH --mem=8G #SBATCH --mem=4G
#SBATCH --cpus-per-task=1 #SBATCH --cpus-per-task=1
#SBATCH --ntasks-per-node=1 #SBATCH --ntasks-per-node=1
#SBATCH --chdir /gscratch/comdata/users/nathante/ml_measurement_error_public/simulations #SBATCH --chdir /gscratch/comdata/users/nathante/ml_measurement_error_public/simulations
#SBATCH --output=simulation_jobs/%A_%a.out #SBATCH --output=simulation_jobs/%A_%a.out
#SBATCH --error=simulation_jobs/%A_%a.err #SBATCH --error=simulation_jobs/%A_%a.err
source ~/.bashrc
TASK_NUM=$(($SLURM_ARRAY_TASK_ID + $1)) TASK_NUM=$(($SLURM_ARRAY_TASK_ID + $1))
TASK_CALL=$(sed -n ${TASK_NUM}p $2) TASK_CALL=$(sed -n ${TASK_NUM}p $2)

35
simulations/simulation_base.R
View File

@ -210,11 +210,19 @@ run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formu
accuracy <- df[,mean(w_pred==y)] accuracy <- df[,mean(w_pred==y)]
result <- append(result, list(accuracy=accuracy)) result <- append(result, list(accuracy=accuracy))
error.cor.x <- cor(df$x, df$w - df$x)
result <- append(result, list(error.cor.x = error.cor.x))
model.null <- glm(y~1, data=df,family=binomial(link='logit'))
(model.true <- glm(y ~ x + z, data=df,family=binomial(link='logit'))) (model.true <- glm(y ~ x + z, data=df,family=binomial(link='logit')))
(lik.ratio <- exp(logLik(model.true) - logLik(model.null)))
true.ci.Bxy <- confint(model.true)['x',] true.ci.Bxy <- confint(model.true)['x',]
true.ci.Bzy <- confint(model.true)['z',] true.ci.Bzy <- confint(model.true)['z',]
result <- append(result, list(lik.ratio=lik.ratio))
result <- append(result, list(Bxy.est.true=coef(model.true)['x'], result <- append(result, list(Bxy.est.true=coef(model.true)['x'],
Bzy.est.true=coef(model.true)['z'], Bzy.est.true=coef(model.true)['z'],
Bxy.ci.upper.true = true.ci.Bxy[2], Bxy.ci.upper.true = true.ci.Bxy[2],
@ -322,8 +330,33 @@ run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formu
run_simulation <- function(df, result, outcome_formula=y~x+z, proxy_formula=NULL, truth_formula=NULL){ run_simulation <- function(df, result, outcome_formula=y~x+z, proxy_formula=NULL, truth_formula=NULL){
accuracy <- df[,mean(w_pred==x)] accuracy <- df[,mean(w_pred==x)]
result <- append(result, list(accuracy=accuracy)) accuracy.y0 <- df[y<=0,mean(w_pred==x)]
accuracy.y1 <- df[y>=0,mean(w_pred==x)]
cor.y.xi <- cor(df$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)) (model.true <- lm(y ~ x + z, data=df))
true.ci.Bxy <- confint(model.true)['x',] true.ci.Bxy <- confint(model.true)['x',]
true.ci.Bzy <- confint(model.true)['z',] true.ci.Bzy <- confint(model.true)['z',]

16
simulations/summarize_estimator.R
View File

@ -13,10 +13,11 @@ summarize.estimator <- function(df, suffix='naive', coefname='x'){
'accuracy_imbalance_difference' 'accuracy_imbalance_difference'
), ),
with=FALSE] 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)]])) 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)]]) 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)]])) sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))
part <- part[,':='(true.in.ci = true.in.ci, 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.bias = mean(bias),
mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]), mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
var.est = var(.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.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)]],0.05,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, N.sims = .N,
p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))), p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
variable=coefname, variable=coefname,