Add another robustness check for varying levels of accuracy.

[ml_measurement_error_public.git] / simulations / Makefile

diff --git a/simulations/Makefile b/simulations/Makefile
index b3ab77a3c62d773b5b577940c8e1e373bac955d5..e6a3bbe6d5823de6471110b81ed368a64fce2990 100644 (file)
--- a/simulations/Makefile
+++ b/simulations/Makefile
@@ -120,15 +120,21 @@ remember_irr.RDS: example_5.feather plot_irr_example.R plot_irr_dv_example.R sum
 #      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-$(shell cat robustness_1_jobs | wc -l)  run_simulation.sbatch 0 robustness_1_jobs
+       sbatch --wait --verbose --array=1-1000  run_simulation.sbatch 0 robustness_1_jobs
+       sbatch --wait --verbose --array=1001-2000  run_simulation.sbatch 0 robustness_1_jobs
+       sbatch --wait --verbose --array=2001-3000  run_simulation.sbatch 0 robustness_1_jobs
+       sbatch --wait --verbose --array=3001-4000  run_simulation.sbatch 0 robustness_1_jobs
+       sbatch --wait --verbose --array=4001-$(shell cat robustness_1_jobs | wc -l)  run_simulation.sbatch 0 robustness_1_jobs
+
+robustness_1.RDS: robustness_1.feather
+       rm -f robustness_1.RDS
+       ${srun} Rscript plot_example.R --infile $< --name "robustness_1" --remember-file $@
 
 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':${explained_variances}, 'proxy_formula':['w_pred~y']}\" --outfile robustness_1_dv_jobs"
@@ -139,12 +145,26 @@ robustness_1_dv.feather: robustness_1_dv_jobs
        sbatch --wait --verbose --array=1-$(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"
+robustness_1_dv.RDS: robustness_1_dv.feather
+       rm -f $@
+       ${srun} Rscript plot_dv_example.R --infile $< --name "robustness_1_dv" --outfile $@
+ 
+
+robustness_2_jobs: grid_sweep.py 01_two_covariates.R simulation_base.R grid_sweep.py
+       rm -f $@
+       ${srun} $< --command 'Rscript 01_two_covariates.R'  --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_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+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.6,0.73,0.8,0.85,0.9,0.95]}' --outfile $@
+
+
+
+START=1
+END=$(shell cat robustness_2_jobs | wc -l)
+STEP=1000
+ITEMS=$(shell seq $(START) $(STEP) $(END))
 
+robustness_2.feather: robustness_2_jobs
+       $(foreach item,$(ITEMS),sbatch --wait --verbose --array=$(shell expr $(item))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 $<)
 
+#      
 clean:
        rm *.feather
        rm -f remembr.RDS