From: Nathan TeBlunthuis Date: Fri, 6 Jan 2023 20:22:41 +0000 (-0800) Subject: check in some old simulation updates and a dv examples with real data X-Git-Url: https://code.communitydata.science/ml_measurement_error_public.git/commitdiff_plain/fa05dbab6bd2c5db6ed4eccf38cff03bb4fd6683?ds=sidebyside check in some old simulation updates and a dv examples with real data --- diff --git a/civil_comments/01_dv_example.R b/civil_comments/01_dv_example.R new file mode 100644 index 0000000..4092243 --- /dev/null +++ b/civil_comments/01_dv_example.R @@ -0,0 +1,54 @@ +source('load_perspective_data.R') +source("../simulations/measerr_methods.R") +source("../simulations/RemembR/R/RemembeR.R") + +change.remember.file("dv_perspective_example.RDS") + +# for reproducibility +set.seed(1111) + +## another simple enough example: is P(toxic | funny and white) > P(toxic | funny nand white)? Or, are funny comments more toxic when people disclose that they are white? + +compare_dv_models <-function(pred_formula, outcome_formula, proxy_formula, df, sample.prop, remember_prefix){ + pred_model <- glm(pred_formula, df, family=binomial(link='logit')) + + remember(coef(pred_model), paste0(remember_prefix, "coef_pred_model")) + remember(diag(vcov((pred_model))), paste0(remember_prefix, "se_pred_model")) + + coder_model <- glm(outcome_formula, df, family=binomial(link='logit')) + remember(coef(coder_model), paste0(remember_prefix, "coef_coder_model")) + remember(diag(vcov((coder_model))), paste0(remember_prefix, "se_coder_model")) + + df_measerr_method <- copy(df)[sample(1:.N, sample.prop * .N), toxicity_coded_1 := toxicity_coded] + df_measerr_method <- df_measerr_method[,toxicity_coded := toxicity_coded_1] + sample_model <- glm(outcome_formula, df_measerr_method, family=binomial(link='logit')) + remember(coef(sample_model), paste0(remember_prefix, "coef_sample_model")) + remember(diag(vcov((sample_model))), paste0(remember_prefix, "se_sample_model")) + + measerr_model <- measerr_mle_dv(df_measerr_method, outcome_formula, outcome_family=binomial(link='logit'), proxy_formula=proxy_formula, proxy_family=binomial(link='logit')) + + inv_hessian = solve(measerr_model$hessian) + stderr = diag(inv_hessian) + remember(stderr, paste0(remember_prefix, "measerr_model_stderr")) + remember(measerr_model$par, paste0(remember_prefix, "measerr_model_par")) +} + +print("running first example") + +compare_dv_models(pred_formula = toxicity_pred ~ funny*white, + outcome_formula = toxicity_coded ~ funny*white, proxy_formula, + proxy_formula = toxicity_pred ~ toxicity_coded*funny*white, + df=df, + sample.prop=0.01, + remember_prefix='cc_ex_tox.funny.white') + + +print("running second example") + +compare_dv_models(pred_formula = toxicity_pred ~ likes+race_disclosed, + outcome_formula = toxicity_coded ~ likes + race_disclosed, proxy_formula, + proxy_formula = toxicity_pred ~ toxicity_coded*likes*race_disclosed, + df=df, + sample.prop=0.01, + remember_prefix='cc_ex_tox.funny.race_disclosed') + diff --git a/civil_comments/design_example.R b/civil_comments/design_example.R index 5991334..1a83a81 100644 --- a/civil_comments/design_example.R +++ b/civil_comments/design_example.R @@ -1,18 +1,5 @@ -library(data.table) -library(MASS) - -scores <- fread("perspective_scores.csv") -scores <- scores[,id:=as.character(id)] - -df <- fread("all_data.csv") - -# only use the data that has identity annotations -df <- df[identity_annotator_count > 0] - -(df[!(df$id %in% scores$id)]) - -df <- df[scores,on='id',nomatch=NULL] - +set.seed(1111) +source('load_perspective_data.R') ## how accurate are the classifiers? ## the API claims that these scores are "probabilities" @@ -27,21 +14,6 @@ F1 <- function(y, predictions){ return (2 * precision * recall ) / (precision + recall) } -df[, ":="(identity_attack_pred = identity_attack_prob >=0.5, - insult_pred = insult_prob >= 0.5, - profanity_pred = profanity_prob >= 0.5, - severe_toxicity_pred = severe_toxicity_prob >= 0.5, - threat_pred = threat_prob >= 0.5, - toxicity_pred = toxicity_prob >= 0.5, - identity_attack_coded = identity_attack >= 0.5, - insult_coded = insult >= 0.5, - profanity_coded = obscene >= 0.5, - severe_toxicity_coded = severe_toxicity >= 0.5, - threat_coded = threat >= 0.5, - toxicity_coded = toxicity >= 0.5 - )] - - ## toxicity is about 93% accurate, with an f1 of 0.8 ## identity_attack has high accuracy 97%, but an unfortunant f1 of 0.5. @@ -88,6 +60,7 @@ df <- df[,":="(identity_error = identity_attack_coded - identity_attack_pred, ## what's correlated with toxicity_error ? df <- df[,approved := rating == "approved"] +df <- df[,white := white > 0.5] cortab <- cor(df[,.(toxicity_error, identity_error, @@ -134,14 +107,62 @@ cortab['toxicity_coded',] cortab['identity_error',] cortab['white',] -glm(white ~ toxicity_coded + psychiatric_or_mental_illness, data = df, family=binomial(link='logit')) +cortab <- cor(df[,.(toxicity_error, + identity_error, + toxicity_coded, + funny, + approved, + sad, + wow, + likes, + disagree, + gender_disclosed, + sexuality_disclosed, + religion_disclosed, + race_disclosed, + disability_disclosed)]) + + +## here's a simple example, is P(white | toxic and mentally ill) > P(white | toxic or mentally ill). Are people who discuss their mental illness in a toxic way more likely to be white compared to those who just talk about their mental illness or are toxic? +summary(glm(white ~ toxicity_coded*psychiatric_or_mental_illness, data = df, family=binomial(link='logit'))) + +summary(glm(white ~ toxicity_pred*psychiatric_or_mental_illness, data = df, family=binomial(link='logit'))) + +summary(glm(white ~ toxicity_coded*male, data = df, family=binomial(link='logit'))) + +summary(glm(white ~ toxicity_pred*male, data = df, family=binomial(link='logit'))) -glm(white ~ toxicity_pred + psychiatric_or_mental_illness, data = df, family=binomial(link='logit')) +summary(glm(toxicity_coded ~ white*psychiatric_or_mental_illness, data = df, family=binomial(link='logit'))) -m1 <- glm.nb(funny ~ (male + female + transgender + other_gender + heterosexual + bisexual + other_sexual_orientation + christian + jewish + hindu + buddhist + atheist + other_religion + asian + latino + other_race_or_ethnicity + physical_disability + intellectual_or_learning_disability + white + black + psychiatric_or_mental_illness)*toxicity_coded, data = df) +summary(glm(toxicity_pred ~ white*psychiatric_or_mental_illness, data = df, family=binomial(link='logit'))) + + +## another simple enough example: is P(toxic | funny and white) > P(toxic | funny nand white)? Or, are funny comments more toxic when people disclose that they are white? + +summary(glm(toxicity_pred ~ funny*white, data=df, family=binomial(link='logit'))) +summary(glm(toxicity_coded ~ funny*white, data=df, family=binomial(link='logit'))) + +source("../simulations/measerr_methods.R") + +saved_model_file <- "measerr_model_tox.eq.funny.cross.white.RDS" +overwrite_model <- TRUE + +# it works so far with a 20% and 15% sample. Smaller is better. let's try a 10% sample again. It didn't work out. We'll go forward with a 15% sample. +df_measerr_method <- copy(df)[sample(1:.N, 0.05 * .N), toxicity_coded_1 := toxicity_coded] +df_measerr_method <- df_measerr_method[,toxicity_coded := toxicity_coded_1] +summary(glm(toxicity_coded ~ funny*white, data=df_measerr_method[!is.na(toxicity_coded)], family=binomial(link='logit'))) + +if(!file.exists(saved_model_file) || (overwrite_model == TRUE)){ + measerr_model <- measerr_mle_dv(df_measerr_method,toxicity_coded ~ funny*white,outcome_family=binomial(link='logit'), proxy_formula=toxicity_pred ~ toxicity_coded*funny*white) +saveRDS(measerr_model, saved_model_file) +} else { + measerr_model <- readRDS(saved_model_file) +} -m2 <- glm.nb(funny ~ (male + female + transgender + other_gender + heterosexual + bisexual + other_sexual_orientation + christian + jewish + hindu + buddhist + atheist + other_religion + asian + latino + other_race_or_ethnicity + physical_disability + intellectual_or_learning_disability + white + black + psychiatric_or_mental_illness)*toxicity_pred, data = df) +inv_hessian <- solve(measerr_model$hessian) +se <- diag(inv_hessian) +lm2 <- glm.nb(funny ~ (male + female + transgender + other_gender + heterosexual + bisexual + other_sexual_orientation + christian + jewish + hindu + buddhist + atheist + other_religion + asian + latino + other_race_or_ethnicity + physical_disability + intellectual_or_learning_disability + white + black + psychiatric_or_mental_illness)*toxicity_pred, data = df) m3 <- glm.nb(funny ~ (male + female + transgender + other_gender + heterosexual + bisexual + other_sexual_orientation + christian + jewish + hindu + buddhist + atheist + other_religion + asian + latino + other_race_or_ethnicity + physical_disability + intellectual_or_learning_disability + white + black + psychiatric_or_mental_illness)*toxicity, data = df) diff --git a/civil_comments/load_perspective_data.R b/civil_comments/load_perspective_data.R new file mode 100644 index 0000000..636c423 --- /dev/null +++ b/civil_comments/load_perspective_data.R @@ -0,0 +1,41 @@ +library(data.table) +library(MASS) + +set.seed(1111) + +scores <- fread("perspective_scores.csv") +scores <- scores[,id:=as.character(id)] + +df <- fread("all_data.csv") + +# only use the data that has identity annotations +df <- df[identity_annotator_count > 0] + +(df[!(df$id %in% scores$id)]) + +df <- df[scores,on='id',nomatch=NULL] + +df[, ":="(identity_attack_pred = identity_attack_prob >=0.5, + insult_pred = insult_prob >= 0.5, + profanity_pred = profanity_prob >= 0.5, + severe_toxicity_pred = severe_toxicity_prob >= 0.5, + threat_pred = threat_prob >= 0.5, + toxicity_pred = toxicity_prob >= 0.5, + identity_attack_coded = identity_attack >= 0.5, + insult_coded = insult >= 0.5, + profanity_coded = obscene >= 0.5, + severe_toxicity_coded = severe_toxicity >= 0.5, + threat_coded = threat >= 0.5, + toxicity_coded = toxicity >= 0.5 + )] + +gt.0.5 <- function(v) { v >= 0.5 } +dt.apply.any <- function(fun, ...){apply(apply(cbind(...), 2, fun),1,any)} + +df <- df[,":="(gender_disclosed = dt.apply.any(gt.0.5, male, female, transgender, other_gender), + sexuality_disclosed = dt.apply.any(gt.0.5, heterosexual, bisexual, other_sexual_orientation), + religion_disclosed = dt.apply.any(gt.0.5, christian, jewish, hindu, buddhist, atheist, muslim, other_religion), + race_disclosed = dt.apply.any(gt.0.5, white, black, asian, latino, other_race_or_ethnicity), + disability_disclosed = dt.apply.any(gt.0.5,physical_disability, intellectual_or_learning_disability, psychiatric_or_mental_illness, other_disability))] + +df <- df[,white:=gt.0.5(white)] diff --git a/simulations/02_indep_differential.R b/simulations/02_indep_differential.R index 80e19be..4e3a132 100644 --- a/simulations/02_indep_differential.R +++ b/simulations/02_indep_differential.R @@ -159,7 +159,7 @@ if(args$m < args$N){ ## pc.df <- pc(suffStat=list(C=cor(df.pc),n=nrow(df.pc)),indepTest=gaussCItest,labels=names(df.pc),alpha=0.05) ## plot(pc.df) - result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy, 'Bzx'=args$Bzx, 'Bzy'=Bzy, 'Px'=Px, .'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'accuracy_imbalance_difference'=args$accuracy_imbalance_difference, 'y_bias'=args$y_bias,'outcome_formula'=args$outcome_formula, 'proxy_formula'=args$proxy_formula,truth_formula=args$truth_formula, error='') + result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy, 'Bzx'=args$Bzx, 'Bzy'=Bzy, 'Px'=Px, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'accuracy_imbalance_difference'=args$accuracy_imbalance_difference, 'y_bias'=args$y_bias,'outcome_formula'=args$outcome_formula, 'proxy_formula'=args$proxy_formula,truth_formula=args$truth_formula, error='') outline <- run_simulation(df, result, outcome_formula=as.formula(args$outcome_formula), proxy_formula=as.formula(args$proxy_formula), truth_formula=as.formula(args$truth_formula)) diff --git a/simulations/Makefile b/simulations/Makefile index feeeaa5..1cab473 100644 --- a/simulations/Makefile +++ b/simulations/Makefile @@ -1,4 +1,4 @@ - +.ONESHELL: SHELL=bash Ns=[1000, 5000, 10000] @@ -6,8 +6,9 @@ ms=[100, 200, 400] seeds=[$(shell seq -s, 1 500)] explained_variances=[0.1] -all:remembr.RDS remember_irr.RDS -supplement: remember_robustness_misspec.RDS +all:main supplement +main:remembr.RDS +supplement:robustness_1.RDS robustness_1_dv.RDS robustness_2.RDS robustness_2_dv.RDS robustness_3.RDS robustness_3_dv.RDS robustness_4.RDS robustness_4_dv.RDS srun=sbatch --wait --verbose run_job.sbatch @@ -24,7 +25,7 @@ joblists:example_1_jobs example_2_jobs example_3_jobs example_1_jobs: 01_two_covariates.R simulation_base.R grid_sweep.py pl_methods.R - sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 01_two_covariates.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_1.feather"], "y_explained_variance":${explained_variances}, "Bzx":[1]}' --outfile example_1_jobs + ${srun} grid_sweep.py --command "Rscript 01_two_covariates.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_1.feather"], "y_explained_variance":${explained_variances}, "Bzx":[1]}' --outfile example_1_jobs example_1.feather: example_1_jobs rm -f example_1.feather @@ -124,7 +125,14 @@ robustness_1_jobs: 02_indep_differential.R simulation_base.R grid_sweep.py sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 02_indep_differential.R" --arg_dict '{"N":${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 + +START=0 +STEP=1000 +ONE=1 + robustness_1.feather: robustness_1_jobs + $(eval END_1!=cat robustness_1_jobs | wc -l) + $(eval ITEMS_1!=seq $(START) $(STEP) $(END_1)) rm -f robustness_1.feather sbatch --wait --verbose --array=1-1000 run_simulation.sbatch 0 robustness_1_jobs sbatch --wait --verbose --array=1001-2000 run_simulation.sbatch 0 robustness_1_jobs @@ -132,22 +140,25 @@ robustness_1.feather: robustness_1_jobs sbatch --wait --verbose --array=3001-4000 run_simulation.sbatch 0 robustness_1_jobs sbatch --wait --verbose --array=4001-$(shell cat robustness_1_jobs | wc -l) run_simulation.sbatch 0 robustness_1_jobs + $(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) 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" - + ${srun} grid_sweep.py --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${Ns},"Bxy":[0.7],"Bzy":[-0.7],"m":${ms}, "seed":${seeds}, "outfile":["robustness_1_dv.feather"], "proxy_formula":["w_pred~y"],"z_bias":[0.5]}' --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 + $(eval END_1!=cat robustness_1_dv_jobs | wc -l) + $(eval ITEMS_1!=seq $(START) $(STEP) $(END_1)) + $(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_1_dv_jobs;) robustness_1_dv.RDS: robustness_1_dv.feather rm -f $@ - ${srun} Rscript plot_dv_example.R --infile $< --name "robustness_1_dv" --outfile $@ + ${srun} Rscript plot_dv_example.R --infile $< --name "robustness_1_dv" --remember-file $@ robustness_2_jobs_p1: grid_sweep.py 01_two_covariates.R simulation_base.R grid_sweep.py @@ -166,59 +177,59 @@ robustness_2_jobs_p4: grid_sweep.py 01_two_covariates.R simulation_base.R grid_s 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":[0.90,0.95]}' --outfile $@ -START=0 -END_1=$(shell cat robustness_2_jobs_p1 | wc -l) -END_2=$(shell cat robustness_2_jobs_p2 | wc -l) -END_3=$(shell cat robustness_2_jobs_p3 | wc -l) -END_4=$(shell cat robustness_2_jobs_p4 | wc -l) -STEP=1000 -ONE=1 -ITEMS_1=$(shell seq $(START) $(STEP) $(END_1)) -ITEMS_2=$(shell seq $(START) $(STEP) $(END_2)) -ITEMS_3=$(shell seq $(START) $(STEP) $(END_3)) -ITEMS_4=$(shell seq $(START) $(STEP) $(END_4)) - robustness_2.feather: robustness_2_jobs_p1 robustness_2_jobs_p2 robustness_2_jobs_p3 robustness_2_jobs_p4 - $(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_2_jobs_p1) + $(eval END_1!=cat robustness_2_jobs_p1 | wc -l) + $(eval ITEMS_1!=seq $(START) $(STEP) $(END_1)) + $(eval END_2!=cat robustness_2_jobs_p2 | wc -l) + $(eval ITEMS_2!=seq $(START) $(STEP) $(END_2)) + $(eval END_3!=cat robustness_2_jobs_p3 | wc -l) + $(eval ITEMS_3!=seq $(START) $(STEP) $(END_3)) + $(eval END_4!=cat robustness_2_jobs_p4 | wc -l) + $(eval ITEMS_4!=seq $(START) $(STEP) $(END_4)) + + $(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_2_jobs_p1;) $(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_2_jobs_p2;) $(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_2_jobs_p3;) $(foreach item,$(ITEMS_4),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_2_jobs_p4;) +robustness_2.RDS: plot_example.R robustness_2.feather + rm -f $@ + ${srun} Rscript $< --infile $(word 2, $^) --name "robustness_2" --remember-file $@ robustness_2_dv_jobs_p1: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py rm -f $@ - ${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${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"], "prediction_accuracy":[0.60,0.65]}' --outfile $@ + ${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_2_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.60,0.65]}' --outfile $@ robustness_2_dv_jobs_p2: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py rm -f $@ - ${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${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"], "prediction_accuracy":[0.70,0.75]}' --outfile $@ + ${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_2_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.70,0.75]}' --outfile $@ robustness_2_dv_jobs_p3: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py rm -f $@ - ${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${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"], "prediction_accuracy":[0.80,0.85]}' --outfile $@ + ${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_2_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.80,0.85]}' --outfile $@ robustness_2_dv_jobs_p4: grid_sweep.py 03_depvar.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"], "prediction_accuracy":[0.90,0.95]}' --outfile $@ - -START=0 -END_1=$(shell cat robustness_2_dv_jobs_p1 | wc -l) -END_2=$(shell cat robustness_2_dv_jobs_p2 | wc -l) -END_3=$(shell cat robustness_2_dv_jobs_p3 | wc -l) -END_4=$(shell cat robustness_2_dv_jobs_p4 | wc -l) -STEP=1000 -ONE=1 -ITEMS_1=$(shell seq $(START) $(STEP) $(END_1)) -ITEMS_2=$(shell seq $(START) $(STEP) $(END_2)) -ITEMS_3=$(shell seq $(START) $(STEP) $(END_3)) -ITEMS_4=$(shell seq $(START) $(STEP) $(END_4)) + ${srun} $< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_2_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.90,0.95]}' --outfile $@ robustness_2_dv.feather: robustness_2_dv_jobs_p1 robustness_2_dv_jobs_p2 robustness_2_dv_jobs_p3 robustness_2_dv_jobs_p4 - $(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_2_dv_jobs_p1) + $(eval END_1!=cat robustness_2_dv_jobs_p1 | wc -l) + $(eval ITEMS_1!=seq $(START) $(STEP) $(END_1)) + $(eval END_2!=cat robustness_2_dv_jobs_p2 | wc -l) + $(eval ITEMS_2!=seq $(START) $(STEP) $(END_2)) + $(eval END_3!=cat robustness_2_dv_jobs_p3 | wc -l) + $(eval ITEMS_3!=seq $(START) $(STEP) $(END_3)) + $(eval END_4!=cat robustness_2_dv_jobs_p4 | wc -l) + $(eval ITEMS_4!=seq $(START) $(STEP) $(END_4)) + + $(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_2_dv_jobs_p1;) $(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_2_dv_jobs_p2;) $(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_2_dv_jobs_p3;) $(foreach item,$(ITEMS_4),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_2_dv_jobs_p4;) +robustness_2_dv.RDS: plot_example.R robustness_2_dv.feather + rm -f $@ + ${srun} Rscript $< --infile $(word 2, $^) --name "robustness_2_dv" --remember-file $@ robustness_3_jobs_p1: grid_sweep.py 01_two_covariates.R simulation_base.R grid_sweep.py @@ -233,125 +244,131 @@ robustness_3_jobs_p3: grid_sweep.py 01_two_covariates.R simulation_base.R grid_s rm -f $@ ${srun} $< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_3.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3],"Px":[0.9,0.95], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85]}' --outfile $@ -START=0 -END_1=$(shell cat robustness_3_jobs_p1 | wc -l) -END_2=$(shell cat robustness_3_jobs_p2 | wc -l) -END_3=$(shell cat robustness_3_jobs_p3 | wc -l) - -STEP=1000 -ONE=1 -ITEMS_1=$(shell seq $(START) $(STEP) $(END_1)) -ITEMS_2=$(shell seq $(START) $(STEP) $(END_2)) -ITEMS_3=$(shell seq $(START) $(STEP) $(END_3)) - robustness_3.feather: robustness_3_jobs_p1 robustness_3_jobs_p2 robustness_3_jobs_p3 - $(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_3_jobs_p1) + $(eval END_1!=cat robustness_3_jobs_p1 | wc -l) + $(eval ITEMS_1!=seq $(START) $(STEP) $(END_1)) + $(eval END_2!=cat robustness_3_jobs_p2 | wc -l) + $(eval ITEMS_2!=seq $(START) $(STEP) $(END_2)) + $(eval END_3!=cat robustness_3_jobs_p3 | wc -l) + $(eval ITEMS_3!=seq $(START) $(STEP) $(END_3)) + + $(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_3_jobs_p1;) $(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_3_jobs_p2;) $(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_3_jobs_p3;) +robustness_3.RDS: plot_example.R robustness_3.feather + rm -f $@ + ${srun} Rscript $< --infile $(word 2, $^) --name "robustness_3" --remember-file $@ robustness_3_dv_jobs_p1: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py rm -f $@ - ${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_3.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3],"B0":[0.5,0.6], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85]}' --outfile $@ + ${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_3_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3],"B0":[0.5,0.6], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85]}' --outfile $@ + + robustness_3_dv_jobs_p2: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py rm -f $@ - ${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_3.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3],"B0":[0.7,0.8], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85]}' --outfile $@ + ${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_3_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3],"B0":[0.7,0.8], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85]}' --outfile $@ + robustness_3_dv_jobs_p3: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py rm -f $@ - ${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_3.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "B0":[0.9,0.95], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85]}' --outfile $@ - -START=0 -END_1=$(shell cat robustness_3_dv_jobs_p1 | wc -l) -END_2=$(shell cat robustness_3_dv_jobs_p2 | wc -l) -END_3=$(shell cat robustness_3_dv_jobs_p3 | wc -l) - -STEP=1000 -ONE=1 -ITEMS_1=$(shell seq $(START) $(STEP) $(END_1)) -ITEMS_2=$(shell seq $(START) $(STEP) $(END_2)) -ITEMS_3=$(shell seq $(START) $(STEP) $(END_3)) + ${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_3_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "B0":[0.9,0.95], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85]}' --outfile $@ robustness_3_dv.feather: robustness_3_dv_jobs_p1 robustness_3_dv_jobs_p2 robustness_3_dv_jobs_p3 - $(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_3_dv_jobs_p1) + $(eval END_1!=cat robustness_3_dv_jobs_p1 | wc -l) + $(eval ITEMS_1!=seq $(START) $(STEP) $(END_1)) + $(eval END_2!=cat robustness_3_dv_jobs_p2 | wc -l) + $(eval ITEMS_2!=seq $(START) $(STEP) $(END_2)) + $(eval END_3!=cat robustness_3_dv_jobs_p3 | wc -l) + $(eval ITEMS_3!=seq $(START) $(STEP) $(END_3)) + + $(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_3_dv_jobs_p1;) $(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_3_dv_jobs_p2;) - $(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_3_dv_jobs_p3;) + $(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_3_dv_jobs_p3;) +robustness_3_dv.RDS: plot_dv_example.R robustness_3_dv.feather + rm -f $@ + ${srun} Rscript $< --infile $(word 2, $^) --name "robustness_3_dv" --remember-file $@ + robustness_4_jobs_p1: grid_sweep.py 02_indep_differential.R simulation_base.R grid_sweep.py rm -f $@ - ${srun} $< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.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":[0.85],y_bias=[-1,-0.85]}' --outfile $@ + ${srun} $< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.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":[0.85],"y_bias":[-1,-0.85]}' --outfile $@ robustness_4_jobs_p2: grid_sweep.py 02_indep_differential.R simulation_base.R grid_sweep.py rm -f $@ - ${srun} $< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.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":[0.85], y_bias=[-0.70,-0.55]}' --outfile $@ + ${srun} $< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.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":[0.85], "y_bias":[-0.70,-0.55]}' --outfile $@ robustness_4_jobs_p3: grid_sweep.py 02_indep_differential.R simulation_base.R grid_sweep.py rm -f $@ - ${srun} $< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.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":[0.85],y_bias=[-0.4,-0.25]}' --outfile $@ + ${srun} $< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.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":[0.85],"y_bias":[-0.4,-0.25]}' --outfile $@ robustness_4_jobs_p4: grid_sweep.py 02_indep_differential.R simulation_base.R grid_sweep.py rm -f $@ - ${srun} $< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.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":[0.85],y_bias=[-0.1,0]}' --outfile $@ - -START=0 -END_1=$(shell cat robustness_4_jobs_p1 | wc -l) -END_2=$(shell cat robustness_4_jobs_p2 | wc -l) -END_3=$(shell cat robustness_4_jobs_p3 | wc -l) -END_4=$(shell cat robustness_4_jobs_p3 | wc -l) - -STEP=1000 -ONE=1 -ITEMS_1=$(shell seq $(START) $(STEP) $(END_1)) -ITEMS_2=$(shell seq $(START) $(STEP) $(END_2)) -ITEMS_3=$(shell seq $(START) $(STEP) $(END_3)) -ITEMS_4=$(shell seq $(START) $(STEP) $(END_4)) + ${srun} $< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.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":[0.85],"y_bias":[-0.1,0]}' --outfile $@ robustness_4.feather: robustness_4_jobs_p1 robustness_4_jobs_p2 robustness_4_jobs_p3 - $(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_jobs_p1) + $(eval END_1!=cat robustness_4_jobs_p1 | wc -l) + $(eval ITEMS_1!=seq $(START) $(STEP) $(END_1)) + $(eval END_2!=cat robustness_4_jobs_p2 | wc -l) + $(eval ITEMS_2!=seq $(START) $(STEP) $(END_2)) + $(eval END_3!=cat robustness_4_jobs_p3 | wc -l) + $(eval ITEMS_3!=seq $(START) $(STEP) $(END_3)) + + $(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_jobs_p1;) $(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_jobs_p2;) $(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_jobs_p3;) - -robustness_4_dv_jobs_p1: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py +robustness_4.RDS: plot_example.R robustness_4.feather rm -f $@ - ${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3],"B0":[0.5] "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],z_bias=[0,0.1]}' --outfile $@ + ${srun} Rscript $< --infile $(word 2, $^) --name "robustness_4" --remember-file $@ -robustness_4_dv_jobs_p2: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py - rm -f $@ - ${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3],"B0":[0.5] "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],z_bias=[0.25,0.4]}' --outfile $@ -robustness_4_dv_jobs_p3: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py - rm -f $@ - ${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "B0":[0.5], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],z_bias=[0.55,0.7]}' --outfile $@ -robustness_4_dv_jobs_p4: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py +# '{"N":${Ns},"Bxy":[0.7],"Bzy":[-0.7],"m":${ms}, "seed":${seeds}, "outfile":["example_4.feather"], "z_bias":[0.5]}' --outfile example_4_jobs + +robustness_4_dv_jobs_p1: grid_sweep.py 04_depvar_differential.R simulation_base.R grid_sweep.py rm -f $@ - ${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "B0":[0.5], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],z_bias=[0.85,1]}' --outfile $@ + ${srun} $< --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],"z_bias":[0,0.1]}' --outfile $@ +robustness_4_dv_jobs_p2: grid_sweep.py 04_depvar_differential.R simulation_base.R grid_sweep.py + rm -f $@ + ${srun} $< --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],"z_bias":[0.25,0.4]}' --outfile $@ -START=0 -END_1=$(shell cat robustness_4_dv_jobs_p1 | wc -l) -END_2=$(shell cat robustness_4_dv_jobs_p2 | wc -l) -END_3=$(shell cat robustness_4_dv_jobs_p3 | wc -l) +robustness_4_dv_jobs_p3: grid_sweep.py 04_depvar_differential.R simulation_base.R grid_sweep.py + rm -f $@ + ${srun} $< --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7],"outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],"z_bias":[0.55,0.7]}' --outfile $@ -STEP=1000 -ONE=1 -ITEMS_1=$(shell seq $(START) $(STEP) $(END_1)) -ITEMS_2=$(shell seq $(START) $(STEP) $(END_2)) -ITEMS_3=$(shell seq $(START) $(STEP) $(END_3)) +robustness_4_dv_jobs_p4: grid_sweep.py 04_depvar_differential.R simulation_base.R grid_sweep.py + rm -f $@ + ${srun} $< --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4_dv.feather"],"Bzy":[-0.7],"Bxy":[0.7], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],"z_bias":[0.85,1]}' --outfile $@ robustness_4_dv.feather: robustness_4_dv_jobs_p1 robustness_4_dv_jobs_p2 robustness_4_dv_jobs_p3 - $(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_dv_jobs_p1) + $(eval END_1!=cat robustness_4_dv_jobs_p1 | wc -l) + $(eval ITEMS_1!=seq $(START) $(STEP) $(END_1)) + $(eval END_2!=cat robustness_4_dv_p2 | wc -l) + $(eval ITEMS_2!=seq $(START) $(STEP) $(END_2)) + $(eval END_3!=cat robustness_4_dv_p3 | wc -l) + $(eval ITEMS_3!=seq $(START) $(STEP) $(END_3)) + + $(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_dv_jobs_p1;) $(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_dv_jobs_p2;) $(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_dv_jobs_p3;) + +robustness_4_dv.RDS: plot_dv_example.R robustness_4_dv.feather + rm -f $@ + ${srun} Rscript $< --infile $(word 2, $^) --name "robustness_4" --remember-file $@ + # clean: rm *.feather rm -f remembr.RDS + rm -f remembr*.RDS + rm -f robustness*.RDS rm -f example_*_jobs + rm -f robustness_*_jobs_* # sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 example_2_B_jobs # example_2_B_mecor_jobs: diff --git a/simulations/grid_sweep.py b/simulations/grid_sweep.py index 7db920d..b428729 100755 --- a/simulations/grid_sweep.py +++ b/simulations/grid_sweep.py @@ -5,6 +5,7 @@ from itertools import product import pyRemembeR def main(command, arg_dict, outfile, remember_file='remember_grid_sweep.RDS'): + print(remember_file) remember = pyRemembeR.remember.Remember() remember.set_file(remember_file) remember[outfile] = arg_dict diff --git a/simulations/measerr_methods.R b/simulations/measerr_methods.R index 63f8bc1..fdc4978 100644 --- a/simulations/measerr_methods.R +++ b/simulations/measerr_methods.R @@ -19,14 +19,29 @@ library(bbmle) ## 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'),method='optim'){ + df.obs <- model.frame(outcome_formula, df) + proxy.model.matrix <- model.matrix(proxy_formula, df) + proxy.variable <- all.vars(proxy_formula)[1] + + df.proxy.obs <- model.frame(proxy_formula,df) + proxy.obs <- with(df.proxy.obs, eval(parse(text=proxy.variable))) + + response.var <- all.vars(outcome_formula)[1] + y.obs <- with(df.obs,eval(parse(text=response.var))) + outcome.model.matrix <- model.matrix(outcome_formula, df.obs) + + df.unobs <- df[is.na(df[[response.var]])] + df.unobs.y1 <- copy(df.unobs) + df.unobs.y1[[response.var]] <- 1 + df.unobs.y0 <- copy(df.unobs) + df.unobs.y0[[response.var]] <- 0 + + outcome.model.matrix.y1 <- model.matrix(outcome_formula, df.unobs.y1) + proxy.model.matrix.y1 <- model.matrix(proxy_formula, df.unobs.y1) + proxy.model.matrix.y0 <- model.matrix(proxy_formula, df.unobs.y0) + proxy.unobs <- with(df.unobs, eval(parse(text=proxy.variable))) 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) - response.var <- all.vars(outcome_formula)[1] - y.obs <- with(df.obs,eval(parse(text=response.var))) - outcome.model.matrix <- model.matrix(outcome_formula, df.obs) param.idx <- 1 n.outcome.model.covars <- dim(outcome.model.matrix)[2] @@ -39,12 +54,9 @@ measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='lo ll.y.obs[y.obs==0] <- plogis(outcome.params %*% t(outcome.model.matrix[y.obs==0,]),log=TRUE,lower.tail=FALSE) } - df.obs <- model.frame(proxy_formula,df) n.proxy.model.covars <- dim(proxy.model.matrix)[2] proxy.params <- params[param.idx:(n.proxy.model.covars+param.idx-1)] - param.idx <- param.idx + n.proxy.model.covars - proxy.obs <- with(df.obs, eval(parse(text=proxy.variable))) if( (proxy_family$family=="binomial") & (proxy_family$link=='logit')){ ll.w.obs <- vector(mode='numeric',length=dim(proxy.model.matrix)[1]) @@ -53,15 +65,8 @@ measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='lo } ll.obs <- sum(ll.y.obs + ll.w.obs) - - df.unobs <- df[is.na(df[[response.var]])] - df.unobs.y1 <- copy(df.unobs) - df.unobs.y1[[response.var]] <- 1 - df.unobs.y0 <- copy(df.unobs) - df.unobs.y0[[response.var]] <- 0 ## integrate out y - outcome.model.matrix.y1 <- model.matrix(outcome_formula, df.unobs.y1) if((outcome_family$family == "binomial") & (outcome_family$link == 'logit')){ ll.y.unobs.1 <- vector(mode='numeric', length=dim(outcome.model.matrix.y1)[1]) @@ -70,10 +75,6 @@ measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='lo ll.y.unobs.0 <- plogis(outcome.params %*% t(outcome.model.matrix.y1),log=TRUE,lower.tail=FALSE) } - proxy.model.matrix.y1 <- model.matrix(proxy_formula, df.unobs.y1) - proxy.model.matrix.y0 <- model.matrix(proxy_formula, df.unobs.y0) - proxy.unobs <- with(df.unobs, eval(parse(text=proxy.variable))) - if( (proxy_family$family=="binomial") & (proxy_family$link=='logit')){ ll.w.unobs.1 <- vector(mode='numeric',length=dim(proxy.model.matrix.y1)[1]) ll.w.unobs.0 <- vector(mode='numeric',length=dim(proxy.model.matrix.y0)[1]) @@ -431,7 +432,7 @@ measerr_irr_mle <- function(df, outcome_formula, outcome_family=gaussian(), code ## Experimental, and does not work. measerr_irr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='logit'), coder_formulas=list(y.obs.0~y+w_pred+y.obs.1,y.obs.1~y+w_pred+y.obs.0), proxy_formula=w_pred~y, proxy_family=binomial(link='logit'),method='optim'){ integrate.grid <- expand.grid(replicate(1 + length(coder_formulas), c(0,1), simplify=FALSE)) - print(integrate.grid) +# print(integrate.grid) outcome.model.matrix <- model.matrix(outcome_formula, df) @@ -527,8 +528,8 @@ measerr_irr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link ## likelihood of observed data target <- -1 * sum(lls) - print(target) - print(params) +# print(target) +# print(params) return(target) } } diff --git a/simulations/pl_methods.R b/simulations/pl_methods.R index b3007d1..f014eec 100644 --- a/simulations/pl_methods.R +++ b/simulations/pl_methods.R @@ -31,8 +31,8 @@ zhang.mle.dv <- function(df){ (1-w_pred) * (log(1-fpr) - exp(log(1-fnr-fpr)+pi.y.1))))) ll <- ll + sum(lls) - print(paste0(B0,Bxy,Bzy)) - print(ll) +# print(paste0(B0,Bxy,Bzy)) +# print(ll) return(-ll) } mlefit <- mle2(minuslogl = nll, control=list(maxit=1e6),method='L-BFGS-B',lower=c(B0=-Inf, Bxy=-Inf, Bzy=-Inf), diff --git a/simulations/robustness_check_notes.md b/simulations/robustness_check_notes.md index 1c786e9..64a472d 100644 --- a/simulations/robustness_check_notes.md +++ b/simulations/robustness_check_notes.md @@ -10,11 +10,11 @@ Like `robustness\_1.RDS` but with a less precise model for $w_pred$. In the mai # robustness_2.RDS -This is just example 1 with varying levels of classifier accuracy. +This is just example 1 with varying levels of classifier accuracy indicated by the `prediction_accuracy` variable.. # robustness_2_dv.RDS -Example 3 with varying levels of classifier accuracy +Example 3 with varying levels of classifier accuracy indicated by the `prediction_accuracy` variable. # robustness_3.RDS diff --git a/simulations/run_job.sbatch b/simulations/run_job.sbatch new file mode 100644 index 0000000..3ff4f80 --- /dev/null +++ b/simulations/run_job.sbatch @@ -0,0 +1,17 @@ +#!/bin/bash +#SBATCH --job-name="simulate measurement error models" +## Allocation Definition +#SBATCH --account=comdata +#SBATCH --partition=compute-bigmem,compute-hugemem +## Resources +#SBATCH --nodes=1 +## Walltime (4 hours) +#SBATCH --time=4:00:00 +## Memory per node +#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 +"$@" diff --git a/simulations/simulation_base.R b/simulations/simulation_base.R index 08b11ec..e715edf 100644 --- a/simulations/simulation_base.R +++ b/simulations/simulation_base.R @@ -180,26 +180,35 @@ run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formu # amelia says use normal distribution for binary variables. - - amelia.out.k <- amelia(df, m=200, p2s=0, idvars=c('y','ystar','w')) - mod.amelia.k <- zelig(y.obs~x+z, model='ls', data=amelia.out.k$imputations, cite=FALSE) - (coefse <- combine_coef_se(mod.amelia.k, messages=FALSE)) - est.x.mi <- coefse['x','Estimate'] - est.x.se <- coefse['x','Std.Error'] - result <- append(result, - list(Bxy.est.amelia.full = est.x.mi, + amelia_result <- list(Bxy.est.amelia.full = NA, + Bxy.ci.upper.amelia.full = NA, + Bxy.ci.lower.amelia.full = NA, + Bzy.est.amelia.full = NA, + Bzy.ci.upper.amelia.full = NA, + Bzy.ci.lower.amelia.full = NA + ) + + tryCatch({ + amelia.out.k <- amelia(df, m=200, p2s=0, idvars=c('y','ystar','w')) + mod.amelia.k <- zelig(y.obs~x+z, model='ls', data=amelia.out.k$imputations, cite=FALSE) + (coefse <- combine_coef_se(mod.amelia.k, messages=FALSE)) + est.x.mi <- coefse['x','Estimate'] + est.x.se <- coefse['x','Std.Error'] + + est.z.mi <- coefse['z','Estimate'] + est.z.se <- coefse['z','Std.Error'] + amelia_result <- list(Bxy.est.amelia.full = est.x.mi, Bxy.ci.upper.amelia.full = est.x.mi + 1.96 * est.x.se, - Bxy.ci.lower.amelia.full = est.x.mi - 1.96 * est.x.se - )) - - est.z.mi <- coefse['z','Estimate'] - est.z.se <- coefse['z','Std.Error'] - - result <- append(result, - list(Bzy.est.amelia.full = est.z.mi, + Bxy.ci.lower.amelia.full = est.x.mi - 1.96 * est.x.se, + Bzy.est.amelia.full = est.z.mi, Bzy.ci.upper.amelia.full = est.z.mi + 1.96 * est.z.se, Bzy.ci.lower.amelia.full = est.z.mi - 1.96 * est.z.se - )) + ) + }, + error = function(e){ + result[['error']] <- e} + ) + result <- append(result,amelia_result) return(result)