From: Nathan TeBlunthuis <nathante@uw.edu>
Date: Fri, 7 Oct 2022 17:42:50 +0000 (-0700)
Subject: Update stuff.
X-Git-Url: https://code.communitydata.science/ml_measurement_error_public.git/commitdiff_plain/b52b4f7daaba8a877b041ddb24c8f36b466ddc5b?hp=-c

Update stuff.
---

b52b4f7daaba8a877b041ddb24c8f36b466ddc5b
diff --git a/simulations/02_indep_differential.R b/simulations/02_indep_differential.R
index c6907d3..bcfad65 100644
--- a/simulations/02_indep_differential.R
+++ b/simulations/02_indep_differential.R
@@ -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$w_pred == df$x))
 
-    odds.x1 <- qlogis(prediction_accuracy) + y_bias*qlogis(pnorm(scale(df[x==1]$y)))
-    odds.x0 <- qlogis(prediction_accuracy,lower.tail=F) + y_bias*qlogis(pnorm(scale(df[x==0]$y)))
+    resids <- resid(lm(y~x + z))
+    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.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)]
 
-    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[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 <- 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, "--seed", default=51, help='seed for the rng')
 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, "--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, "--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")
 
 args <- parse_args(parser)
diff --git a/simulations/03_depvar.R b/simulations/03_depvar.R
index a2d88e0..79a516f 100644
--- a/simulations/03_depvar.R
+++ b/simulations/03_depvar.R
@@ -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.
 ## 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)
 
     # 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
     y <- rbinom(N,1,plogis(ystar))
 
-    # glm(y ~ x + z, family="binomial")
-
     df <- data.table(x=x,y=y,ystar=ystar,z=z)
 
     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 <- 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, "--seed", default=17, help='seed for the rng')
 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, "--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, "--Bxy", help='coefficient of x on y', default=0.3)
-parser <- add_argument(parser, "--Bzy", help='coeffficient of z 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=-0.01)
 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")
 
diff --git a/simulations/Makefile b/simulations/Makefile
index 44910cb..af54727 100644
--- a/simulations/Makefile
+++ b/simulations/Makefile
@@ -2,19 +2,20 @@
 SHELL=bash
 
 Ns=[1000, 2000, 4000]
-ms=[200, 400, 800]
+ms=[100, 200, 400, 800]
 seeds=[$(shell seq -s, 1 250)]
 explained_variances=[0.1]
 
 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
 
 # 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 
 # 	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
 
 
-example_1_jobs: 01_two_covariates.R simulation_base.R
-	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
+example_1_jobs: 01_two_covariates.R simulation_base.R grid_sweep.py
+	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 
 	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=3001-6001 run_simulation.sbatch 0 example_1_jobs
 
-example_2_jobs: 02_indep_differential.R simulation_base.R
-	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_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":${seeds}, "outfile":["example_2.feather"],"y_explained_variance":${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 
 	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
 
 # 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
 # 	rm -f example_2_B.feather
 # 	sbatch --wait --verbose --array=1-3000 run_simulation.sbatch 0 example_2_B_jobs
 
-example_3_jobs: 03_depvar.R simulation_base.R
-	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
+example_3_jobs: 03_depvar.R simulation_base.R grid_sweep.py
+	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
 	rm -f example_3.feather	
 	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
-	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
+example_4_jobs: 04_depvar_differential.R simulation_base.R grid_sweep.py
+	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
 	rm -f example_4.feather	
 	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
 	${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"
@@ -73,25 +74,51 @@ irr_ms = ${ms}
 irr_seeds=${seeds}
 irr_explained_variances=${explained_variances}
 
-example_5_jobs: 05_irr_indep.R irr_simulation_base.R
-	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_jobs: 05_irr_indep.R irr_simulation_base.R grid_sweep.py
+	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
 	rm -f example_5.feather
 	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
-	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_jobs: 06_irr_dv.R irr_dv_simulation_base.R grid_sweep.py
+	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
 	rm -f example_6.feather
 	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
-	${srun} Rscript plot_irr_example.R --infile example_5.feather --name "plot.df.example.5"
-	${srun} Rscript plot_irr_dv_example.R --infile example_6.feather --name "plot.df.example.6"
+	sbatch --wait --verbose run_job.sbatch Rscript plot_irr_example.R --infile example_5.feather --name "plot.df.example.5"
+	sbatch --wait --verbose run_job.sbatch Rscript plot_irr_dv_example.R --infile example_6.feather --name "plot.df.example.6"
+
+
+
+robustness_1_jobs: 02_indep_differential.R simulation_base.R grid_sweep.py
+	sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 02_indep_differential.R" --arg_dict '{"N":${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:
 	rm *.feather
@@ -100,7 +127,7 @@ clean:
 #	sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 example_2_B_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
 # 	rm -f example_2_B_mecor.feather
@@ -109,3 +136,4 @@ clean:
 
 
 
+.PHONY: supplement
diff --git a/simulations/grid_sweep.py b/simulations/grid_sweep.py
index 86312ea..7db920d 100755
--- a/simulations/grid_sweep.py
+++ b/simulations/grid_sweep.py
@@ -2,8 +2,13 @@
 
 import fire
 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 = []
     values = []
         
diff --git a/simulations/irr_dv_simulation_base.R b/simulations/irr_dv_simulation_base.R
index 3f63d7a..059473c 100644
--- a/simulations/irr_dv_simulation_base.R
+++ b/simulations/irr_dv_simulation_base.R
@@ -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.lower.loco.mle = ci.lower['x'],
                                   Bzy.ci.upper.loco.mle = ci.upper['z'],
-                                  Bzy.ci.lower.loco.mle = ci.upper['z']))
+                                  Bzy.ci.lower.loco.mle = ci.lower['z']))
 
     print(rater_formula)
     print(proxy_formula)
diff --git a/simulations/irr_simulation_base.R b/simulations/irr_simulation_base.R
index ebb215b..ee7112a 100644
--- a/simulations/irr_simulation_base.R
+++ b/simulations/irr_simulation_base.R
@@ -82,7 +82,7 @@ run_simulation <- function(df, result, outcome_formula = y ~ x + z, proxy_formul
                                   Bxy.ci.upper.loco.mle = ci.upper['x'],
                                   Bxy.ci.lower.loco.mle = ci.lower['x'],
                                   Bzy.ci.upper.loco.mle = ci.upper['z'],
-                                  Bzy.ci.lower.loco.mle = ci.upper['z']))
+                                  Bzy.ci.lower.loco.mle = ci.lower['z']))
 
     ## print(rater_formula)
     ## print(proxy_formula)
diff --git a/simulations/measerr_methods.R b/simulations/measerr_methods.R
index 00f1746..087c608 100644
--- a/simulations/measerr_methods.R
+++ b/simulations/measerr_methods.R
@@ -1,6 +1,6 @@
 library(formula.tools)
 library(matrixStats)
-
+library(bbmle)
 ## df: dataframe to model
 ## outcome_formula: formula for y | x, z
 ## outcome_family: family for y | x, z
@@ -17,7 +17,7 @@ library(matrixStats)
 
 
 ## outcome_formula <- y ~ x + z; proxy_formula <- w_pred ~ y + x + z + x:z + x:y + z:y 
-measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='logit'), proxy_formula, proxy_family=binomial(link='logit')){
+measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='logit'), proxy_formula, proxy_family=binomial(link='logit'),method='optim'){
 
     nll <- function(params){
         df.obs <- model.frame(outcome_formula, df)
@@ -98,12 +98,23 @@ measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='lo
     start <- rep(0.1,length(params))
     names(start) <- params
     
-    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)
 }
 
 ## Experimental, and not necessary if errors are independent.
-measerr_irr_mle <- function(df, outcome_formula, outcome_family=gaussian(), rater_formula, proxy_formula, proxy_family=binomial(link='logit'), truth_formula, truth_family=binomial(link='logit')){
+measerr_irr_mle <- function(df, outcome_formula, outcome_family=gaussian(), rater_formula, proxy_formula, proxy_family=binomial(link='logit'), truth_formula, truth_family=binomial(link='logit'),method='optim'){
 
     ### in this scenario, the ground truth also has measurement error, but we have repeated measures for it. 
 
@@ -293,14 +304,28 @@ measerr_irr_mle <- function(df, outcome_formula, outcome_family=gaussian(), rate
     start <- rep(0.1,length(params))
     names(start) <- params
     
-    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)
 }
 
 
-measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_formula, proxy_family=binomial(link='logit'), truth_formula, truth_family=binomial(link='logit')){
+measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_formula, proxy_family=binomial(link='logit'), truth_formula, truth_family=binomial(link='logit'),method='optim'){
 
-    measrr_mle_nll <- function(params){
+    measerr_mle_nll <- function(params){
         df.obs <- model.frame(outcome_formula, df)
         proxy.variable <- all.vars(proxy_formula)[1]
         proxy.model.matrix <- model.matrix(proxy_formula, df)
@@ -425,8 +450,21 @@ measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_fo
     lower <- c(lower, rep(-Inf, length(truth.params)))
     start <- rep(0.1,length(params))
     names(start) <- params
-    
-    fit <- optim(start, fn = measrr_mle_nll, lower=lower, method='L-BFGS-B', hessian=TRUE, control=list(maxit=1e6))
+
+    if(method=='optim'){
+        fit <- optim(start, fn = measerr_mle_nll, lower=lower, method='L-BFGS-B', hessian=TRUE, control=list(maxit=1e6))
+    } else { # method='mle2'
+                
+        quoted.names <- gsub("[\\(\\)]",'',names(start))
+
+        text <- paste("function(", paste0(quoted.names,'=',start,collapse=','),"){params<-c(",paste0(quoted.names,collapse=','),");return(measerr_mle_nll(params))}")
+
+        measerr_mle_nll_mle <- eval(parse(text=text))
+        names(start) <- quoted.names
+        names(lower) <- quoted.names
+        fit <- mle2(minuslogl=measerr_mle_nll_mle, start=start, lower=lower, parnames=params,control=list(maxit=1e6),method='L-BFGS-B')
+    }
 
     return(fit)
 }
+
diff --git a/simulations/plot_dv_example.R b/simulations/plot_dv_example.R
index 4052c38..71963b1 100644
--- a/simulations/plot_dv_example.R
+++ b/simulations/plot_dv_example.R
@@ -7,49 +7,51 @@ library(argparser)
 
 parser <- arg_parser("Simulate data and fit corrected models.")
 parser <- add_argument(parser, "--infile", default="", help="name of the file to read.")
+parser <- add_argument(parser, "--remember-file", default="remembr.RDS", help="name of the remember file.")
 parser <- add_argument(parser, "--name", default="", help="The name to safe the data to in the remember file.")
 args <- parse_args(parser)
 
-summarize.estimator <- function(df, suffix='naive', coefname='x'){
-
-    part <- df[,c('N',
-                  'm',
-                  'Bxy',
-                  paste0('B',coefname,'y.est.',suffix),
-                  paste0('B',coefname,'y.ci.lower.',suffix),
-                  paste0('B',coefname,'y.ci.upper.',suffix),
-                  'y_explained_variance',
-                  'Bzy'
-                  ),
-               with=FALSE]
+## summarize.estimator <- function(df, suffix='naive', coefname='x'){
+
+##     part <- df[,c('N',
+##                   'm',
+##                   'Bxy',
+##                   paste0('B',coefname,'y.est.',suffix),
+##                   paste0('B',coefname,'y.ci.lower.',suffix),
+##                   paste0('B',coefname,'y.ci.upper.',suffix),
+##                   'y_explained_variance',
+##                   'Bzy'
+##                   ),
+##                with=FALSE]
     
-    true.in.ci <- as.integer((part$Bxy >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (part$Bxy <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]]))
-    zero.in.ci <- as.integer(0 >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (0 <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]])
-    bias <- part$Bxy - part[[paste0('B',coefname,'y.est.',suffix)]]
-    sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))
-
-    part <- part[,':='(true.in.ci = true.in.ci,
-                       zero.in.ci = zero.in.ci,
-                       bias=bias,
-                       sign.correct =sign.correct)]
-
-    part.plot <- part[, .(p.true.in.ci = mean(true.in.ci),
-                          mean.bias = mean(bias),
-                          mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
-                          var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
-                          est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.95),
-                          est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.05),
-                          N.sims = .N,
-                          p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
-                          variable=coefname,
-                          method=suffix
-                          ),
-                      by=c("N","m",'Bzy','y_explained_variance')
-                      ]
+##     true.in.ci <- as.integer((part$Bxy >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (part$Bxy <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]]))
+##     zero.in.ci <- as.integer(0 >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (0 <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]])
+##     bias <- part$Bxy - part[[paste0('B',coefname,'y.est.',suffix)]]
+##     sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))
+
+##     part <- part[,':='(true.in.ci = true.in.ci,
+##                        zero.in.ci = zero.in.ci,
+##                        bias=bias,
+##                        sign.correct =sign.correct)]
+
+##     part.plot <- part[, .(p.true.in.ci = mean(true.in.ci),
+##                           mean.bias = mean(bias),
+##                           mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
+##                           var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
+##                           est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.95),
+##                           est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.05),
+##                           N.sims = .N,
+##                           p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
+##                           variable=coefname,
+##                           method=suffix
+##                           ),
+##                       by=c("N","m",'Bzy','y_explained_variance')
+##                       ]
     
-    return(part.plot)
-}
+##     return(part.plot)
+## }
 
+source("summarize_estimator.R")
 
 build_plot_dataset <- function(df){
 
@@ -82,12 +84,23 @@ build_plot_dataset <- function(df){
     return(plot.df)
 }
 
-
-df <- read_feather(args$infile)
-plot.df <- build_plot_dataset(df)
+change.remember.file(args$remember_file, clear=TRUE)
+sims.df <- read_feather(args$infile)
+sims.df[,Bzx:=NA]
+sims.df[,accuracy_imbalance_difference:=NA]
+plot.df <- build_plot_dataset(sims.df)
 
 remember(plot.df,args$name)
 
+set.remember.prefix(gsub("plot.df.","",args$name))
+
+remember(median(sims.df$cor.xz),'med.cor.xz')
+remember(median(sims.df$accuracy),'med.accuracy')
+remember(median(sims.df$error.cor.x),'med.error.cor.x')
+remember(median(sims.df$lik.ratio),'med.lik.ratio')
+
+
+
 
 ## df[gmm.ER_pval<0.05]
 ## plot.df.test <- plot.df[,':='(method=factor(method,levels=c("Naive","Multiple imputation", "Multiple imputation (Classifier features unobserved)","Regression Calibration","2SLS+gmm","Bespoke MLE", "Feasible"),ordered=T),
diff --git a/simulations/plot_example.R b/simulations/plot_example.R
index 7a853b7..8e6c477 100644
--- a/simulations/plot_example.R
+++ b/simulations/plot_example.R
@@ -5,52 +5,58 @@ library(ggplot2)
 library(filelock)
 library(argparser)
 
+source("summarize_estimator.R")
+
+
 parser <- arg_parser("Simulate data and fit corrected models.")
 parser <- add_argument(parser, "--infile", default="", help="name of the file to read.")
+parser <- add_argument(parser, "--remember-file", default="remembr.RDS", help="name of the remember file.")
 parser <- add_argument(parser, "--name", default="", help="The name to safe the data to in the remember file.")
 args <- parse_args(parser)
 
-summarize.estimator <- function(df, suffix='naive', coefname='x'){
-
-    part <- df[,c('N',
-                  'm',
-                  'Bxy',
-                  paste0('B',coefname,'y.est.',suffix),
-                  paste0('B',coefname,'y.ci.lower.',suffix),
-                  paste0('B',coefname,'y.ci.upper.',suffix),
-                  'y_explained_variance',
-                  'Bzx',
-                  'Bzy',
-                  'accuracy_imbalance_difference'
-                  ),
-               with=FALSE]
+
+
+## summarize.estimator <- function(df, suffix='naive', coefname='x'){
+
+##     part <- df[,c('N',
+##                   'm',
+##                   'Bxy',
+##                   paste0('B',coefname,'y.est.',suffix),
+##                   paste0('B',coefname,'y.ci.lower.',suffix),
+##                   paste0('B',coefname,'y.ci.upper.',suffix),
+##                   'y_explained_variance',
+##                   'Bzx',
+##                   'Bzy',
+##                   'accuracy_imbalance_difference'
+##                   ),
+##                with=FALSE]
     
-    true.in.ci <- as.integer((part$Bxy >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (part$Bxy <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]]))
-    zero.in.ci <- as.integer(0 >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (0 <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]])
-    bias <- part$Bxy - part[[paste0('B',coefname,'y.est.',suffix)]]
-    sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))
-
-    part <- part[,':='(true.in.ci = true.in.ci,
-                       zero.in.ci = zero.in.ci,
-                       bias=bias,
-                       sign.correct =sign.correct)]
-
-    part.plot <- part[, .(p.true.in.ci = mean(true.in.ci),
-                          mean.bias = mean(bias),
-                          mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
-                          var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
-                          est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.95,na.rm=T),
-                          est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.05,na.rm=T),
-                          N.sims = .N,
-                          p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
-                          variable=coefname,
-                          method=suffix
-                          ),
-                      by=c("N","m",'y_explained_variance','Bzx', 'Bzy', 'accuracy_imbalance_difference')
-                      ]
+##     true.in.ci <- as.integer((part$Bxy >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (part$Bxy <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]]))
+##     zero.in.ci <- as.integer(0 >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (0 <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]])
+##     bias <- part$Bxy - part[[paste0('B',coefname,'y.est.',suffix)]]
+##     sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))
+
+##     part <- part[,':='(true.in.ci = true.in.ci,
+##                        zero.in.ci = zero.in.ci,
+##                        bias=bias,
+##                        sign.correct =sign.correct)]
+
+##     part.plot <- part[, .(p.true.in.ci = mean(true.in.ci),
+##                           mean.bias = mean(bias),
+##                           mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
+##                           var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
+##                           est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.95,na.rm=T),
+##                           est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.05,na.rm=T),
+##                           N.sims = .N,
+##                           p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
+##                           variable=coefname,
+##                           method=suffix
+##                           ),
+##                       by=c("N","m",'y_explained_variance','Bzx', 'Bzy', 'accuracy_imbalance_difference')
+##                       ]
     
-    return(part.plot)
-}
+##     return(part.plot)
+## }
 
 build_plot_dataset <- function(df){
     
@@ -98,24 +104,40 @@ build_plot_dataset <- function(df){
 }
 
 
-plot.df <- read_feather(args$infile)
-print(unique(plot.df$N))
+sims.df <- read_feather(args$infile)
+print(unique(sims.df$N))
 
 # df <- df[apply(df,1,function(x) !any(is.na(x)))]
 
-if(!('Bzx' %in% names(plot.df)))
-    plot.df[,Bzx:=NA]
+if(!('Bzx' %in% names(sims.df)))
+    sims.df[,Bzx:=NA]
 
-if(!('accuracy_imbalance_difference' %in% names(plot.df)))
-    plot.df[,accuracy_imbalance_difference:=NA]
+if(!('accuracy_imbalance_difference' %in% names(sims.df)))
+    sims.df[,accuracy_imbalance_difference:=NA]
 
-unique(plot.df[,'accuracy_imbalance_difference'])
+unique(sims.df[,'accuracy_imbalance_difference'])
 
+change.remember.file(args$remember_file, clear=TRUE)
 #plot.df <- build_plot_dataset(df[accuracy_imbalance_difference==0.1][N==700])
-plot.df <- build_plot_dataset(plot.df)
+plot.df <- build_plot_dataset(sims.df)
 
 remember(plot.df,args$name)
 
+set.remember.prefix(gsub("plot.df.","",args$name))
+
+remember(median(sims.df$cor.xz),'med.cor.xz')
+remember(median(sims.df$accuracy),'med.accuracy')
+remember(median(sims.df$accuracy.y0),'med.accuracy.y0')
+remember(median(sims.df$accuracy.y1),'med.accuracy.y1')
+remember(median(sims.df$fpr),'med.fpr')
+remember(median(sims.df$fpr.y0),'med.fpr.y0')
+remember(median(sims.df$fpr.y1),'med.fpr.y1')
+remember(median(sims.df$fnr),'med.fnr')
+remember(median(sims.df$fnr.y0),'med.fnr.y0')
+remember(median(sims.df$fnr.y1),'med.fnr.y1')
+
+remember(median(sims.df$cor.resid.w_pred),'cor.resid.w_pred')
+
 #ggplot(df,aes(x=Bxy.est.mle)) + geom_histogram() + facet_grid(accuracy_imbalance_difference ~ Bzy)
 
 ## ## ## df[gmm.ER_pval<0.05]
diff --git a/simulations/run_simulation.sbatch b/simulations/run_simulation.sbatch
index 54f56be..9dce9ea 100644
--- a/simulations/run_simulation.sbatch
+++ b/simulations/run_simulation.sbatch
@@ -5,15 +5,16 @@
 #SBATCH --partition=compute-bigmem
 ## Resources
 #SBATCH --nodes=1    
-## Walltime (12 hours)
-#SBATCH --time=1:00:00
+## Walltime (4 hours)
+#SBATCH --time=4:00:00
 ## Memory per node
-#SBATCH --mem=8G
+#SBATCH --mem=4G
 #SBATCH --cpus-per-task=1
 #SBATCH --ntasks-per-node=1
 #SBATCH --chdir /gscratch/comdata/users/nathante/ml_measurement_error_public/simulations
 #SBATCH --output=simulation_jobs/%A_%a.out
 #SBATCH --error=simulation_jobs/%A_%a.err
+source ~/.bashrc
 
 TASK_NUM=$(($SLURM_ARRAY_TASK_ID + $1))
 TASK_CALL=$(sed -n ${TASK_NUM}p $2)
diff --git a/simulations/simulation_base.R b/simulations/simulation_base.R
index ee46ec6..27f0276 100644
--- a/simulations/simulation_base.R
+++ b/simulations/simulation_base.R
@@ -210,11 +210,19 @@ run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formu
 
     accuracy <- df[,mean(w_pred==y)]
     result <- append(result, list(accuracy=accuracy))
+    error.cor.x <- cor(df$x, df$w - df$x)
+    result <- append(result, list(error.cor.x = error.cor.x))
 
+    model.null <- glm(y~1, data=df,family=binomial(link='logit'))
     (model.true <- glm(y ~ x + z, data=df,family=binomial(link='logit')))
+    (lik.ratio <- exp(logLik(model.true) - logLik(model.null)))
+
     true.ci.Bxy <- confint(model.true)['x',]
     true.ci.Bzy <- confint(model.true)['z',]
 
+
+    result <- append(result, list(lik.ratio=lik.ratio))
+
     result <- append(result, list(Bxy.est.true=coef(model.true)['x'],
                                   Bzy.est.true=coef(model.true)['z'],
                                   Bxy.ci.upper.true = true.ci.Bxy[2],
@@ -322,8 +330,33 @@ run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formu
 run_simulation <-  function(df, result, outcome_formula=y~x+z, proxy_formula=NULL, truth_formula=NULL){
 
     accuracy <- df[,mean(w_pred==x)]
-    result <- append(result, list(accuracy=accuracy))
-
+    accuracy.y0 <- df[y<=0,mean(w_pred==x)]
+    accuracy.y1 <- df[y>=0,mean(w_pred==x)]
+    cor.y.xi <- cor(df$x - df$w_pred, df$y)
+
+    fnr <- df[w_pred==0,mean(w_pred!=x)]
+    fnr.y0 <- df[(w_pred==0) & (y<=0),mean(w_pred!=x)]
+    fnr.y1 <- df[(w_pred==0) & (y>=0),mean(w_pred!=x)]
+
+    fpr <- df[w_pred==1,mean(w_pred!=x)]
+    fpr.y0 <- df[(w_pred==1) & (y<=0),mean(w_pred!=x)]
+    fpr.y1 <- df[(w_pred==1) & (y>=0),mean(w_pred!=x)]
+    cor.resid.w_pred <- cor(resid(lm(y~x+z,df)),df$w_pred)
+
+    result <- append(result, list(accuracy=accuracy,
+                                  accuracy.y0=accuracy.y0,
+                                  accuracy.y1=accuracy.y1,
+                                  cor.y.xi=cor.y.xi,
+                                  fnr=fnr,
+                                  fnr.y0=fnr.y0,
+                                  fnr.y1=fnr.y1,
+                                  fpr=fpr,
+                                  fpr.y0=fpr.y0,
+                                  fpr.y1=fpr.y1,
+                                  cor.resid.w_pred=cor.resid.w_pred
+                                  ))
+
+    result <- append(result, list(cor.xz=cor(df$x,df$z)))
     (model.true <- lm(y ~ x + z, data=df))
     true.ci.Bxy <- confint(model.true)['x',]
     true.ci.Bzy <- confint(model.true)['z',]
diff --git a/simulations/summarize_estimator.R b/simulations/summarize_estimator.R
index 8199c06..e0e7622 100644
--- a/simulations/summarize_estimator.R
+++ b/simulations/summarize_estimator.R
@@ -13,10 +13,11 @@ summarize.estimator <- function(df, suffix='naive', coefname='x'){
                   'accuracy_imbalance_difference'
                   ),
                with=FALSE]
-    
+
+
     true.in.ci <- as.integer((part$Bxy >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (part$Bxy <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]]))
     zero.in.ci <- as.integer(0 >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (0 <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]])
-    bias <- part$Bxy - part[[paste0('B',coefname,'y.est.',suffix)]]
+    bias <- part[[paste0('B',coefname,'y')]] - part[[paste0('B',coefname,'y.est.',suffix)]]
     sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))
 
     part <- part[,':='(true.in.ci = true.in.ci,
@@ -28,8 +29,15 @@ summarize.estimator <- function(df, suffix='naive', coefname='x'){
                           mean.bias = mean(bias),
                           mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
                           var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
-                          est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.95,na.rm=T),
-                          est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.05,na.rm=T),
+                          est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.975,na.rm=T),
+                          est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.025,na.rm=T),
+                          mean.ci.upper = mean(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]]),
+                          mean.ci.lower = mean(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]]),
+                          ci.upper.975 = quantile(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]],0.975,na.rm=T),
+                          ci.upper.025 = quantile(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]],0.025,na.rm=T),
+                          ci.lower.975 = quantile(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]],0.975,na.rm=T),
+                          ci.lower.025 = quantile(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]],0.025,na.rm=T),
+                          N.ci.is.NA = sum(is.na(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]])),
                           N.sims = .N,
                           p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
                           variable=coefname,