From: Nathan TeBlunthuis <nathante@uw.edu>
Date: Wed, 11 Jan 2023 18:59:50 +0000 (-0800)
Subject: update real data examples code and rerun project.
X-Git-Url: https://code.communitydata.science/ml_measurement_error_public.git/commitdiff_plain/c45ea9dfebca86dfddc1e9237aa74866c5166519?ds=sidebyside;hp=--cc

update real data examples code and rerun project.
---

c45ea9dfebca86dfddc1e9237aa74866c5166519
diff --git a/civil_comments/01_dv_example.R b/civil_comments/01_dv_example.R
index c26d640..12af561 100644
--- a/civil_comments/01_dv_example.R
+++ b/civil_comments/01_dv_example.R
@@ -3,15 +3,33 @@ source("../simulations/measerr_methods.R")
 source("../simulations/RemembR/R/RemembeR.R")
 
 change.remember.file("dv_perspective_example.RDS")
-
+remember(accuracies, "civil_comments_accuracies")
+remember(f1s, "civil_comments_f1s")
+remember(positive_cases, "civil_comments_positive_cases")
+remember(proportions_cases, "civil_comments_proportions_cases")
+remember(cortab, "civil_comments_cortab")
+remember(nrow(df), 'n.annotated.comments')
 # for reproducibility
-set.seed(1111)
+set.seed(111)
 
 ## 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){
+compare_dv_models <-function(pred_formula, outcome_formula, proxy_formula, df, sample.prop, sample.size, remember_prefix){
+    if(is.null(sample.prop)){
+        sample.prop <- sample.size / nrow(df)
+    }
+    if(is.null(sample.size)){
+        sample.size <- nrow(df) * sample.prop
+    }
+
     pred_model <- glm(pred_formula, df, family=binomial(link='logit'))
 
+    remember(sample.size, paste0(remember_prefix, "sample.size"))
+    remember(sample.prop, paste0(remember_prefix, "sample.prop"))
+    remember(pred_formula, paste0(remember_prefix, "pred_formula"))
+    remember(outcome_formula, paste0(remember_prefix, 'outcome_formula'))
+    remember(proxy_formula, paste0(remember_prefix, 'proxy_formula'))
+
     remember(coef(pred_model), paste0(remember_prefix, "coef_pred_model"))
     remember(diag(vcov((pred_model))), paste0(remember_prefix, "se_pred_model"))
 
@@ -19,7 +37,7 @@ compare_dv_models <-function(pred_formula, outcome_formula, proxy_formula, df, s
     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 <- copy(df)[sample(1:.N, sample.size), 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"))
@@ -35,20 +53,37 @@ compare_dv_models <-function(pred_formula, outcome_formula, proxy_formula, df, s
 
 print("running first example")
 
-compare_dv_models(pred_formula = toxicity_pred ~ funny*white,
-                  outcome_formula = toxicity_coded ~ funny*white,
-                  proxy_formula = toxicity_pred ~ toxicity_coded*funny*white,
+pred_formula = toxicity_pred ~ likes + race_disclosed
+outcome_formula = toxicity_coded ~ likes + race_disclosed
+proxy_formula = toxicity_pred ~ toxicity_coded*race_disclosed*likes
+
+compare_dv_models(pred_formula = pred_formula,
+                  outcome_formula = outcome_formula,
+                  proxy_formula = proxy_formula,
                   df=df,
                   sample.prop=0.01,
-                  remember_prefix='cc_ex_tox.funny.white')
+                  sample.size=NULL,
+                  remember_prefix='cc_ex_tox.likes.race_disclosed')
 
 
 print("running second example")
 
-compare_dv_models(pred_formula = toxicity_pred ~ likes+race_disclosed,
-                  outcome_formula = toxicity_coded ~ likes + race_disclosed,KKJ
-                  proxy_formula = toxicity_pred ~ toxicity_coded*likes*race_disclosed,
+compare_dv_models(pred_formula = pred_formula,
+                  outcome_formula = outcome_formula,
+                  proxy_formula = proxy_formula,
                   df=df,
-                  sample.prop=0.01,
-                  remember_prefix='cc_ex_tox.funny.race_disclosed')
+                  sample.size=10000,
+                  sample.prop=NULL,
+                  remember_prefix='cc_ex_tox.likes.race_disclosed.medsamp')
+
+
+print("running third example")
+
+compare_dv_models(pred_formula = pred_formula,
+                  outcome_formula = outcome_formula,
+                  proxy_formula = proxy_formula,
+                  df=df,
+                  sample.prop=0.05,
+                  sample.size=NULL,
+                  remember_prefix='cc_ex_tox.likes.race_disclosed.largesamp')
 
diff --git a/civil_comments/02_iv_example.R b/civil_comments/02_iv_example.R
new file mode 100644
index 0000000..ba5ab12
--- /dev/null
+++ b/civil_comments/02_iv_example.R
@@ -0,0 +1,107 @@
+source("../simulations/RemembR/R/RemembeR.R")
+change.remember.file("iv_perspective_example.RDS")
+
+source('load_perspective_data.R')
+source("../simulations/measerr_methods.R")
+
+remember(accuracies, "civil_comments_accuracies")
+remember(f1s, "civil_comments_f1s")
+remember(positive_cases, "civil_comments_positive_cases")
+remember(proportions_cases, "civil_comments_proportions_cases")
+remember(cortab, "civil_comments_cortab")
+remember(nrow(df), 'n.annotated.comments')
+# for reproducibility
+set.seed(1)
+
+## 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_iv_models <-function(pred_formula, outcome_formula, proxy_formula, truth_formula, df, sample.prop, sample.size, remember_prefix){
+
+    if(is.null(sample.prop)){
+        sample.prop <- sample.size / nrow(df)
+    }
+    if(is.null(sample.size)){
+        sample.size <- nrow(df) * sample.prop
+    }
+
+    remember(sample.size, paste0(remember_prefix, "sample.size"))
+    remember(sample.prop, paste0(remember_prefix, "sample.prop"))
+    remember(pred_formula, paste0(remember_prefix, "pred_formula"))
+    remember(outcome_formula, paste0(remember_prefix, 'outcome_formula'))
+    remember(proxy_formula, paste0(remember_prefix, 'proxy_formula'))
+    remember(truth_formula, paste0(remember_prefix, 'truth_formula'))
+
+    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.size), 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(df_measerr_method, outcome_formula, outcome_family=binomial(link='logit'), proxy_formula=proxy_formula, proxy_family=binomial(link='logit'),truth_formula=truth_formula, truth_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 iv example")
+
+## sample.prop <- 0.05
+
+## compare_iv_models(white ~ toxicity_pred*funny,
+##                   outcome_formula = white ~ toxicity_coded*funny,
+##                   proxy_formula = toxicity_pred ~ toxicity_coded*funny*white,
+##                   truth_formula = toxicity_coded ~ 1,
+##                   df=df,
+##                   sample.prop=sample.prop,
+##                   remember_prefix='cc_ex_tox.funny.white')
+
+
+
+pred_formula <- race_disclosed ~ likes * toxicity_pred
+outcome_formula <- race_disclosed ~ likes * toxicity_coded
+proxy_formula <- toxicity_pred ~ toxicity_coded * race_disclosed * likes
+truth_formula <- toxicity_coded ~ 1
+
+print("running first example")
+
+compare_iv_models(pred_formula = pred_formula,
+                  outcome_formula = outcome_formula,
+                  proxy_formula = proxy_formula,
+                  truth_formula = truth_formula,
+                  df=df,
+                  sample.prop=0.01,
+                  sample.size=NULL,
+                  remember_prefix='cc_ex_tox.likes.race_disclosed')
+
+print("running second example")
+
+compare_iv_models(pred_formula = pred_formula,
+                  outcome_formula = outcome_formula,
+                  proxy_formula = proxy_formula,
+                  truth_formula = truth_formula,
+                  df=df,
+                  sample.prop=NULL,
+                  sample.size=10000,
+                  remember_prefix='cc_ex_tox.likes.race_disclosed.medsamp')
+
+print("running third example")
+
+compare_iv_models(pred_formula = race_disclosed ~ likes * toxicity_pred,
+                  outcome_formula = race_disclosed ~ likes * toxicity_coded,
+                  proxy_formula = toxicity_pred ~ toxicity_coded + race_disclosed,
+                  truth_formula = toxicity_coded ~ 1,
+                  df=df,
+                  sample.prop=0.05,
+                  sample.size=NULL,
+                  remember_prefix='cc_ex_tox.likes.race_disclosed.largesamp')
+
diff --git a/civil_comments/Makefile b/civil_comments/Makefile
index 3c331c2..ab05a35 100644
--- a/civil_comments/Makefile
+++ b/civil_comments/Makefile
@@ -1,6 +1,14 @@
+qall: iv_perspective_example.RDS dv_perspective_example.RDS
+
 srun_1core=srun -A comdata -p compute-bigmem --mem=4G --time=12:00:00 -c 1 --pty /usr/bin/bash -l
+srun=srun -A comdata -p compute-bigmem --mem=4G --time=12:00:00 --pty /usr/bin/bash -l
+
 perspective_scores.csv: perspective_json_to_csv.sh perspective_results.json
 	$(srun_1core) ./$^ $@
 
+iv_perspective_example.RDS: 02_iv_example.R perspective_scores.csv
+	$(srun) Rscript $<
 
+dv_perspective_example.RDS: 01_dv_example.R perspective_scores.csv
+	 $(srun) Rscript $<
 
diff --git a/civil_comments/design_example.R b/civil_comments/design_example.R
index 1a83a81..4907688 100644
--- a/civil_comments/design_example.R
+++ b/civil_comments/design_example.R
@@ -5,95 +5,6 @@ source('load_perspective_data.R')
 ## the API claims that these scores are "probabilities"
 ## say we care about the model of the classification, not the probability
 
-F1 <- function(y, predictions){
-    tp <- sum( (predictions == y) & (predictions==1))
-    fn <- sum( (predictions != y) & (predictions!=1))
-    fp <- sum( (predictions != y) & (predictions==1))
-    precision <- tp / (tp + fp)
-    recall <- tp / (tp + fn)
-    return (2 * precision * recall ) / (precision + recall)
-}
-
-
-## toxicity is about 93% accurate, with an f1 of 0.8
-## identity_attack has high accuracy 97%, but an unfortunant f1 of 0.5.
-## threat has high accuracy 99%, but a really bad looking f1 of 0.48.
-accuracies <- df[,.(identity_attack_acc = mean(identity_attack_pred == identity_attack_coded),
-                    insult_pred_acc = mean(insult_pred == insult_coded),
-                    profanity_acc = mean(profanity_pred == profanity_coded),
-                    severe_toxicity_acc = mean(severe_toxicity_pred == severe_toxicity_coded),
-                    theat_acc = mean(threat_pred == threat_coded),
-                    toxicity_acc = mean(toxicity_pred == toxicity_coded))]
-
-f1s <- df[,.(identity_attack_f1 = F1(identity_attack_coded,identity_attack_pred),
-                    insult_f1 = F1(insult_coded,insult_pred),
-                    profanity_f1 = F1(profanity_coded,profanity_pred),
-                    severe_toxicity_f1 = F1(severe_toxicity_coded,severe_toxicity_pred),
-                    theat_f1 = F1(threat_coded,threat_pred),
-                    toxicity_f1 = F1(toxicity_coded,toxicity_pred))]
-
-positive_cases <- df[,.(identity_attacks = sum(identity_attack_coded),
-                        insults = sum(insult_coded),
-                        profanities = sum(profanity_coded),
-                        severe_toxic_comments = sum(severe_toxicity_coded),
-                        threats = sum(threat_coded),
-                        toxic_comments = sum(toxicity_coded))]
-
-## there are 50,000 toxic comments, 13000 identity attacks, 30000 insults, 3000 profanities, 8 severe toxic, and 1000 threats.
-
-proportions_cases <- df[,.(prop_identity = mean(identity_attack_coded),
-                           prop_insults = mean(insult_coded),
-                           prop_profanity = mean(profanity_coded),
-                           prop_severe = mean(severe_toxicity_coded),
-                           prop_threats = mean(threat_coded),
-                           prop_toxic = mean(toxicity_coded))]
-
-## at 11% of comments, "toxicity" seems not so badly skewed. Try toxicity first, and if it doesn't work out try insults.
-
-## now look for an example where differential error affects an identity, or a reaction.
-df <- df[,":="(identity_error = identity_attack_coded - identity_attack_pred,
-               insult_error = insult_coded - insult_pred,
-               profanity_error = profanity_coded - profanity_pred,
-               severe_toxic_error = severe_toxicity_coded - severe_toxicity_pred,
-               threat_error = threat_coded - threat_pred,
-               toxicity_error = toxicity_coded - toxicity_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,
-                    toxicity_coded,
-                    funny,
-                    approved,
-                    sad,
-                    wow,
-                    likes,
-                    disagree,
-                    male,
-                    female,
-                    transgender,
-                    other_gender,
-                    heterosexual,
-                    bisexual,
-                    other_sexual_orientation,
-                    christian,
-                    jewish,
-                    hindu,
-                    buddhist,
-                    atheist,
-                    other_religion,
-                    black,
-                    white,
-                    asian,
-                    latino,
-                    other_race_or_ethnicity,
-                    physical_disability,
-                    intellectual_or_learning_disability,
-                    psychiatric_or_mental_illness,
-                    other_disability)])
-
 ## toxicity error is weakly correlated pearson's R = 0.1 with both "white" and "black".
 
 ## compare regressions with "white" or "black" as the outcome and "toxicity_coded" or "toxicity_pred" as a predictor.
@@ -107,22 +18,6 @@ cortab['toxicity_coded',]
 cortab['identity_error',]
 cortab['white',]
 
-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')))
 
diff --git a/civil_comments/load_perspective_data.R b/civil_comments/load_perspective_data.R
index 636c423..d6ef492 100644
--- a/civil_comments/load_perspective_data.R
+++ b/civil_comments/load_perspective_data.R
@@ -39,3 +39,98 @@ df <- df[,":="(gender_disclosed = dt.apply.any(gt.0.5, male, female, transgender
                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)]
+
+
+F1 <- function(y, predictions){
+    tp <- sum( (predictions == y) & (predictions==1))
+    fn <- sum( (predictions != y) & (predictions!=1))
+    fp <- sum( (predictions != y) & (predictions==1))
+    precision <- tp / (tp + fp)
+    recall <- tp / (tp + fn)
+    return (2 * precision * recall ) / (precision + recall)
+}
+
+
+## toxicity is about 93% accurate, with an f1 of 0.8
+## identity_attack has high accuracy 97%, but an unfortunant f1 of 0.5.
+## threat has high accuracy 99%, but a really bad looking f1 of 0.48.
+accuracies <- df[,.(identity_attack_acc = mean(identity_attack_pred == identity_attack_coded),
+                    insult_pred_acc = mean(insult_pred == insult_coded),
+                    profanity_acc = mean(profanity_pred == profanity_coded),
+                    severe_toxicity_acc = mean(severe_toxicity_pred == severe_toxicity_coded),
+                    theat_acc = mean(threat_pred == threat_coded),
+                    toxicity_acc = mean(toxicity_pred == toxicity_coded))]
+
+f1s <- df[,.(identity_attack_f1 = F1(identity_attack_coded,identity_attack_pred),
+                    insult_f1 = F1(insult_coded,insult_pred),
+                    profanity_f1 = F1(profanity_coded,profanity_pred),
+                    severe_toxicity_f1 = F1(severe_toxicity_coded,severe_toxicity_pred),
+                    theat_f1 = F1(threat_coded,threat_pred),
+                    toxicity_f1 = F1(toxicity_coded,toxicity_pred))]
+
+positive_cases <- df[,.(identity_attacks = sum(identity_attack_coded),
+                        insults = sum(insult_coded),
+                        profanities = sum(profanity_coded),
+                        severe_toxic_comments = sum(severe_toxicity_coded),
+                        threats = sum(threat_coded),
+                        toxic_comments = sum(toxicity_coded))]
+
+## there are 50,000 toxic comments, 13000 identity attacks, 30000 insults, 3000 profanities, 8 severe toxic, and 1000 threats.
+
+proportions_cases <- df[,.(prop_identity = mean(identity_attack_coded),
+                           prop_insults = mean(insult_coded),
+                           prop_profanity = mean(profanity_coded),
+                           prop_severe = mean(severe_toxicity_coded),
+                           prop_threats = mean(threat_coded),
+                           prop_toxic = mean(toxicity_coded))]
+
+## at 11% of comments, "toxicity" seems not so badly skewed. Try toxicity first, and if it doesn't work out try insults.
+
+## now look for an example where differential error affects an identity, or a reaction.
+df <- df[,":="(identity_error = identity_attack_coded - identity_attack_pred,
+               insult_error = insult_coded - insult_pred,
+               profanity_error = profanity_coded - profanity_pred,
+               severe_toxic_error = severe_toxicity_coded - severe_toxicity_pred,
+               threat_error = threat_coded - threat_pred,
+               toxicity_error = toxicity_coded - toxicity_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,
+                    toxicity_coded,
+                    funny,
+                    approved,
+                    sad,
+                    wow,
+                    likes,
+                    disagree,
+                    male,
+                    female,
+                    transgender,
+                    other_gender,
+                    heterosexual,
+                    bisexual,
+                    other_sexual_orientation,
+                    christian,
+                    jewish,
+                    hindu,
+                    buddhist,
+                    atheist,
+                    other_religion,
+                    black,
+                    white,
+                    asian,
+                    latino,
+                    other_race_or_ethnicity,
+                    physical_disability,
+                    intellectual_or_learning_disability,
+                    psychiatric_or_mental_illness,
+                    other_disability,
+                    gender_disclosed,
+                    sexuality_disclosed,
+                    religion_disclosed,
+                    race_disclosed,
+                    disability_disclosed)])
diff --git a/simulations/measerr_methods.R b/simulations/measerr_methods.R
index fdc4978..92309ed 100644
--- a/simulations/measerr_methods.R
+++ b/simulations/measerr_methods.R
@@ -15,7 +15,12 @@ library(bbmle)
 
 ### ideal formulas for example 2
 # test.fit.2 <- measerr_mle(df, y ~ x + z, gaussian(), w_pred ~ x + z + y + y:x, binomial(link='logit'), x ~ z)
-
+likelihood.logistic <- function(model.params, outcome, model.matrix){
+    ll <- vector(mode='numeric', length=length(outcome))
+    ll[outcome == 1] <- plogis(model.params %*% t(model.matrix[outcome==1,]), log=TRUE)
+    ll[outcome == 0] <- plogis(model.params %*% t(model.matrix[outcome==0,]), log=TRUE, lower.tail=FALSE)
+    return(ll)
+}
 
 ## 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'){
@@ -126,6 +131,31 @@ measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_fo
     proxy.model.matrix <- model.matrix(proxy_formula, df)
     y.obs <- with(df.obs,eval(parse(text=response.var)))
 
+    df.proxy.obs <- model.frame(proxy_formula,df)
+    proxy.obs <- with(df.proxy.obs, eval(parse(text=proxy.variable)))
+    n.proxy.model.covars <- dim(proxy.model.matrix)[2]
+
+    df.truth.obs <- model.frame(truth_formula, df)
+    truth.obs <- with(df.truth.obs, eval(parse(text=truth.variable)))
+    truth.model.matrix <- model.matrix(truth_formula,df.truth.obs)
+    n.truth.model.covars <- dim(truth.model.matrix)[2]
+
+    df.unobs <- df[is.na(eval(parse(text=truth.variable)))]
+    df.unobs.x1 <- copy(df.unobs)
+    df.unobs.x1[,truth.variable] <- 1
+    df.unobs.x0 <- copy(df.unobs)
+    df.unobs.x0[,truth.variable] <- 0
+    outcome.unobs <- with(df.unobs, eval(parse(text=response.var)))
+            
+    outcome.model.matrix.x0 <- model.matrix(outcome_formula, df.unobs.x0)
+    outcome.model.matrix.x1 <- model.matrix(outcome_formula, df.unobs.x1)
+        
+    proxy.model.matrix.x0 <- model.matrix(proxy_formula, df.unobs.x0)
+    proxy.model.matrix.x1 <- model.matrix(proxy_formula, df.unobs.x1)
+    proxy.unobs <- df.unobs[[proxy.variable]]
+
+    truth.model.matrix.unobs <- model.matrix(truth_formula, df.unobs.x0)
+
     measerr_mle_nll <- function(params){
         param.idx <- 1
         n.outcome.model.covars <- dim(outcome.model.matrix)[2]
@@ -138,82 +168,48 @@ measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_fo
             param.idx <- param.idx + 1
                                         #  outcome_formula likelihood using linear regression
             ll.y.obs <- dnorm(y.obs, outcome.params %*% t(outcome.model.matrix),sd=sigma.y, log=TRUE)
-        }
-        
-        df.obs <- model.frame(proxy_formula,df)
-        n.proxy.model.covars <- dim(proxy.model.matrix)[2]
+        } else if( (outcome_family$family == "binomial") & (outcome_family$link == "logit") )
+            ll.y.obs <- likelihood.logistic(outcome.params, y.obs, outcome.model.matrix)
+
+    
         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])
-
-                                        # proxy_formula likelihood using logistic regression
-            ll.w.obs[proxy.obs==1] <- plogis(proxy.params %*% t(proxy.model.matrix[proxy.obs==1,]),log=TRUE)
-            ll.w.obs[proxy.obs==0] <- plogis(proxy.params %*% t(proxy.model.matrix[proxy.obs==0,]),log=TRUE, lower.tail=FALSE)
-        }
-
-        df.obs <- model.frame(truth_formula, df)
+        if( (proxy_family$family=="binomial") & (proxy_family$link=='logit'))
+            ll.w.obs <- likelihood.logistic(proxy.params, proxy.obs, proxy.model.matrix)
 
-        truth.obs <- with(df.obs, eval(parse(text=truth.variable)))
-        truth.model.matrix <- model.matrix(truth_formula,df)
-        n.truth.model.covars <- dim(truth.model.matrix)[2]
-        
         truth.params <- params[param.idx:(n.truth.model.covars + param.idx - 1)]
 
-        if( (truth_family$family=="binomial") & (truth_family$link=='logit')){
-            ll.x.obs <- vector(mode='numeric',length=dim(truth.model.matrix)[1])
-
-                                        # truth_formula likelihood using logistic regression
-            ll.x.obs[truth.obs==1] <- plogis(truth.params %*% t(truth.model.matrix[truth.obs==1,]),log=TRUE)
-            ll.x.obs[truth.obs==0] <- plogis(truth.params %*% t(truth.model.matrix[truth.obs==0,]),log=TRUE, lower.tail=FALSE)
-        }
+        if( (truth_family$family=="binomial") & (truth_family$link=='logit'))
+            ll.x.obs <- likelihood.logistic(truth.params, truth.obs, truth.model.matrix)
         
-                                        # add the three likelihoods
+                                # add the three likelihoods
         ll.obs <- sum(ll.y.obs + ll.w.obs + ll.x.obs)
 
         ## likelihood for the predicted data
         ## integrate out the "truth" variable. 
         
         if(truth_family$family=='binomial'){
-            df.unobs <- df[is.na(eval(parse(text=truth.variable)))]
-            df.unobs.x1 <- copy(df.unobs)
-            df.unobs.x1[,'x'] <- 1
-            df.unobs.x0 <- copy(df.unobs)
-            df.unobs.x0[,'x'] <- 0
-            outcome.unobs <- with(df.unobs, eval(parse(text=response.var)))
-            
-            outcome.model.matrix.x0 <- model.matrix(outcome_formula, df.unobs.x0)
-            outcome.model.matrix.x1 <- model.matrix(outcome_formula, df.unobs.x1)
             if(outcome_family$family=="gaussian"){
-
                                         # likelihood of outcome
                 ll.y.x0 <- dnorm(outcome.unobs, outcome.params %*% t(outcome.model.matrix.x0), sd=sigma.y, log=TRUE)
                 ll.y.x1 <- dnorm(outcome.unobs, outcome.params %*% t(outcome.model.matrix.x1), sd=sigma.y, log=TRUE)
+            } else if( (outcome_family$family == "binomial") & (outcome_family$link == "logit") ){
+                ll.y.x1 <- likelihood.logistic(outcome.params, outcome.unobs, outcome.model.matrix.x1)
+                ll.y.x0 <- likelihood.logistic(outcome.params, outcome.unobs, outcome.model.matrix.x0)
             }
 
             if( (proxy_family$family=='binomial') & (proxy_family$link=='logit')){
 
-                proxy.model.matrix.x0 <- model.matrix(proxy_formula, df.unobs.x0)
-                proxy.model.matrix.x1 <- model.matrix(proxy_formula, df.unobs.x1)
-                proxy.unobs <- df.unobs[[proxy.variable]]
-                ll.w.x0 <- vector(mode='numeric', length=dim(df.unobs)[1])
-                ll.w.x1 <- vector(mode='numeric', length=dim(df.unobs)[1])
-
-                                        # likelihood of proxy
-                ll.w.x0[proxy.unobs==1] <- plogis(proxy.params %*% t(proxy.model.matrix.x0[proxy.unobs==1,]), log=TRUE)
-                ll.w.x1[proxy.unobs==1] <- plogis(proxy.params %*% t(proxy.model.matrix.x1[proxy.unobs==1,]), log=TRUE)
+                ll.w.x0 <- likelihood.logistic(proxy.params, proxy.unobs, proxy.model.matrix.x0)
+                ll.w.x1 <- likelihood.logistic(proxy.params, proxy.unobs, proxy.model.matrix.x1)
 
-                ll.w.x0[proxy.unobs==0] <- plogis(proxy.params %*% t(proxy.model.matrix.x0[proxy.unobs==0,]), log=TRUE,lower.tail=FALSE)
-                ll.w.x1[proxy.unobs==0] <- plogis(proxy.params %*% t(proxy.model.matrix.x1[proxy.unobs==0,]), log=TRUE,lower.tail=FALSE)
             }
 
             if(truth_family$link=='logit'){
-                truth.model.matrix <- model.matrix(truth_formula, df.unobs.x0)
                                         # likelihood of truth
-                ll.x.x1 <- plogis(truth.params %*% t(truth.model.matrix), log=TRUE)
-                ll.x.x0 <- plogis(truth.params %*% t(truth.model.matrix), log=TRUE, lower.tail=FALSE)
+                ll.x.x1 <- plogis(truth.params %*% t(truth.model.matrix.unobs), log=TRUE)
+                ll.x.x0 <- plogis(truth.params %*% t(truth.model.matrix.unobs), log=TRUE, lower.tail=FALSE)
             }
         }
 
diff --git a/simulations/run_job.sbatch b/simulations/run_job.sbatch
index 3ff4f80..8561788 100644
--- a/simulations/run_job.sbatch
+++ b/simulations/run_job.sbatch
@@ -2,7 +2,7 @@
 #SBATCH --job-name="simulate measurement error models"
 ## Allocation Definition
 #SBATCH --account=comdata
-#SBATCH --partition=compute-bigmem,compute-hugemem
+#SBATCH --partition=compute-bigmem
 ## Resources
 #SBATCH --nodes=1    
 ## Walltime (4 hours)