changes from klone

Also fix a possible bug in the MI logic and simplify the error
correction formula in example 2.

.gitmodules

@@ -1,3 +1,9 @@
 [submodule "paper"]
 	path = paper
 	url = git@github.com:chainsawriot/measure.git
+[submodule "overleaf"]
+	path = overleaf
+	url = https://git.overleaf.com/62a956eb9b9254783cc84c82
+[submodule "misclassificationmodels"]
+	path = misclassificationmodels
+	url = https://github.com/chainsawriot/misclassificationmodels.git

Makefile

@@ -0,0 +1,3 @@
+all:
+	+$(MAKE) -C simulations
+	+$(MAKE) -C civil_comments

civil_comments/01_dv_example.R

@@ -0,0 +1,89 @@
+source('load_perspective_data.R')
+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(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, 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"))
+
+    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_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")
+
+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,
+                  sample.size=NULL,
+                  remember_prefix='cc_ex_tox.likes.race_disclosed')
+
+
+print("running second example")
+
+compare_dv_models(pred_formula = pred_formula,
+                  outcome_formula = outcome_formula,
+                  proxy_formula = proxy_formula,
+                  df=df,
+                  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')
+

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')
+

civil_comments/03_prob_not_pred.R

@@ -0,0 +1,21 @@
+source('load_perspective_data.R')
+source("../simulations/RemembR/R/RemembeR.R")
+library(xtable)
+change.remember.file("prob_not_pred.RDS")
+### to respond to the reviewer show what happens if we don't recode the predictions.
+
+non_recoded_dv <- lm(toxicity_prob ~ likes * race_disclosed, data=df)
+remember(coef(non_recoded_dv), "coef_dv")
+remember(diag(vcov(non_recoded_dv)), "se_dv")
+remember(xtable(non_recoded_dv),'dv_xtable')
+
+non_recoded_iv <- glm(race_disclosed ~ likes * toxicity_prob, data=df, family='binomial')
+remember(coef(non_recoded_iv), "coef_iv")
+remember(diag(vcov(non_recoded_iv)), "se_iv")
+remember(xtable(non_recoded_iv),'iv_xtable')
+
+remember(extract(non_recoded_iv,include.aic=F,include.bic=F,include.nobs=F,include.deviance=F,include.loglik=F),'non_recoded_iv')
+remember(extract(non_recoded_dv,include.rsquared=F,include.adjrs=F,include.nobs=F),'non_recoded_dv')
+tr <- texreg(list(r$non_recoded_iv, r$non_recoded_dv),custom.model.names=c("Example 1","Example 2"),custom.coef.map=list("(Intercept)"="Intercept","race_disclosedTRUE"="Identity Disclosure","toxicity_prob"="Toxicity Score","likes"="Likes","likes:race_disclosedTRUE"="Likes:Identity Disclosure","likes:toxicity_prob"="Likes:Toxicity Score"),single.row=T,dcolumn=T)
+print(tr)
+remember(tr, 'texregobj')

civil_comments/Makefile

@@ -0,0 +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 $<
+

civil_comments/design_example.R

@@ -0,0 +1,72 @@
+set.seed(1111)
+source('load_perspective_data.R')
+## how accurate are the classifiers?
+
+## the API claims that these scores are "probabilities"
+## say we care about the model of the classification, not the probability
+
+## 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.
+## here's a great example with presumambly non-differential error: about what identities is toxicity found humorous?
+## a bunch of stars reappear when you used the ground-truth data instead of the predictions.
+## pro/con of this example: will have to implement family='poisson'.
+## shouldn't be that bad though haha.
+cortab['toxicity_error',]
+cortab['toxicity_error','funny']
+cortab['toxicity_coded',]
+cortab['identity_error',]
+cortab['white',]
+
+## 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')))
+
+summary(glm(toxicity_coded ~ white*psychiatric_or_mental_illness, data = df, family=binomial(link='logit')))
+
+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)
+}
+
+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)
+
+
+glm(white ~ disagree, data = df, family=binomial(link='logit'))
+
+## example with differential error
+
+glm(white ~ toxicity_coded + toxicity_error, data=df,family=binomial(link='logit'))
+
+glm(toxicity_coded ~ white, data = df, family=binomial(link='logit'))
+
+glm(toxicity_pred ~ white, data = df, family=binomial(link='logit'))

civil_comments/load_perspective_data.R

@@ -0,0 +1,136 @@
+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)]
+
+
+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)])

civil_comments/perspective_json_to_csv.jq

@@ -0,0 +1,2 @@
+#!/usr/bin/bash
+cat $1 | jq "[.attributeScores.IDENTITY_ATTACK.summaryScore.value, .attributeScores.INSULT.summaryScore.value, .attributeScores.PROFANITY.summaryScore.value,.attributeScores.SEVERE_TOXICITY.summaryScore.value, .attributeScores.THREAT.summaryScore.value, .attributeScores.TOXICITY.summaryScore.value] | @csv" > $2

civil_comments/perspective_json_to_csv.sh

@@ -0,0 +1,4 @@
+#!/usr/bin/bash
+header=id,identity_attack_prob,insult_prob,profanity_prob,severe_toxicity_prob,threat_prob,toxicity_prob
+echo "$header" > $2
+cat $1 | jq -r '[.id, .attributeScores.IDENTITY_ATTACK.summaryScore.value, .attributeScores.INSULT.summaryScore.value, .attributeScores.PROFANITY.summaryScore.value,.attributeScores.SEVERE_TOXICITY.summaryScore.value, .attributeScores.THREAT.summaryScore.value, .attributeScores.TOXICITY.summaryScore.value] | @csv' >>  $2

simulations/01_two_covariates.R

@@ -30,11 +30,11 @@ source("simulation_base.R")
 #### how much power do we get from the model in the first place? (sweeping N and m)
 #### 
 
-simulate_data <- function(N, m, B0=0, Bxy=0.2, Bzy=-0.2, Bzx=0.2, y_explained_variance=0.025, prediction_accuracy=0.73, seed=1){
+simulate_data <- function(N, m, B0=0, Bxy=0.2, Bzy=-0.2, Bzx=0.2, y_explained_variance=0.025, prediction_accuracy=0.73, Px=0.5, seed=1){
     set.seed(seed)
     z <- rnorm(N,sd=0.5)
                                         #    x.var.epsilon <- var(Bzx *z) * ((1-zx_explained_variance)/zx_explained_variance)
-    xprime <- Bzx * z #+ x.var.epsilon
+    xprime <- Bzx * z + qlogis(Px)
     x <- rbinom(N,1,plogis(xprime))
 
     y.var.epsilon <- (var(Bzy * z) + var(Bxy *x) + 2*cov(Bxy*x,Bzy*z)) * ((1-y_explained_variance)/y_explained_variance)
@@ -78,18 +78,21 @@ parser <- add_argument(parser, "--truth_formula", help='formula for the true var
 parser <- add_argument(parser, "--Bzx", help='Effect of z on x', default=0.3)
 parser <- add_argument(parser, "--Bzy", help='Effect of z on y', default=-0.3)
 parser <- add_argument(parser, "--Bxy", help='Effect of x on y', default=0.3)
+parser <- add_argument(parser, "--Px", help='Base rate of x', default=0.5)
+parser <- add_argument(parser, "--confint_method", help='method for approximating confidence intervals', default='quad')
 
 args <- parse_args(parser)
 B0 <- 0
+Px <- args$Px
 Bxy <- args$Bxy
 Bzy <- args$Bzy
 Bzx <- args$Bzx
 
-df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, Bzx, seed=args$seed + 500, y_explained_variance = args$y_explained_variance,  prediction_accuracy=args$prediction_accuracy)
+df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, Bzx, Px, seed=args$seed + 500, y_explained_variance = args$y_explained_variance,  prediction_accuracy=args$prediction_accuracy)
 
-result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy, Bzx=Bzx, 'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'accuracy_imbalance_difference'=args$accuracy_imbalance_difference, 'outcome_formula'=args$outcome_formula, 'proxy_formula'=args$proxy_formula,truth_formula=args$truth_formula, error='')
+result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy, 'Bzx'=Bzx, 'Bzy'=Bzy, 'Px'=Px, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'accuracy_imbalance_difference'=args$accuracy_imbalance_difference, 'outcome_formula'=args$outcome_formula, 'proxy_formula'=args$proxy_formula,truth_formula=args$truth_formula, confint_method=args$confint_method,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$truth_formula))
+outline <- run_simulation(df, result, outcome_formula=as.formula(args$outcome_formula), proxy_formula=as.formula(args$proxy_formula), truth_formula=as.formula(args$truth_formula),confint_method=args$confint_method)
     
 outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE)
 if(file.exists(args$outfile)){

simulations/02_indep_differential.R

@@ -31,11 +31,11 @@ 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, Bzx, Bzy, seed, y_explained_variance=0.025, prediction_accuracy=0.73, y_bias=-0.8,z_bias=0,accuracy_imbalance_difference=0.3){
+simulate_data <- function(N, m, B0, Bxy, Bzx, Bzy, seed, y_explained_variance=0.025, prediction_accuracy=0.73, y_bias=-0.8,z_bias=0,Px=0.5,accuracy_imbalance_difference=0.3){
     set.seed(seed)
     # make w and y dependent
     z <- rnorm(N,sd=0.5)
-    x <- rbinom(N, 1, plogis(Bzx * z))
+    x <- rbinom(N, 1, plogis(Bzx * z + qlogis(Px)))
 
     y.var.epsilon <- (var(Bzy * z) + var(Bxy *x) + 2*cov(Bzy*z,Bxy*x)) * ((1-y_explained_variance)/y_explained_variance)
     y.epsilon <- rnorm(N, sd = sqrt(y.var.epsilon))
@@ -104,9 +104,10 @@ 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))
 
+
     resids <- resid(lm(y~x + z))
-    odds.x1 <- qlogis(prediction_accuracy) + y_bias*qlogis(pnorm(resids[x==1])) + z_bias * qlogis(pnorm(z,sd(z)))
+    odds.x1 <- qlogis(prediction_accuracy) + y_bias*qlogis(pnorm(resids[x==1])) + z_bias * qlogis(pnorm(z[x==1],sd(z)))
-    odds.x0 <- qlogis(prediction_accuracy,lower.tail=F) + y_bias*qlogis(pnorm(resids[x==0])) + z_bias * qlogis(pnorm(z,sd(z)))
+    odds.x0 <- qlogis(prediction_accuracy,lower.tail=F) + y_bias*qlogis(pnorm(resids[x==0])) + z_bias * qlogis(pnorm(z[x==0],sd(z)))
 
     ## acc.x0 <- p.correct[df[,x==0]]
     ## acc.x1 <- p.correct[df[,x==1]]
@@ -139,10 +140,12 @@ parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy va
 parser <- add_argument(parser, "--y_bias", help='coefficient of y on the probability a classification is correct', default=-0.5)
 parser <- add_argument(parser, "--z_bias", help='coefficient of z on the probability a classification is correct', default=0)
 parser <- add_argument(parser, "--truth_formula", help='formula for the true variable', default="x~z")
-
+parser <- add_argument(parser, "--Px", help='base rate of x', default=0.5)
+parser <- add_argument(parser, "--confint_method", help='method for approximating confidence intervals', default='quad')
 args <- parse_args(parser)
 
 B0 <- 0
+Px <- args$Px
 Bxy <- args$Bxy
 Bzy <- args$Bzy
 Bzx <- args$Bzx
@@ -156,9 +159,9 @@ 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$Bzx, 'Bzy'=Bzy, 'seed'=args$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, confint_method=args$confint_method, 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$truth_formula))
+    outline <- run_simulation(df, result, outcome_formula=as.formula(args$outcome_formula), proxy_formula=as.formula(args$proxy_formula), truth_formula=as.formula(args$truth_formula),confint_method=args$confint_method)
     
    
  outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE)

simulations/03_depvar.R

@@ -73,15 +73,17 @@ 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=0.01)
+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=-0.01)
+parser <- add_argument(parser, "--Bzy", help='coeffficient of z on y', default=-0.3)
 parser <- add_argument(parser, "--Bzx", help='coeffficient of z on x', default=-0.5)
+parser <- add_argument(parser, "--B0", help='Base rate of y', default=0.5)
 parser <- add_argument(parser, "--outcome_formula", help='formula for the outcome variable', default="y~x+z")
 parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~y")
+parser <- add_argument(parser, "--confint_method", help='method for getting confidence intervals', default="quad")
 
 args <- parse_args(parser)
 
-B0 <- 0
+B0 <- args$B0
 Bxy <- args$Bxy
 Bzy <- args$Bzy
 Bzx <- args$Bzx
@@ -90,9 +92,9 @@ if(args$m < args$N){
     df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, Bzx, args$seed, args$prediction_accuracy)
 
 #    result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'x_bias_y0'=args$x_bias_y0,'x_bias_y1'=args$x_bias_y1,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)
-    result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'Bzx'=Bzx,'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)
+    result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'Bzx'=Bzx,'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula, 'confint_method'=args$confint_method)
 
-    outline <- run_simulation_depvar(df, result, outcome_formula = as.formula(args$outcome_formula), proxy_formula = as.formula(args$proxy_formula))
+    outline <- run_simulation_depvar(df, result, outcome_formula = as.formula(args$outcome_formula), proxy_formula = as.formula(args$proxy_formula), confint_method=args$confint_method)
 
     outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE)
 

simulations/03_depvar_differential.R

@@ -31,14 +31,14 @@ 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, x_bias=-0.75){
+simulate_data <- function(N, m, B0, Bxy, Bzy, Py, seed, prediction_accuracy=0.73, x_bias=-0.75){
     set.seed(seed)
 
     # make w and y dependent
     z <- rbinom(N, 1, 0.5)
     x <- rbinom(N, 1, 0.5)
 
-    ystar <- Bzy * z + Bxy * x + B0
+    ystar <- Bzy * z + Bxy * x + B0 + qlogix(Py)
     y <- rbinom(N,1,plogis(ystar))
 
     # glm(y ~ x + z, family="binomial")
@@ -77,6 +77,7 @@ parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is th
 parser <- add_argument(parser, "--x_bias", help='how is the classifier biased?', 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, "--Py", help='Base rate of y', default=0.5)
 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*x")
 

simulations/03_indep_differential_nonnorm.R

@@ -0,0 +1,185 @@
+### EXAMPLE 1: demonstrates how measurement error can lead to a type sign error in a covariate
+### What kind of data invalidates fong + tyler?
+### Even when you have a good predictor, if it's biased against a covariate you can get the wrong sign.
+### Even when you include the proxy variable in the regression.
+### But with some ground truth and multiple imputation, you can fix it.
+
+library(argparser)
+library(mecor)
+library(ggplot2)
+library(data.table)
+library(filelock)
+library(arrow)
+library(Amelia)
+library(Zelig)
+library(predictionError)
+options(amelia.parallel="no",
+        amelia.ncpus=1)
+setDTthreads(40)
+
+source("simulation_base.R")
+
+## SETUP:
+### we want to estimate x -> y; x is MAR
+### we have x -> k; k -> w; x -> w is used to predict x via the model w.
+### A realistic scenario is that we have an NLP model predicting something like "racial harassment" in social media comments
+### The labels x are binary, but the model provides a continuous predictor
+
+### simulation:
+#### how much power do we get from the model in the first place? (sweeping N and m)
+#### 
+
+## 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, Bzx, Bzy, seed, y_explained_variance=0.025, prediction_accuracy=0.73, y_bias=-0.8,z_bias=0,Px=0.5,accuracy_imbalance_difference=0.3,sd_y_mixin=1){
+    set.seed(seed)
+    # make w and y dependent
+    z <- rnorm(N,sd=0.5)
+    x <- rbinom(N, 1, plogis(Bzx * z + qlogis(Px)))
+    ## following Fong + Tyler: mix y with a Bernoulli(0.15) × |N (0, 20)| to make a skewed non-normal distribution
+    y.var.epsilon <- (var(Bzy * z) + var(Bxy *x) + 2*cov(Bzy*z,Bxy*x)) * ((1-y_explained_variance)/y_explained_variance)
+    y.epsilon <- rnorm(N, sd = sqrt(y.var.epsilon))
+    y <- Bzy * z + Bxy * x + y.epsilon + rbinom(N,1,0.15) * rnorm(N,0,sd_y_mixin)
+    
+    df <- data.table(x=x,y=y,z=z)
+
+    if(m < N){
+        df <- df[sample(nrow(df), m), x.obs := x]
+    } else {
+        df <- df[, x.obs := x]
+    }
+
+    ## probablity of an error is correlated with y
+    ## pz <- mean(z)
+    ## accuracy_imbalance_ratio <- (prediction_accuracy + accuracy_imbalance_difference/2) / (prediction_accuracy - accuracy_imbalance_difference/2)
+
+    ## # this works because of conditional probability
+    ## accuracy_z0 <- prediction_accuracy / (pz*(accuracy_imbalance_ratio) + (1-pz))
+    ## accuracy_z1 <- accuracy_imbalance_ratio * accuracy_z0
+
+    ## z0x0 <- df[(z==0) & (x==0)]$x
+    ## z0x1 <- df[(z==0) & (x==1)]$x
+    ## z1x0 <- df[(z==1) & (x==0)]$x
+    ## z1x1 <- df[(z==1) & (x==1)]$x
+
+    ## yz0x0 <- df[(z==0) & (x==0)]$y
+    ## yz0x1 <- df[(z==0) & (x==1)]$y
+    ## yz1x0 <- df[(z==1) & (x==0)]$y
+    ## yz1x1 <- df[(z==1) & (x==1)]$y
+
+    ## nz0x0 <- nrow(df[(z==0) & (x==0)])
+    ## nz0x1 <- nrow(df[(z==0) & (x==1)])
+    ## nz1x0 <- nrow(df[(z==1) & (x==0)])
+    ## nz1x1 <- nrow(df[(z==1) & (x==1)])
+
+    ## yz1 <- df[z==1]$y 
+    ## yz1 <- df[z==1]$y 
+
+    ## # tranform yz0.1 into a logistic distribution with mean accuracy_z0
+    ## acc.z0x0 <- plogis(0.5*scale(yz0x0) + qlogis(accuracy_z0))
+    ## acc.z0x1 <- plogis(0.5*scale(yz0x1) + qlogis(accuracy_z0))
+    ## acc.z1x0 <- plogis(1.5*scale(yz1x0) + qlogis(accuracy_z1))
+    ## acc.z1x1 <- plogis(1.5*scale(yz1x1) + qlogis(accuracy_z1))
+
+    ## w0z0x0 <- (1-z0x0)**2 + (-1)**(1-z0x0) * acc.z0x0
+    ## w0z0x1 <- (1-z0x1)**2 + (-1)**(1-z0x1) * acc.z0x1
+    ## w0z1x0 <- (1-z1x0)**2 + (-1)**(1-z1x0) * acc.z1x0
+    ## w0z1x1 <- (1-z1x1)**2 + (-1)**(1-z1x1) * acc.z1x1
+
+    ## ##perrorz0 <- w0z0*(pyz0)
+    ## ##perrorz1 <- w0z1*(pyz1)
+
+    ## w0z0x0.noisy.odds <- rlogis(nz0x0,qlogis(w0z0x0))
+    ## w0z0x1.noisy.odds <- rlogis(nz0x1,qlogis(w0z0x1))
+    ## w0z1x0.noisy.odds <- rlogis(nz1x0,qlogis(w0z1x0))
+    ## w0z1x1.noisy.odds <- rlogis(nz1x1,qlogis(w0z1x1))
+
+    ## df[(z==0)&(x==0),w:=plogis(w0z0x0.noisy.odds)]
+    ## df[(z==0)&(x==1),w:=plogis(w0z0x1.noisy.odds)]    
+    ## df[(z==1)&(x==0),w:=plogis(w0z1x0.noisy.odds)]    
+    ## df[(z==1)&(x==1),w:=plogis(w0z1x1.noisy.odds)]    
+
+    ## 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))
+
+
+
+    resids <- resid(lm(y~x + z))
+    odds.x1 <- qlogis(prediction_accuracy) + y_bias*qlogis(pnorm(resids[x==1],log.p=T),log.p=T) + z_bias * qlogis(pnorm(z[x==1],sd(z),log.p=T),log.p=T)
+    odds.x0 <- qlogis(prediction_accuracy,lower.tail=F) + y_bias*qlogis(pnorm(resids[x==0],log.p=T),log.p=T) + z_bias * qlogis(pnorm(z[x==0],sd(z),log.p=T),log.p=T)
+
+    ## acc.x0 <- p.correct[df[,x==0]]
+    ## acc.x1 <- p.correct[df[,x==1]]
+
+    df[x==0,w:=plogis(rlogis(.N,odds.x0))]
+    df[x==1,w:=plogis(rlogis(.N,odds.x1))]
+
+    print(prediction_accuracy)
+    print(resids[is.na(df$w)])
+    print(odds.x0[is.na(df$w)])
+    print(odds.x1[is.na(df$w)])
+
+    df[,w_pred := as.integer(w > 0.5)]
+
+
+    print(mean(df$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))
+    return(df)
+}
+
+parser <- arg_parser("Simulate data and fit corrected models")
+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')
+parser <- add_argument(parser, "--y_explained_variance", help='what proportion of the variance of y can be explained?', default=0.1)
+parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.75)
+parser <- add_argument(parser, "--accuracy_imbalance_difference", help='how much more accurate is the predictive model for one class than the other?', default=0.3)
+parser <- add_argument(parser, "--Bzx", help='Effect of z on x', default=0.3)
+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=-0.5)
+parser <- add_argument(parser, "--z_bias", help='coefficient of z on the probability a classification is correct', default=0)
+parser <- add_argument(parser, "--truth_formula", help='formula for the true variable', default="x~z")
+parser <- add_argument(parser, "--Px", help='base rate of x', default=0.5)
+parser <- add_argument(parser, "--confint_method", help='method for approximating confidence intervals', default='quad')
+parser <- add_argument(parser, "--sd_y_mixin", help='varience of the non-normal part of Y', default=10)
+args <- parse_args(parser)
+
+B0 <- 0
+Px <- args$Px
+Bxy <- args$Bxy
+Bzy <- args$Bzy
+Bzx <- args$Bzx
+
+if(args$m < args$N){
+
+    df <- simulate_data(args$N, args$m, B0, Bxy, Bzx, Bzy, args$seed, args$y_explained_variance, args$prediction_accuracy, y_bias=args$y_bias, sd_y_mixin=args$sd_y_mixin)
+
+    ## df.pc <- df[,.(x,y,z,w_pred,w)]
+    ##                                     #    df.pc <- df.pc[,err:=x-w_pred]
+    ## 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$truth_formula, confint_method=args$confint_method, error='', 'sd_y_mixin'=args$sd_y_mixin)
+
+    outline <- run_simulation(df, result, outcome_formula=as.formula(args$outcome_formula), proxy_formula=as.formula(args$proxy_formula), truth_formula=as.formula(args$truth_formula),confint_method=args$confint_method)
+    
+   
+ outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE)
+    if(file.exists(args$outfile)){
+        logdata <- read_feather(args$outfile)
+        logdata <- rbind(logdata,as.data.table(outline), fill=TRUE)
+    } else {
+        logdata <- as.data.table(outline)
+    }
+
+    print(outline)
+    write_feather(logdata, args$outfile)
+    unlock(outfile_lock)
+}

simulations/04_depvar_differential.R

@@ -31,12 +31,12 @@ 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, z_bias=-0.75){
+simulate_data <- function(N, m, B0, Bxy, Bzy, Bxz=0, seed=0, prediction_accuracy=0.73, z_bias=-0.75){
     set.seed(seed)
 
     # make w and y dependent
     z <- rnorm(N,sd=0.5)
-    x <- rbinom(N,1,0.5)
+    x <- rbinom(N,1,plogis(Bxz*z))
 
     ystar <- Bzy * z + Bxy * x + B0
     y <- rbinom(N,1,plogis(ystar))
@@ -70,29 +70,32 @@ parser <- add_argument(parser, "--N", default=1000, help="number of observations
 parser <- add_argument(parser, "--m", default=500, help="m the number of ground truth observations")
 parser <- add_argument(parser, "--seed", default=17, help='seed for the rng')
 parser <- add_argument(parser, "--outfile", help='output file', default='example_4.feather')
-parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.79)
+parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.75)
 ## parser <- add_argument(parser, "--z_bias_y1", help='how is the classifier biased when y = 1?', default=-0.75)
 ## parser <- add_argument(parser, "--z_bias_y0", help='how is the classifier biased when y = 0 ?', default=0.75)
-parser <- add_argument(parser, "--z_bias", help='how is the classifier biased?', default=1.5)
+parser <- add_argument(parser, "--z_bias", help='how is the classifier biased?', default=-0.5)
-parser <- add_argument(parser, "--Bxy", help='coefficient of x on y', default=0.1)
+parser <- add_argument(parser, "--Bxy", help='coefficient of x on y', default=0.7)
-parser <- add_argument(parser, "--Bzy", help='coeffficient of z on y', default=-0.1)
+parser <- add_argument(parser, "--Bzy", help='coeffficient of z on y', default=-0.7)
+parser <- add_argument(parser, "--Bzx", help='coeffficient of z on y', default=1)
+parser <- add_argument(parser, "--B0", help='coeffficient of z on y', default=0)
 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")
-
+parser <- add_argument(parser, "--confint_method", help='method for approximating confidence intervals', default='quad')
 args <- parse_args(parser)
 
-B0 <- 0
+B0 <- args$B0
 Bxy <- args$Bxy
 Bzy <- args$Bzy
-
+Bzx <- args$Bzx
 
 if(args$m < args$N){
-    df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, args$seed, args$prediction_accuracy, args$z_bias)
+    df <- simulate_data(args$N, args$m, B0, Bxy, Bzx, Bzy, args$seed, args$prediction_accuracy, args$z_bias)
 
-#    result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'z_bias_y0'=args$z_bias_y0,'z_bias_y1'=args$z_bias_y1,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)
+#    result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy,'Bzx'=Bzx, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'z_bias_y0'=args$z_bias_y0,'z_bias_y1'=args$z_bias_y1,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)
-    result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'z_bias'=args$z_bias,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)
+    result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy,'Bzx'=Bzx, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'z_bias'=args$z_bias,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula, confint_method=args$confint_method)
 
-    outline <- run_simulation_depvar(df, result, outcome_formula = as.formula(args$outcome_formula), proxy_formula = as.formula(args$proxy_formula))
+    outline <- run_simulation_depvar(df, result, outcome_formula = as.formula(args$outcome_formula), proxy_formula = as.formula(args$proxy_formula),confint_method=args$confint_method)
+    print(outline$error.cor.z)
 
     outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE)
 

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
+all:main supplement
-supplement: remember_robustness_misspec.RDS
+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_3_proflik.RDS robustness_3_dv_proflik.RDS robustness_4.RDS robustness_4_dv.RDS robustness_5.RDS robustness_5_dv.RDS robustness_6.feather
 
 srun=sbatch --wait --verbose run_job.sbatch
 
@@ -15,7 +16,7 @@ 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
-# 	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":${Bzx}}' --outfile test_true_z_jobsb
+# 	sbatch --wait --verbose run_job.sbatch  ./grid_sweep.py --command "Rscript test_true_z.R" --arg_dict '{"N":${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
@@ -24,57 +25,54 @@ 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
 	sbatch --wait --verbose --array=1-1000  run_simulation.sbatch 0 example_1_jobs
-	sbatch --wait --verbose --array=1001-2000  run_simulation.sbatch 0 example_1_jobs
+	sbatch --wait --verbose --array=1001-2000 run_simulation.sbatch 0 example_1_jobs
 	sbatch --wait --verbose --array=2001-3000  run_simulation.sbatch 0 example_1_jobs
-	sbatch --wait --verbose --array=3001-4000  run_simulation.sbatch 0 example_1_jobs
+	sbatch --wait --verbose --array=3001-$(shell cat example_1_jobs | wc -l) run_simulation.sbatch 0 example_1_jobs
-	sbatch --wait --verbose --array=4001-$(shell cat example_1_jobs | wc -l)  run_simulation.sbatch 0 example_1_jobs
 
 example_2_jobs: 02_indep_differential.R simulation_base.R grid_sweep.py pl_methods.R
-	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":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y*z*x"]}' --outfile example_2_jobs
+	sbatch --wait --verbose run_job.sbatch ./grid_sweep.py --command "Rscript 02_indep_differential.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_2.feather"],"y_explained_variance":${explained_variances},  "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "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=1-1000  run_simulation.sbatch 0 example_2_jobs
-	sbatch --wait --verbose --array=1001-2000  run_simulation.sbatch 0 example_2_jobs
+	sbatch --wait --verbose --array=1001-2000 run_simulation.sbatch 0 example_2_jobs
 	sbatch --wait --verbose --array=2001-3000  run_simulation.sbatch 0 example_2_jobs
-	sbatch --wait --verbose --array=3001-4000  run_simulation.sbatch 0 example_2_jobs
+	sbatch --wait --verbose --array=3001-$(shell cat example_2_jobs | wc -l) run_simulation.sbatch 0 example_2_jobs
-	sbatch --wait --verbose --array=4001-$(shell cat example_2_jobs | wc -l)  run_simulation.sbatch 0 example_2_jobs
+
 
 
 # example_2_B_jobs: example_2_B.R
-# 	sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript example_2_B.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_2_B.feather"]}' --outfile example_2_B_jobs
+# 	sbatch --wait --verbose run_job.sbatch python3 ./grid_sweep.py --command "Rscript example_2_B.R" --arg_dict '{"N":${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 pl_methods.R
-	sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 03_depvar.R" --arg_dict '{"N":${Ns},"m":${ms}, "Bxy":[0.7],"Bzy":[-0.7],"seed":${seeds}, "outfile":["example_3.feather"], "y_explained_variance":${explained_variances}}' --outfile example_3_jobs
+	sbatch --wait --verbose run_job.sbatch ./grid_sweep.py --command "Rscript 03_depvar.R" --arg_dict '{"N":${Ns},"m":${ms}, "Bxy":[0.7],"Bzy":[-0.7],"Bzx":[1],"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=1-1000  run_simulation.sbatch 0 example_3_jobs
-	sbatch --wait --verbose --array=1001-2000  run_simulation.sbatch 0 example_3_jobs
+	sbatch --wait --verbose --array=1001-2000 run_simulation.sbatch 0 example_3_jobs
-	sbatch --wait --verbose --array=2001-3000  run_simulation.sbatch 0 example_3_jobs
+	 sbatch --wait --verbose --array=2001-3000  run_simulation.sbatch 0 example_3_jobs
-	sbatch --wait --verbose --array=3001-4000  run_simulation.sbatch 0 example_3_jobs
+	sbatch --wait --verbose --array=3001-$(shell cat example_3_jobs | wc -l) run_simulation.sbatch 0 example_3_jobs
-	sbatch --wait --verbose --array=4001-$(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 pl_methods.R
-	sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 04_depvar_differential.R" --arg_dict '{"N":${Ns},"Bxy":[0.7],"Bzy":[-0.7],"m":${ms}, "seed":${seeds}, "outfile":["example_4.feather"], "z_bias":[0.5]}' --outfile example_4_jobs
+	sbatch --wait --verbose run_job.sbatch ./grid_sweep.py --command "Rscript 04_depvar_differential.R" --arg_dict '{"N":${Ns},"Bxy":[0.7],"Bzy":[-0.7],"Bzx":[1], "m":${ms}, "seed":${seeds}, "outfile":["example_4.feather"], "z_bias":[-0.5], "prediction_accuracy":[0.73]}' --outfile example_4_jobs
 
 example_4.feather: example_4_jobs
 	rm -f example_4.feather
 	sbatch --wait --verbose --array=1-1000  run_simulation.sbatch 0 example_4_jobs
 	sbatch --wait --verbose --array=1001-2000  run_simulation.sbatch 0 example_4_jobs
+	sbatch --wait --verbose --array=2001-3001 run_simulation.sbatch 0 example_4_jobs
 	sbatch --wait --verbose --array=2001-3000  run_simulation.sbatch 0 example_4_jobs
-	sbatch --wait --verbose --array=3001-4000  run_simulation.sbatch 0 example_4_jobs
+	sbatch --wait --verbose --array=3001-$(shell cat example_4_jobs | wc -l)  run_simulation.sbatch 0 example_4_jobs
-	sbatch --wait --verbose --array=4001-$(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 summarize_estimator.R
@@ -85,70 +83,397 @@ remembr.RDS:example_1.feather example_2.feather example_3.feather example_4.feat
 	${srun} Rscript plot_dv_example.R --infile example_4.feather --name "plot.df.example.4"
 
 
-irr_Ns = [1000]
+START=0
-irr_ms = [150,300,600]
+STEP=1000
-irr_seeds=${seeds}
+ONE=1
-irr_explained_variances=${explained_variances}
-irr_coder_accuracy=[0.80]
 
-example_5_jobs: 05_irr_indep.R irr_simulation_base.R grid_sweep.py pl_methods.R measerr_methods.R
+robustness_Ns=[1000,5000]
-	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}, "coder_accuracy":${irr_coder_accuracy}}' --outfile example_5_jobs
+robustness_ms=[100,200]
 
-example_5.feather:example_5_jobs
+#in robustness 1 / example 2 misclassification is correlated with Y.
-	rm -f example_5.feather
+robustness_1_jobs_p1: 02_indep_differential.R simulation_base.R grid_sweep.py
-	sbatch --wait --verbose --array=1-1000  run_simulation.sbatch 0 example_5_jobs
+	sbatch --wait --verbose run_job.sbatch ./grid_sweep.py --command "Rscript 02_indep_differential.R" --arg_dict '{"N":[1000],"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_1.feather"],"y_explained_variance":${explained_variances},  "Bzy":[-0.3,0],"Bxy":[0.3],"Bzx":[1,0], "outcome_formula":["y~x+z"], "z_bias":[0, 0.5], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~1"]}' --outfile robustness_1_jobs_p1
-	sbatch --wait --verbose --array=1001-$(shell cat example_5_jobs | wc -l)  run_simulation.sbatch 1000 example_5_jobs
+
-	# sbatch --wait --verbose --array=2001-3000  run_simulation.sbatch 2000 example_5_jobs
+robustness_1_jobs_p2: 02_indep_differential.R simulation_base.R grid_sweep.py
-	# sbatch --wait --verbose --array=3001-4000  run_simulation.sbatch 3000 example_5_jobs
+	sbatch --wait --verbose run_job.sbatch ./grid_sweep.py --command "Rscript 02_indep_differential.R" --arg_dict '{"N":[5000],"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_1.feather"],"y_explained_variance":${explained_variances},  "Bzy":[-0.3,0],"Bxy":[0.3],"Bzx":[1,0], "outcome_formula":["y~x+z"], "z_bias":[0, 0.5], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~1"]}' --outfile robustness_1_jobs_p2
-	# sbatch --wait --verbose --array=2001-$(shell cat example_5_jobs | wc -l)  run_simulation.sbatch 4000 example_5_jobs
+
+robustness_1.feather: robustness_1_jobs_p1 robustness_1_jobs_p2
+	rm -f $@
+	$(eval END_1!=cat robustness_1_jobs_p1 | wc -l)
+	$(eval ITEMS_1!=seq $(START) $(STEP) $(END_1))
+	$(eval END_2!=cat robustness_1_jobs_p2 | wc -l)
+	$(eval ITEMS_2!=seq $(START) $(STEP) $(END_2))
+	$(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_1_jobs_p1;)
+	$(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_1_jobs_p2;)
+
+robustness_1.RDS: robustness_1.feather summarize_estimator.R
+	rm -f robustness_1.RDS
+	${srun} Rscript plot_example.R --infile $< --name "robustness_1" --remember-file $@
+
+# when Bzy is 0 and zbias is not zero, we have the case where P(W|Y,X,Z) has an omitted variable that is conditionanlly independent from Y.  Note that X and Z are independent in this scenario.
+robustness_1_dv_jobs_p1: simulation_base.R 04_depvar_differential.R grid_sweep.py
+	${srun} ./grid_sweep.py --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":[1000],"Bzx":[1], "Bxy":[0.7,0],"Bzy":[-0.7,0],"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_1_dv.feather"], "proxy_formula":["w_pred~y"],"z_bias":[-0.5]}' --outfile robustness_1_dv_jobs_p1
+
+robustness_1_dv_jobs_p2: simulation_base.R 04_depvar_differential.R grid_sweep.py
+	${srun} ./grid_sweep.py --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":[5000],"Bzx":[1], "Bxy":[0.7,0],"Bzy":[-0.7,0],"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_1_dv.feather"], "proxy_formula":["w_pred~y"],"z_bias":[-0.5]}' --outfile robustness_1_dv_jobs_p2
+
+robustness_1_dv.feather: robustness_1_dv_jobs_p1 robustness_1_dv_jobs_p2
+	rm -f $@
+	$(eval END_1!=cat robustness_1_dv_jobs_p1 | wc -l)
+	$(eval ITEMS_1!=seq $(START) $(STEP) $(END_1))
+	$(eval END_2!=cat robustness_1_dv_jobs_p2 | wc -l)
+	$(eval ITEMS_2!=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_p1;)
+	$(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_1_dv_jobs_p2;)
+
+robustness_1_dv.RDS: robustness_1_dv.feather summarize_estimator.R
+	rm -f $@
+	${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
+	rm -f $@
+	${srun} ./$< --command 'Rscript 01_two_covariates.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2.feather"],"y_explained_variance":${explained_variances},  "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.60,0.65]}' --outfile $@
+
+robustness_2_jobs_p2: grid_sweep.py 01_two_covariates.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 01_two_covariates.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2.feather"],"y_explained_variance":${explained_variances},  "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.70,0.75]}' --outfile $@
+
+robustness_2_jobs_p3: grid_sweep.py 01_two_covariates.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 01_two_covariates.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2.feather"],"y_explained_variance":${explained_variances},  "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.80,0.85]}' --outfile $@
+
+robustness_2_jobs_p4: grid_sweep.py 01_two_covariates.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 01_two_covariates.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2.feather"],"y_explained_variance":${explained_variances},  "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.90,0.95]}' --outfile $@
+
+robustness_2.feather: robustness_2_jobs_p1 robustness_2_jobs_p2 robustness_2_jobs_p3 robustness_2_jobs_p4
+	rm $@
+	$(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 summarize_estimator.R
+	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
+	rm -f $@
+	${srun} ./$< --command 'Rscript 03_depvar.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2_dv.feather"],"y_explained_variance":${explained_variances},  "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "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
+	rm -f $@
+	${srun} ./$< --command 'Rscript 03_depvar.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2_dv.feather"],"y_explained_variance":${explained_variances},  "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "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
+	rm -f $@
+	${srun} ./$< --command 'Rscript 03_depvar.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2_dv.feather"],"y_explained_variance":${explained_variances},  "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "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
+	rm -f $@
+	${srun} ./$< --command 'Rscript 03_depvar.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_2_dv.feather"],"y_explained_variance":${explained_variances},  "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "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
+	rm -f $@
+	$(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_dv_example.R robustness_2_dv.feather summarize_estimator.R
+	rm -f $@
+	${srun} Rscript $< --infile $(word 2, $^) --name "robustness_2_dv" --remember-file $@
+
+
+robustness_3_proflik_jobs: grid_sweep.py 01_two_covariates.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 01_two_covariates.R'  --arg_dict '{"N":[1000],"m":[100], "seed":${seeds}, "outfile":["robustness_3_proflik.feather"],"y_explained_variance":${explained_variances},  "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"Px":[0.5,0.6,0.7,0.8,0.9,0.95], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85], "confint_method":['spline']}' --outfile $@
+
+robustness_3_proflik.feather: robustness_3_proflik_jobs
+	rm -f $@
+	$(eval END_1!=cat robustness_3_proflik_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_3_proflik_jobs;)
+
+robustness_3_proflik.RDS: plot_example.R robustness_3_proflik.feather summarize_estimator.R
+	rm -f $@
+	${srun} Rscript $< --infile $(word 2, $^) --name "robustness_3_proflik" --remember-file $@
+
+
+robustness_3_jobs_p1: grid_sweep.py 01_two_covariates.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 01_two_covariates.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_3.feather"],"y_explained_variance":${explained_variances},  "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"Px":[0.5,0.6], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85]}' --outfile $@
+
+robustness_3_jobs_p2: grid_sweep.py 01_two_covariates.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 01_two_covariates.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_3.feather"],"y_explained_variance":${explained_variances},  "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"Px":[0.7,0.8], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85]}' --outfile $@
+
+robustness_3_jobs_p3: grid_sweep.py 01_two_covariates.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 01_two_covariates.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_3.feather"],"y_explained_variance":${explained_variances},  "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"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 $@
+
+robustness_3.feather: robustness_3_jobs_p1 robustness_3_jobs_p2 robustness_3_jobs_p3
+	rm -f $@
+	$(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 summarize_estimator.R
+	rm -f $@
+	${srun} Rscript $< --infile $(word 2, $^) --name "robustness_3" --remember-file $@
+
+robustness_3_dv_proflik_jobs: grid_sweep.py 03_depvar.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 03_depvar.R'  --arg_dict '{"N":[1000],"m":[100], "seed":${seeds}, "outfile":["robustness_3_dv_proflik.feather"],"y_explained_variance":${explained_variances},  "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"B0":[0,0.405,0.846,1.386,2.197,2.944], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],"confint_method":['spline']}' --outfile $@
+
+robustness_3_dv_proflik.feather: robustness_3_dv_proflik_jobs
+	rm -f $@
+	$(eval END_1!=cat robustness_3_dv_proflik_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_3_dv_proflik_jobs;)
+
+robustness_3_dv_proflik.RDS: plot_dv_example.R robustness_3_dv_proflik.feather summarize_estimator.R
+	rm -f $@
+	${srun} Rscript $< --infile $(word 2, $^) --name "robustness_3_dv_proflik" --remember-file $@
+
+
+ robustness_3_dv_jobs_p1: grid_sweep.py 03_depvar.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 03_depvar.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_3_dv.feather"],"y_explained_variance":${explained_variances},  "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"B0":[0,0.405], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85]}' --outfile $@
+
+robustness_3_dv_jobs_p2: grid_sweep.py 03_depvar.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 03_depvar.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_3_dv.feather"],"y_explained_variance":${explained_variances},  "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1],"B0":[0.847,1.386], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85]}' --outfile $@
+
+
+robustness_3_dv_jobs_p3: grid_sweep.py 03_depvar.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 03_depvar.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_3_dv.feather"],"y_explained_variance":${explained_variances},  "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "B0":[2.197,2.944], "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
+	rm -f $@
+	$(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;)
+
+
+robustness_3_dv.RDS: plot_dv_example.R robustness_3_dv.feather summarize_estimator.R
+	rm -f $@
+	${srun} Rscript $< --infile $(word 2, $^) --name "robustness_3_dv" --remember-file $@
 
 
 
-# example_6_jobs: 06_irr_dv.R irr_dv_simulation_base.R grid_sweep.py pl_methods.R
+robustness_4_jobs_p1: grid_sweep.py 02_indep_differential.R simulation_base.R
-# 	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},"coder_accuracy":${irr_coder_accuracy}}' --outfile example_6_jobs
+	rm -f $@
+	${srun} ./$< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"], "y_bias":[-2.944,-2.197]}' --outfile $@
 
-# example_6.feather:example_6_jobs
+robustness_4_jobs_p2: grid_sweep.py 02_indep_differential.R simulation_base.R
-# 	rm -f example_6.feather
+	rm -f $@
-# 	sbatch --wait --verbose --array=1-1000  run_simulation.sbatch 0 example_6_jobs
+	${srun} ./$< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"], "y_bias":[-1.386,-0.846]}' --outfile $@
-# 	sbatch --wait --verbose --array=1001-2000  run_simulation.sbatch 1000 example_6_jobs
+
-# 	sbatch --wait --verbose --array=2001-$(shell cat example_6_jobs | wc -l)  run_simulation.sbatch 2000 example_6_jobs
+robustness_4_jobs_p3: grid_sweep.py 02_indep_differential.R simulation_base.R
+	rm -f ./$@
+	${srun} ./$< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"], "y_bias":[-0.405,-0.25]}' --outfile $@
+
+robustness_4_jobs_p4: grid_sweep.py 02_indep_differential.R simulation_base.R
+	rm -f ./$@
+	${srun} ./$< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"], "y_bias":[0,-0.1]}' --outfile $@
 
 
-remember_irr.RDS: example_5.feather plot_irr_example.R plot_irr_dv_example.R summarize_estimator.R
+robustness_4.feather: robustness_4_jobs_p1 robustness_4_jobs_p2 robustness_4_jobs_p3
-	rm -f remember_irr.RDS
+	rm -f $@
-	sbatch --wait --verbose run_job.sbatch Rscript plot_irr_example.R --infile example_5.feather --name "plot.df.example.5"
+	$(eval END_1!=cat robustness_4_jobs_p1 | wc -l)
-#	sbatch --wait --verbose run_job.sbatch Rscript plot_irr_dv_example.R --infile example_6.feather --name "plot.df.example.6"
+	$(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))
+	$(eval END_4!=cat robustness_4_jobs_p3 | wc -l)
+	$(eval ITEMS_3!=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_4_jobs_p1;)
-robustness_1_jobs: 02_indep_differential.R simulation_base.R grid_sweep.py
+	$(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_jobs_p2;)
-	sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 02_indep_differential.R" --arg_dict '{"N":${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
+	$(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_jobs_p3;)
+	$(foreach item,$(ITEMS_4),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_jobs_p3;)
 
 
-
+robustness_4.RDS: plot_example.R robustness_4.feather summarize_estimator.R
-robustness_1.feather: robustness_1_jobs
+	rm -f $@
-	rm -f robustness_1.feather
+	${srun} Rscript $< --infile $(word 2, $^) --name "robustness_4" --remember-file $@
-	sbatch --wait --verbose --array=1-$(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
+# '{"N":${robustness_Ns},"Bxy":[0.7],"Bzy":[-0.7],"m":${ms}, "seed":${seeds}, "outfile":["example_4.feather"], "z_bias":[0.5]}' --example_4_jobs
-	rm -f robustness_1_dv.feather
+
-	sbatch --wait --verbose --array=1-$(shell cat example_3_jobs | wc -l)  run_simulation.sbatch 0 robustness_1_dv_jobs
+robustness_4_dv_jobs_p1: grid_sweep.py 04_depvar_differential.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 04_depvar_differential.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7],"Bzx":[1], "outcome_formula":["y~x+z"], "z_bias":[0,0.1]}' --outfile $@
+
+robustness_4_dv_jobs_p2: grid_sweep.py 04_depvar_differential.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 04_depvar_differential.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7],"Bzx":[1], "outcome_formula":["y~x+z"], "z_bias":[0.25,0.405]}' --outfile $@
+
+robustness_4_dv_jobs_p3: grid_sweep.py 04_depvar_differential.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 04_depvar_differential.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7],"Bzx":[1],"outcome_formula":["y~x+z"],"z_bias":[0.846,1.386]}' --outfile $@ 
+
+robustness_4_dv_jobs_p4: grid_sweep.py 04_depvar_differential.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 04_depvar_differential.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_4_dv.feather"],"Bzy":[-0.7],"Bxy":[0.7],"Bzx":[1], "outcome_formula":["y~x+z"], "z_bias":[2.197,2.944]}' --outfile $@
+
+robustness_4_dv.feather: robustness_4_dv_jobs_p1 robustness_4_dv_jobs_p2 robustness_4_dv_jobs_p3 robustness_4_dv_jobs_p4
+	rm -f $@
+	$(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_jobs_p2 | wc -l)
+	$(eval ITEMS_2!=seq $(START) $(STEP) $(END_2))
+	$(eval END_3!=cat robustness_4_dv_jobs_p3 | wc -l)
+	$(eval ITEMS_3!=seq $(START) $(STEP) $(END_3))
+	$(eval END_4!=cat robustness_4_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_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;)
+	$(foreach item,$(ITEMS_4),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_dv_jobs_p4;)
 
 
-remember_robustness_misspec.RDS: robustness_1.feather robustness_1_dv.feather
+robustness_4_dv.RDS: plot_dv_example.R robustness_4_dv.feather summarize_estimator.R
-	rm -f remember_robustness_misspec.RDS
+	rm -f $@
-	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"
+	${srun} Rscript $< --infile $(word 2, $^) --name "robustness_4_dv" --remember-file $@
-	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:
+robustness_5_jobs_p1: grid_sweep.py 02_indep_differential.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_5.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1.386,2.197], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"]}' --outfile $@
+
+robustness_5_jobs_p2: grid_sweep.py 02_indep_differential.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_5.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.405,0.846], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"]}' --outfile $@
+
+robustness_5_jobs_p3: grid_sweep.py 02_indep_differential.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_5.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0,0.25], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"]}' --outfile $@
+
+robustness_5_jobs_p4: grid_sweep.py 02_indep_differential.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_5.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[2.944], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"]}' --outfile $@
+
+
+robustness_5.feather: robustness_5_jobs_p1 robustness_5_jobs_p2 robustness_5_jobs_p3
+	rm -f $@
+	$(eval END_1!=cat robustness_5_jobs_p1 | wc -l)
+	$(eval ITEMS_1!=seq $(START) $(STEP) $(END_1))
+	$(eval END_2!=cat robustness_5_jobs_p2 | wc -l)
+	$(eval ITEMS_2!=seq $(START) $(STEP) $(END_2))
+	$(eval END_3!=cat robustness_5_jobs_p3 | wc -l)
+	$(eval ITEMS_3!=seq $(START) $(STEP) $(END_3))
+	$(eval END_4!=cat robustness_5_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_5_jobs_p1;)
+	$(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_5_jobs_p2;)
+	$(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_5_jobs_p3;)
+	$(foreach item,$(ITEMS_4),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_5_jobs_p4;)
+
+robustness_5.RDS: plot_example.R robustness_5.feather summarize_estimator.R
+	rm -f $@
+	${srun} Rscript $< --infile $(word 2, $^) --name "robustness_5" --remember-file $@
+
+
+# '{"N":${robustness_Ns},"Bxy":[0.7],"Bzy":[-0.7],"m":${ms}, "seed":${seeds}, "outfile":["example_4.feather"], "z_bias":[0.5]}' --example_4_jobs
+
+robustness_5_dv_jobs_p1: grid_sweep.py 04_depvar_differential.R simulation_base.R
+	rm -f $@
+	 ${srun} ./$< --command 'Rscript 04_depvar_differential.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_5_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7],"Bzx":[0,0.25], "outcome_formula":["y~x+z"], "z_bias":[-0.5]}' --outfile $@
+
+robustness_5_dv_jobs_p2: grid_sweep.py 04_depvar_differential.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 04_depvar_differential.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_5_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7],"Bzx":[0.405,0.846], "outcome_formula":["y~x+z"],  "z_bias":[-0.5]}' --outfile $@
+
+robustness_5_dv_jobs_p3: grid_sweep.py 04_depvar_differential.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 04_depvar_differential.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_5_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7],"Bzx":[1.386,2.197],"outcome_formula":["y~x+z"], "z_bias":[-0.5]}' --outfile $@ 
+
+robustness_5_dv_jobs_p4: grid_sweep.py 04_depvar_differential.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 04_depvar_differential.R'  --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_5_dv.feather"],"Bzy":[-0.7],"Bxy":[0.7],"Bzx":[2.944], "outcome_formula":["y~x+z"], "z_bias":[-0.5]}' --outfile $@
+
+robustness_5_dv.feather: robustness_5_dv_jobs_p1 robustness_5_dv_jobs_p2 robustness_5_dv_jobs_p3 robustness_5_dv_jobs_p4
+	rm -f $@
+	$(eval END_1!=cat robustness_5_dv_jobs_p1 | wc -l)
+	$(eval ITEMS_1!=seq $(START) $(STEP) $(END_1))
+	$(eval END_2!=cat robustness_5_dv_jobs_p2 | wc -l)
+	$(eval ITEMS_2!=seq $(START) $(STEP) $(END_2))
+	$(eval END_3!=cat robustness_5_dv_jobs_p3 | wc -l)
+	$(eval ITEMS_3!=seq $(START) $(STEP) $(END_3))
+	$(eval END_4!=cat robustness_5_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_5_dv_jobs_p1;)
+	$(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_5_dv_jobs_p2;)
+	$(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_5_dv_jobs_p3;)
+	$(foreach item,$(ITEMS_4),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_5_dv_jobs_p4;)
+
+
+robustness_5_dv.RDS: plot_dv_example.R robustness_5_dv.feather summarize_estimator.R
+	rm -f $@
+	${srun} Rscript $< --infile $(word 2, $^) --name "robustness_5_dv" --remember-file $@
+
+
+clean_main:
+	rm -f remembr.RDS
+	rm -f example_1_jobs
+	rm -f example_2_jobs
+	rm -f example_3_jobs
+	rm -f example_4_jobs
+	rm -f example_1.feather
+	rm -f example_2.feather
+	rm -f example_3.feather
+	rm -f example_4.feather
+
+
+#	
+clean_all:
 	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:
@@ -159,6 +484,44 @@ clean:
 # 	sbatch --wait --verbose --array=1-3000 run_simulation.sbatch 0 example_2_B_mecor_jobs
 # 	sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 example_2_B_mecor_jobs
 
+robustness_6_jobs_p1: grid_sweep.py 03_indep_differential_nonnorm.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 03_indep_differential_nonnorm.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_6.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"],"sd_y_mixin":[0,1,2.5]}' --outfile $@
+
+robustness_6_jobs_p2: grid_sweep.py 03_indep_differential_nonnorm.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 03_indep_differential_nonnorm.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_6.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"],"sd_y_mixin":[5,10]}' --outfile $@
+
+robustness_6_jobs_p3: grid_sweep.py 03_indep_differential_nonnorm.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 03_indep_differential_nonnorm.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_6.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"],"sd_y_mixin":[0,1,2.5],"y_bias":[0]}' --outfile $@
+
+robustness_6_jobs_p4: grid_sweep.py 03_indep_differential_nonnorm.R simulation_base.R
+	rm -f $@
+	${srun} ./$< --command 'Rscript 03_indep_differential_nonnorm.R' --arg_dict '{"N":${robustness_Ns},"m":${robustness_ms}, "seed":${seeds}, "outfile":["robustness_6.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x+z"], "truth_formula":["x~z"],"sd_y_mixin":[5,10],"y_bias":[0]}' --outfile $@
+
+
+robustness_6.feather: robustness_6_jobs_p1 robustness_6_jobs_p2 robustness_6_jobs_p3 robustness_6_jobs_p4
+	rm -f $@
+	$(eval END_1!=cat robustness_6_jobs_p1 | wc -l)
+	$(eval ITEMS_1!=seq $(START) $(STEP) $(END_1))
+	$(eval END_2!=cat robustness_6_jobs_p2 | wc -l)
+	$(eval ITEMS_2!=seq $(START) $(STEP) $(END_2))
+	$(eval END_3!=cat robustness_6_jobs_p3 | wc -l)
+	$(eval ITEMS_3!=seq $(START) $(STEP) $(END_3))
+	$(eval END_4!=cat robustness_6_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_6_jobs_p1;)
+	# $(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_6_jobs_p2;)
+	# $(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_6_jobs_p3;)
+	$(foreach item,$(ITEMS_4),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_6_jobs_p4;)
+
+
+robustness_6.RDS: plot_example.R robustness_6.feather summarize_estimator.R
+	rm -f $@
+	${srun} Rscript $< --infile $(word 2, $^) --name "robustness_6" --remember-file $@
 
 
 .PHONY: supplement

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

simulations/measerr_methods.R

@@ -15,18 +15,38 @@ 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'){
+measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='logit'), proxy_formula, proxy_family=binomial(link='logit'),maxit=1e6, method='optim',optim_method='L-BFGS-B'){
+    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 +59,9 @@ 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])
@@ -54,14 +71,7 @@ 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 +80,6 @@ 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])
@@ -100,7 +106,7 @@ measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='lo
     names(start) <- params
     
     if(method=='optim'){
-        fit <- optim(start, fn = nll, lower=lower, method='L-BFGS-B', hessian=TRUE, control=list(maxit=1e6))
+        fit <- optim(start, fn = nll, lower=lower, method=optim_method, hessian=TRUE, control=list(maxit=maxit))
     } else {
         quoted.names <- gsub("[\\(\\)]",'',names(start))
         print(quoted.names)
@@ -109,13 +115,13 @@ measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='lo
         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')
+        fit <- mle2(minuslogl=measerr_mle_nll, start=start, lower=lower, parnames=params,control=list(maxit=maxit),method=optim_method)
     }
     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'),method='optim'){
+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', maxit=1e6, optim_method='L-BFGS-B'){
 
     df.obs <- model.frame(outcome_formula, df)
     response.var <- all.vars(outcome_formula)[1]
@@ -125,6 +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]
@@ -137,82 +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)
-        }
+        } else if( (outcome_family$family == "binomial") & (outcome_family$link == "logit") )
+            ll.y.obs <- likelihood.logistic(outcome.params, y.obs, outcome.model.matrix)
+
     
-        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')){
+        if( (proxy_family$family=="binomial") & (proxy_family$link=='logit'))
-            ll.w.obs <- vector(mode='numeric',length=dim(proxy.model.matrix)[1])
+            ll.w.obs <- likelihood.logistic(proxy.params, proxy.obs, proxy.model.matrix)
-
-                                        # 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)
-
-        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')){
+        if( (truth_family$family=="binomial") & (truth_family$link=='logit'))
-            ll.x.obs <- vector(mode='numeric',length=dim(truth.model.matrix)[1])
+            ll.x.obs <- likelihood.logistic(truth.params, truth.obs, truth.model.matrix)
         
-                                        # truth_formula likelihood using logistic regression
+                                # add the three likelihoods
-            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)
-        }
-        
-                                        # 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)
+                ll.w.x0 <- likelihood.logistic(proxy.params, proxy.unobs, proxy.model.matrix.x0)
-                proxy.model.matrix.x1 <- model.matrix(proxy_formula, df.unobs.x1)
+                ll.w.x1 <- likelihood.logistic(proxy.params, proxy.unobs, proxy.model.matrix.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[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.x1 <- plogis(truth.params %*% t(truth.model.matrix.unobs), log=TRUE)
-                ll.x.x0 <- plogis(truth.params %*% t(truth.model.matrix), log=TRUE, lower.tail=FALSE)
+                ll.x.x0 <- plogis(truth.params %*% t(truth.model.matrix.unobs), log=TRUE, lower.tail=FALSE)
             }
         }
 
@@ -243,7 +240,7 @@ measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_fo
     names(start) <- params
 
     if(method=='optim'){
-        fit <- optim(start, fn = measerr_mle_nll, lower=lower, method='L-BFGS-B', hessian=TRUE, control=list(maxit=1e6))
+        fit <- optim(start, fn = measerr_mle_nll, lower=lower, method=optim_method, hessian=TRUE, control=list(maxit=maxit))
     } else { # method='mle2'
                 
         quoted.names <- gsub("[\\(\\)]",'',names(start))
@@ -253,7 +250,7 @@ measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_fo
         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')
+        fit <- mle2(minuslogl=measerr_mle_nll_mle, start=start, lower=lower, parnames=params,control=list(maxit=maxit),method=optim_method)
     }
 
     return(fit)
@@ -431,7 +428,7 @@ 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 +524,8 @@ measerr_irr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link
 
             ## likelihood of observed data 
             target <- -1 * sum(lls)
-            print(target)
+#            print(target)
-            print(params)
+#            print(params)
             return(target)
         }
     }

simulations/pl_methods.R

@@ -0,0 +1,86 @@
+library(stats4)
+library(bbmle)
+library(matrixStats)
+
+zhang.mle.dv <- function(df){
+    df.obs <- df[!is.na(y.obs)]
+    df.unobs <- df[is.na(y.obs)]
+
+    fp <- df.obs[(w_pred==1) & (y.obs != w_pred),.N]
+    tn <- df.obs[(w_pred == 0) & (y.obs == w_pred),.N]
+    fpr <- fp / (fp+tn)
+
+    fn <- df.obs[(w_pred==0) & (y.obs != w_pred), .N]
+    tp <- df.obs[(w_pred==1) & (y.obs == w_pred),.N]
+    fnr <- fn / (fn+tp)
+
+    nll <- function(B0=0, Bxy=0, Bzy=0){
+
+
+        ## observed case
+        ll.y.obs <- vector(mode='numeric', length=nrow(df.obs))
+        ll.y.obs[df.obs$y.obs==1] <- with(df.obs[y.obs==1], plogis(B0 + Bxy * x + Bzy * z,log=T))
+        ll.y.obs[df.obs$y.obs==0] <- with(df.obs[y.obs==0], plogis(B0 + Bxy * x + Bzy * z,log=T,lower.tail=FALSE))
+
+        ll <- sum(ll.y.obs)
+
+        pi.y.1 <- with(df.unobs,plogis(B0 + Bxy * x + Bzy*z, log=T))
+        #pi.y.0 <- with(df.unobs,plogis(B0 + Bxy * x + Bzy*z, log=T,lower.tail=FALSE))
+
+        lls <- with(df.unobs, colLogSumExps(rbind(w_pred * colLogSumExps(rbind(log(fpr), log(1 - fnr - fpr)+pi.y.1)),
+        (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)
+        return(-ll)
+    }    
+    mlefit <- mle2(minuslogl = nll, control=list(maxit=1e6),method='L-BFGS-B',lower=c(B0=-Inf, Bxy=-Inf, Bzy=-Inf),
+                   upper=c(B0=Inf, Bxy=Inf, Bzy=Inf))
+    return(mlefit)
+}
+
+
+## model from Zhang's arxiv paper, with predictions for y
+## Zhang got this model from Hausman 1998
+zhang.mle.iv <- function(df){
+    df.obs <- df[!is.na(x.obs)]
+    df.unobs <- df[is.na(x.obs)]
+
+    tn <- df.obs[(w_pred == 0) & (x.obs == w_pred),.N]
+    fn <- df.obs[(w_pred==0) & (x.obs==1), .N]
+    npv <- tn / (tn + fn)
+
+
+    tp <- df.obs[(w_pred==1) & (x.obs == w_pred),.N]
+    fp <- df.obs[(w_pred==1) & (x.obs == 0),.N]
+    ppv <- tp / (tp + fp)
+
+    nll <- function(B0=0, Bxy=0, Bzy=0, sigma_y=9){
+
+    ## fpr = 1 - TNR
+    ### Problem: accounting for uncertainty in ppv / npv
+
+    ## fnr = 1 - TPR
+    ll.y.obs <- with(df.obs, dnorm(y, B0 + Bxy * x + Bzy * z, sd=sigma_y,log=T))
+
+    ll <- sum(ll.y.obs)
+    # unobserved case; integrate out x
+    ll.x.1 <- with(df.unobs, dnorm(y, B0 + Bxy + Bzy * z, sd = sigma_y, log=T))
+    ll.x.0 <- with(df.unobs, dnorm(y, B0 + Bzy * z, sd = sigma_y,log=T))
+
+    ## case x == 1
+    lls.x.1 <- colLogSumExps(rbind(log(ppv) + ll.x.1, log(1-ppv) + ll.x.0))
+    
+    ## case x == 0
+    lls.x.0 <- colLogSumExps(rbind(log(1-npv) + ll.x.1, log(npv) + ll.x.0))
+
+    lls <- colLogSumExps(rbind(df.unobs$w_pred * lls.x.1, (1-df.unobs$w_pred) * lls.x.0))
+        
+    ll <- ll + sum(lls)
+
+    }    
+    mlefit <- mle2(minuslogl = nll, control=list(maxit=1e6), lower=list(sigma_y=0.00001, B0=-Inf, Bxy=-Inf, Bzy=-Inf),
+                   upper=list(sigma_y=Inf, B0=Inf, Bxy=Inf, Bzy=Inf),method='L-BFGS-B')
+    return(mlefit)
+}

simulations/plot_dv_example.R

@@ -6,7 +6,7 @@ library(filelock)
 library(argparser)
 
 parser <- arg_parser("Simulate data and fit corrected models.")
-parser <- add_argument(parser, "--infile", default="example_4.feather", help="name of the file to read.")
+parser <- add_argument(parser, "--infile", default="robustness_3_dv.feather", 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)
@@ -86,9 +86,6 @@ build_plot_dataset <- function(df){
 
 change.remember.file(args$remember_file, clear=TRUE)
 sims.df <- read_feather(args$infile)
-sims.df[,Bzx:=NA]
-sims.df[,y_explained_variance:=NA]
-sims.df[,accuracy_imbalance_difference:=NA]
 plot.df <- build_plot_dataset(sims.df)
 
 remember(plot.df,args$name)

simulations/plot_example.R

@@ -9,7 +9,7 @@ source("summarize_estimator.R")
 
 
 parser <- arg_parser("Simulate data and fit corrected models.")
-parser <- add_argument(parser, "--infile", default="example_2.feather", help="name of the file to read.")
+parser <- add_argument(parser, "--infile", default="robustness_2.feather", 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)
@@ -47,7 +47,7 @@ args <- parse_args(parser)
 ##                           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,
+##                           N.sims = .
 ##                           p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
 ##                           variable=coefname,
 ##                           method=suffix

simulations/robustness_check_notes.md

@@ -0,0 +1,39 @@
+# robustness\_1.RDS
+
+Tests how robust the MLE method for independent variables with differential error is when the model for $X$ is less precise. In the main paper, we include $Z$ on the right-hand-side of the `truth_formula`. 
+In this robustness check, the `truth_formula` is an intercept-only model.
+The stats are in the list named `robustness_1` in the `.RDS` 
+# robustness\_1\_dv.RDS
+
+Like `robustness\_1.RDS` but with a less precise model for $w_pred$.  In the main paper, we included $Z$ in the `proxy_formula`. In this robustness check, we do not.
+
+# robustness_2.RDS
+
+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 indicated by the `prediction_accuracy` variable.
+
+# robustness_3.RDS
+
+Example 1 with varying levels of skewness in the classified variable. The variable `Px` is the baserate of $X$ and controls the skewness of $X$.
+It probably makes more sense to report the mean of $X$ instead of `Px` in the supplement.
+
+# robustness_3_dv.RDS
+
+Example 3 with varying levels of skewness in the classified variable. The variable `B0` is the intercept of the main model and controls the skewness of $Y$.
+It probably makes more sense to report the mean of $Y$ instead of B0 in the supplement. 
+
+# robustness_4.RDS
+
+Example 2 with varying amounts of differential error. The variable `y_bias` controls the amount of differential error.
+It probably makes more sense to report the corrleation between $Y$ and $X-~$, or the difference in accuracy from when when $Y=1$ to $Y=0$ in the supplement instead of `y_bias`.
+
+# robustness_4_dv.RDS
+
+Example 4 with varying amounts of bias. The variable `z_bias` controls the amount of differential error.
+It probably makes more sense to report the corrleation between $Z$ and $Y-W$, or the difference in accuracy from when when $Z=1$ to $Z=0$ in the supplement instead of `z_bias`.
+
+
+

simulations/run_job.sbatch

@@ -0,0 +1,18 @@
+#!/bin/bash
+#SBATCH --job-name="simulate measurement error models"
+## Allocation Definition
+#SBATCH --account=comdata-ckpt
+#SBATCH --partition=ckpt
+## 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
+echo "$@"
+"$@"

simulations/simulation_base.R

@@ -89,7 +89,7 @@ my.mle <- function(df){
     return(mlefit)
 }
 
-run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formula=w_pred~y){
+run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formula=w_pred~y, confint_method='quad'){
 
     (accuracy <- df[,mean(w_pred==y)])
     result <- append(result, list(accuracy=accuracy))
@@ -150,11 +150,13 @@ run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formu
 
     temp.df <- copy(df)
     temp.df[,y:=y.obs]
+
     mod.caroll.lik <- measerr_mle_dv(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula)
-    fisher.info <- solve(mod.caroll.lik$hessian)
+    fischer.info <- solve(mod.caroll.lik$hessian)
     coef <- mod.caroll.lik$par
-    ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
+    ci.upper <- coef + sqrt(diag(fischer.info)) * 1.96
-    ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
+    ci.lower <- coef - sqrt(diag(fischer.info)) * 1.96
+
     result <- append(result,
                      list(Bxy.est.mle = coef['x'],
                           Bxy.ci.upper.mle = ci.upper['x'],
@@ -163,6 +165,19 @@ run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formu
                           Bzy.ci.upper.mle = ci.upper['z'],
                           Bzy.ci.lower.mle = ci.lower['z']))
 
+    mod.caroll.profile.lik <- measerr_mle_dv(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula, method='bbmle')
+    coef <- coef(mod.caroll.profile.lik)
+    ci <- confint(mod.caroll.profile.lik, method='spline')
+    ci.lower <- ci[,'2.5 %']
+    ci.upper <- ci[,'97.5 %']
+
+    result <- append(result,
+                     list(Bxy.est.mle.profile = coef['x'],
+                          Bxy.ci.upper.mle.profile = ci.upper['x'],
+                          Bxy.ci.lower.mle.profile = ci.lower['x'],
+                          Bzy.est.mle.profile = coef['z'],
+                          Bzy.ci.upper.mle.profile = ci.upper['z'],
+                          Bzy.ci.lower.mle.profile = ci.lower['z']))
 
     ## my implementatoin of liklihood based correction
     mod.zhang <- zhang.mle.dv(df)
@@ -180,26 +195,35 @@ run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formu
     
 
     # amelia says use normal distribution for binary variables.
+    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
+                          )
 
-    amelia.out.k <- amelia(df, m=200, p2s=0, idvars=c('y','ystar','w'))
+    tryCatch({
-    mod.amelia.k <- zelig(y.obs~x+z, model='ls', data=amelia.out.k$imputations, cite=FALSE)
+        amelia.out.k <- amelia(df, m=200, p2s=0, idvars=c('y','ystar','w'),ords="y.obs")
-    (coefse <- combine_coef_se(mod.amelia.k, messages=FALSE))
+        mod.amelia.k <- zelig(y.obs~x+z, model='logit', data=amelia.out.k$imputations, cite=FALSE)
-    est.x.mi <- coefse['x','Estimate']
+        (coefse <- combine_coef_se(mod.amelia.k, messages=FALSE))
-    est.x.se <- coefse['x','Std.Error']
+        est.x.mi <- coefse['x','Estimate']
-    result <- append(result,
+        est.x.se <- coefse['x','Std.Error']
-                     list(Bxy.est.amelia.full = est.x.mi,
+
+        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
+                          Bxy.ci.lower.amelia.full = est.x.mi - 1.96 * est.x.se,
-                          ))
+                          Bzy.est.amelia.full = est.z.mi,
-
-    est.z.mi <- coefse['z','Estimate']
-    est.z.se <- coefse['z','Std.Error']
-
-    result <- append(result,
-                     list(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)
 
@@ -207,7 +231,7 @@ run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formu
 
 
 ## outcome_formula, proxy_formula, and truth_formula are passed to measerr_mle 
-run_simulation <-  function(df, result, outcome_formula=y~x+z, proxy_formula=NULL, truth_formula=NULL){
+run_simulation <-  function(df, result, outcome_formula=y~x+z, proxy_formula=NULL, truth_formula=NULL, confint_method='quad'){
 
     accuracy <- df[,mean(w_pred==x)]
     accuracy.y0 <- df[y<=0,mean(w_pred==x)]
@@ -272,57 +296,119 @@ run_simulation <-  function(df, result, outcome_formula=y~x+z, proxy_formula=NUL
                                   Bzy.ci.upper.naive = naive.ci.Bzy[2],
                                   Bzy.ci.lower.naive = naive.ci.Bzy[1]))
 
+    amelia_result <- list(
+        Bxy.est.amelia.full = NULL,
+        Bxy.ci.upper.amelia.full = NULL,
+        Bxy.ci.lower.amelia.full = NULL,
+        Bzy.est.amelia.full = NULL,
+        Bzy.ci.upper.amelia.full = NULL,
+        Bzy.ci.lower.amelia.full = NULL
+        )
+
+    tryCatch({
+        amelia.out.k <- amelia(df, m=200, p2s=0, idvars=c('x','w'))
+        mod.amelia.k <- zelig(y~x.obs+z, model='ls', data=amelia.out.k$imputations, cite=FALSE)
+        (coefse <- combine_coef_se(mod.amelia.k))
+
+        est.x.mi <- coefse['x.obs','Estimate']
+        est.x.se <- coefse['x.obs','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,
+                              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}
+    )
 
 
-    amelia.out.k <- amelia(df, m=200, p2s=0, idvars=c('x','w'))
+    result <- append(result, amelia_result)
-    mod.amelia.k <- zelig(y~x.obs+z, model='ls', data=amelia.out.k$imputations, cite=FALSE)
-    (coefse <- combine_coef_se(mod.amelia.k, messages=FALSE))
-
-    est.x.mi <- coefse['x.obs','Estimate']
-    est.x.se <- coefse['x.obs','Std.Error']
-    result <- append(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,
-                          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
-                          ))
 
 
+   mle_result <- list(Bxy.est.mle = NULL,
+                      Bxy.ci.upper.mle = NULL,
+                      Bxy.ci.lower.mle = NULL,
+                      Bzy.est.mle = NULL,
+                      Bzy.ci.upper.mle = NULL,
+                      Bzy.ci.lower.mle = NULL)
+
+    tryCatch({
         temp.df <- copy(df)
         temp.df <- temp.df[,x:=x.obs]
-        mod.caroll.lik <- measerr_mle(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula, truth_formula=truth_formula)
+        mod.caroll.lik <- measerr_mle(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula, truth_formula=truth_formula, method='optim')
-        fisher.info <- solve(mod.caroll.lik$hessian)
+        fischer.info <- solve(mod.caroll.lik$hessian)
         coef <- mod.caroll.lik$par
-        ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
+        ci.upper <- coef + sqrt(diag(fischer.info)) * 1.96
-        ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
+        ci.lower <- coef - sqrt(diag(fischer.info)) * 1.96
 
+        mle_result <- list(Bxy.est.mle = coef['x'],
+                           Bxy.ci.upper.mle = ci.upper['x'],
+                           Bxy.ci.lower.mle = ci.lower['x'],
+                           Bzy.est.mle = coef['z'],
+                           Bzy.ci.upper.mle = ci.upper['z'],
+                           Bzy.ci.lower.mle = ci.lower['z'])
+        },
+        error=function(e) {result[['error']] <- as.character(e)
+        })
 
-        result <- append(result,
+    result <- append(result, mle_result)
-                         list(Bxy.est.mle = coef['x'],
+    mle_result_proflik <- list(Bxy.est.mle.profile = NULL,
-                              Bxy.ci.upper.mle = ci.upper['x'],
+                               Bxy.ci.upper.mle.profile = NULL,
-                              Bxy.ci.lower.mle = ci.lower['x'],
+                               Bxy.ci.lower.mle.profile = NULL,
-                              Bzy.est.mle = coef['z'],
+                               Bzy.est.mle.profile = NULL,
-                              Bzy.ci.upper.mle = ci.upper['z'],
+                               Bzy.ci.upper.mle.profile = NULL,
-                              Bzy.ci.lower.mle = ci.lower['z']))
+                               Bzy.ci.lower.mle.profile = NULL)
 
-        mod.zhang.lik <- zhang.mle.iv(df)
+    tryCatch({
-        coef <- coef(mod.zhang.lik)
+        ## confint_method == 'bbmle'
-        ci <- confint(mod.zhang.lik,method='quad')
+        mod.caroll.lik <- measerr_mle(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula, truth_formula=truth_formula, method='bbmle')
-        result <- append(result,
+        coef <- coef(mod.caroll.lik)
-                         list(Bxy.est.zhang = coef['Bxy'],
+        ci <- confint(mod.caroll.lik, method='spline')
-                              Bxy.ci.upper.zhang = ci['Bxy','97.5 %'],
+        ci.lower <- ci[,'2.5 %']
-                              Bxy.ci.lower.zhang = ci['Bxy','2.5 %'],
+        ci.upper <- ci[,'97.5 %']
-                              Bzy.est.zhang = coef['Bzy'],
+
-                              Bzy.ci.upper.zhang = ci['Bzy','97.5 %'],
+        mle_result_proflik <- list(Bxy.est.mle.profile = coef['x'],
-                              Bzy.ci.lower.zhang = ci['Bzy','2.5 %']))
+                                   Bxy.ci.upper.mle.profile = ci.upper['x'],
+                                   Bxy.ci.lower.mle.profile = ci.lower['x'],
+                                   Bzy.est.mle.profile = coef['z'],
+                                   Bzy.ci.upper.mle.profile = ci.upper['z'],
+                                   Bzy.ci.lower.mle.profile = ci.lower['z'])
+    },
+
+    error=function(e) {result[['error']] <- as.character(e)
+    })
+        
+    result <- append(result, mle_result_proflik)
+
+    zhang_result <- list(Bxy.est.mle.zhang = NULL,
+                       Bxy.ci.upper.mle.zhang = NULL,
+                       Bxy.ci.lower.mle.zhang = NULL,
+                       Bzy.est.mle.zhang = NULL,
+                       Bzy.ci.upper.mle.zhang = NULL,
+                       Bzy.ci.lower.mle.zhang = NULL)
+
+    tryCatch({
+    mod.zhang.lik <- zhang.mle.iv(df)
+    coef <- coef(mod.zhang.lik)
+    ci <- confint(mod.zhang.lik,method='quad')
+    zhang_result <- list(Bxy.est.zhang = coef['Bxy'],
+                         Bxy.ci.upper.zhang = ci['Bxy','97.5 %'],
+                         Bxy.ci.lower.zhang = ci['Bxy','2.5 %'],
+                         Bzy.est.zhang = coef['Bzy'],
+                         Bzy.ci.upper.zhang = ci['Bzy','97.5 %'],
+                         Bzy.ci.lower.zhang = ci['Bzy','2.5 %'])
+    },
+    error=function(e) {result[['error']] <- as.character(e)
+    })
+    result <- append(result, zhang_result)
 
     ## What if we can't observe k -- most realistic scenario. We can't include all the ML features in a model.
     ## amelia.out.nok <- amelia(df, m=200, p2s=0, idvars=c("x","w_pred"), noms=noms)

simulations/summarize_estimator.R

@@ -1,18 +1,23 @@
+library(ggdist)
 
-summarize.estimator <- function(df, suffix='naive', coefname='x'){
+summarize.estimator <- function(sims.df, suffix='naive', coefname='x'){
 
-    part <- df[,c('N',
+    reported_vars <- c(
-                  'm',
+                       'Bxy',
-                  'Bxy',
+                       paste0('B',coefname,'y.est.',suffix),
-                  paste0('B',coefname,'y.est.',suffix),
+                       paste0('B',coefname,'y.ci.lower.',suffix),
-                  paste0('B',coefname,'y.ci.lower.',suffix),
+                       paste0('B',coefname,'y.ci.upper.',suffix)
-                  paste0('B',coefname,'y.ci.upper.',suffix),
+                       )
-                  'y_explained_variance',
+
-                  'Bzx',
+    
-                  'Bzy',
+    grouping_vars <- c('N','m','B0', 'Bxy', 'Bzy', 'Bzx', 'Px', 'Py','y_explained_variance', 'prediction_accuracy','outcome_formula','proxy_formula','truth_formula','z_bias','y_bias')
-                  'accuracy_imbalance_difference'
+
-                  ),
+    grouping_vars <- grouping_vars[grouping_vars %in% names(df)]
-               with=FALSE]
+
+    part <- sims.df[,
+                    unique(c(reported_vars,
+                             grouping_vars)),
+                    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)]]))
@@ -25,14 +30,17 @@ summarize.estimator <- function(df, suffix='naive', coefname='x'){
                        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)]]),
+                          mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]],na.rm=T),
-                          var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
+                          var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]],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)]],na.rm=T),
                           mean.ci.lower = mean(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]],na.rm=T),
+                          median.ci.upper = median(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]],na.rm=T),
+                          median.ci.lower = median(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]],na.rm=T),
                           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),
@@ -43,7 +51,7 @@ summarize.estimator <- function(df, suffix='naive', coefname='x'){
                           variable=coefname,
                           method=suffix
                           ),
-                      by=c("N","m",'y_explained_variance','Bzx', 'Bzy', 'accuracy_imbalance_difference')
+                      by=grouping_vars,
                       ]
     
     return(part.plot)