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update real data examples code and rerun project.

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This commit is contained in:
Nathan TeBlunthuis 2023-01-11 10:59:50 -08:00
parent c066f900d3
commit c45ea9dfeb
7 changed files with 306 additions and 170 deletions
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61
civil_comments/01_dv_example.R
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@ -3,15 +3,33 @@ source("../simulations/measerr_methods.R")
source("../simulations/RemembR/R/RemembeR.R")
change.remember.file("dv_perspective_example.RDS")
remember(accuracies, "civil_comments_accuracies")
remember(f1s, "civil_comments_f1s")
remember(positive_cases, "civil_comments_positive_cases")
remember(proportions_cases, "civil_comments_proportions_cases")
remember(cortab, "civil_comments_cortab")
remember(nrow(df), 'n.annotated.comments')
# for reproducibility
set.seed(1111)
set.seed(111)
## another simple enough example: is P(toxic | funny and white) > P(toxic | funny nand white)? Or, are funny comments more toxic when people disclose that they are white?
compare_dv_models <-function(pred_formula, outcome_formula, proxy_formula, df, sample.prop, remember_prefix){
compare_dv_models <-function(pred_formula, outcome_formula, proxy_formula, df, sample.prop, sample.size, remember_prefix){
if(is.null(sample.prop)){
sample.prop <- sample.size / nrow(df)
}
if(is.null(sample.size)){
sample.size <- nrow(df) * sample.prop
}
pred_model <- glm(pred_formula, df, family=binomial(link='logit'))
remember(sample.size, paste0(remember_prefix, "sample.size"))
remember(sample.prop, paste0(remember_prefix, "sample.prop"))
remember(pred_formula, paste0(remember_prefix, "pred_formula"))
remember(outcome_formula, paste0(remember_prefix, 'outcome_formula'))
remember(proxy_formula, paste0(remember_prefix, 'proxy_formula'))
remember(coef(pred_model), paste0(remember_prefix, "coef_pred_model"))
remember(diag(vcov((pred_model))), paste0(remember_prefix, "se_pred_model"))
@ -19,7 +37,7 @@ compare_dv_models <-function(pred_formula, outcome_formula, proxy_formula, df, s
remember(coef(coder_model), paste0(remember_prefix, "coef_coder_model"))
remember(diag(vcov((coder_model))), paste0(remember_prefix, "se_coder_model"))
df_measerr_method <- copy(df)[sample(1:.N, sample.prop * .N), toxicity_coded_1 := toxicity_coded]
df_measerr_method <- copy(df)[sample(1:.N, sample.size), toxicity_coded_1 := toxicity_coded]
df_measerr_method <- df_measerr_method[,toxicity_coded := toxicity_coded_1]
sample_model <- glm(outcome_formula, df_measerr_method, family=binomial(link='logit'))
remember(coef(sample_model), paste0(remember_prefix, "coef_sample_model"))
@ -35,20 +53,37 @@ compare_dv_models <-function(pred_formula, outcome_formula, proxy_formula, df, s
print("running first example")
compare_dv_models(pred_formula = toxicity_pred ~ funny*white,
outcome_formula = toxicity_coded ~ funny*white,
proxy_formula = toxicity_pred ~ toxicity_coded*funny*white,
pred_formula = toxicity_pred ~ likes + race_disclosed
outcome_formula = toxicity_coded ~ likes + race_disclosed
proxy_formula = toxicity_pred ~ toxicity_coded*race_disclosed*likes
compare_dv_models(pred_formula = pred_formula,
outcome_formula = outcome_formula,
proxy_formula = proxy_formula,
df=df,
sample.prop=0.01,
remember_prefix='cc_ex_tox.funny.white')
sample.size=NULL,
remember_prefix='cc_ex_tox.likes.race_disclosed')
print("running second example")
compare_dv_models(pred_formula = toxicity_pred ~ likes+race_disclosed,
outcome_formula = toxicity_coded ~ likes + race_disclosed,KKJ
proxy_formula = toxicity_pred ~ toxicity_coded*likes*race_disclosed,
compare_dv_models(pred_formula = pred_formula,
outcome_formula = outcome_formula,
proxy_formula = proxy_formula,
df=df,
sample.prop=0.01,
remember_prefix='cc_ex_tox.funny.race_disclosed')
sample.size=10000,
sample.prop=NULL,
remember_prefix='cc_ex_tox.likes.race_disclosed.medsamp')
print("running third example")
compare_dv_models(pred_formula = pred_formula,
outcome_formula = outcome_formula,
proxy_formula = proxy_formula,
df=df,
sample.prop=0.05,
sample.size=NULL,
remember_prefix='cc_ex_tox.likes.race_disclosed.largesamp')

107
civil_comments/02_iv_example.R Normal file
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@ -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')

8
civil_comments/Makefile
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@ -1,6 +1,14 @@
qall: iv_perspective_example.RDS dv_perspective_example.RDS
srun_1core=srun -A comdata -p compute-bigmem --mem=4G --time=12:00:00 -c 1 --pty /usr/bin/bash -l
srun=srun -A comdata -p compute-bigmem --mem=4G --time=12:00:00 --pty /usr/bin/bash -l
perspective_scores.csv: perspective_json_to_csv.sh perspective_results.json
$(srun_1core) ./$^ $@
iv_perspective_example.RDS: 02_iv_example.R perspective_scores.csv
$(srun) Rscript $<
dv_perspective_example.RDS: 01_dv_example.R perspective_scores.csv
$(srun) Rscript $<

105
civil_comments/design_example.R
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@ -5,95 +5,6 @@ source('load_perspective_data.R')
## the API claims that these scores are "probabilities"
## say we care about the model of the classification, not the probability
F1 <- function(y, predictions){
tp <- sum( (predictions == y) & (predictions==1))
fn <- sum( (predictions != y) & (predictions!=1))
fp <- sum( (predictions != y) & (predictions==1))
precision <- tp / (tp + fp)
recall <- tp / (tp + fn)
return (2 * precision * recall ) / (precision + recall)
}
## toxicity is about 93% accurate, with an f1 of 0.8
## identity_attack has high accuracy 97%, but an unfortunant f1 of 0.5.
## threat has high accuracy 99%, but a really bad looking f1 of 0.48.
accuracies <- df[,.(identity_attack_acc = mean(identity_attack_pred == identity_attack_coded),
insult_pred_acc = mean(insult_pred == insult_coded),
profanity_acc = mean(profanity_pred == profanity_coded),
severe_toxicity_acc = mean(severe_toxicity_pred == severe_toxicity_coded),
theat_acc = mean(threat_pred == threat_coded),
toxicity_acc = mean(toxicity_pred == toxicity_coded))]
f1s <- df[,.(identity_attack_f1 = F1(identity_attack_coded,identity_attack_pred),
insult_f1 = F1(insult_coded,insult_pred),
profanity_f1 = F1(profanity_coded,profanity_pred),
severe_toxicity_f1 = F1(severe_toxicity_coded,severe_toxicity_pred),
theat_f1 = F1(threat_coded,threat_pred),
toxicity_f1 = F1(toxicity_coded,toxicity_pred))]
positive_cases <- df[,.(identity_attacks = sum(identity_attack_coded),
insults = sum(insult_coded),
profanities = sum(profanity_coded),
severe_toxic_comments = sum(severe_toxicity_coded),
threats = sum(threat_coded),
toxic_comments = sum(toxicity_coded))]
## there are 50,000 toxic comments, 13000 identity attacks, 30000 insults, 3000 profanities, 8 severe toxic, and 1000 threats.
proportions_cases <- df[,.(prop_identity = mean(identity_attack_coded),
prop_insults = mean(insult_coded),
prop_profanity = mean(profanity_coded),
prop_severe = mean(severe_toxicity_coded),
prop_threats = mean(threat_coded),
prop_toxic = mean(toxicity_coded))]
## at 11% of comments, "toxicity" seems not so badly skewed. Try toxicity first, and if it doesn't work out try insults.
## now look for an example where differential error affects an identity, or a reaction.
df <- df[,":="(identity_error = identity_attack_coded - identity_attack_pred,
insult_error = insult_coded - insult_pred,
profanity_error = profanity_coded - profanity_pred,
severe_toxic_error = severe_toxicity_coded - severe_toxicity_pred,
threat_error = threat_coded - threat_pred,
toxicity_error = toxicity_coded - toxicity_pred)]
## what's correlated with toxicity_error ?
df <- df[,approved := rating == "approved"]
df <- df[,white := white > 0.5]
cortab <- cor(df[,.(toxicity_error,
identity_error,
toxicity_coded,
funny,
approved,
sad,
wow,
likes,
disagree,
male,
female,
transgender,
other_gender,
heterosexual,
bisexual,
other_sexual_orientation,
christian,
jewish,
hindu,
buddhist,
atheist,
other_religion,
black,
white,
asian,
latino,
other_race_or_ethnicity,
physical_disability,
intellectual_or_learning_disability,
psychiatric_or_mental_illness,
other_disability)])
## toxicity error is weakly correlated pearson's R = 0.1 with both "white" and "black".
## compare regressions with "white" or "black" as the outcome and "toxicity_coded" or "toxicity_pred" as a predictor.
@ -107,22 +18,6 @@ cortab['toxicity_coded',]
cortab['identity_error',]
cortab['white',]
cortab <- cor(df[,.(toxicity_error,
identity_error,
toxicity_coded,
funny,
approved,
sad,
wow,
likes,
disagree,
gender_disclosed,
sexuality_disclosed,
religion_disclosed,
race_disclosed,
disability_disclosed)])
## here's a simple example, is P(white | toxic and mentally ill) > P(white | toxic or mentally ill). Are people who discuss their mental illness in a toxic way more likely to be white compared to those who just talk about their mental illness or are toxic?
summary(glm(white ~ toxicity_coded*psychiatric_or_mental_illness, data = df, family=binomial(link='logit')))

95
civil_comments/load_perspective_data.R
View File

@ -39,3 +39,98 @@ df <- df[,":="(gender_disclosed = dt.apply.any(gt.0.5, male, female, transgender
disability_disclosed = dt.apply.any(gt.0.5,physical_disability, intellectual_or_learning_disability, psychiatric_or_mental_illness, other_disability))]
df <- df[,white:=gt.0.5(white)]
F1 <- function(y, predictions){
tp <- sum( (predictions == y) & (predictions==1))
fn <- sum( (predictions != y) & (predictions!=1))
fp <- sum( (predictions != y) & (predictions==1))
precision <- tp / (tp + fp)
recall <- tp / (tp + fn)
return (2 * precision * recall ) / (precision + recall)
}
## toxicity is about 93% accurate, with an f1 of 0.8
## identity_attack has high accuracy 97%, but an unfortunant f1 of 0.5.
## threat has high accuracy 99%, but a really bad looking f1 of 0.48.
accuracies <- df[,.(identity_attack_acc = mean(identity_attack_pred == identity_attack_coded),
insult_pred_acc = mean(insult_pred == insult_coded),
profanity_acc = mean(profanity_pred == profanity_coded),
severe_toxicity_acc = mean(severe_toxicity_pred == severe_toxicity_coded),
theat_acc = mean(threat_pred == threat_coded),
toxicity_acc = mean(toxicity_pred == toxicity_coded))]
f1s <- df[,.(identity_attack_f1 = F1(identity_attack_coded,identity_attack_pred),
insult_f1 = F1(insult_coded,insult_pred),
profanity_f1 = F1(profanity_coded,profanity_pred),
severe_toxicity_f1 = F1(severe_toxicity_coded,severe_toxicity_pred),
theat_f1 = F1(threat_coded,threat_pred),
toxicity_f1 = F1(toxicity_coded,toxicity_pred))]
positive_cases <- df[,.(identity_attacks = sum(identity_attack_coded),
insults = sum(insult_coded),
profanities = sum(profanity_coded),
severe_toxic_comments = sum(severe_toxicity_coded),
threats = sum(threat_coded),
toxic_comments = sum(toxicity_coded))]
## there are 50,000 toxic comments, 13000 identity attacks, 30000 insults, 3000 profanities, 8 severe toxic, and 1000 threats.
proportions_cases <- df[,.(prop_identity = mean(identity_attack_coded),
prop_insults = mean(insult_coded),
prop_profanity = mean(profanity_coded),
prop_severe = mean(severe_toxicity_coded),
prop_threats = mean(threat_coded),
prop_toxic = mean(toxicity_coded))]
## at 11% of comments, "toxicity" seems not so badly skewed. Try toxicity first, and if it doesn't work out try insults.
## now look for an example where differential error affects an identity, or a reaction.
df <- df[,":="(identity_error = identity_attack_coded - identity_attack_pred,
insult_error = insult_coded - insult_pred,
profanity_error = profanity_coded - profanity_pred,
severe_toxic_error = severe_toxicity_coded - severe_toxicity_pred,
threat_error = threat_coded - threat_pred,
toxicity_error = toxicity_coded - toxicity_pred)]
## what's correlated with toxicity_error ?
df <- df[,approved := rating == "approved"]
df <- df[,white := white > 0.5]
cortab <- cor(df[,.(toxicity_error,
identity_error,
toxicity_coded,
funny,
approved,
sad,
wow,
likes,
disagree,
male,
female,
transgender,
other_gender,
heterosexual,
bisexual,
other_sexual_orientation,
christian,
jewish,
hindu,
buddhist,
atheist,
other_religion,
black,
white,
asian,
latino,
other_race_or_ethnicity,
physical_disability,
intellectual_or_learning_disability,
psychiatric_or_mental_illness,
other_disability,
gender_disclosed,
sexuality_disclosed,
religion_disclosed,
race_disclosed,
disability_disclosed)])

96
simulations/measerr_methods.R
View File

@ -15,7 +15,12 @@ library(bbmle)
### ideal formulas for example 2
# test.fit.2 <- measerr_mle(df, y ~ x + z, gaussian(), w_pred ~ x + z + y + y:x, binomial(link='logit'), x ~ z)
likelihood.logistic <- function(model.params, outcome, model.matrix){
ll <- vector(mode='numeric', length=length(outcome))
ll[outcome == 1] <- plogis(model.params %*% t(model.matrix[outcome==1,]), log=TRUE)
ll[outcome == 0] <- plogis(model.params %*% t(model.matrix[outcome==0,]), log=TRUE, lower.tail=FALSE)
return(ll)
}
## outcome_formula <- y ~ x + z; proxy_formula <- w_pred ~ y + x + z + x:z + x:y + z:y
measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='logit'), proxy_formula, proxy_family=binomial(link='logit'),method='optim'){
@ -126,6 +131,31 @@ measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_fo
proxy.model.matrix <- model.matrix(proxy_formula, df)
y.obs <- with(df.obs,eval(parse(text=response.var)))
df.proxy.obs <- model.frame(proxy_formula,df)
proxy.obs <- with(df.proxy.obs, eval(parse(text=proxy.variable)))
n.proxy.model.covars <- dim(proxy.model.matrix)[2]
df.truth.obs <- model.frame(truth_formula, df)
truth.obs <- with(df.truth.obs, eval(parse(text=truth.variable)))
truth.model.matrix <- model.matrix(truth_formula,df.truth.obs)
n.truth.model.covars <- dim(truth.model.matrix)[2]
df.unobs <- df[is.na(eval(parse(text=truth.variable)))]
df.unobs.x1 <- copy(df.unobs)
df.unobs.x1[,truth.variable] <- 1
df.unobs.x0 <- copy(df.unobs)
df.unobs.x0[,truth.variable] <- 0
outcome.unobs <- with(df.unobs, eval(parse(text=response.var)))
outcome.model.matrix.x0 <- model.matrix(outcome_formula, df.unobs.x0)
outcome.model.matrix.x1 <- model.matrix(outcome_formula, df.unobs.x1)
proxy.model.matrix.x0 <- model.matrix(proxy_formula, df.unobs.x0)
proxy.model.matrix.x1 <- model.matrix(proxy_formula, df.unobs.x1)
proxy.unobs <- df.unobs[[proxy.variable]]
truth.model.matrix.unobs <- model.matrix(truth_formula, df.unobs.x0)
measerr_mle_nll <- function(params){
param.idx <- 1
n.outcome.model.covars <- dim(outcome.model.matrix)[2]
@ -138,82 +168,48 @@ measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_fo
param.idx <- param.idx + 1
# outcome_formula likelihood using linear regression
ll.y.obs <- dnorm(y.obs, outcome.params %*% t(outcome.model.matrix),sd=sigma.y, log=TRUE)
}
} 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')){
ll.w.obs <- vector(mode='numeric',length=dim(proxy.model.matrix)[1])
# proxy_formula likelihood using logistic regression
ll.w.obs[proxy.obs==1] <- plogis(proxy.params %*% t(proxy.model.matrix[proxy.obs==1,]),log=TRUE)
ll.w.obs[proxy.obs==0] <- plogis(proxy.params %*% t(proxy.model.matrix[proxy.obs==0,]),log=TRUE, lower.tail=FALSE)
}
df.obs <- model.frame(truth_formula, df)
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]
if( (proxy_family$family=="binomial") & (proxy_family$link=='logit'))
ll.w.obs <- likelihood.logistic(proxy.params, proxy.obs, proxy.model.matrix)
truth.params <- params[param.idx:(n.truth.model.covars + param.idx - 1)]
if( (truth_family$family=="binomial") & (truth_family$link=='logit')){
ll.x.obs <- vector(mode='numeric',length=dim(truth.model.matrix)[1])
if( (truth_family$family=="binomial") & (truth_family$link=='logit'))
ll.x.obs <- likelihood.logistic(truth.params, truth.obs, truth.model.matrix)
# truth_formula likelihood using logistic regression
ll.x.obs[truth.obs==1] <- plogis(truth.params %*% t(truth.model.matrix[truth.obs==1,]),log=TRUE)
ll.x.obs[truth.obs==0] <- plogis(truth.params %*% t(truth.model.matrix[truth.obs==0,]),log=TRUE, lower.tail=FALSE)
}
# add the three likelihoods
# add the three likelihoods
ll.obs <- sum(ll.y.obs + ll.w.obs + ll.x.obs)
## likelihood for the predicted data
## integrate out the "truth" variable.
if(truth_family$family=='binomial'){
df.unobs <- df[is.na(eval(parse(text=truth.variable)))]
df.unobs.x1 <- copy(df.unobs)
df.unobs.x1[,'x'] <- 1
df.unobs.x0 <- copy(df.unobs)
df.unobs.x0[,'x'] <- 0
outcome.unobs <- with(df.unobs, eval(parse(text=response.var)))
outcome.model.matrix.x0 <- model.matrix(outcome_formula, df.unobs.x0)
outcome.model.matrix.x1 <- model.matrix(outcome_formula, df.unobs.x1)
if(outcome_family$family=="gaussian"){
# likelihood of outcome
ll.y.x0 <- dnorm(outcome.unobs, outcome.params %*% t(outcome.model.matrix.x0), sd=sigma.y, log=TRUE)
ll.y.x1 <- dnorm(outcome.unobs, outcome.params %*% t(outcome.model.matrix.x1), sd=sigma.y, log=TRUE)
} else if( (outcome_family$family == "binomial") & (outcome_family$link == "logit") ){
ll.y.x1 <- likelihood.logistic(outcome.params, outcome.unobs, outcome.model.matrix.x1)
ll.y.x0 <- likelihood.logistic(outcome.params, outcome.unobs, outcome.model.matrix.x0)
}
if( (proxy_family$family=='binomial') & (proxy_family$link=='logit')){
proxy.model.matrix.x0 <- model.matrix(proxy_formula, df.unobs.x0)
proxy.model.matrix.x1 <- model.matrix(proxy_formula, df.unobs.x1)
proxy.unobs <- df.unobs[[proxy.variable]]
ll.w.x0 <- vector(mode='numeric', length=dim(df.unobs)[1])
ll.w.x1 <- vector(mode='numeric', length=dim(df.unobs)[1])
ll.w.x0 <- likelihood.logistic(proxy.params, proxy.unobs, proxy.model.matrix.x0)
ll.w.x1 <- likelihood.logistic(proxy.params, proxy.unobs, proxy.model.matrix.x1)
# 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.x0 <- plogis(truth.params %*% t(truth.model.matrix), log=TRUE, lower.tail=FALSE)
ll.x.x1 <- plogis(truth.params %*% t(truth.model.matrix.unobs), log=TRUE)
ll.x.x0 <- plogis(truth.params %*% t(truth.model.matrix.unobs), log=TRUE, lower.tail=FALSE)
}
}

2
simulations/run_job.sbatch
View File

@ -2,7 +2,7 @@
#SBATCH --job-name="simulate measurement error models"
## Allocation Definition
#SBATCH --account=comdata
#SBATCH --partition=compute-bigmem,compute-hugemem
#SBATCH --partition=compute-bigmem
## Resources
#SBATCH --nodes=1
## Walltime (4 hours)