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"))
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"))
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')
--- /dev/null
+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')
+
+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 $<
## 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.
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')))
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)])
### 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'){
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]
param.idx <- param.idx + 1
# outcome_formula likelihood using linear regression
ll.y.obs <- dnorm(y.obs, outcome.params %*% t(outcome.model.matrix),sd=sigma.y, log=TRUE)
- }
-
- df.obs <- model.frame(proxy_formula,df)
- n.proxy.model.covars <- dim(proxy.model.matrix)[2]
+ } else if( (outcome_family$family == "binomial") & (outcome_family$link == "logit") )
+ ll.y.obs <- likelihood.logistic(outcome.params, y.obs, outcome.model.matrix)
+
+
proxy.params <- params[param.idx:(n.proxy.model.covars+param.idx-1)]
param.idx <- param.idx + n.proxy.model.covars
- proxy.obs <- with(df.obs, eval(parse(text=proxy.variable)))
- if( (proxy_family$family=="binomial") & (proxy_family$link=='logit')){
- ll.w.obs <- vector(mode='numeric',length=dim(proxy.model.matrix)[1])
-
- # proxy_formula likelihood using logistic regression
- ll.w.obs[proxy.obs==1] <- plogis(proxy.params %*% t(proxy.model.matrix[proxy.obs==1,]),log=TRUE)
- ll.w.obs[proxy.obs==0] <- plogis(proxy.params %*% t(proxy.model.matrix[proxy.obs==0,]),log=TRUE, lower.tail=FALSE)
- }
-
- df.obs <- model.frame(truth_formula, df)
+ if( (proxy_family$family=="binomial") & (proxy_family$link=='logit'))
+ ll.w.obs <- likelihood.logistic(proxy.params, proxy.obs, proxy.model.matrix)
- truth.obs <- with(df.obs, eval(parse(text=truth.variable)))
- truth.model.matrix <- model.matrix(truth_formula,df)
- n.truth.model.covars <- dim(truth.model.matrix)[2]
-
truth.params <- params[param.idx:(n.truth.model.covars + param.idx - 1)]
- if( (truth_family$family=="binomial") & (truth_family$link=='logit')){
- ll.x.obs <- vector(mode='numeric',length=dim(truth.model.matrix)[1])
-
- # truth_formula likelihood using logistic regression
- ll.x.obs[truth.obs==1] <- plogis(truth.params %*% t(truth.model.matrix[truth.obs==1,]),log=TRUE)
- ll.x.obs[truth.obs==0] <- plogis(truth.params %*% t(truth.model.matrix[truth.obs==0,]),log=TRUE, lower.tail=FALSE)
- }
+ if( (truth_family$family=="binomial") & (truth_family$link=='logit'))
+ ll.x.obs <- likelihood.logistic(truth.params, truth.obs, truth.model.matrix)
- # add the three likelihoods
+ # add the three likelihoods
ll.obs <- sum(ll.y.obs + ll.w.obs + ll.x.obs)
## likelihood for the predicted data
## integrate out the "truth" variable.
if(truth_family$family=='binomial'){
- df.unobs <- df[is.na(eval(parse(text=truth.variable)))]
- df.unobs.x1 <- copy(df.unobs)
- df.unobs.x1[,'x'] <- 1
- df.unobs.x0 <- copy(df.unobs)
- df.unobs.x0[,'x'] <- 0
- outcome.unobs <- with(df.unobs, eval(parse(text=response.var)))
-
- outcome.model.matrix.x0 <- model.matrix(outcome_formula, df.unobs.x0)
- outcome.model.matrix.x1 <- model.matrix(outcome_formula, df.unobs.x1)
if(outcome_family$family=="gaussian"){
-
# likelihood of outcome
ll.y.x0 <- dnorm(outcome.unobs, outcome.params %*% t(outcome.model.matrix.x0), sd=sigma.y, log=TRUE)
ll.y.x1 <- dnorm(outcome.unobs, outcome.params %*% t(outcome.model.matrix.x1), sd=sigma.y, log=TRUE)
+ } else if( (outcome_family$family == "binomial") & (outcome_family$link == "logit") ){
+ ll.y.x1 <- likelihood.logistic(outcome.params, outcome.unobs, outcome.model.matrix.x1)
+ ll.y.x0 <- likelihood.logistic(outcome.params, outcome.unobs, outcome.model.matrix.x0)
}
if( (proxy_family$family=='binomial') & (proxy_family$link=='logit')){
- proxy.model.matrix.x0 <- model.matrix(proxy_formula, df.unobs.x0)
- proxy.model.matrix.x1 <- model.matrix(proxy_formula, df.unobs.x1)
- proxy.unobs <- df.unobs[[proxy.variable]]
- ll.w.x0 <- vector(mode='numeric', length=dim(df.unobs)[1])
- ll.w.x1 <- vector(mode='numeric', length=dim(df.unobs)[1])
-
- # likelihood of proxy
- ll.w.x0[proxy.unobs==1] <- plogis(proxy.params %*% t(proxy.model.matrix.x0[proxy.unobs==1,]), log=TRUE)
- ll.w.x1[proxy.unobs==1] <- plogis(proxy.params %*% t(proxy.model.matrix.x1[proxy.unobs==1,]), log=TRUE)
+ ll.w.x0 <- likelihood.logistic(proxy.params, proxy.unobs, proxy.model.matrix.x0)
+ ll.w.x1 <- likelihood.logistic(proxy.params, proxy.unobs, proxy.model.matrix.x1)
- ll.w.x0[proxy.unobs==0] <- plogis(proxy.params %*% t(proxy.model.matrix.x0[proxy.unobs==0,]), log=TRUE,lower.tail=FALSE)
- ll.w.x1[proxy.unobs==0] <- plogis(proxy.params %*% t(proxy.model.matrix.x1[proxy.unobs==0,]), log=TRUE,lower.tail=FALSE)
}
if(truth_family$link=='logit'){
- truth.model.matrix <- model.matrix(truth_formula, df.unobs.x0)
# likelihood of truth
- ll.x.x1 <- plogis(truth.params %*% t(truth.model.matrix), log=TRUE)
- ll.x.x0 <- plogis(truth.params %*% t(truth.model.matrix), log=TRUE, lower.tail=FALSE)
+ ll.x.x1 <- plogis(truth.params %*% t(truth.model.matrix.unobs), log=TRUE)
+ ll.x.x0 <- plogis(truth.params %*% t(truth.model.matrix.unobs), log=TRUE, lower.tail=FALSE)
}
}
#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)
projects / ml_measurement_error_public.git / commitdiff