108 lines
4.6 KiB
R
108 lines
4.6 KiB
R
source("../simulations/RemembR/R/RemembeR.R")
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change.remember.file("iv_perspective_example.RDS")
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source('load_perspective_data.R')
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source("../simulations/measerr_methods.R")
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remember(accuracies, "civil_comments_accuracies")
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remember(f1s, "civil_comments_f1s")
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remember(positive_cases, "civil_comments_positive_cases")
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remember(proportions_cases, "civil_comments_proportions_cases")
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remember(cortab, "civil_comments_cortab")
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remember(nrow(df), 'n.annotated.comments')
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# for reproducibility
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set.seed(1)
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## 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?
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compare_iv_models <-function(pred_formula, outcome_formula, proxy_formula, truth_formula, df, sample.prop, sample.size, remember_prefix){
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if(is.null(sample.prop)){
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sample.prop <- sample.size / nrow(df)
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}
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if(is.null(sample.size)){
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sample.size <- nrow(df) * sample.prop
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}
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remember(sample.size, paste0(remember_prefix, "sample.size"))
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remember(sample.prop, paste0(remember_prefix, "sample.prop"))
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remember(pred_formula, paste0(remember_prefix, "pred_formula"))
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remember(outcome_formula, paste0(remember_prefix, 'outcome_formula'))
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remember(proxy_formula, paste0(remember_prefix, 'proxy_formula'))
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remember(truth_formula, paste0(remember_prefix, 'truth_formula'))
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pred_model <- glm(pred_formula, df, family=binomial(link='logit'))
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remember(coef(pred_model), paste0(remember_prefix, "coef_pred_model"))
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remember(diag(vcov((pred_model))), paste0(remember_prefix, "se_pred_model"))
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coder_model <- glm(outcome_formula, df, family=binomial(link='logit'))
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remember(coef(coder_model), paste0(remember_prefix, "coef_coder_model"))
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remember(diag(vcov((coder_model))), paste0(remember_prefix, "se_coder_model"))
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df_measerr_method <- copy(df)[sample(1:.N, sample.size), toxicity_coded_1 := toxicity_coded]
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df_measerr_method <- df_measerr_method[,toxicity_coded := toxicity_coded_1]
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sample_model <- glm(outcome_formula, df_measerr_method, family=binomial(link='logit'))
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remember(coef(sample_model), paste0(remember_prefix, "coef_sample_model"))
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remember(diag(vcov((sample_model))), paste0(remember_prefix, "se_sample_model"))
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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'))
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inv_hessian = solve(measerr_model$hessian)
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stderr = diag(inv_hessian)
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remember(stderr, paste0(remember_prefix, "measerr_model_stderr"))
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remember(measerr_model$par, paste0(remember_prefix, "measerr_model_par"))
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}
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## print("running first iv example")
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## sample.prop <- 0.05
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## compare_iv_models(white ~ toxicity_pred*funny,
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## outcome_formula = white ~ toxicity_coded*funny,
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## proxy_formula = toxicity_pred ~ toxicity_coded*funny*white,
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## truth_formula = toxicity_coded ~ 1,
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## df=df,
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## sample.prop=sample.prop,
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## remember_prefix='cc_ex_tox.funny.white')
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pred_formula <- race_disclosed ~ likes * toxicity_pred
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outcome_formula <- race_disclosed ~ likes * toxicity_coded
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proxy_formula <- toxicity_pred ~ toxicity_coded * race_disclosed * likes
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truth_formula <- toxicity_coded ~ 1
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print("running first example")
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compare_iv_models(pred_formula = pred_formula,
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outcome_formula = outcome_formula,
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proxy_formula = proxy_formula,
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truth_formula = truth_formula,
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df=df,
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sample.prop=0.01,
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sample.size=NULL,
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remember_prefix='cc_ex_tox.likes.race_disclosed')
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print("running second example")
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compare_iv_models(pred_formula = pred_formula,
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outcome_formula = outcome_formula,
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proxy_formula = proxy_formula,
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truth_formula = truth_formula,
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df=df,
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sample.prop=NULL,
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sample.size=10000,
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remember_prefix='cc_ex_tox.likes.race_disclosed.medsamp')
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print("running third example")
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compare_iv_models(pred_formula = race_disclosed ~ likes * toxicity_pred,
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outcome_formula = race_disclosed ~ likes * toxicity_coded,
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proxy_formula = toxicity_pred ~ toxicity_coded + race_disclosed,
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truth_formula = toxicity_coded ~ 1,
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df=df,
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sample.prop=0.05,
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sample.size=NULL,
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remember_prefix='cc_ex_tox.likes.race_disclosed.largesamp')
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