55 lines
2.5 KiB
R
55 lines
2.5 KiB
R
source('load_perspective_data.R')
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source("../simulations/measerr_methods.R")
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source("../simulations/RemembR/R/RemembeR.R")
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change.remember.file("dv_perspective_example.RDS")
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# for reproducibility
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set.seed(1111)
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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_dv_models <-function(pred_formula, outcome_formula, proxy_formula, df, sample.prop, remember_prefix){
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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.prop * .N), 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_dv(df_measerr_method, outcome_formula, outcome_family=binomial(link='logit'), proxy_formula=proxy_formula, proxy_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 example")
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compare_dv_models(pred_formula = toxicity_pred ~ funny*white,
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outcome_formula = toxicity_coded ~ funny*white,
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proxy_formula = toxicity_pred ~ toxicity_coded*funny*white,
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df=df,
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sample.prop=0.01,
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remember_prefix='cc_ex_tox.funny.white')
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print("running second example")
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compare_dv_models(pred_formula = toxicity_pred ~ likes+race_disclosed,
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outcome_formula = toxicity_coded ~ likes + race_disclosed,KKJ
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proxy_formula = toxicity_pred ~ toxicity_coded*likes*race_disclosed,
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df=df,
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sample.prop=0.01,
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remember_prefix='cc_ex_tox.funny.race_disclosed')
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