73 lines
2.2 KiB
R
73 lines
2.2 KiB
R
library(tidyverse)
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library(dsl)
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dsl_csv <-"111725_DSL_frame.csv"
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dsl_df <- read.csv(dsl_csv, header = TRUE)
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base_model <- dsl(
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model = "logit",
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formula = dsl_score ~ human_EP_prop_adac,
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predicted_var = "human_EP_prop_adac",
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prediction = "olmo_EP_prop_adac",
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sample_prob = "sampling_prob",
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data=dsl_df
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)
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summary(base_model)
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case_model <- dsl(
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model = "logit",
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formula = dsl_score ~ human_EP_prop_adac + as.factor(source),
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predicted_var = "human_EP_prop_adac",
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prediction = "olmo_EP_prop_adac",
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sample_prob = "sampling_prob",
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data=dsl_df
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)
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summary(case_model)
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trial_model <- dsl(
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model = "logit",
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formula = dsl_score ~ human_EP_prop_adac + human_TSOL_prop_adac + human_RK_prop_adac
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+ as.factor(source) + week_index + as.factor(isAuthorWMF) + median_PC4_adac + n_comments_before,
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predicted_var = c("human_EP_prop_adac", "human_TSOL_prop_adac", "human_RK_prop_adac"),
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prediction = c("olmo_EP_prop_adac", "olmo_TSOL_prop_adac", "olmo_RK_prop_adac"),
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sample_prob = "sampling_prob",
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data=dsl_df
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)
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summary(trial_model)
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anova(dsl_df$olmo_RK_prop, dsl_df$median_gerrit_reviewers)
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chisq.test(table(dsl_df$isAuthorWMF, dsl_df$author_closer))
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c1_df <- dsl_df |>
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dplyr::filter(source=="c1")
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felm_model <- dsl(
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model = "felm",
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formula = TTR ~ human_EP_prop_adac + human_TSOL_prop_adac + human_RK_prop_adac
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+ week_index + as.factor(isAuthorWMF) + median_PC4_adac + n_comments_before,
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predicted_var = c("human_EP_prop_adac", "human_TSOL_prop_adac", "human_RK_prop_adac"),
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prediction = c("olmo_EP_prop_adac", "olmo_TSOL_prop_adac", "olmo_RK_prop_adac"),
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sample_prob = "sampling_prob",
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fixed_effect = "oneway",
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index = c("source"),
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cluster="source",
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data=dsl_df
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)
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summary(felm_model)
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#https://github.com/naoki-egami/dsl/blob/537664a54163dda52ee277071fdfd9e8df2572a6/R/estimate_g.R#L39
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felm_df <- dsl_df |>
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dplyr::mutate(ttr_days = TTR / 24)
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felm_model <- dsl(
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model = "felm",
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formula = ttr_days ~ human_EP_prop_adac,
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predicted_var = c("human_EP_prop_adac"),
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prediction = c("olmo_EP_prop_adac"),
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sample_prob = "sampling_prob",
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fixed_effect = "oneway",
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index = c("phase"),
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cluster="phase",
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data=felm_df
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)
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summary(felm_model)
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