added random slope
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@ -5,8 +5,8 @@ library(plyr)
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
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readme_df <- read_csv("../final_data/deb_readme_did.csv")
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# 1 preprocessing
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colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
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col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
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#colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
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col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
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readme_df <- readme_df[,col_order]
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readme_df$ct_before_all <- str_split(gsub("[][]","", readme_df$before_all_ct), ", ")
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readme_df$ct_after_all <- str_split(gsub("[][]","", readme_df$after_all_ct), ", ")
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@ -36,19 +36,21 @@ window_num <- 8
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expanded_data <- expanded_data |>
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filter(week >= (26 - window_num) & week <= (26 + window_num)) |>
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mutate(D = ifelse(week > 26, 1, 0))
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#separate out the cleaning
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#separate out the cleaning d
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all_actions_data <- expanded_data[which(expanded_data$observation_type == "all"),]
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mean(all_actions_data$count)
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mrg_actions_data <- expanded_data[which(expanded_data$observation_type == "mrg"),]
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# 3 rdd in lmer analysis
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# rdd: https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
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# lmer: https://www.youtube.com/watch?v=LzAwEKrn2Mc
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library(lme4)
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draft_all_model <- lmer(count ~ D * I(week - 26) + (1|upstream_vcs_link), REML=FALSE, data=all_actions_data)
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draft_all_model <- lmer(count ~ D * I(week - 26) + age_of_project + (1 + D |upstream_vcs_link), REML=FALSE, data=all_actions_data)
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summary(draft_all_model)
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draft_mrg_model <- lmer(count ~ D * I(week - 26) + (1|upstream_vcs_link), REML=FALSE, data=mrg_actions_data)
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draft_mrg_model <- lmer(count ~ D * I(week - 26) + age_of_project + (1 + D |upstream_vcs_link), REML=FALSE, data=mrg_actions_data)
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summary(draft_mrg_model)
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# need to calculate inter-class correlation coefficient?
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library(merTools)
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ICC(outcome="count", group="upstream_vcs_link", data=all_actions_data)
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ICC(outcome="count", group="week", data=all_actions_data)
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#testing for different types of models
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# anova
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# BIC
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library(flexmix)
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@ -1 +0,0 @@
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Matt Gaughan,mgone,dhcp-10-105-58-199.wireless.northwestern.private,19.04.2024 14:27,file:///Users/mgone/Library/Application%20Support/OpenOffice/4;
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