fit the mems

2025-02-07 13:11:29 -08:00 · 2025-02-07 13:11:29 -08:00 · e56eac2f76
commit e56eac2f76
parent 8e57295a21
10 changed files with 4984 additions and 22 deletions
--- a/.RData
+++ b/.RData
--- a/mg-govdoc-cr_24074016.out
+++ b/mg-govdoc-cr_24074016.out
@ -0,0 +1,17 @@
+1. SSH tunnel from your workstation using the following command:
+
+   ssh -N -L 8787:n3434:49157 mjilg@klone.hyak.uw.edu
+
+   and point your web browser to http://localhost:8787
+
+2. log in to RStudio Server using the following credentials:
+
+   user: mjilg
+   password: shDpem/m/RHo7HO1CuWG
+
+When done using RStudio Server, terminate the job by:
+
+1. Exit the RStudio Session ("power" button in the top right corner of the RStudio window)
+2. Issue the following command on the login node:
+
+      scancel -f 24074016
--- a/mlm/contributing_did_model_fit.R
+++ b/mlm/contributing_did_model_fit.R
@ -2,12 +2,12 @@ library(dplyr)
 library(lubridate)
 library(rdd)

-contributing_df_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/CONTRIBUTING_weekly_count_data.csv"
+contributing_df_filepath <-  "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/metadata/final_0207_CONTRIBUTING_weekly_count_data.csv"
 contributing_df = read.csv(contributing_df_filepath, header = TRUE) 

 window_num <- 5
 contributing_df <- contributing_df |>
-  filter(week_index >= (- window_num) & week_index <= (window_num)) |>
+  filter(relative_week >= (- window_num) & relative_week <= (window_num)) |>
  mutate(scaled_age = scale(age)) |>
  mutate(scaled_age_at_commit = scale(age_at_commit))|>
  mutate(log1p_count = log1p(commit_count))
@ -16,13 +16,16 @@ library(lme4)
 library(optimx)
 library(lattice)

-#all_gmodel <- glmer.nb(log1p_count ~ before_after * week_index + scaled_age + (before_after * week_index | project_id),
-#                       control=glmerControl(optimizer="bobyqa",
-#                                            optCtrl=list(maxfun=2e5)), nAGQ=0,
-#                       data=contributing_df)
-all_gmodel <- readRDS("mlm/models/020125_contributing_model.rda")                 
+all_gmodel <- glmer.nb(log1p_count ~ before_after * relative_week + scaled_age + (before_after * relative_week | project_id),
+                       control=glmerControl(optimizer="bobyqa",
+                                            optCtrl=list(maxfun=2e5)), nAGQ=0,
+                       data=contributing_df)
+#all_gmodel <- readRDS("mlm/models/020125_contributing_model.rda")                 
 summary(all_gmodel)
-#saveRDS(all_gmodel, "020125_contributing_model.rda")
+
+
+
+saveRDS(all_gmodel, "020725_contributing_model.rda")
 model_residuals <- residuals(all_gmodel)
 acf(model_residuals)
 vif(all_gmodel)
@ -33,7 +36,7 @@ variance_components <- as.data.frame(VarCorr(all_gmodel))
 library(broom.mixed)
 library(ggplot2)
 condvals <- broom.mixed::tidy(all_gmodel, effects = "ran_vals", conf.int = TRUE)
-glmer_ranef_Dweek <- condvals [which(condvals $term == "before_after:week_index"),]
+glmer_ranef_Dweek <- condvals [which(condvals $term == "before_after:relative_week"),]
 has_zero <- function(estimate, low, high){
  return(ifelse((low < 0),ifelse((high > 0), 1, 0), 2))
 }
@ -46,4 +49,4 @@ g <- glmer_ranef_Dweek |>
  theme_bw()
 g

-write.csv(glmer_ranef_Dweek, "0201_contributing_dweek_ranefs.csv")
+write.csv(glmer_ranef_Dweek, "0207_contributing_dweek_ranefs.csv")
--- a/mlm/data/0207_contributing_dweek_ranefs.csv
+++ b/mlm/data/0207_contributing_dweek_ranefs.csv
@ -0,0 +1,715 @@
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--- a/mlm/data/0207_readme_dweek_ranefs.csv
+++ b/mlm/data/0207_readme_dweek_ranefs.csv
--- a/mlm/gam_plot.R
+++ b/mlm/gam_plot.R
@ -1,17 +1,17 @@
 library(dplyr)
-contributing_df_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/CONTRIBUTING_weekly_count_data.csv"
+contributing_df_filepath <-"/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/metadata/final_0207_CONTRIBUTING_weekly_count_data.csv"
 contributing_df = read.csv(contributing_df_filepath, header = TRUE) 

-readme_df_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/README_weekly_count_data.csv"
+readme_df_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/metadata/final_0207_README_weekly_count_data.csv"
 readme_df = read.csv(readme_df_filepath, header = TRUE)

 window_num <- 5
 contributing_df <- contributing_df |>
-  filter(week_index >= (- window_num) & week_index <= (window_num)) |>
+  filter(relative_week >= (- window_num) & relative_week <= (window_num)) |>
  mutate(doc_type = "CONTRIBUTING")

 readme_df <- readme_df |>
-  filter(week_index >= (- window_num) & week_index <= (window_num)) |>
+  filter(relative_week >= (- window_num) & relative_week <= (window_num)) |>
  mutate(doc_type = "README")

 main_df <- rbind(contributing_df, readme_df)
@ -31,7 +31,7 @@ doctypeColors <-
            , c("CONTRIBUTING", "README"))

 time_plot <- main_df |>
-  ggplot(aes(x=week_index, y=commit_count, color=factor(doc_type))) +
+  ggplot(aes(x=relative_week, y=commit_count, color=factor(doc_type))) +
  scale_y_continuous(trans = 'log1p', labels = scales::comma) +
  labs(x="Weekly Offset", y="Commit Count", color="Document Type: ") +
  scale_color_manual(values = doctypeColors) + 
@ -41,4 +41,4 @@ time_plot <- main_df |>
  theme(legend.position = "top")
 time_plot

-ggsave(filename = "plots/cr-020325-gam-introduction.png", plot = time_plot, width = 9, height = 9, dpi = 800)
+ggsave(filename = "plots/cr-020725-gam-introduction.png", plot = time_plot, width = 9, height = 9, dpi = 800)
--- a/mlm/models/020725_contributing_model.rda
+++ b/mlm/models/020725_contributing_model.rda
--- a/mlm/models/020725_readme_model.rda
+++ b/mlm/models/020725_readme_model.rda
--- a/mlm/readme_did_model_fit.R
+++ b/mlm/readme_did_model_fit.R
@ -2,12 +2,12 @@ library(dplyr)
 library(lubridate)
 library(rdd)

-readme_df_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/README_weekly_count_data.csv"
+readme_df_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/metadata/final_0207_README_weekly_count_data.csv"
 readme_df = read.csv(readme_df_filepath, header = TRUE)

 window_num <- 5
 readme_df <- readme_df |>
-  filter(week_index >= (- window_num) & week_index <= (window_num)) |>
+  filter(relative_week >= (- window_num) & relative_week <= (window_num)) |>
  mutate(scaled_age = scale(age)) |>
  mutate(scaled_age_at_commit = scale(age_at_commit))|>
  mutate(log1p_count = log1p(commit_count))
@ -16,13 +16,13 @@ library(lme4)
 library(optimx)
 library(lattice)

-all_gmodel <- glmer.nb(log1p_count ~ before_after * week_index + scaled_age + (before_after * week_index | project_id),
+all_gmodel <- glmer.nb(log1p_count ~ before_after * relative_week + scaled_age + (before_after * relative_week | project_id),
                       control=glmerControl(optimizer="bobyqa",
                                            optCtrl=list(maxfun=2e5)), nAGQ=0,
                       data=readme_df)

 summary(all_gmodel)
-#saveRDS(all_gmodel, "020325_readme_model.rda")
+saveRDS(all_gmodel, "020725_readme_model.rda")

 model_residuals <- residuals(all_gmodel)
 acf(model_residuals)
@ -35,7 +35,7 @@ variance_components <- as.data.frame(VarCorr(all_gmodel))
 library(broom.mixed)
 library(ggplot2)
 condvals <- broom.mixed::tidy(all_gmodel, effects = "ran_vals", conf.int = TRUE)
-glmer_ranef_Dweek <- condvals [which(condvals $term == "before_after:week_index"),]
+glmer_ranef_Dweek <- condvals [which(condvals $term == "before_after:relative_week"),]
 has_zero <- function(estimate, low, high){
  return(ifelse((low < 0),ifelse((high > 0), 1, 0), 2))
 }
@ -48,4 +48,4 @@ g <- glmer_ranef_Dweek |>
  theme_bw()
 g

-write.csv(glmer_ranef_Dweek, "0203_readme_dweek_ranefs.csv")
+write.csv(glmer_ranef_Dweek, "0207_readme_dweek_ranefs.csv")
--- a/plots/cr-020725-gam-introduction.png
+++ b/plots/cr-020725-gam-introduction.png