diff --git a/R/final_models/0624_pop_rm_collab_better.rda b/R/final_models/0624_pop_rm_collab_better.rda
new file mode 100644
index 0000000..7c8f37a
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diff --git a/R/final_models/0624_pop_rm_contrib.rda b/R/final_models/0624_pop_rm_contrib.rda
new file mode 100644
index 0000000..fa149bb
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diff --git a/R/popRDDAnalyssis.R b/R/popRDDAnalyssis.R
index de104ac..6f52d66 100644
--- a/R/popRDDAnalyssis.R
+++ b/R/popRDDAnalyssis.R
@@ -9,20 +9,6 @@ readme_df <- read_csv("../final_data/deb_readme_pop_change.csv")
 contrib_df <- merge(full_df, contrib_df, by="upstream_vcs_link")
 readme_df <- merge(full_df, readme_df, by="upstream_vcs_link")
 # age is calculated against December 11, 2023
-contrib_df <- contrib_df |>
-  mutate(start_date = as.Date("2023-12-11") - age_of_project) |>
-  mutate(event_date_days = 
-           as.numeric(
-             difftime(as.POSIXct("2024-06-24 00:00:00", format = "%Y-%m-%d %H:%M:%S"),
-                     as.POSIXct(event_date, format = "%Y-%m-%d %H:%M:%S"), 
-                     units = "days"))) 
-readme_df <- readme_df |>
-  mutate(start_date = as.Date("2023-12-11") - age_of_project) |>
-  mutate(event_date_days = 
-           as.numeric(
-             difftime(as.POSIXct("2024-06-24 00:00:00", format = "%Y-%m-%d %H:%M:%S"),
-                      as.POSIXct(event_date, format = "%Y-%m-%d %H:%M:%S"), 
-                      units = "days"))) 
 #some expansion needs to happens for each project 
 expand_timeseries <- function(project_row) {
   longer <- project_row |> 
@@ -46,6 +32,9 @@ expanded_readme_data$log1pcount <- log1p(expanded_readme_data$count)
 expanded_contrib_data$log1pcount <- log1p(expanded_contrib_data$count)
 expanded_readme_data$logcount <- log(expanded_readme_data$count)
 expanded_contrib_data$logcount <- log(expanded_contrib_data$count)
+#scale age
+expanded_readme_data$scaled_age <- scale(expanded_readme_data$age_in_days)
+expanded_contrib_data$scaled_age <- scale(expanded_contrib_data$age_in_days)
 #breaking out the types of population counts
 collab_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 1),]
 contrib_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 0),]
@@ -55,30 +44,22 @@ contrib_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_coll
 library(lme4)
 library(optimx)
 library(MASS)
-simple_collab_readme_model <- glm.nb(count ~ as.factor(after_doc), data=collab_pop_readme)
+#readme docs
+simple_collab_readme_model <- glm.nb(log1pcount ~ as.factor(after_doc) + scale(age_in_days), data=collab_pop_readme)
 summary(simple_collab_readme_model)
 qqnorm(residuals(simple_collab_readme_model))
+simple_contrib_readme_model <- glm.nb(log1pcount ~ as.factor(after_doc) + scale(age_in_days), data=collab_pop_readme)
+summary(simple_contrib_readme_model)
+qqnorm(residuals(simple_contrib_readme_model))
 # I don't think MLM is the right one
-collab_readme_model <- glmer.nb(log1pcount ~ as.factor(after_doc) +  (after_doc| upstream_vcs_link), data=collab_pop_readme)
-collab_readme_model_plus <- glmer.nb(log1pcount ~ as.factor(after_doc) + event_date_days +  (after_doc| upstream_vcs_link), data=collab_pop_readme)
-collab_readme_model <- readRDS("final_models/0623_pop_rm_collab.rda")
+collab_readme_model <- glmer.nb(log1pcount ~ as.factor(after_doc) + scaled_age + (after_doc| upstream_vcs_link), data=collab_pop_readme)
 summary(collab_readme_model)
-saveRDS(collab_readme_model, "final_models/0623_pop_rm_collab_better.rda")
-contrib_readme_model <- glmer.nb(log1pcount ~ after_doc + (after_doc| upstream_vcs_link), data=contrib_pop_readme)
-contrib_readme_model_plus <- glmer.nb(log1pcount ~ after_doc + event_date_days+ (after_doc| upstream_vcs_link), data=contrib_pop_readme)
+saveRDS(collab_readme_model, "final_models/0624_pop_rm_collab_better.rda")
+contrib_readme_model <- glmer.nb(log1pcount ~ as.factor(after_doc) + scaled_age + (after_doc| upstream_vcs_link), data=contrib_pop_readme)
 summary(contrib_readme_model)
-saveRDS(contrib_readme_model, "final_models/0623_pop_rm_contrib.rda")
-contrib_readme_model <- readRDS("final_models/0623_pop_rm_contrib.rda")
-conrm_residuals <- residuals(contrib_readme_model)
-qqnorm(conrm_residuals)
-collab_contrib_model <- glmer.nb(log1pcount ~ after_doc + (after_doc| upstream_vcs_link), data=collab_pop_contrib)
-summary(collab_contrib_model)
-saveRDS(collab_contrib_model, "final_models/0623_pop_contrib_collab.rda")
-contrib_readme_model <- readRDS("final_models/0623_pop_contrib_collab.rda")
-contrib_contrib_model <- glmer.nb(log1pcount ~ after_doc + (after_doc| upstream_vcs_link), data=contrib_pop_contrib)
-summary(contrib_contrib_model)
-saveRDS(contrib_contrib_model, "final_models/0623_pop_contrib_contrib.rda")
-
+saveRDS(contrib_readme_model, "final_models/0624_pop_rm_contrib.rda")
+#contrib_readme_model <- readRDS("final_models/0623_pop_rm_contrib.rda")
+#contributing models are not statistically significant``
 library(texreg)
 
 texreg(list(collab_readme_model, contrib_readme_model), stars=NULL, digits=2,