change trend

analysis_trend.R

@@ -12,6 +12,7 @@ require(lmerTest)
 library(dplyr)
 library(pscl)
 require(MASS)
+library(MuMIn)
 
 categorical_factors = c('has_goal', 'freelancer', 'company_null', 'email_null', 'isEmployee', 'isBountyHunter', 'isCampusExpert', 'isDeveloperProgramMember', 'isGitHubStar', 'isHireable', 'isSiteAdmin', 'location_null', 'twitterUsername_null', "other_way", "intervention")
 
@@ -60,10 +61,36 @@ df$location_null = relevel(df$location_null, ref = "1")
 df$other_way = relevel(df$other_way, ref = "0")
 
 df$slope_change = df$after_slope - df$before_slope
-df$slope_change = df$slope_change + abs(df$slope_change)
+df$slope_change_convert = (df$slope_change - min(df$slope_change)) / max(df$slope_change)
 
 m_account_commit_slope = lm(
-    formula=scale(log(slope_change+0.5))~
+    formula=log(slope_change_convert+0.5)~
+        scale(log(user_age+0.5)) +
+        scale(log(min_tier+0.5)) + 
+        scale(log(max_tier+0.5)) + 
+        has_goal + 
+        scale(log(bio_length+0.5)) + 
+        scale(log(followers+0.5)) + 
+        scale(log(followings+0.5)) +
+        scale(log(history_commit_num+0.5)) +
+        scale(log(history_discussion_num+0.5)) +
+        scale(log(history_star_num+0.5)) + 
+        company_null + 
+        email_null + 
+        isHireable + 
+        location_null + 
+        other_way + 
+        scale(log(num_repo_dependents+0.5)),
+    data=df
+)
+r.squaredGLMM(m_account_commit_slope)
+
+
+df$intercept_change = df$after_intercept - df$before_intercept
+df$intercept_change = (df$intercept_change - min(df$intercept_change)) / max(df$intercept_change)
+
+m_account_commit_intercept = lm(
+    formula=scale(log(intercept_change+0.5))~
         scale(log(user_age+0.5)) +
         scale(log(min_tier+0.5)) + 
         scale(log(max_tier+0.5)) +