diff --git a/R/contribRDDAnalysis.R b/R/contribRDDAnalysis.R
index 1d9671e..0420bf5 100644
--- a/R/contribRDDAnalysis.R
+++ b/R/contribRDDAnalysis.R
@@ -67,11 +67,18 @@ print("fitting model")
 #all_gmodel <- glmer.nb(log1p_count ~ D * week_offset + scaled_project_age + scaled_event_gap + (D * week_offset | upstream_vcs_link),
 #              control=glmerControl(optimizer="bobyqa",
 #                                           optCtrl=list(maxfun=2e5)), nAGQ=0, data=all_actions_data)
-#all_gmodel <- readRDS("0711_contrib_all.rda")
+library(car)
+library(forecast)
+
+all_gmodel <- readRDS("final_models/0711_contrib_all_rdd.rda")
 summary(all_gmodel)
 #saveRDS(all_gmodel, "0711_contrib_all_01.rda")
 
+#autocorrelation
+tes <- vif(all_gmodel)
+tes
 all_residuals <- residuals(all_gmodel)
+acf(all_residuals)
 qqnorm(all_residuals)
 #identifying the quartiles of effect for D
 test_condvals <- broom.mixed::tidy(all_gmodel, effects = "ran_vals", conf.int = TRUE)
diff --git a/R/readmeRDDAnalysis.R b/R/readmeRDDAnalysis.R
index c5a3d65..474752b 100644
--- a/R/readmeRDDAnalysis.R
+++ b/R/readmeRDDAnalysis.R
@@ -70,9 +70,18 @@ median(all_actions_data$count) # 0
 print("fitting model")
 #all_log1p_gmodel <- glmer.nb(log1p_count ~ D * week_offset+ scaled_project_age + scaled_event_gap + (D * week_offset | upstream_vcs_link), data=all_actions_data, nAGQ=1,  control=glmerControl(optimizer="bobyqa",
 #                         optCtrl=list(maxfun=1e5)))
-#all_log1p_gmodel <- readRDS("final_models/0624_readme_all_rdd.rda")
+all_log1p_gmodel <- readRDS("final_models/0624_readme_all_rdd.rda")
 summary(all_log1p_gmodel)
 print("model fit")
+
+library(car)
+library(forecast)
+
+tes <- vif(all_log1p_gmodel)
+tes
+all_residuals <- residuals(all_log1p_gmodel)
+acf(all_residuals)
+
 #I grouped the ranef D effects on 0624
 all_residuals <- residuals(all_log1p_gmodel)
 qqnorm(all_residuals)