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running readme again for event_gap

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Matthew Gaughan 2024-06-24 19:13:58 -05:00
parent 45a6e729b5
commit 19c8f4eb10
5 changed files with 10 additions and 9 deletions
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7
R/contribRDDAnalysis.R
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@ -58,9 +58,10 @@ library(optimx)
library(lattice) library(lattice)
#model #model
print("fitting model") print("fitting model")
all_gmodel <- glmer.nb(log1p_count ~ D * week_offset + scaled_project_age + scaled_event_gap + (D * week_offset | upstream_vcs_link), #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", # control=glmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5)), nAGQ=0, data=all_actions_data) # optCtrl=list(maxfun=2e5)), nAGQ=0, data=all_actions_data)
all_gmodel <- readRDS("0512_contrib_all.rda")
summary(all_gmodel) summary(all_gmodel)
saveRDS(all_gmodel, "0512_contrib_all.rda") saveRDS(all_gmodel, "0512_contrib_all.rda")
all_residuals <- residuals(all_gmodel) all_residuals <- residuals(all_gmodel)

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R/final_models/0623_pop_rm_collab.rda
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R/final_models/0623_pop_rm_collab_better.rda
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R/final_models/0623_pop_rm_contrib.rda
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12
R/readmeRDDAnalysis.R
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@ -38,6 +38,7 @@ windowed_data <- expanded_data |>
mutate(D = ifelse(week > 27, 1, 0)) mutate(D = ifelse(week > 27, 1, 0))
#scale the age numbers #scale the age numbers
windowed_data$scaled_project_age <- scale(windowed_data$age_in_days) windowed_data$scaled_project_age <- scale(windowed_data$age_in_days)
windowed_data$scaled_event_gap <- scale(event_gap)
windowed_data$week_offset <- windowed_data$week - 27 windowed_data$week_offset <- windowed_data$week - 27
#break out the different types of commit actions that are studied #break out the different types of commit actions that are studied
all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),] all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
@ -61,11 +62,11 @@ var(all_actions_data$count) # 268.4449
mean (all_actions_data$count) # 3.757298 mean (all_actions_data$count) # 3.757298
median(all_actions_data$count) # 0 median(all_actions_data$count) # 0
print("fitting model") print("fitting model")
all_log1p_gmodel <- glmer.nb(log1p_count ~ D * week_offset+ scaled_project_age + (D * week_offset | upstream_vcs_link), data=all_actions_data, nAGQ=1, control=glmerControl(optimizer="bobyqa", 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))) optCtrl=list(maxfun=1e5)))
#all_log1p_gmodel <- readRDS("final_models/0510_rm_all.rda") #all_log1p_gmodel <- readRDS("0624_log1p_nagq_gmodel_backup.rda")
summary(all_log1p_gmodel) summary(all_log1p_gmodel)
saveRDS(all_log1p_gmodel, "0624_log1p_nagq_gmodel_backup.rda") saveRDS(all_log1p_gmodel, "0624_eventgap_rm_rdd.rda")
print("model fit") print("model fit")
#I grouped the ranef D effects on 0624 #I grouped the ranef D effects on 0624
all_residuals <- residuals(all_log1p_gmodel) all_residuals <- residuals(all_log1p_gmodel)
@ -84,7 +85,6 @@ g <- test_glmer_ranef_D |>
geom_linerange(aes(ymin= conf.low, ymax= conf.high)) + geom_linerange(aes(ymin= conf.low, ymax= conf.high)) +
theme_bw() theme_bw()
g g
write.csv(test_glmer_ranef_D, "062424_readme_grouped.csv") write.csv(test_glmer_ranef_D, "062424_readme_grouped_1.csv")
ggsave("0624caterpillar.png", g) ggsave("0624caterpillar.png", g)
# NOTE: below is the merge model for the same analysis, but it won't converge
print("all pau") print("all pau")