got the neg binom to work
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R/0509_gmodel.rda
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R/0509_gmodel.rda
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R/0509caterpillar.png
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R/0509caterpillar.png
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@ -82,8 +82,12 @@ mean (all_actions_data$log1p_count) # 0.6426873
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#all_gmodel <- glmer.nb(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset) | upstream_vcs_link),
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# control=glmerControl(optimizer="bobyqa",
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# optCtrl=list(maxfun=2e5)), data=all_actions_data)
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all_gmodel <- glmer.nb(count ~ D * I(week_offset)+ scaled_project_age + (D | upstream_vcs_link), data=all_actions_data)
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all_gmodel <- glmer.nb(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset) | upstream_vcs_link), data=all_actions_data, nAGQ=0, control=glmerControl(optimizer="bobyqa",
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optCtrl=list(maxfun=1e5)))
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summary(all_gmodel)
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saveRDS(all_gmodel, "0509_gmodel.rda")
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#readRDS(path)
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library(broom.mixed)
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test_condvals <- broom.mixed::tidy(all_gmodel, effects = "ran_vals", conf.int = TRUE)
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test_glmer_ranef_D <- test_condvals [which(test_condvals $term == "D"),]
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has_zero <- function(estimate, low, high){
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@ -92,10 +96,11 @@ has_zero <- function(estimate, low, high){
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test_glmer_ranef_D <- test_glmer_ranef_D |>
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mutate(ranef_grouping = has_zero(estimate, conf.low, conf.high)) |>
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mutate(rank = rank(estimate))
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test_glmer_ranef_D |>
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g <- test_glmer_ranef_D |>
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ggplot(aes(x=rank, y=estimate, col = as.factor(ranef_grouping))) +
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geom_linerange(aes(ymin= conf.low, ymax= conf.high)) +
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theme_bw()
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ggsave("0509caterpillar.png", g)
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#below this groups the ranefs
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"""
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has_zero <- function(condval, condsd){
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