log1p nbinom save
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R/0509_log1p_gmodel.rda
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R/0509_log1p_gmodel.rda
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@ -82,10 +82,10 @@ 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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#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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# control=glmerControl(optimizer="bobyqa",
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# optCtrl=list(maxfun=2e5)), data=all_actions_data)
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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 * I(week_offset) | upstream_vcs_link), data=all_actions_data, nAGQ=0, control=glmerControl(optimizer="bobyqa",
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all_log1p_gmodel <- glmer.nb(log1p_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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optCtrl=list(maxfun=1e5)))
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summary(all_gmodel)
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summary(all_log1p_gmodel)
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saveRDS(all_gmodel, "0509_gmodel.rda")
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saveRDS(all_log1p_gmodel, "0509_log1p_gmodel.rda")
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#readRDS(path)
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#readRDS(path)
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library(broom.mixed)
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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_condvals <- broom.mixed::tidy(all_gmodel, effects = "ran_vals", conf.int = TRUE)
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