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nbinom fit

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This commit is contained in:
Matthew Gaughan 2024-05-10 14:11:24 -05:00
parent 6076d161ad
commit c3816d64e1
4 changed files with 16 additions and 7 deletions
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23
R/readmeRDDAnalysis.R
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@ -77,18 +77,27 @@ all_standard_errors <- sqrt(diag(vcov(all_model)))[1]
var(all_actions_data$log1p_count) # 1.125429
mean (all_actions_data$log1p_count) # 0.6426873
var(all_actions_data$count) # 268.4449
mean (all_actions_data$count) # 3.757298
summary(all_actions_data$week_offset)
#all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, nAGQ=0, family = poisson)
#all_gmodel <- glmer.nb(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset) | upstream_vcs_link),
# control=glmerControl(optimizer="bobyqa",
# optCtrl=list(maxfun=2e5)), data=all_actions_data)
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",
all_gmodel <- glmer.nb(count ~ D * week_offset + scaled_project_age + (D * week_offset | upstream_vcs_link),
control=glmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5)), data=all_actions_data)
#all_log1p_gmodel <- glmer.nb(log1p_count ~ D * week_offset+ scaled_project_age + (D * week_offset | upstream_vcs_link), data=all_actions_data, nAGQ=0, control=glmerControl(optimizer="bobyqa",
# optCtrl=list(maxfun=1e5)))
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",
optCtrl=list(maxfun=1e5)))
summary(all_log1p_gmodel)
saveRDS(all_log1p_gmodel, "0509_log1p_gmodel.rda")
#readRDS(path)
warnings(all_log1p_gmodel)
saveRDS(all_log1p_gmodel, "0510_log1p_nagq_gmodel_backup.rda")
#yesterdays_model <- readRDS("0509_log1p_gmodel.rda")
all_residuals <- residuals(all_log1p_gmodel)
qqnorm(all_residuals)
library(broom.mixed)
test_condvals <- broom.mixed::tidy(all_gmodel, effects = "ran_vals", conf.int = TRUE)
test_condvals <- broom.mixed::tidy(all_log1p_gmodel, effects = "ran_vals", conf.int = TRUE)
test_glmer_ranef_D <- test_condvals [which(test_condvals $term == "D"),]
has_zero <- function(estimate, low, high){
return(ifelse((low < 0),ifelse((high > 0), 1, 0), 2))