7-poissonBA.R

(allTabs <- excel_sheets("data.xlsx")) # list tabs

# Read female reproductive output
fro <- read_xlsx("data.xlsx", sheet = allTabs[2])

# Assess missingness
sum(complete.cases(fro)) / nrow(fro)
# only 0.57 complete records; which vars have at least one NA?
names(which(apply(fro, 2, function(x){any(is.na(x))})))

# Filter out missingness in fledged eggs, the model does not cope with it
fro %<>% slice(which(!is.na(Eggs_fledged))) %>% 
      as.data.frame()

fro %<>% mutate(female_id = as.integer(factor(Female_ID_coded)),
                year_id = as.integer(factor(Year)),
                group_id = as.integer(factor(Group_ID_coded)),
                Min_age_Z = scale(Min_age),
                Group_size_Z = scale(Group_size),
                Mean_eggsize_Z = scale(Mean_eggsize))