bage

Fast Bayesian estimation and forecasting of age-specific rates.

Installation

install.packages("bage") ## CRAN version
devtools::install_github("bayesiandemography/bage") ## development version

Example

Fit Poisson model to data on injuries

library(bage)
mod <- mod_pois(injuries ~ age:sex + ethnicity + year,
                data = nzl_injuries,
                exposure = popn) |>
  fit()
mod
#> 
#>     ------ Fitted Poisson model ------
#> 
#> 
#>    injuries ~ age:sex + ethnicity + year
#> 
#>   exposure = popn
#> 
#> 
#>         term  prior along n_par n_par_free std_dev
#>  (Intercept) NFix()     -     1          1       -
#>    ethnicity NFix()     -     2          2    0.45
#>         year   RW()  year    19         18    0.09
#>      age:sex   RW()   age    24         22    0.88
#> 
#> 
#>  n_draw pr_mean_disp var_time var_age var_sexgender optimizer
#>    1000            1     year     age           sex    nlminb
#> 
#> 
#>  time_total time_optim time_draws iter converged                    message
#>        1.00       0.29       0.02   11      TRUE   relative convergence (4)

Extract model-based and direct estimates

augment(mod)
#> # A tibble: 912 × 9
#>    age   sex    ethnicity  year injuries  popn .observed
#>    <fct> <chr>  <chr>     <int>    <int> <int>     <dbl>
#>  1 0-4   Female Maori      2000       12 35830 0.000335 
#>  2 5-9   Female Maori      2000        6 35120 0.000171 
#>  3 10-14 Female Maori      2000        3 32830 0.0000914
#>  4 15-19 Female Maori      2000        6 27130 0.000221 
#>  5 20-24 Female Maori      2000        6 24380 0.000246 
#>  6 25-29 Female Maori      2000        6 24160 0.000248 
#>  7 30-34 Female Maori      2000       12 22560 0.000532 
#>  8 35-39 Female Maori      2000        3 22230 0.000135 
#>  9 40-44 Female Maori      2000        6 18130 0.000331 
#> 10 45-49 Female Maori      2000        6 13770 0.000436 
#> # ℹ 902 more rows
#> # ℹ 2 more variables: .fitted <rdbl<1000>>, .expected <rdbl<1000>>