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Fast Bayesian estimation and forecasting of age-specific rates.
install.packages("bage") ## CRAN version
devtools::install_github("bayesiandemography/bage") ## development versionFit 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 19 0.09
#> age:sex RW() age 24 24 0.88
#>
#> disp: mean = 1
#>
#> n_draw var_time var_age var_sexgender optimizer
#> 1000 year age sex multi
#>
#> time_total time_optim time_report iter converged message
#> 1.04 0.31 0.30 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>>These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.