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Fit a Model

Source: R/bage_mod-methods.R
fit.bage_mod.Rd

Calculate the posterior distribution for a model.

Usage

# S3 method for class 'bage_mod'
fit(object, method = c("standard", "inner-outer"), vars_inner = NULL, ...)

Arguments

object

A bage_mod object, created with mod_pois(), mod_binom(), or mod_norm().

method

Estimation method. Current choices are "standard" (the default) and "inner-outer". See below for details.

vars_inner

Names of variables to use for inner model when method is "inner-outer". If NULL(the default)vars_inner` is the age, sex/gender, and time variables.

...

Not currently used.

Value

A bage_mod object

Estimation methods

  • "standard" All parameters, other than the lowest-level rates, probabilities, or means are jointly estimated within TMB. The default.

  • "inner-outer". Multiple-stage estimation, which can be faster than "standard" for models with many parameters. In Step 1, the data is aggregated across all dimensions other than those specified in var_inner, and a model for the inner variables is fitted to the data. In Step 2, the data is aggregated across the remaining variables, and a model for the outer variables is fitted to the data. Parameter estimtes from steps 1 and 2 are then combined.

See also

Examples

## specify model
mod <- mod_pois(injuries ~ age + sex + year,
                data = injuries,
                exposure = popn)

## examine unfitted model
mod
#> -- Unfitted Poisson model --
#> 
#>    injuries ~ age + sex + year
#> 
#> (Intercept) ~ NFix()
#>         age ~ RW()
#>         sex ~ NFix()
#>        year ~ RW()
#> 
#>  term n_par n_par_free
#>   age    12         12
#>   sex     2          2
#>  year    19         19
#> 
#>      dispersion: mean=1
#>        exposure: popn
#>         var_age: age
#>   var_sexgender: sex
#>        var_time: year
#>          n_draw: 1000

## fit model
mod <- fit(mod)

## examine fitted model
mod
#> -- Fitted Poisson model --
#> 
#>    injuries ~ age + sex + year
#> 
#> (Intercept) ~ NFix()
#>         age ~ RW()
#>         sex ~ NFix()
#>        year ~ RW()
#> 
#>  term n_par n_par_free std_dev
#>   age    12         12   0.760
#>   sex     2          2   0.720
#>  year    19         19   0.081
#> 
#>      dispersion: mean=1
#>        exposure: popn
#>         var_age: age
#>   var_sexgender: sex
#>        var_time: year
#>          n_draw: 1000

## extract rates
aug <- augment(mod)
aug
#> # 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>>

## extract hyper-parameters
comp <- components(mod)
comp
#> # A tibble: 37 × 4
#>    term        component level                    .fitted
#>    <chr>       <chr>     <chr>               <rdbl<1000>>
#>  1 (Intercept) effect    (Intercept)   -2.5 (-4.1, -0.86)
#>  2 age         effect    0-4         -0.43 (-0.52, -0.35)
#>  3 age         effect    5-9            -1.7 (-1.9, -1.6)
#>  4 age         effect    10-14          -1.3 (-1.4, -1.2)
#>  5 age         effect    15-19           0.44 (0.38, 0.5)
#>  6 age         effect    20-24          0.59 (0.53, 0.64)
#>  7 age         effect    25-29          0.43 (0.37, 0.49)
#>  8 age         effect    30-34           0.33 (0.27, 0.4)
#>  9 age         effect    35-39           0.34 (0.28, 0.4)
#> 10 age         effect    40-44          0.33 (0.27, 0.39)
#> # ℹ 27 more rows