Specify Number of Draws from Prior or Posterior Distribution

Specify the number of draws from the posterior distribution to be used in model output. A newly-created bage_mod object has an n_draw value of 1000. Higher values may be appropriate for characterizing the tails of distributions, or for publication-quality graphics and summaries.

Usage

set_n_draw(mod, n_draw = 1000L)

Arguments

mod

An object of class "bage_mod", created with mod_pois(), mod_binom(), or mod_norm().

n_draw

Number of draws.

Value

A bage_mod object

Details

If the new value for n_draw is greater than the old value, and the model has already been fitted, then the model is unfitted, and function fit() may need to be called again.

See also

• augment(), components() functions for drawing from prior or posterior distribution - the output of which is affected by the value of n_draw.

• mod_pois(), mod_binom(), mod_norm() Specify a model

• set_prior() Specify prior for a term

• set_disp() Specify prior for dispersion

• fit() Fit a model

• unfit() Reset a model

Examples

mod <- mod_pois(injuries ~ age:sex + ethnicity + year,
                data = nzl_injuries,
                exposure = popn)
mod
#> 
#>     ------ Unfitted Poisson model ------
#> 
#> 
#>    injuries ~ age:sex + ethnicity + year
#> 
#>   exposure = popn
#> 
#> 
#>         term  prior along n_par n_par_free
#>  (Intercept) NFix()     -     1          1
#>    ethnicity NFix()     -     2          2
#>         year   RW()  year    19         18
#>      age:sex   RW()   age    24         22
#> 
#> 
#>  n_draw pr_mean_disp var_time var_age var_sexgender
#>    1000            1     year     age           sex
#> 

mod |>
  set_n_draw(n_draw = 5000)
#> 
#>     ------ Unfitted Poisson model ------
#> 
#> 
#>    injuries ~ age:sex + ethnicity + year
#> 
#>   exposure = popn
#> 
#> 
#>         term  prior along n_par n_par_free
#>  (Intercept) NFix()     -     1          1
#>    ethnicity NFix()     -     2          2
#>         year   RW()  year    19         18
#>      age:sex   RW()   age    24         22
#> 
#> 
#>  n_draw pr_mean_disp var_time var_age var_sexgender
#>    5000            1     year     age           sex
#>