Printing a Model

After calling a function such as mod_pois() or set_prior() it is good practice to print the model object at the console, to check the model's structure. The output from print() has the following components:

• A header giving the class of the model and noting whether the model has been fitted.

• A formula giving the outcome variable and terms for the model.

• A table giving the number of parameters, and (fitted models only) the standard deviation across those parameters, a measure of the term's importance. See priors() and tidy().

• Values for other model settings. See set_disp(), set_var_age(), set_var_sexgender(), set_var_time(), set_n_draw()

• Details on computations (fitted models only). See computations().

Usage

# S3 method for class 'bage_mod'
print(x, ...)

Arguments

x

Object of class "bage_mod", typically created with mod_pois(), mod_binom(), or mod_norm().

...

Unused. Included for generic consistency only.

Value

x, invisibly.

See also

• mod_pois() Specify a Poisson model

• mod_binom() Specify a binomial model

• mod_norm() Specify a normal model

• fit.bage_mod() and is_fitted() Model fitting

• augment() Extract values for rates, probabilities, or means, together with original data

• components() Extract values for hyper-parameters

• dispersion() Extract values for dispersion

• priors Overview of priors for model terms

• tidy.bage_mod() Number of parameters, and standard deviations

• set_disp() Dispersion

• set_var_age(), set_var_sexgender(), set_var_time() Age, sex/gender and time variables

• set_n_draw() Model draws

Examples

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

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

mod <- fit(mod)
#> Building log-posterior function...
#> Finding maximum...
#> Drawing values for hyper-parameters...

## print fitted model
mod
#> 
#>     ------ Fitted Poisson model ------
#> 
#>    injuries ~ age + sex + year
#> 
#>                  exposure: popn
#> 
#>         term  prior along n_par n_par_free std_dev
#>  (Intercept) NFix()     -     1          1       -
#>          age   RW()   age    12         12    0.76
#>          sex NFix()     -     2          2    0.71
#>         year   RW()  year    19         19    0.09
#> 
#>  disp: mean = 1
#> 
#>  n_draw var_time var_age var_sexgender optimizer
#>    1000     year     age           sex    nlminb
#> 
#>  time_total time_max time_draw iter converged                    message
#>        0.46     0.20      0.22   12      TRUE   relative convergence (4)
#>