bage (version 0.9.4)

fit.bage_mod: Fit a Model

Description

Derive the posterior distribution for a model.

Usage

# S3 method for bage_mod
fit(
  object,
  method = c("standard", "inner-outer"),
  vars_inner = NULL,
  optimizer = c("multi", "nlminb", "BFGS", "CG"),
  quiet = TRUE,
  start_oldpar = FALSE,
  ...
)

Value

A bage_mod object

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.

optimizer

Which optimizer to use. Current choices are "multi", "nlminb", "BFGS", and "CG". Default is "multi". See below for details.

quiet

Whether to suppress warnings and progress messages from the optimizer. Default is TRUE.

start_oldpar

Whether the optimizer should start at previous estimates. Used only when fit() is being called on a fitted model. Default is FALSE.

...

Not currently used.

Estimation methods

When method is "standard" (the default), all parameters, other than the lowest-level rates, probabilities, or means are jointly estimated within TMB.

When method is "inner-outer", estimation is carried out in multiple steps, which, in large models, can sometimes reduce computation times. 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. In Step 3, values for dispersion are calculated. Parameter estimates from steps 1, 2, and 3 are then combined. "inner-outer" methods are still experimental, and may change in future.

Optimizer

The choices for the optimizer argument are:

• "multi" Try "nlminb", and if that fails, restart from the parameter values where "nlminb" stopped, using "BFGS". The default.

• "nlminb" stats::nlminb()

• "BFGS" stats::optim() using method "BFGS".

• "GC" stats::optim() using method "CG" (conjugate gradient).

See Also

• mod_pois() Specify a Poisson model

• mod_binom() Specify a binomial model

• mod_norm() Specify a normal model

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

• components() Extract values for hyper-parameters

• forecast() Forecast, based on a model

• report_sim() Simulation study of a model

• unfit() Reset a model

• is_fitted() Check if a model has been fitted

• Mathematical Details vignette

Examples

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

## examine unfitted model
mod

## fit model
mod <- fit(mod)

## examine fitted model
mod

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

## extract hyper-parameters
comp <- components(mod)
comp