fit.bage_mod {bage} | R Documentation |
Fit a Model
Description
Derive the posterior distribution for a model.
Usage
## S3 method for class 'bage_mod'
fit(
object,
method = c("standard", "inner-outer"),
vars_inner = NULL,
optimizer = c("multi", "nlminb", "BFGS", "CG"),
quiet = TRUE,
start_oldpar = FALSE,
...
)
Arguments
object |
A |
method |
Estimation method. Current
choices are |
vars_inner |
Names of variables to use
for inner model when |
optimizer |
Which optimizer to use.
Current choices are |
quiet |
Whether to suppress warnings and
progress messages from the optimizer.
Default is |
start_oldpar |
Whether the optimizer should start
at previous estimates. Used only
when |
... |
Not currently used. |
Value
A bage_mod
object
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