fit.bage_mod {bage} | R Documentation |
Fit a Model
Description
Calculate 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
-
"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 invar_inner
, and a model for theinner
variables is fitted to the data. In Step 2, the data is aggregated across the remaining variables, and a model for theouter
variables is fitted to the data. In Step 3, values for dispersion are calculated. Parameter estimtes from steps 1, 2, and 3 are then combined."inner-outer"
methods are still experimental, and may change in future, eg dividing calculations into chunks in Step 2.
Optimizer
The choices for the optimizer
argument are:
-
"multi"
Try"nlminb"
, and if that fails, retart from the value 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()
,mod_binom()
,mod_norm()
Specify a model -
augment()
,components()
,tidy()
Examine output from a model -
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
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