MAP fit object

MAP fit object

Details

An R6 class storing optimization results from maximum a posteriori (MAP) estimation.

Super class

BayesRTMB::RTMB_Fit_Base -> map_fit

Public fields

model: The `RTMB_Model` object used for estimation.
par_vec: Parameter vector on the unconstrained scale (constrained values unlisted).
par: Parameter list on the constrained scale.
par_unc: Parameter vector on the unconstrained scale (raw unconstrained values).
ci_method: Method used for CI estimation ("wald", "sampling", or "none").
objective: RTMB objective function object.
log_ml: Log marginal likelihood or related model criterion.
convergence: Optimizer convergence code.
sd_rep: Standard deviation report object.
df_fixed: Summary table for fixed-effect parameters.
random_effects: Random effect estimates.
df_transform: Summary table for transformed parameter estimates.
df_generate: Summary table for generated quantity estimates.
opt_history: A vector of optimize objective history.
transform: List of transformed parameters maintaining their original dimensions.
generate: List of generated quantities maintaining their original dimensions.
se_samples: List of simulated samples for standard error estimation.
laplace: Logical; whether Laplace approximation was used.
vcov_unc: Variance-covariance matrix of parameters in unconstrained space.
map: List; the parameter mapping used.
marginal_vars: Character vector of parameter names requested through `optimize(marginal = ...)`.
laplace_random_vars: Character vector of all parameter names passed to `MakeADFun(random = ...)` during Laplace approximation.
idx_fix_active: Numeric vector; mapping between active parameters and full unconstrained vector.
show_df: Logical; whether to display degrees of freedom in the summary output.
view: Character vector of parameter names to prioritize in summary.
fallback_needed: Logical; whether Hessian/SE fallback was used during optimization.

Methods

Inherited methods

Method `get_point_estimate()`

Get point estimate for a target parameter.

Usage

MAP_Fit$get_point_estimate(target, ...)

Arguments

target: Target parameter name.
...: Additional arguments, ignored for MAP fits.

Returns

Matrix, array, vector, or scalar point estimate.

Method `new()`

Create a new `MAP_Fit` object.

Usage

MAP_Fit$new(
  model,
  par_vec = NULL,
  par = NULL,
  objective = NULL,
  log_ml = NULL,
  convergence = NULL,
  sd_rep = NULL,
  df_fixed = NULL,
  random_effects = NULL,
  df_transform = NULL,
  df_generate = NULL,
  opt_history = NULL,
  transform = NULL,
  generate = NULL,
  se_samples = NULL,
  par_unc = NULL,
  ci_method = "wald",
  laplace = TRUE,
  map = NULL,
  vcov_unc = NULL,
  marginal_vars = NULL,
  laplace_random_vars = NULL,
  idx_fix_active = NULL,
  show_df = TRUE,
  view = NULL,
  fallback_needed = NULL
)

Arguments

model: The `RTMB_Model` object used for estimation.
par_vec: Parameter vector on the unconstrained scale (constrained values unlisted).
par: Parameter list on the constrained scale.
objective: The objective function value at the optimum.
log_ml: Log marginal likelihood.
convergence: Optimizer convergence code.
sd_rep: The `sdreport` object from TMB.
df_fixed: Data frame of fixed effects estimates and CIs.
random_effects: Data frame of random effects estimates and CIs.
df_transform: Data frame of transformed parameters.
df_generate: Data frame of generated quantities.
opt_history: Data frame of optimization history.
transform: List of transformed parameters maintaining their original dimensions.
generate: List of generated quantities maintaining their original dimensions.
se_samples: List of simulated samples for standard error estimation.
par_unc: Parameter vector on the unconstrained scale (raw values).
ci_method: Method used for CI estimation ("wald", "sampling", or "none").
laplace: Logical; whether Laplace approximation was used.
map: List; the parameter mapping used.
vcov_unc: Variance-covariance matrix of parameters in unconstrained space.
marginal_vars: Character vector of parameter names requested through `optimize(marginal = ...)`.
laplace_random_vars: Character vector of all parameter names passed to `MakeADFun(random = ...)` during Laplace approximation.
idx_fix_active: Numeric vector; mapping between active parameters and full unconstrained vector.
show_df: Logical; whether to display degrees of freedom in the summary output. Default is TRUE.
view: Character vector of parameter names to prioritize in summary.
fallback_needed: Logical; whether Hessian/SE fallback was used during optimization.

Method `ranef()`

Return random effect estimates as a named list.

Usage

MAP_Fit$ranef()

Returns

A named list of random effect estimates.

Method `draws()`

Extract samples from the asymptotic posterior distribution.

Usage

MAP_Fit$draws(
  pars = NULL,
  inc_random = FALSE,
  inc_transform = TRUE,
  inc_generate = TRUE,
  ...
)

Arguments

pars: Character or numeric vector specifying the names or indices of parameters to extract.
inc_random: Logical; whether to include random effects.
inc_transform: Logical; whether to include transformed parameters.
inc_generate: Logical; whether to include generated quantities.
...: Ignored.

Returns

An array of samples [iterations, 1, parameters].

Method `summary()`

Summarize MAP estimates.

Usage

MAP_Fit$summary(
  pars = NULL,
  max_rows = 10,
  digits = 5,
  ranef = FALSE,
  view = NULL
)

Arguments

pars: Character vector specifying the names of parameters to summarize. If NULL, all available parameters are summarized.
max_rows: Maximum number of rows to print in summaries. Default is 10.
digits: Number of digits to print.
ranef: Logical; whether to also display random effect estimates. Default is FALSE.
view: Character vector of parameter names to prioritize or filter by.

Returns

A summary object, typically a data frame.

Method `print()`

Print a brief summary of the fitted object.

Usage

MAP_Fit$print(pars = NULL, max_rows = 10, digits = 5, ...)

Arguments

pars: Character vector specifying the names of parameters to summarize.
max_rows: Maximum number of rows to print in summaries.
digits: Number of digits to print.
...: Additional arguments passed to the `summary` method.

Returns

The object itself, invisibly.

Method `generated_quantities()`

Compute generated quantities from the MAP estimate.

Usage

MAP_Fit$generated_quantities(code)

Arguments

code: An `rtmb_code({ ... })` or `{ ... }` block containing the logic to be calculated using the MAP estimate.

Returns

The `MAP_Fit` object itself (invisibly). Results are added or updated in the `generate` list and `df_generate`.

Method `WAIC()`

Compute an approximate WAIC from sampling-based uncertainty propagation.

Usage

MAP_Fit$WAIC()

Returns

A `waic_BayesRTMB` object.

Method `diagnose()`

Run basic diagnostics for the MAP fit.

Usage

MAP_Fit$diagnose(...)

Arguments

...: Additional arguments passed to `diagnose_map_fit()`.

Returns

A `diagnose_BayesRTMB` object.

Method `profile()`

Calculate Profile Likelihood confidence intervals for specific parameters.

Usage

MAP_Fit$profile(
  pars = NULL,
  level = 0.95,
  trace = FALSE,
  digits = 5,
  show_plot = FALSE,
  quiet = FALSE,
  jacobian = "none",
  ...
)

Arguments

pars: Character vector of parameter names to profile. If NULL, all fixed parameters are profiled.
level: Confidence level (default is 0.95).
trace: Logical; whether to print profiling progress. Default is FALSE.
digits: Integer; number of decimal places to print. Default is 5.
show_plot: Logical; whether to plot the profile likelihood curves. Default is FALSE.
quiet: Logical; whether to suppress text output. Default is FALSE.
jacobian: Character; "none" (default), "random", or "all". Whether to include Jacobian adjustments for transformations.
...: Additional arguments passed to TMB::tmbprofile (e.g., ytol).

Returns

A data frame containing the profile-based confidence intervals, with the raw profile objects stored in the "profiles" attribute.

Method `clone()`

The objects of this class are cloneable with this method.

Usage

MAP_Fit$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Details

Super class

Public fields

Methods

Public methods

Method get_point_estimate()

Usage

Arguments

Returns

Method new()

Usage

Arguments

Method ranef()

Usage

Returns

Method draws()

Usage

Arguments

Returns

Method summary()

Usage

Arguments

Returns

Method print()

Usage

Arguments

Returns

Method generated_quantities()

Usage

Arguments

Returns

Method WAIC()

Usage

Returns

Method diagnose()

Usage

Arguments

Returns

Method profile()

Usage

Arguments

Returns

Method clone()

Usage

Arguments

Method `get_point_estimate()`

Method `new()`

Method `ranef()`

Method `draws()`

Method `summary()`

Method `print()`

Method `generated_quantities()`

Method `WAIC()`

Method `diagnose()`

Method `profile()`

Method `clone()`