Classic fit object

An R6 class representing the results of a classical (frequentist) estimation.

Super class

BayesRTMB::RTMB_Fit_Base -> Classic_Fit

Public fields

model

The `RTMB_Model` object used for estimation.

fit

The result of the estimation (dataframe or lm object).

par

a named list of parameter estimates.

vcov

Variance-covariance matrix of fixed effects.

se_method

Character string specifying the method used for standard errors.

cluster

Character string specifying the cluster variable name, if any.

robust_type

Character string specifying the robust standard error correction type, if used.

bootstrap_results

A matrix containing bootstrap samples, if applicable.

test_results

List of additional test results (e.g., chisq.test).

view

Character vector of parameter names to prioritize in summary.

par_vec

Numeric vector of parameter estimates.

objective

Final objective value.

log_lik

Log-likelihood.

restricted_log_lik

Restricted log-likelihood, if classical estimation used REML-style marginalization.

rss

Residual sum of squares for classical linear models, if available.

df_residual

Residual degrees of freedom for classical linear models, if available.

convergence

Convergence code.

sd_rep

TMB sdreport object.

df_fixed

Dataframe of fixed effects results.

random_effects

Dataframe of random effects results.

df_transform

Dataframe of transformed parameters.

df_generate

Dataframe of generated quantities.

opt_history

Dataframe of optimization history.

transform

List of transformed parameters.

generate

List of generated quantities.

se_samples

List of simulated samples for SE estimation.

par_unc

Numeric vector of unconstrained parameter estimates.

vcov_unc

Variance-covariance matrix of parameters in unconstrained space.

ci_method

Method used for CI estimation.

laplace

Whether Laplace approximation was used.

map

Parameter mapping used.

df_method

Character string specifying the degrees of freedom calculation method.

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.

Methods

Public methods

• Classic_Fit$get_point_estimate()

• Classic_Fit$new()

• Classic_Fit$robust_se()

• Classic_Fit$compute_robust()

• Classic_Fit$bootstrap()

• Classic_Fit$compute_bootstrap()

• Classic_Fit$AIC()

• Classic_Fit$BIC()

• Classic_Fit$WAIC()

• Classic_Fit$diagnose()

• Classic_Fit$print()

• Classic_Fit$logLik()

• Classic_Fit$EAP()

• Classic_Fit$MAP()

• Classic_Fit$summary()

• Classic_Fit$anova()

• Classic_Fit$lsmeans()

• Classic_Fit$.calc_contrast()

• Classic_Fit$.get_lsmeans_df()

• Classic_Fit$.construct_par_list()

• Classic_Fit$clone()

Inherited methods

• BayesRTMB::RTMB_Fit_Base$estimate()
• BayesRTMB::RTMB_Fit_Base$fa_rotate()
• BayesRTMB::RTMB_Fit_Base$rotate()

---

Method get_point_estimate()

Get point estimate for a target parameter.

Usage

Classic_Fit$get_point_estimate(target, ...)

Arguments

target

Target parameter name.

...

Additional arguments, ignored for classic fits.

Returns

Matrix, array, vector, or scalar point estimate.

---

Method new()

Create a new `Classic_Fit` object.

Usage

Classic_Fit$new(
  model,
  par_vec = NULL,
  par = NULL,
  objective = NULL,
  log_lik = NULL,
  restricted_log_lik = 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,
  vcov_unc = NULL,
  ci_method = "wald",
  laplace = TRUE,
  map = NULL,
  test_results = list(),
  view = NULL,
  fit = NULL,
  vcov = NULL,
  df_method = "auto",
  idx_fix_active = NULL,
  show_df = TRUE,
  rss = NULL,
  df_residual = NULL,
  ...
)

Arguments

model

The `RTMB_Model` object.

par_vec

Numeric vector of parameter estimates.

par

List of parameter estimates.

objective

Final objective value.

log_lik

Full log-likelihood used for information criteria.

restricted_log_lik

Restricted log-likelihood, if classical estimation used REML-style marginalization.

convergence

Convergence code.

sd_rep

TMB sdreport object.

df_fixed

Dataframe of fixed effects results.

random_effects

Dataframe of random effects results.

df_transform

Dataframe of transformed parameters.

df_generate

Dataframe of generated quantities.

opt_history

Dataframe of optimization history.

transform

List of transformed parameters.

generate

List of generated quantities.

se_samples

List of simulated samples for SE estimation.

par_unc

Parameter vector on unconstrained scale.

vcov_unc

Covariance matrix on unconstrained scale.

ci_method

Method used for CI estimation.

laplace

Whether Laplace approximation was used.

map

Parameter mapping used.

test_results

List of additional test results (e.g., chisq.test).

view

Character vector of parameter names to prioritize in summary.

fit

Legacy argument for backward compatibility (maps to df_fixed).

vcov

Variance-covariance matrix of parameters.

df_method

Method for degrees of freedom calculation.

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.

rss

Residual sum of squares for classical linear models, if available.

df_residual

Residual degrees of freedom for classical linear models, if available.

...

Additional arguments passed to the constructor.

---

Method robust_se()

Compute robust standard errors (sandwich estimator).

Usage

Classic_Fit$robust_se(
  cluster = NULL,
  type = c("HC3", "HC0", "HC1", "CR1", "CR0"),
  inplace = FALSE,
  ...
)

Arguments

cluster

Character; variable name for clustering.

type

Character; "HC3" (default), "HC0", or "HC1" for non-clustered robust SE; "CR1" or "CR0" for clustered robust SE.

inplace

Logical; if FALSE, return a robust-SE copy without modifying the original fit. If TRUE, update the object in place.

...

Additional arguments.

Returns

Self.

---

Method compute_robust()

(Deprecated) Use robust_se() instead.

Usage

Classic_Fit$compute_robust(...)

Arguments

...

Arguments passed to robust_se(), including inplace.

Returns

Self.

---

Method bootstrap()

Compute nonparametric bootstrap standard errors and confidence intervals. Currently implemented only for mediation models. Bootstrap refits mediation equations with base R lm.fit()/glm.fit() when possible, and falls back to RTMB refits for unsupported families.

Usage

Classic_Fit$bootstrap(n_boot = 1000, seed = NULL, inplace = FALSE, ...)

Arguments

n_boot

Integer; number of bootstrap samples.

seed

Optional integer random seed.

inplace

Logical; if FALSE, return a bootstrap-SE copy without modifying the original fit. If TRUE, update the object in place.

...

Additional arguments.

Returns

A Classic_Fit object.

---

Method compute_bootstrap()

Compute nonparametric bootstrap standard errors and confidence intervals. Currently implemented only for mediation models. Bootstrap refits mediation equations with base R lm.fit()/glm.fit() when possible, and falls back to RTMB refits for unsupported families.

Usage

Classic_Fit$compute_bootstrap(n_boot = 1000, seed = NULL, inplace = FALSE, ...)

Arguments

n_boot

Integer; number of bootstrap samples.

seed

Optional integer random seed.

inplace

Logical; if FALSE, return a bootstrap-SE copy without modifying the original fit. If TRUE, update the object in place.

...

Additional arguments.

Returns

A Classic_Fit object.

---

Method AIC()

Get the AIC of the fitted model.

Usage

Classic_Fit$AIC()

---

Method BIC()

Get the BIC of the fitted model.

Usage

Classic_Fit$BIC()

---

Method WAIC()

WAIC is not defined for classical fits.

Usage

Classic_Fit$WAIC()

---

Method diagnose()

Run basic diagnostics for the classical fit.

Usage

Classic_Fit$diagnose(...)

Arguments

...

Additional arguments passed to `diagnose_classic_fit()`.

Returns

A `diagnose_BayesRTMB` object.

---

Method print()

Print the fit results.

Usage

Classic_Fit$print(...)

Arguments

...

Additional arguments passed to `summary()`.

---

Method logLik()

Get the Log-Likelihood of the fitted model.

Usage

Classic_Fit$logLik()

---

Method EAP()

EAP estimates are not available for Classic_Fit.

Usage

Classic_Fit$EAP(...)

Arguments

...

Ignored.

---

Method MAP()

MAP estimates are not available for Classic_Fit.

Usage

Classic_Fit$MAP(...)

Arguments

...

Ignored.

---

Method summary()

Display a summary of the estimation results.

Usage

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

Arguments

view

Character vector of parameter names to prioritize or filter by.

digits

Number of digits to print for estimates.

max_rows

Maximum number of rows to display in the coefficient table.

ranef

Logical; whether to show random effects in the summary.

---

Method anova()

Perform ANOVA (Wald F-tests / Chisq-tests) on the fitted model.

Usage

Classic_Fit$anova(method = c("reml", "ls"), type = 3)

Arguments

method

Character; "reml" (standard) or "ls" (experimental).

type

Integer; Type of Sum of Squares (only Type III supported currently).

Returns

A data frame containing the ANOVA table.

---

Method lsmeans()

Calculate Least Squares Means (Marginal Means) and contrasts.

Usage

Classic_Fit$lsmeans(
  specs = NULL,
  pairwise = FALSE,
  simple = NULL,
  adjust = "holm",
  protect = FALSE
)

Arguments

specs

Character vector of factors to calculate means for.

pairwise

Logical; whether to perform pairwise comparisons.

simple

Character vector of factors to hold constant for simple main effects.

adjust

Character; p-value adjustment method (e.g., "bonferroni", "holm", "none").

protect

Logical; whether to use hierarchical (protected) testing.

Returns

A data frame containing the marginal means or contrasts.

---

Method .calc_contrast()

(Internal) Calculate metrics for a contrast.

Usage

Classic_Fit$.calc_contrast(L, specs)

Arguments

L

Contrast matrix.

specs

Variable names for lsmeans.

Returns

A data frame with estimate, SE, df, t-value, and p-value.

---

Method .get_lsmeans_df()

(Internal) Get representative DF for lsmeans.

Usage

Classic_Fit$.get_lsmeans_df(specs)

Arguments

specs

Variable names for lsmeans.

Returns

Degrees of freedom.

---

Method .construct_par_list()

(Internal) Construct a list of parameters from the fit.

Usage

Classic_Fit$.construct_par_list(fit)

Arguments

fit

The fit result (dataframe or lm object).

Returns

A named list of parameters.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

Classic_Fit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.