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This helper does some simple post-processing of the TMB object and optimization results, including setting names, inverting matrices etc.


h_mmrm_tmb_fit(tmb_object, tmb_opt, formula_parts, tmb_data)



created with TMB::MakeADFun().


optimization result.


produced by h_mmrm_tmb_formula_parts().


produced by h_mmrm_tmb_data().


List of class mmrm_tmb with:

  • cov: estimated covariance matrix, or named list of estimated group specific covariance matrices.

  • beta_est: vector of coefficient estimates.

  • beta_vcov: Variance-covariance matrix for coefficient estimates.

  • beta_vcov_inv_L: Lower triangular matrix L of the inverse variance-covariance matrix decomposition.

  • beta_vcov_inv_D: vector of diagonal matrix D of the inverse variance-covariance matrix decomposition.

  • theta_est: vector of variance parameter estimates.

  • theta_vcov: variance-covariance matrix for variance parameter estimates.

  • neg_log_lik: obtained negative log-likelihood.

  • formula_parts: input.

  • data: input.

  • weights: input.

  • reml: input as a flag.

  • opt_details: list with optimization details including convergence code.

  • tmb_object: original TMB object created with TMB::MakeADFun().

  • tmb_data: input.


Instead of inverting or decomposing beta_vcov, it can be more efficient to use its robust Cholesky decomposition LDL^T, therefore we return the corresponding two components L and D as well since they have been available on the C++ side already.