new_ergmito_ptr {ergmito}

Creates a new ergmito_ptr

Description

After calculating the support of the sufficient statistics, the second most computationally expensive task is computing log-likelihoods, Gradients, and Hessian matrices of ERGMs. This function creates a pointer to an underlying class that is optimized to improve memory allocation and save computation time when possible.

Usage

new_ergmito_ptr(
  target_stats,
  stats_weights,
  stats_statmat,
  target_offset,
  stats_offset
)

Arguments

target_stats, stats_weights, stats_statmat, target_offset, stats_offset

see exact_loglik.

Details

This function is for internal used only. Non-advanced users are not encouraged to use it. See ergmito_formulae and exact_loglik for user friendly wrappers of this function.

Recycling computations

Some components of the likelihood, its gradient, and hessian can be pre-computed and recycled when needed. For example, it is usually the case that in optimization gradients are computed using a current state of the model's parameter, which implies that the normalizing constant and some other matrix products will be the same between the log-likelihood and the gradient. Because of this, the underlying class ergmito_ptr will only re-calculate these shared components if the parameter used changes as well. This saves a significant amount of computation time.

Scope of the class methods

To save space, the class creates pointers to the matrices of sufficient statistics that the model uses. This means that once these objects are deleted the log-likelihood and the gradient functions become invalid from the computational point of view.