optim_model_space_params: Finds MLE parameters for each model in the given model space

In bdsm: Bayesian Dynamic Systems Modeling

Finds MLE parameters for each model in the given model space

Description

Given a dataset and an initial value for parameters, initializes a model space with parameters equal to the initial value for each model. Then for each model performs a numerical optimization and finds parameters which maximize the likelihood.

Usage

optim_model_space_params(
  df,
  timestamp_col,
  entity_col,
  dep_var_col,
  init_value,
  exact_value = FALSE,
  cl = NULL,
  control = list(trace = 0, maxit = 10000, fnscale = -1, REPORT = 100, scale = 0.05)
)

Arguments

df	Data frame with data for the analysis.
timestamp_col	The name of the column with time stamps.
entity_col	Column with entities (e.g. countries).
dep_var_col	Column with the dependent variable.
init_value	The value with which the model space will be initialized. This will be the starting point for the numerical optimization.
exact_value	Whether the exact value of the likelihood should be computed (TRUE) or just the proportional part (FALSE). Check sem_likelihood for details.
cl	An optional cluster object. If supplied, the function will use this cluster for parallel processing. If NULL (the default), pbapply::pblapply will run sequentially.
control	a list of control parameters for the optimization which are passed to optim. Default is list(trace = 2, maxit = 10000, fnscale = -1, REPORT = 100, scale = 0.05).

Value

List (or matrix) of parameters describing analyzed models.

bdsm documentation built on Sept. 9, 2025, 5:55 p.m.