bdsm (version 0.3.0)

optim_model_space: Calculation of the model_space object

Description

This function calculates model space, values of the maximized likelihood function, BICs, and standard deviations of the parameters that will be used in Bayesian model averaging.

Usage

optim_model_space(
  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)
)

Value

List with two objects:

1. params - table with parameters of all estimated models
   

2. stats - table with the value of maximized likelihood function, BIC, and standard errors for all estimated models

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), but note that scale is used only for adjusting the parscale element added later in the function code.

Examples

if (FALSE) {
library(magrittr)

data_prepared <- bdsm::economic_growth[, 1:5] %>%
  bdsm::feature_standardization(
    excluded_cols = c(country, year, gdp)
  ) %>%
  bdsm::feature_standardization(
    group_by_col  = year,
    excluded_cols = country,
    scale         = FALSE
  )

optim_model_space(
  df            = data_prepared,
  dep_var_col   = gdp,
  timestamp_col = year,
  entity_col    = country,
  init_value    = 0.5
)
}