likelihoods_summary: Approximate standard deviations for the models

In bdsm: Bayesian Dynamic Systems Modeling

Approximate standard deviations for the models

Description

Approximate standard deviations are computed for the models in the given model space. Two versions are computed.

Usage

likelihoods_summary(
  df,
  dep_var_col,
  timestamp_col,
  entity_col,
  model_space,
  exact_value = TRUE,
  model_prior = "uniform",
  run_parallel = FALSE
)

Arguments

df	Data frame with data for the SEM analysis.
dep_var_col	Column with the dependent variable
timestamp_col	The name of the column with timestamps
entity_col	Column with entities (e.g. countries)
model_space	A matrix (with named rows) with each column corresponding to a model. Each column specifies model parameters. Compare with optimal_model_space
exact_value	Whether the exact value of the likelihood should be computed (TRUE) or just the proportional part (FALSE). Check SEM_likelihood for details.
model_prior	Which model prior to use. For now there are two options: 'uniform' and 'binomial-beta'. Default is 'uniform'.
run_parallel	If TRUE the optimization is run in parallel using the parApply function. If FALSE (default value) the base apply function is used. Note that using the parallel computing requires setting the default cluster. See README.

Value

Matrix with columns describing likelihood and standard deviations for each model. The first row is the likelihood for the model (computed using the parameters in the provided model space). The second row is almost 1/2 * BIC_k as in Raftery's Bayesian Model Selection in Social Research eq. 19 (see TODO in the code below). The third row is model posterior probability. Then there are rows with standard deviations for each parameter. After that we have rows with robust standard deviation (not sure yet what exactly "robust" means).

Examples

  data_centered_scaled <-
    feature_standardization(df = bdsm::economic_growth[,1:7],
                            timestamp_col = year, entity_col = country)
  data_cross_sectional_standarized <-
    feature_standardization(df = data_centered_scaled, timestamp_col = year,
                            entity_col = country, time_effects = TRUE,
                            scale = FALSE)

    likelihoods_summary(df = data_cross_sectional_standarized,
                        dep_var_col = gdp, timestamp_col = year,
                        entity_col = country, model_space = economic_growth_ms)

bdsm documentation built on April 4, 2025, 1:06 a.m.