model_pmp: Graphs of the prior and posterior model probabilities for the...

In bdsm: Bayesian Dynamic Systems Modeling

Graphs of the prior and posterior model probabilities for the best individual models

Description

This function draws four graphs of prior and posterior model probabilities for the best individual models:
a) The results with binomial model prior (based on PMP - posterior model probability)
b) The results with binomial-beta model prior (based on PMP - posterior model probability)
Models on the graph are ordered according to their posterior model probability.

Arguments

bma_list	bma_list object (the result of the bma function)
top	The number of the best model to be placed on the graphs

Value

A list with three graphs with prior and posterior model probabilities for individual models:

1. The results with binomial model prior (based on PMP - posterior model probability)
   

2. The results with binomial-beta model prior (based on PMP - posterior model probability)
   

3. On graph combining the aforementioned graphs

Examples

library(magrittr)

data_prepared <- economic_growth[,1:7] %>%
   feature_standardization(timestamp_col = year, entity_col = country) %>%
   feature_standardization(timestamp_col = year, entity_col = country,
                           time_effects = TRUE, scale = FALSE)

model_space <- optimal_model_space(df = data_prepared, dep_var_col = gdp,
                                   timestamp_col = year, entity_col = country,
                                   init_value = 0.5)

bma_results <- bma(df = data_prepared, dep_var_col = gdp, timestamp_col = year,
entity_col = country, model_space = model_space, run_parallel = FALSE, dilution = 0)

model_graphs <- model_pmp(bma_results, top = 16)

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