## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set( fig.width = 5, fig.height = 3, dpi = 72 ) ## ----eval=FALSE--------------------------------------------------------------- # install.packages("badp") ## ----------------------------------------------------------------------------- library(badp) ## ----------------------------------------------------------------------------- economic_growth[1:12,1:10] ## ----------------------------------------------------------------------------- original_economic_growth[1:12,1:10] ## ----------------------------------------------------------------------------- economic_growth <- join_lagged_col( df = original_economic_growth, col = gdp, col_lagged = lag_gdp, timestamp_col = year, entity_col = country, timestep = 10 ) ## ----------------------------------------------------------------------------- data_standardized_features <- feature_standardization( df = economic_growth, excluded_cols = c(country, year, gdp) ) ## ----------------------------------------------------------------------------- data_prepared <- feature_standardization( df = data_standardized_features, group_by_col = year, excluded_cols = country, scale = FALSE ) ## ----eval=FALSE--------------------------------------------------------------- # full_model_space <- optim_model_space( # df = data_prepared, # dep_var_col = gdp, # timestamp_col = year, # entity_col = country, # init_value = function(n) rep(0.5, n) # ) ## ----------------------------------------------------------------------------- n_models(full_model_space) regressors(full_model_space) ## ----eval=FALSE--------------------------------------------------------------- # model_space_nonnested <- optim_model_space( # df = data_prepared, # dep_var_col = gdp, # timestamp_col = year, # entity_col = country, # init_value = function(n) rep(0.5, n), # nested = FALSE # ) ## ----------------------------------------------------------------------------- convergence(full_model_space)[, 1:5] ## ----------------------------------------------------------------------------- full_model_space$params[1:10, 1:5] ## ----------------------------------------------------------------------------- full_model_space$stats[, 1:5] ## ----eval=FALSE--------------------------------------------------------------- # model_space <- optim_model_space( # df = data_prepared, # dep_var_col = gdp, # timestamp_col = year, # entity_col = country, # init_value = function(n) rep(0.5, n) # ) ## ----eval=FALSE--------------------------------------------------------------- # library(parallel) # # Leave one core free so the session remains responsive. # cores <- max(1, detectCores() - 1) # cl <- makeCluster(cores) # # Only needed when init_value draws at random; set.seed() does not reach # # the workers, where the per-model optimization runs. # clusterSetRNGStream(cl, 20240812) ## ----eval=FALSE--------------------------------------------------------------- # model_space <- optim_model_space( # df = data_prepared, # dep_var_col = gdp, # timestamp_col = year, # entity_col = country, # init_value = function(n) rep(0.5, n), # cl = cl # ) # # stopCluster(cl) ## ----------------------------------------------------------------------------- bma_results <- bma(full_model_space, round = 3) ## ----eval = FALSE------------------------------------------------------------- # bma_results_mb2012 <- bma(full_model_space, round = 3, weighting = "mb2012") ## ----eval = FALSE------------------------------------------------------------- # bma_results_eta <- bma(full_model_space, round = 3, eta = 0.1) ## ----------------------------------------------------------------------------- n_models(bma_results) weighting(bma_results) learning_rate(bma_results) ## ----------------------------------------------------------------------------- bma_table(bma_results) ## ----------------------------------------------------------------------------- sort(pip(bma_results), decreasing = TRUE) ## ----------------------------------------------------------------------------- bma_table(bma_results, prior = "beta") ## ----------------------------------------------------------------------------- model_size_table(bma_results) ## ----------------------------------------------------------------------------- sum(pmp(bma_results)) pmp(bma_results, top = 5) ## ----fig=TRUE----------------------------------------------------------------- model_pmp(bma_results) ## ----fig=TRUE----------------------------------------------------------------- model_pmp(bma_results, top = 16) ## ----fig=TRUE----------------------------------------------------------------- model_pmp(bma_results, top = 3, type = "histogram") ## ----fig=TRUE----------------------------------------------------------------- model_sizes(bma_results) ## ----------------------------------------------------------------------------- best_8_models <- best_models(bma_results, prior = "binomial", best = 8) best_8_models ## ----------------------------------------------------------------------------- best_3_models <- best_models(bma_results, prior = "beta", best = 3) summary(best_3_models) ## ----fig=TRUE----------------------------------------------------------------- plot(best_3_models, robust = TRUE) ## ----------------------------------------------------------------------------- best_3_models[[1]] ## ----------------------------------------------------------------------------- coef(best_3_models[[1]], se = TRUE) ## ----------------------------------------------------------------------------- jointness(bma_results) ## ----------------------------------------------------------------------------- jointness(bma_results, measure = "LS") ## ----------------------------------------------------------------------------- jointness(bma_results, measure = "DW") ## ----fig=TRUE----------------------------------------------------------------- coef_plots <- coef_hist(bma_results) coef_plots[["gdp_lag"]] ## ----fig=TRUE----------------------------------------------------------------- coef_plots2 <- coef_hist(bma_results, use_kernel = 1) coef_plots2[["gdp_lag"]] ## ----fig=TRUE----------------------------------------------------------------- library(gridExtra) grid.arrange(coef_plots[["gdp_lag"]], coef_plots[["ish"]], coef_plots2[["gdp_lag"]], coef_plots2[["ish"]], nrow = 2, ncol = 2) ## ----fig=TRUE----------------------------------------------------------------- coef_plots3 <- coef_hist(bma_results, weight = "beta") coef_plots3[["gdp_lag"]] ## ----fig=TRUE----------------------------------------------------------------- distPlots <- posterior_dens(bma_results, prior = "binomial", SE = "standard") grid.arrange(distPlots[["ish"]], distPlots[["sed"]], nrow = 2, ncol = 1) ## ----------------------------------------------------------------------------- bma_results2 <- bma(full_model_space, round = 3, EMS = 2) ## ----------------------------------------------------------------------------- model_size_table(bma_results2) ## ----fig=TRUE----------------------------------------------------------------- model_sizes(bma_results2) ## ----fig=TRUE----------------------------------------------------------------- model_pmp(bma_results2) ## ----------------------------------------------------------------------------- bma_table(bma_results2) ## ----------------------------------------------------------------------------- bma_table(bma_results2, prior = "beta") ## ----------------------------------------------------------------------------- jointness(bma_results2, measure = "HCGHM", rho = 0.5, round = 3) ## ----------------------------------------------------------------------------- bma_results8 <- bma(full_model_space, round = 3, EMS = 8) model_size_table(bma_results8) ## ----fig=TRUE----------------------------------------------------------------- model_sizes(bma_results8) ## ----fig=TRUE----------------------------------------------------------------- model_pmp(bma_results8) ## ----------------------------------------------------------------------------- bma_table(bma_results8) ## ----------------------------------------------------------------------------- bma_table(bma_results8, prior = "beta") ## ----------------------------------------------------------------------------- jointness(bma_results8, measure = "HCGHM", rho = 0.5, round = 3) ## ----------------------------------------------------------------------------- bma_results_dil <- bma( model_space = full_model_space, round = 3, dilution = 1 ) ## ----fig=TRUE----------------------------------------------------------------- model_sizes(bma_results_dil) ## ----fig=TRUE----------------------------------------------------------------- bma_results_dil01 <- bma( model_space = full_model_space, round = 3, dilution = 1, omega = 0.1 ) model_sizes(bma_results_dil01) ## ----fig=TRUE----------------------------------------------------------------- bma_results_dil2 <- bma( model_space = full_model_space, round = 3, dilution = 1, omega = 2 ) model_sizes(bma_results_dil2) ## ----------------------------------------------------------------------------- bma_table(bma_results_dil2, prior = "beta")