## ----------------------------------------------------------------------------- library(spfcICOMP) ## ----------------------------------------------------------------------------- set.seed(123) sim <- simulate_spfc_continuous( n = 100, p = 50, d = 1, s = 5, rho_x = 0.5, snr = 2 ) X <- sim$X y <- sim$y ## ----------------------------------------------------------------------------- fit <- spfc_fit( X = X, y = y, d = 1, ytype = "continuous", cov_method = "mec", nslices = 5, poly_degree = 2 ) fit summary(fit) ## ----------------------------------------------------------------------------- coef(fit) ## ----------------------------------------------------------------------------- scores <- fitted(fit) head(scores) ## ----------------------------------------------------------------------------- predict( fit, newdata = X[1:5, ] ) ## ----------------------------------------------------------------------------- dsel <- spfc_select_dimension( X = X, y = y, d_grid = 1:3, cov_method = "mec", ytype = "continuous" ) dsel$criteria dsel$selected ## ----------------------------------------------------------------------------- reduced_model <- fit_reduced_model( Z = scores, y = y, ytype = "continuous" ) vsel <- spfc_select_variables( fit = fit, method = "adaptive_weighted_l1", selection_rule = "c1f", reduced_model = reduced_model ) head(vsel) ## ----------------------------------------------------------------------------- bench <- benchmark_spfc( X = X, y = y, d = 1, methods = c( "mec", "oas", "sre", "sde", "cse" ), verbose = FALSE ) bench summary(bench) ## ----------------------------------------------------------------------------- results <- run_spfc_simulation( response_type = "continuous", nrep = 5, n = 100, p = 50, d = 1, s = 5, rho_x = 0.5, snr = 2, cov_methods = c( "mec", "oas" ) ) summary_results <- summarise_spfc_simulation( results ) summary_results ## ----------------------------------------------------------------------------- plot_rmse_by_covariance(results) plot_runtime_by_covariance(results) plot_subspace_distance_by_covariance(results)