# Export SVEM candidate sets to CSV Given one or more selection objects returned by `svem_select_from_score_table`, concatenate their `$best` rows and `$candidates` and export a CSV suitable for planning new experimental runs. Each row is tagged with: * `candidate_type`: `"best"` or `"medoid"`. * `selection_label`: derived from the `label` argument used in `svem_select_from_score_table()` when available. The function does not modify any response or prediction columns (for example, `Potency`, `Potency_pred`); it simply harmonizes columns across inputs (adding `NA`-filled columns where necessary), concatenates rows, and reorders a few metadata columns for readability. Any columns named `candidate_type`, `selection_label`, or `Notes_from_SVEMnet` that are present in the final data frame are moved to the leftmost positions in that order. ```r svem_export_candidates_csv( ..., file = NULL, overwrite = FALSE, write_file = TRUE ) ``` ## Arguments - `...`: One or more objects returned by `svem_select_from_score_table`. You may also pass a single list of such objects. - `file`: Character scalar; path to the CSV file to be written. Required only when `write_file = TRUE`. - `overwrite`: Logical; if `FALSE` (default) and `file` already exists, an error is thrown. If `TRUE`, any existing file at `file` is overwritten. Only used when `write_file = TRUE`. - `write_file`: Logical; if `TRUE` (default), write the combined table to `file` as CSV and print the full path. If `FALSE`, no file is written and `file` may be `NULL`; the concatenated `data.frame` is still returned (invisibly). ## Returns Invisibly, the `data.frame` that was written to CSV (or would be written, when `write_file = FALSE`). ## Examples ```r # 1) Load example data data(lipid_screen) # 2) Build a deterministic expansion using bigexp_terms() spec <- bigexp_terms( Potency ~ PEG + Helper + Ionizable + Cholesterol + Ionizable_Lipid_Type + N_P_ratio + flow_rate, data = lipid_screen, factorial_order = 3, # up to 3-way interactions polynomial_order = 3, # include up to cubic terms I(X^2), I(X^3) include_pc_2way = TRUE, include_pc_3way = FALSE ) # 3) Shared deterministic expansion for all three responses form_pot <- bigexp_formula(spec, "Potency") form_siz <- bigexp_formula(spec, "Size") form_pdi <- bigexp_formula(spec, "PDI") # 4) Fit SVEM models set.seed(1) fit_pot <- SVEMnet(form_pot, lipid_screen) fit_siz <- SVEMnet(form_siz, lipid_screen) fit_pdi <- SVEMnet(form_pdi, lipid_screen) objs <- list(Potency = fit_pot, Size = fit_siz, PDI = fit_pdi) # 5) Multi-response goals (DS desirabilities under the hood) goals <- list( Potency = list(goal = "max", weight = 0.6), Size = list(goal = "min", weight = 0.3), PDI = list(goal = "min", weight = 0.1) ) # 6) Mixture constraints on the four lipid components mix <- list(list( vars = c("PEG", "Helper", "Ionizable", "Cholesterol"), lower = c(0.01, 0.10, 0.10, 0.10), upper = c(0.05, 0.60, 0.60, 0.60), total = 1.0 )) # 7) Optional process-mean specifications for a design-space example specs_ds <- list( Potency = list(lower = 78), Size = list(upper = 100), PDI = list(upper = 0.25) ) # 8) Random-search scoring (predictions stored in *_pred columns) set.seed(3) scored <- svem_score_random( objects = objs, goals = goals, data = lipid_screen, n = 2500, mixture_groups = mix, level = 0.95, combine = "geom", numeric_sampler = "random", specs = specs_ds, verbose = FALSE ) # 9) Build several selection objects from the scored table # High-score optimal medoids (user-weighted score) opt_sel <- svem_select_from_score_table( score_table = scored$score_table, target = "score", direction = "max", k = 5, top_type = "frac", top = 0.02, label = "round1_score_optimal" ) # High-uncertainty exploration medoids explore_sel <- svem_select_from_score_table( score_table = scored$score_table, target = "uncertainty_measure", direction = "max", k = 5, top_type = "frac", top = 0.05, label = "round1_explore" ) # High joint mean-in-spec medoids (design-space view) inspec_sel <- svem_select_from_score_table( score_table = scored$score_table, target = "p_joint_mean", direction = "max", k = 5, top_type = "frac", top = 0.10, label = "round1_inspec" ) # Best existing screened run (from original_data_scored; k <= 0 -> no medoids) best_existing <- svem_select_from_score_table( score_table = scored$original_data_scored, target = "score", direction = "max", k = 0, top_type = "frac", top = 1.0, label = "round1_existing_best" ) # 10) Combine all selection objects in a single list candidate_sels <- list( opt_sel, explore_sel, inspec_sel, best_existing ) # 11a) Export all candidates to CSV for the next experimental round # svem_export_candidates_csv( # candidate_sels, # file = "lipid_screen_round1_candidates.csv", # overwrite = FALSE, # write_file = TRUE # ) # 11b) Or inspect the combined table in-memory without writing a file cand_tbl <- svem_export_candidates_csv( candidate_sels, write_file = FALSE ) head(cand_tbl) # 11c) Alternatively, pass selection objects directly as separate arguments cand_tbl2 <- svem_export_candidates_csv( opt_sel, explore_sel, inspec_sel, best_existing, write_file = FALSE ) head(cand_tbl2) ```

svem_export_candidates_csv function