| Adapts the object that contains PIC for all candidate C's and all subsamples with sigma2_max_model. | adapt_pic_with_sigma2maxmodel |
| When running the algorithm with a different number of observable variables then the number that is available, reformat X. (Mainly used for testing) | adapt_X_estimating_less_variables |
| Adds the current configuration (number of groups and factors) to df_results. | add_configuration |
| Adds several metrics to df_results. | add_metrics |
| Fills in df_pic: adds a row with the calculated PIC for the current configuration. | add_pic |
| Calculates the PIC for the current configuration. | add_pic_parallel |
| Helpfunction in create_true_beta() for the option beta_true_heterogeneous_groups. (This is the default option.) | beta_true_heterogroups |
| Function that returns for each candidate C the best number of groups and factors, based on the PIC. | calculate_best_config |
| Calculates the error term Y - X*beta_est - LF - LgFg. | calculate_error_term |
| Helpfunction for update_g(). Calculates the errors for one of the possible groups time series can be placed in. | calculate_errors_virtual_groups |
| Returns the estimated groupfactorstructure. | calculate_FL_group_estimated |
| Calculate the true groupfactorstructure. | calculate_FL_group_true |
| calculates factor loadings of common factors | calculate_lambda |
| calculates factor loadings of groupfactors | calculate_lambda_group |
| Calculates the group factor structure: the matrix product of the group factors and their loadings. | calculate_lgfg |
| Calculates objective function for individual i and group k in order to estimate group membership. | calculate_obj_for_g |
| Function to determine PIC (panel information criterium) | calculate_PIC |
| Function to calculate the first term of PIC (panel information criterium) | calculate_PIC_term1 |
| Calculates sum of squared errors, divided by NT | calculate_sigma2 |
| Calculates sigma2maxmodel | calculate_sigma2maxmodel |
| Helpfunction. Calculates part of the 4th term of the PIC. | calculate_TN_factor |
| Calculates VC², to determine the stability of the found number of groups and factors over the subsamples. | calculate_VCsquared |
| Helpfunction used in update_g() | calculate_virtual_factor_and_lambda_group |
| Calculates W = Y - X*beta_est. It is used in the initialization step of the algorithm, to initialise the factorstructures. | calculate_W |
| Calculates (the estimated value of) the matrix X*beta_est. | calculate_XB_estimated |
| Calculates the product of X*beta_true . | calculate_XB_true |
| Calculates Z = Y - X*beta_est - LgFg. It is used in the estimate of the common factorstructure. | calculate_Z_common |
| Calculates Z = Y - X*beta_est - LF. It is used to estimate the groupfactorstructure. | calculate_Z_group |
| Checks the rules for stopping the algorithm, based on its convergence speed. | check_stopping_rules |
| Function that puts individuals in a separate "class zero", when their distance to all possible groups is bigger then a certain threshold. | clustering_with_robust_distances |
| Function used in generating simulated data with non normal errors. | create_covMat_crosssectional_dependence |
| Creates an instance of DGP 2, as defined in Boudt and Heyndels (2022). | create_data_dgp2 |
| Creates beta_true, which contains the true values of beta (= the coefficients of X) | create_true_beta |
| Defines the candidate values for C. | define_C_candidates |
| Constructs dataframe where the rows contains all configurations that are included and for which the estimators will be estimated. | define_configurations |
| Defines the set of combinations of group specific factors. | define_kg_candidates |
| Returns a vector with the indices of the subsets. Must start with zero. | define_number_subsets |
| Defines the object that will be used to define a initial clustering. | define_object_for_initial_clustering_macropca |
| Determines parameters of rho-function. | define_rho_parameters |
| Helpfunction in estimate_beta() for estimating beta_est. | determine_beta |
| Help-function for return_robust_lambdaobject(). | determine_robust_lambda |
| An example for df_results. This dataframe contains the estimators for each configuration. | df_results_example |
| Helpfunction to shorten code: are common factors being estimated. | do_we_estimate_common_factors |
| Helpfunction to shorten code: are group factors being estimated. | do_we_estimate_group_factors |
| This function is a wrapper around the initialization and the estimation part of the algorithm, for one configuration. It is only used for the serialized algorithm. | estimate_algorithm |
| Estimates beta. | estimate_beta |
| Estimates common factor(s) F. | estimate_factor |
| Estimates group factors Fg. | estimate_factor_group |
| Solves a very specific issue with MacroPCA. | evade_crashes_macropca |
| Function to evade floating point errors. | evade_floating_point_errors |
| factor_group_true_dgp3 contains the values of the true group factors on which Y_dgp3 is based | factor_group_true_dgp3 |
| Fills in the optimized number of common factors for each C. | fill_rc |
| Fills in the optimized number of groups and group specific factors for each C. | fill_rcj |
| Filters dataframe on the requested group specific factors configuration. | final_estimations_filter_kg |
| g_true_dgp3 contains the true group memberships of the elements of Y_dgp3 | g_true_dgp3 |
| Generates the true groupfactorstructure, to use in simulations. | generate_grouped_factorstructure |
| Generate panel data Y for simulations. | generate_Y |
| Finds the first stable interval after the first unstable point. It then defines the value for C for the begin, middle and end of this interval. | get_best_configuration |
| Defines the convergence speed. | get_convergence_speed |
| Function that returns the final clustering, based on the estimated number of groups and common and group specific factors. | get_final_estimation |
| Function which is used to have a dataframe (called "grid") with data (individualindex, timeindex, XT and LF) available. | grid_add_variables |
| Helpfunction in robustpca(). | handle_macropca_errors |
| Function with as input a dataframe. (this will be "Y" or "to_divide") It filters out rows with NA. | handleNA |
| Removes NA's in LG (in function calculate_virtual_factor_and_lambda_group() ) | handleNA_LG |
| Initialisation of estimation of beta (the coefficients with the observable variables) | initialise_beta |
| Function that clusters time series in a dataframe with kmeans. | initialise_clustering |
| Initialises the estimation of the common factors and their loadings. | initialise_commonfactorstructure_macropca |
| Initialises a dataframe which will contain the PIC for each configuration and for each value of C. | initialise_df_pic |
| Initialises a dataframe that will contain an overview of metrics for each estimated configuration (for example adjusted randindex). | initialise_df_results |
| Initialises rc. | initialise_rc |
| Initialises rcj. | initialise_rcj |
| Creates X (the observable variables) to use in simulations. | initialise_X |
| Wrapper around estimate_beta(), update_g(), and estimating the factorstructures. | iterate |
| Function that returns the set of combinations of groupfactors for which the algorithm needs to run. | kg_candidates_expand |
| lambda_group_true_dgp3 contains the values of the loadings to the group factors on which Y_dgp3 is based | lambda_group_true_dgp3 |
| Wrapper around lmrob. | LMROB |
| Makes a dataframe with the PIC for each configuration and each candidate C. | make_df_pic_parallel |
| Makes a dataframe with information on each configuration. | make_df_results_parallel |
| Selects a subsample of the time series, and of the length of the time series. Based on this it returns a list with a subsample of Y, the corresponding subsample of X and of the true group membership and factorstructures if applicable. | make_subsamples |
| Function to calculate the norm of a matrix. | matrixnorm |
| Helpfunction in OF_vectorized3() | OF_vectorized_helpfunction3 |
| Calculates objective function for the classical algorithm: used in iterate() and in local_search. | OF_vectorized3 |
| Wrapper of the loop over the subsets which in turn use the parallelised algorithm. | parallel_algorithm |
| Plots expression(VC^2) along with the corresponding number of groups (orange), common factors (darkblue) and group factors of the first group (lightblue). | plot_VCsquared |
| Helpfunction: prepares object to perform robust PCA on. | prepare_for_robpca |
| RCTS | RCTS |
| Randomly reassign individual(s) if there are empty groups. This can happen if the total number of time series is low compared to the number of desired groups. | reassign_if_empty_groups |
| Restructures X (which is an 3D-array of dimensions (N,T,p) to a 2D-matrix of dimension (NxT,p). | restructure_X_to_order_slowN_fastT |
| Calculates robust loadings | return_robust_lambdaobject |
| Function that uses robust PCA and estimates robust factors and loadings. | robustpca |
| Wrapper around the non-parallel algorithm, to estimate beta, group membership and the factorstructures. | run_config |
| Scaling of X. | scaling_X |
| Helpfunction in update_g(), to calculate solve(FG x t(FG)) x FG | solveFG |
| Shows the configurations for potential C's of the first stable interval (beginpoint, middlepoint and endpoint) | tabulate_potential_C |
| Function that estimates group membership. | update_g |
| The dataset X_dgp3 contains the values of the 3 observable variables on which Y_dgp3 is based. | X_dgp3 |
| Y_dgp3 contains a simulated dataset for DGP 3. | Y_dgp3 |
