DataMinerXL software includes the most useful predictive modeling functions.
Here is the list of functions organized in terms of the following categories.
The detailed descriptions of all functions, such as function arguments and return values,
can be found in the manual of DataMinerXL on the Downloads page.
You can find the theories and algorithms behind these functions in our book
"Foundations of Predictive Analytics."
| Function Name | Description |
|---|
| freq | Creates frequency tables given a data table |
| freq_from_file | Creates frequency tables given a data file |
| freq_2d | Creates a frequency cross-table for two variables given a data table |
| freq_2d_from_file | Creates a frequency cross-table for two variables given a data file |
| means | Generates basic statistics: sum, average, standard deviation, minimum, and maximum given a data table |
| means_from_file | Generates basic statistics: sum, average, standard deviation, minimum, and maximum given a data file |
| univariate | Generates univariate statistics given a data table |
| univariate_from_file | Generates univariate statistics given a data file |
| percentiles | Calculates p-th percentiles of values in each subgroup |
| summary | Generates descriptive statistics in classes given a data table |
| summary_from_file | Generates descriptive statistics in classes given a data file |
| ranks | Creates 1-based ranks of data points given a column of data |
| ranks_from_file | Creates 1-based ranks of data points given a data file |
| binning | Creates equal interval binning given a column of data table |
| QQ_plot | Tests normality of a univariate sample |
| variable_corr_select | Selects variables by removing highly correlated variables |
| poly_roots | Finds all roots given real coefficients of a polynomial |
| Lagrange_interpolation | Performs Lagrange polynomial interpolation given data points |
| three_moment_match_to_SLN | Performs three moment match to a shifted lognormal distribution |
| set | Creates a set given a string/number matrix |
| set_union | Creates a set from union of two sets |
| set_intersection | Creates a set from intersection of two sets |
| set_difference | Creates a set from difference of two sets |
| Function Name | Description |
|---|
| model_bin_eval | Evaluates a binary target model given a column of actual values and a column of predicted values |
| model_bin_eval_from_file | Evaluates a binary target model given a data file, a name of actual values, and a name of predicted values |
| model_cont_eval | Evaluates a continuous target model given a column of actual values and a column of predicted values |
| model_cont_eval_from_file | Evaluates a continuous target model given a data file, a name of actual values, and a name of predicted values |
| model_eval | Evaluates model performance given a model and a data table |
| model_eval_from_file | Evaluates model performance given a model and a data file |
| model_score | Scores a population given a model and a data table |
| model_score_from_file | Scores a population given a model and a data file |
| model_save_scoring_code | Saves the scoring code of a given model to a file |
| Function Name | Description |
|---|
| woe_xcont_ybin | Generates weight of evidence (WOE) of continous independent variables and a binary dependent variable given a data table |
| woe_xcont_ybin_from_file | Generates weight of evidence (WOE) of continous independent variables and a binary dependent variable given a data file |
| woe_xcont_ycont | Generates weight of evidence (WOE) of continous independent variables and a continous dependent variable given a data table |
| woe_xcont_ycont_from_file | Generates weight of evidence (WOE) of continous independent variables and a continous dependent variable given a data file |
| woe_xcat_ybin | Generates weight of evidence (WOE) of categorical independent variables and a binary dependent variable given a data table |
| woe_xcat_ybin_from_file | Generates weight of evidence (WOE) of categorical independent variables and a binary dependent variable given a data file |
| woe_xcat_ycont | Generates weight of evidence (WOE) of categorical independent variables and a continous dependent variable given a data table |
| woe_xcat_ycont_from_file | Generates weight of evidence (WOE) of categorical independent variables and a continous dependent variable given a data file |
| woe_transform | Performs weight of evidence (WOE) transformation given aWOE model and a data table |
| woe_transform_from_file | Performs weight of evidence (WOE) transformation given a WOE model and a data file |
| Function Name | Description |
|---|
| linear_reg | Builds a linear regression model given a data table |
| linear_reg_from_file | Builds a linear regression model given a data file |
| linear_reg_forward_select | Builds a linear regression model by forward selection given a data table |
| linear_reg_forward_select_from_file | Builds a linear regression model by forward selection given a data file |
| linear_reg_score_from_coefs | Scores a population from the coefficients of a linear regression model given a data table |
| linear_reg_piecewise | Builds a two-segment piecewise linear regression model for each variable given a data table |
| linear_reg_piecewise_from_file | Builds a two-segment piecewise linear regression model for each variable given a data file |
| poly_reg | Builds a polynomial regression model given a data table |
| Function Name | Description |
|---|
| logistic_reg | Builds a logistic regression model given a data table |
| logistic_reg_from_file | Builds a logistic regression model given a data file |
| logistic_reg_forward_select | Builds a logistic regression model by forward selection given a data table |
| logistic_reg_forward_select_from_file | Builds a logistic regression model by forward selection given a data file |
| logistic_reg_score_from_coefs | Scores a population from the coefficients of a logistic regression model given a data table |
| Function Name | Description |
|---|
| ts_acf | Calculates the autocorrelation functions (ACF) given a data table |
| ts_pacf | Calculates the partial autocorrelation functions (PACF) given a data table |
| ts_ccf | Calculates the cross correlation functions (CCF) given two data tables |
| Box_white_noise_test | Tests if a time series is a white noise by Box-Ljung or Box-Pierce test |
| Mann_Kendall_trend_test | Tests if a time series has a trend |
| ADF_test | Tests whether a unit root is in a time series using Augmented Dickey-Fuller (ADF) test |
| ts_diff | Calculates the differences given lag and order |
| ts_sma | Calculates the simple moving average (SMA) of a time series |
| lowess | Performs locally weighted scatterplot smoothing (lowess) |
| natural_cubic_spline | Performs natural cubic spline |
| garch | Estimates the parameters of GARCH(1, 1) model |
| stochastic_process | Estimates the parameters of a stochastic process: normal, lognormal, or shifted lognormal |
| stochastic_process_simulate | Simulates a stochastic process: normal, lognormal, or shifted lognormal |
| Holt_Winters | Performs Holt-Winters exponential smoothing |
| Holt_Winters_forecast | Performs forecast given Holt-Winters exponential smoothing |
| HP_filter | Performs the Hodrick-Prescott filter for a time-series data |
| arima | Builds an ARIMA model |
| sarima | Builds a seasonal ARIMA (SARIMA) model |
| arima_forecast | Performs forecast given an ARIMA model |
| sarima_forecast | Performs forecast given a seasonal ARIMA (SARIMA) model |
| arima_simulate | Simulates an ARIMA process |
| sarima_simulate | Simulates a seasonal ARIMA (SARIMA) process |
| arma_to_ma | Converts an ARMA process to a pure A process |
| arma_to_ar | Converts an ARMA process to a pure AR process |
| acf_of_arma | Calculates the autocorrealtion functions (ACF) of an ARMA process |
| Function Name | Description |
|---|
| tree | Builds a regression or classification tree model given a data table |
| tree_from_file | Builds a regression or classification tree model given a data file |
| tree_logistic_reg_boosting | Builds a logistic regression boosting tree model given a data table |
| tree_logistic_reg_boosting_from_file | Builds a logistic regression boosting tree model given a data file |
| tree_ls_reg_boosting | Builds a least square boosting tree model given a data table |
| tree_ls_reg_boosting_from_file | Builds a least square boosting tree model given a data file |
| Function Name | Description |
|---|
| linear_prog | Solves a linear programming problem |
| quadratic_prog | Solves a quadratic programming problem |
| lcp | Solves a linear complementarity programming problem |
| nls_solver | Solves a nonlinear least-square problem using the Levenberg-Marquardt algorithm |
| diff_evol_solver | Solves a minimization problem given a function and lower/upper bounds of variables using differential evolution solver |
| transportation_solver | Solves a transportation problem |
| assignment_solver | Solves an assignment problem |
| netflow_solver | Solves a minimum or maximum cost network flow problem: to find optimal flows that minimize or maximize the total cost |
| maxflow_solver | Solves a maximum flow problem: to find optimal flows that maximize the total flows from the start node to the end node |
| shortest_path_solver | Solves the shortest path problem: to find the shortest path from the start node to the end node |
| Function Name | Description |
|---|
| matrix_random | Generates a random matrix from a uniform distibution U(0, 1) or a standard normal distribution N(0, 1) |
| matrix_cov | Computes the covariance matrix given a data table |
| matrix_cov_from_file | Computes the covariance matrix given a data file |
| matrix_corr | Computes the correlation matrix given a data table |
| matrix_corr_from_file | Computes the correlation matrix given a data file |
| matrix_corr_from_cov | Computes the correlation matrix from a covariance matrix |
| matrix_prod | Computes the product of two matrices, one matrix could be a number |
| matrix_directprod | Computes the direct product of two matrices |
| matrix_elementprod | Computes the elementwise product of two matrices, one matrix could be a number |
| matrix_plus | Computes the addition of two matrices with the same dimension |
| matrix_minus | Computes the subtraction of two matrices with the same dimension |
| matrix_I | Creates an identity matrix |
| matrix_t | Returns the transpose matrix of a matrix |
| matrix_diag | Creates a diagonal matrix from a matrix or a vector |
| matrix_tr | Returns the trace of a matrix |
| matrix_inv | Computes the inverse of a square matrix |
| matrix_pinv | Computes the pseudoinverse of a real matrix |
| matrix_complex_pinv | Computes the pseudoinverse of a complex matrix |
| matrix_solver | Solves a system of linear equations Ax = B |
| matrix_tridiagonal_solver | Solves a system of tridiagonal linear equations Ax = B |
| matrix_pentadiagonal_solver | Solves a system of pentadiagonal linear equations Ax = B |
| matrix_chol | Computes the Cholesky decomposition of a symmetric positive-definite matrix |
| matrix_sym_eigen | Computes the eigenvalue-eigenvector pairs of a symmetric matrix |
| matrix_eigen | Computes the eigenvalue-eigenvector pairs of a square real matrix |
| matrix_complex_eigen | Computes the eigenvalue-eigenvector pairs of a square complex matrix |
| matrix_svd | Computes the singular value decomposition (SVD) of a matrix |
| matrix_LU | Computes the LU decomposition of a square matrix |
| matrix_QR | Computes the QR decomposition of a square real matrix |
| matrix_complex_QR | Computes the QR decomposition of a square complex matrix |
| matrix_Schur | Computes the Schur decomposition a square real matrix |
| matrix_complex_Schur | Computes the Schur decomposition a square complex matrix |
| matrix_sweep | Sweeps a matrix given indexes |
| matrix_det | Computes the determinant of a square matrix |
| matrix_distance | Computes the distance matrix given a data table |
| matrix_freq | Creates a frequency table given a string matrix |
| matrix_from_vector | Converts a matrix from a vector |
| matrix_to_vector | Converts a matrix into a column vector |
| Function Name | Description |
|---|
| prob_normal | Computes the cumulative probability given z for the standard normal distribution: N(z) = Prob(Z < z) |
| prob_normal_inv | Computes the percentile of a standard normal distribution: Prob(Z < z) = p |
| prob_normal_table | Generates a table of the cumulative probabilities for the standard normal distribution: N(z) = Prob(Z < z) |
| prob_t | Computes the cumulative probability given t and the degree of freeom for the Student's t distribution: Prob(t_n < t) |
| prob_t_inv | Computes the percentile for the Student's t distribution: Prob(t_n < t) = p |
| prob_t_table | Generates a table of the percentiles given a set of degrees of freedom and a set of probabilites for the Student's t distribution: Prob(t_n < t) = P |
| prob_chi | Computes the cumulative probability given c and the degree of freeom for the Student's distribution: Prob(X^2 < c) |
| prob_chi_inv | Computes the percentile for the Chi-Squared distribution: Prob(X^2 < c) = p |
| prob_chi_table | Generates a table of the percentiles given a set of degrees of freedom and a set of probabilites for the Chi-Squared distribution: Prob(X^2 < c) = P |
| prob_f | Computes the cumulative probability given f and the degree of freeom for the F-distribution: Prob(F(df1, df2) < f) |
| prob_f_inv | Computes the percentile for the F-distribution: Prob(F(df1, df2) < f) = p |
| prob_f_table | Generates a table of the percentiles given a set of degrees of freedom and a probability for the F-distribution: Prob(F(df1, df2) < f) = p |
| Cornish_Fisher_expansion | Computes the percentile of a distribution with a skewness and an excess kurtosis by Cornish-Fisher expansion |
| Function Name | Description |
|---|
| variable_list | Lists the variable names in an input data file |
| subset | Gets a subset of a data table |
| data_lookup | Looks up data by matching multiple keys |
| data_save | Saves a data table into a file |
| data_save_tex | Saves a data table into a file in TEX format |
| data_load | Loads a data table from a file |
| data_partition | Gets random data partition |
| data_sort | Sorts a data table given keys and orders |
| sort_file | Sorts a data file given keys and orders |
| merge_tables | Merge two data tables by a single numerical key |
| rank_items | Selects the items from the ranks by keys |