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fitting

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fitting [Mon Jun 4 14:15:05 2018] Alexander |
fitting [Sun Jul 21 14:09:36 2019] (current) Alexander |
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===== Creating a Fit Plot ===== | ===== Creating a Fit Plot ===== | ||

Nonlinear least squares data fitting (nonlinear regression) can be performed using Fit Plot. | Nonlinear least squares data fitting (nonlinear regression) can be performed using Fit Plot. | ||

- | To create a Fit Plot, select your X and Y columns in Table, then select ''Table -> Create Fit Plot'' in main menu, or use the same item in Table context menu, or use ''Create Fit Plot'' button in the toolbar. | + | To create a Fit Plot, select your X and Y columns in Table, then select ''Table -> Create Fit Plot'' in the main menu, or use the same item in the Table context menu, or use ''Create Fit Plot'' button in the toolbar. |

{{:create_fit_plot_context_menu.png?nolink|Creating Fit Plot using Table context menu}} | {{:create_fit_plot_context_menu.png?nolink|Creating Fit Plot using Table context menu}} | ||

==== MagicPlot has been verified with NIST Datasets ==== | ==== MagicPlot has been verified with NIST Datasets ==== | ||

- | National Institute of Standards and Technology (NIST) has created Statistical Reference Datasets Project which includes [[http://www.itl.nist.gov/div898/strd/nls/nls_main.shtml|26 datasets]] for testing the nonlinear fit algorithms. MagicPlot has been successfully tested on these datasets. Our report on MagicPlot testing with NIST datasets is available here: [[http://magicplot.com/downloads/MagicPlot-NIST-Test.pdf|Report]]. | + | National Institute of Standards and Technology (NIST) has created the Statistical Reference Datasets Project which includes [[http://www.itl.nist.gov/div898/strd/nls/nls_main.shtml|26 datasets]] for testing the nonlinear fit algorithms. MagicPlot has been successfully tested on these datasets. Our report on MagicPlot testing with NIST datasets is available here: [[http://magicplot.com/downloads/MagicPlot-NIST-Test.pdf|Report]]. |

===== Fitting Methodology ===== | ===== Fitting Methodology ===== | ||

'Nonlinear' means here that analytical fitting function depends nonlinearly on varying parameters (fit parameters). | 'Nonlinear' means here that analytical fitting function depends nonlinearly on varying parameters (fit parameters). | ||

- | Fit procedure iteratively varies the parameters of fit function to minimize the residual sum of squares. Nonlinear fitting algorithm needs the user to set the initial values of fit parameters. | + | Fit procedure iteratively varies the parameters of the fit function to minimize the residual sum of squares. The nonlinear fitting algorithm needs the user to set the initial values of fit parameters. |

To fit the data, implement these steps: | To fit the data, implement these steps: | ||

- | - Create a Fit Plot, specify Y errors in Data tab of Curve Properties dialog for data curve, if any | + | - Create a Fit Plot, specify Y errors in Data tab of Curve Properties dialog for the data curve, if any |

- Specify fit function by adding Fit Curves | - Specify fit function by adding Fit Curves | ||

- Specify initial values of fit parameters (drag curves or enter accurate values) | - Specify initial values of fit parameters (drag curves or enter accurate values) | ||

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{{:curves_table.png?nolink|Fit Curves table}} | {{:curves_table.png?nolink|Fit Curves table}} | ||

- | * ''Show'': Specifies whether to show this Fit Curve on plot. Active only if Baseline checkbox is not set | + | * ''Show'': Specifies whether to show this Fit Curve on the plot. Active only if Baseline checkbox is not set |

* ''Baseline'': Toggles the subtracting of this Fit Curve from experimental data. You also can use ''Residual'' button to subtract all Fit Sum from data | * ''Baseline'': Toggles the subtracting of this Fit Curve from experimental data. You also can use ''Residual'' button to subtract all Fit Sum from data | ||

* ''Sum'': Specifies whether to use this Fit Curve in sum fit function | * ''Sum'': Specifies whether to use this Fit Curve in sum fit function | ||

- | Below the Fit Curves list is a parameters table which shows names, values, and descriptions of parameters relating to selected Fit Curve. | + | Below the Fit Curves list, is a parameters table which shows names, values, and descriptions of parameters relating to the selected Fit Curve. |

==== Fitting by Sum and Fitting One Curve ==== | ==== Fitting by Sum and Fitting One Curve ==== | ||

MagicPlot allows two alternatives buttons to run the fit: | MagicPlot allows two alternatives buttons to run the fit: | ||

* ''Fit by Sum'' button will fit the data with the sum of Fit Curves for which the ''Sum'' checkbox is set. Data interval from ''Fit Interval'' tab will be used. This button must be used for example to fit the spectrum with the sum of peaks. | * ''Fit by Sum'' button will fit the data with the sum of Fit Curves for which the ''Sum'' checkbox is set. Data interval from ''Fit Interval'' tab will be used. This button must be used for example to fit the spectrum with the sum of peaks. | ||

- | * ''Fit One Curve'' button will fit the data with the one currently selected Fit Curve. Individual interval for each Fit Curve will be used. Set ''Edit Interval'' checkbox to edit individual interval for each Fit Curve. | + | * ''Fit One Curve'' button will fit the data with the one currently selected Fit Curve. The individual interval for each Fit Curve will be used. Set ''Edit Interval'' checkbox to edit individual interval for each Fit Curve. |

==== Copying and Pasting Fit Curves ==== | ==== Copying and Pasting Fit Curves ==== | ||

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==== Fit Curves Reordering ==== | ==== Fit Curves Reordering ==== | ||

- | You can reorder Fit Curves by dragging them in table. The data curve is always drawn the first and fit sum is drawn the last. | + | You can reorder Fit Curves by dragging them in the table. The data curve is always drawn the first and fit sum is drawn the last. |

===== Setting Initial Values of Parameters ===== | ===== Setting Initial Values of Parameters ===== | ||

- | Nonlinear fitting assumes that certain initial values of parameters are set before fitting. This procedure is very easy if you use Fit Curves of predefined types (not custom equation): you can drag curves on plot. Initial parameters values for each Fit Curve can also be set in parameter table. | + | Nonlinear fitting assumes that certain initial values of parameters are set before fitting. This procedure is very easy if you use Fit Curves of predefined types (not custom equation): you can drag curves on the plot. Initial parameters values for each Fit Curve can also be set in the parameter table. |

{{:moving_curves.png?nolink|Moving curves with mouse}} | {{:moving_curves.png?nolink|Moving curves with mouse}} | ||

==== Adjusting Parameters with Mouse Wheel ==== | ==== Adjusting Parameters with Mouse Wheel ==== | ||

- | You can adjust Parameters in table using mouse wheel scrolling when mouse cursor is on the desired parameter: Hold Ctrl key (Cmd key on Mac) and scroll. If Shift key is also pressed the parameter step for one wheel 'click' will be increased. | + | You can adjust Parameters in the table using mouse wheel scrolling when the mouse cursor is on the desired parameter: Hold Ctrl key (Cmd key on Mac) and scroll. If the Shift key is also pressed the parameter step for one wheel 'click' will be increased. |

===== Guessing Peaks ===== | ===== Guessing Peaks ===== | ||

- | If you are fitting a spectrum with multiple peaks, MagicPlot may automatically add and approximately locate peaks before fitting (Pro edition only). See [[guess_peaks]] for details. Guessed peaks should be used only as the initial estimate for fitting. | + | If you are fitting a spectrum with multiple peaks, MagicPlot may automatically add and approximately locate peaks before fitting (Pro edition only). See [[guess_peaks]] for details. Guessed peaks should be used only as of the initial estimate for fitting: don't forget to click the Fit button after peaks are added. |

===== Parameter Locking ===== | ===== Parameter Locking ===== | ||

- | You can lock (fix) parameter(s) to prevent varying this parameter(s) during fit and to prevent its changing due to setting initial values by mouse dragging (for built-in functions). Set the checkbox in ''Lock'' column in parameters list to lock parameter. | + | You can lock (fix) parameter(s) to prevent varying this parameter(s) during the fit and to prevent its changing due to set initial values by mouse dragging (for built-in functions). Set the checkbox in ''Lock'' column in the parameter list to lock parameter. |

{{:parameters_table.png?nolink|Table of Parameters}} | {{:parameters_table.png?nolink|Table of Parameters}} | ||

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===== Weighting of Data Points Using Y Errors ===== | ===== Weighting of Data Points Using Y Errors ===== | ||

- | MagicPlot allows data points weighting with Y error data. You can specify Y error data in Fit Plot properties dialog. If no Y error data are specified weighting is not used. | + | MagicPlot allows the weighting of data points with Y error data. You can specify Y error data in Fit Plot properties dialog. If no Y error data are specified weighting is not used. |

Weights are calculated as ''1 / Y<sub>error</sub><sup>2</sup>'' for every point. See [[fit_formulas]] for details. | Weights are calculated as ''1 / Y<sub>error</sub><sup>2</sup>'' for every point. See [[fit_formulas]] for details. | ||

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You can set the X intervals of the data which will be used for fitting. Data points outside these intervals are not used to compute the minimizing residual sum of squares. You can use this feature if some data points (especially in the beginning or the end) are inaccurate, e.g. noisy. | You can set the X intervals of the data which will be used for fitting. Data points outside these intervals are not used to compute the minimizing residual sum of squares. You can use this feature if some data points (especially in the beginning or the end) are inaccurate, e.g. noisy. | ||

- | Select ''Fit Interval'' tab to set intervals visually or edit accurate borders values in table. | + | Select ''Fit Interval'' tab to set intervals visually or edit accurate borders values in the table. |

- | * Double click on interval to split it | + | * Double click on the interval to split it |

* Drag the interval border to move it. If intervals intersect, they will be merged | * Drag the interval border to move it. If intervals intersect, they will be merged | ||

* Use context menu on the plot to create, delete and split intervals | * Use context menu on the plot to create, delete and split intervals | ||

- | **Note:** Data intervals from ''Fit Interval'' tab are used for fitting Sum only. To set individual data intervals for the one Curve fitting use ''Edit Interval'' checkbox. | + | **Note:** Data intervals from the ''Fit Interval'' tab are used for fitting Sum only. To set individual data intervals for the one Curve fitting use ''Edit Interval'' checkbox. |

{{:interval_context_menu1.png?nolink|Fit interval context menu}} | {{:interval_context_menu1.png?nolink|Fit interval context menu}} | ||

===== Baseline Fitting and Extraction ===== | ===== Baseline Fitting and Extraction ===== | ||

- | Fit Interval is also usable when baseline fitting. Before baseline fitting you can specify the interval which does not contain any signal points and contains baseline only. Set ''Baseline'' checkboxes at baseline Fit Curves after baseline fitting to subtract baseline from data. Then specify the whole interval and fit the data. | + | Fit Interval is also usable when baseline fitting. Before baseline fitting, you can specify the interval which does not contain any signal points and contains baseline only. Set ''Baseline'' checkboxes at baseline Fit Curves after baseline fitting to subtract baseline from data. Then specify the whole interval and fit the data. |

- | Note that if you use data processing (integration, FFT, etc.) on Fit Plot, then the difference between the data and baseline curves (which you do see on the plot) will be processed. You can use this behaviour to exclude baseline from data before integrating, see [[integration]] for more information. | + | Note that if you use data processing (integration, FFT, etc.) on Fit Plot, then the difference between the data and baseline curves (which you do see on the plot) will be processed. You can use this behavior to exclude baseline from data before integrating, see [[integration]] for more information. |

===== 'Data-Baseline' Table Column ===== | ===== 'Data-Baseline' Table Column ===== | ||

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===== Viewing the Residual Plot ===== | ===== Viewing the Residual Plot ===== | ||

Residual means here the difference between initial data, baseline function and Fit Sum function. MagicPlot offers two different ways to view the residual: | Residual means here the difference between initial data, baseline function and Fit Sum function. MagicPlot offers two different ways to view the residual: | ||

- | * Press and hold the ''Residual'' button. The residual will be shown while button is pressed. You can use either mouse or space key (if button is selected) to hold ''Residual'' button. | + | * Press and hold the ''Residual'' button. The residual will be shown while the button is pressed. You can use either mouse or space key (if the button is selected) to hold ''Residual'' button. |

* You can either set ''Baseline'' checkboxes for all summed Fit Curves to subtract them from data and explore the residual plot | * You can either set ''Baseline'' checkboxes for all summed Fit Curves to subtract them from data and explore the residual plot | ||

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To execute the fit click the ''Fit by Sum'' button of ''Fit One Curve'' button (see below). | To execute the fit click the ''Fit by Sum'' button of ''Fit One Curve'' button (see below). | ||

- | MagicPlot indicates fit process with a special window. Fitting curves are periodically updated on plot while fitting so you can see how fit converges. | + | MagicPlot indicates the fit process with a special window. Fitting curves are periodically updated on the plot while fitting so you can see how fit converges. |

{{:fit_progress.png?nolink|Fit progress window}} | {{:fit_progress.png?nolink|Fit progress window}} | ||

- | MagicPlot shows current iteration number and deviation decrement with two progress bars while fit is performed. The fit process stops when one of these progress bars reaches the end. | + | MagicPlot shows the current iteration number and deviation decrement with two progress bars while the fit is performed. The fit process stops when one of these progress bars reaches the end. |

You can see two buttons on fit progress window: | You can see two buttons on fit progress window: | ||

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===== Fitting One Curve ===== | ===== Fitting One Curve ===== | ||

- | You can use MagicPlot to fit the data with single selected Fit Curve by pressing ''Fit One Curve'' button. In this case a specific data interval for each Fit Curve is used and the main fitting data interval (from ''Fit Interval'' tab) is ignored. Select ''Edit Interval'' checkbox in the bottom of the Fit Plot panel to set specific fit intervals for each Fit Curve. | + | You can use MagicPlot to fit the data with single selected Fit Curve by pressing ''Fit One Curve'' button. In this case, a specific data interval for each Fit Curve is used and the main fitting data interval (from ''Fit Interval'' tab) is ignored. Select ''Edit Interval'' checkbox in the bottom of the Fit Plot panel to set specific fit intervals for each Fit Curve. |

- | Because of using individual data interval this method is useful for baseline fitting. In order to fit baseline specify the intervals which does not contain signal (peaks) and contain only noise. | + | Because of using individual data interval this method is useful for baseline fitting. In order to fit baseline specify the intervals which do not contain signal (peaks) and contain only noise. |

{{:fit_one_curve.png?nolink|'Fit One Curve' button}} | {{:fit_one_curve.png?nolink|'Fit One Curve' button}} | ||

===== Why My Fit is Not Converged? ===== | ===== Why My Fit is Not Converged? ===== | ||

- | In some cases the fit procedure may fail to find the optimal parameters values. The actual mathematical reason for this error is impossibility to invert the matrix α calculated from partial derivatives of fit function with respect to fit parameters. This inverted matrix is used to compute the new values of parameters for next step of fit (like gradient descent). In most cases this error occurs when the matrix α is ill-conditioned or nearly singular and the inverse cannot be calculated accurately enough with used floating-point arithmetic. | + | In some cases, the fit procedure may fail to find the optimal parameters values. The actual mathematical reason for this error is the impossibility to invert the matrix α calculated from partial derivatives of the fit function with respect to fit parameters. This inverted matrix is used to compute the new values of parameters for the next step of fit (like gradient descent). In most cases, this error occurs when the matrix α is ill-conditioned or nearly singular and the inverse cannot be calculated accurately enough with used floating-point arithmetic. |

=== The origin of this error may be: === | === The origin of this error may be: === | ||

- | * Fit is not converged through one or more parameters: some parameters were taking unrealistically great values during iterations. There are no local minimum of residual sum of squares near the initial values of these parameters. MagicPlot highlights the suspicious Fit Curve in this case. | + | * Fit is not converged through one or more parameters: some parameters were taking unrealistically great values during iterations. There is no local minimum of residual sum of squares near the initial values of these parameters. MagicPlot highlights the suspicious Fit Curve in this case. |

* Mutual dependency exists between some parameters. The algorithm cannot resolve which parameter to vary. | * Mutual dependency exists between some parameters. The algorithm cannot resolve which parameter to vary. | ||

* Fit function is ill-conditioned: the minimized residual sum of squares depends on some parameters much more than on other ones. | * Fit function is ill-conditioned: the minimized residual sum of squares depends on some parameters much more than on other ones. |

fitting.txt · Last modified: Sun Jul 21 14:09:36 2019 by Alexander

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