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Nonlinear least squares data fitting can be performed using Fit Plot.
To create Fit Plot select x and y columns in table, then select
Table → Create Fit Plot in main menu, or use context menu in table, or use Create Fit Plot button in the toolbar.
'Nonlinear' means here that analytical fitting function depends nonlinearly on varying parameters (fit parameters). Linear fitting is a quite simple method, which is based on solving the system of linear equations. Unlike linear fitting, nonlinear fitting is performed by iterative algorithm which needs the user to set the initial values of fit parameters.
To fit the data you must implement these steps:
MagicPlot considers fit function as a sum of Fit Curves. Ordinarily in peaks fitting each Fit Curve corresponds to one peak on the experimental data. There are a number of predefined Fit Curves (Line, Parabola, Gauss, Lorentz, etc.) You can specify custom Fit Curve too. The baseline fitting components also may be added to the fitting sum.
Fit Plot window contains the list of Fit Curves. Each Fit Curve in the list has 3 check boxes:
Show: Specifies whether to show this Fit Curve on plot. Active only if Baseline check box is not set
Baseline: Toggles the subtracting of this Fit Curve from experimental data
Sum: Specifies whether to use this Fit Curve in sum fit function
Below the Fit Curves list is parameters table which shows parameters names, values, descriptions of the selected Fit Curve.
You can copy and paste Fit Curves in curves table as usual. Use context menu in curves table or press
Ctrl+V on PC and
Cmd V on Mac (curves table must be focused, not the plot itself). MagicPlot copies Fit Curves parameters values, baseline/sum/show checkbox values and style.
Nonlinear fitting assumes that certain initial values of parameters are set before fitting. This procedure is just easy if you use predefined Fit Curves: you can drag curves on plot.
Initial parameters values for each Fit Curve can also be set in parameters table.
You can lock parameter(s) to prevent varying this parameter during fit and to prevent its changing due to setting initial values by mouse dragging (for built-in functions). Use checkbox on
Lock column in parameters list.
You can set the x intervals of the data. The data points outside this intervals are not used to compute the minimizing residual sum of squares (see below). You can use this feature if some data points (especially in the beginning and the end) are inaccurate, e.g. noisy.
Fit Interval tab to set intervals visually or edit accurate borders values:
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 check boxes at baseline Fit Curves after baseline fitting to subtract baseline from data. Then set interval to full and fit the data.
Note that if you execute one of data processing algorithms (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.
The 'Data-Baseline' column is appended to the Table with initial (x, y) fit data when you create Fit Plot. The 'Data-Baseline' column contains the difference between initial y data and baseline approximation (the sum of Fit Curves for which
Baseline checkbox is selected).
It is 'Data-Baseline' column that is actually plotted on Fit Plot.
Use 'Data-Baseline' column in Table if you want to process the data without baseline. This column is also used as initial data if you use
Processing menu when Fit Plot is active.
You can also 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 (set in
Fit Interval tab) is ignored. Select
Set Interval check box in the bottom of the Fit Plot panel to set specific fit intervals for each Fit Curve.
In some cases you may want to fit the data with two Gauss or Lorentz peaks with the same width but different positions and amplitudes, for example. You can do this in two ways: by specifying custom Fit Curve with your equation or by joining the 'width' parameters of two peaks.
To join parameters of two or more Fit Curves select one of desired Fit Curves, select desired parameter in parameters table and press
Join button. Add parameters which will be joint in the opened dialog window. Joined parameters are treated as one fit parameter.
Joined parameters are showed with blue color (instead of black) in parameters table.
Fit procedure iteratively varies the parameters βk of fit function f(x, β1, …, βp) to minimize the residual sum of squares (RSS, χ2):
The calculation of the new guess of parameters on each fit iteration is based on the fit function partial derivatives for current values of fit parameters and for each x value:
To start minimization, you have to provide an initial guess for the parameters.
After each iteration except the first MagicPlot evaluates deviation decrement D:
Deviation decrement shows how the residual sum of squares (RSS) on current iteration relatively differs from that on the previous iteration.
The iterative fit procedure stops on the one of two conditions:
You can change the minimum allowable deviation decrement and maximum number of iterations in Fitting tab of MagicPlot Preferences.
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 shows current iteration number and deviation decrement by two progress bars while fit is performed. The fit process stops when one of this progress bars reaches the end.
Two buttons are located on fit progress window:
Break Iters: Breaks iterations after current iteration. Use this button if you suppose that further iterations will not change the result.
Undo Fit: Breaks iterations and reverts the fit parameters to before fit values. Use this button if you see that fit process converges to wrong result; change initial values of parameters and run fit again.
MagicPlot can use weighting of y values based on y errors si:
here C is normalizing coefficient (to make the sum of wi be equal to one):
Fit Plot Properties dialog (
Plot Data tab) you can set one of the following method to evaluate standard y errors si:
The standard deviations (std. dev.) of fit parameters βk are evaluated after fit using the following formula:
here α is the matrix of partial derivatives of fit function by parameters βm and βn which is used for fitting:
In the table below you can find the formulas which MagicPlot uses to calculate fit parameters and values in
Fit Report tab.
Because of some confusion in the names of the parameters in different sources (books and software), we also give many different names of same parameter in note column.
|Original Data and Fit Model Properties|
|Number of used data points||—||This is the number of data points inside specified Fit Interval.|
|Fit parameters||β1,…,βp||—||For peak-like functions (Gauss, Lorentz) these parameters are amplitude, position and half width at half maximum.|
|Number of fit function parameters β||—||This is the total number of parameters of all fit curves which are summed to fit.|
|Degrees of freedom|
|Estimated mean of data|
|Estimated variance of data||Not used by fit algorithm, only for comparison.|
|Data total sum of squares, TSS||TSS||TSS is also called sum of squares about the mean and acronym SST is also used.|
|Residual sum of squares, RSS|| This value is minimized during the fit to find the optimal fit function parameters.
RSS is also called the sum of squared residuals (SSR), the error sum of squares (ESS), the sum of squares due to error (SSE).
|Reduced χ2|| The advantage of the reduced chi-squared is that it already normalizes for the number of data points and model (fit function) complexity.
Reduced χ2 is also called mean square error (MSE) or the residual mean square.
|Standard deviation of the model||s||Standard deviation is also called root mean square of the error (Root MSE)|
|Coefficient of determination||R2 will be equal to one if fit is perfect, and to zero otherwise. This is a biased estimate of the population R2, and will never decrease if additional fit parameters (fit curves) are added, even if they are irrelevant.|
|Adjusted R2||Adjusted R2 (or degrees of freedom adjusted R-square) is a slightly modified version of R2, designed to penalize for the excess number of fit parameters (fit curves) which do not add to the explanatory power of the regression. This statistic is always smaller than R2, can decrease as you add new fit curves, and even be negative for poorly fitting models|