Returns the logarithmic fit of a data set using a specific fitting method.
Base of the logarithm.
Name | Value | Description |
---|---|---|
e | 2.71828 | Uses the natural logarithm. |
10 | 10 | Uses 10 as the base of the logarithm. |
2 | 2 | Uses 2 as the base of the logarithm. |
Default: e
Array of dependent values. The length of y must be greater than or equal to the number of unknown parameters.
Independent values. x must be the same size as y.
Weights for the observations.
weight must be the same size as y. The elements in weight cannot be 0. If an element in weight is less than 0, this node uses the absolute value of the element. If you do not wire an input to weight, this node sets all elements in weight to 1.
Value that determines when to stop the iterative adjustment of the amplitude and scale.
If tolerance is less than or equal to 0, this node sets tolerance to 0.0001.
How tolerance Affects the Outputs with Different Fitting Methods
For the Least Square and Least Absolute Residual methods, if the relative difference between residue in two successive iterations is less than tolerance, this node returns the resulting residue. For the Bisquare method, if any relative difference between amplitude and scale in two successive iterations is less than tolerance, this node returns the resulting amplitude and scale.
Default: 0.0001
Error conditions that occur before this node runs.
The node responds to this input according to standard error behavior.
Standard Error Behavior
Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.
Default: No error
Upper and lower constraints for the amplitude and scale. If you know the exact value of certain parameters, you can set the lower and upper bounds of those parameters equal to the known values.
Lower bound for the amplitude.
Default: -Infinity, which means no lower bound is imposed on the amplitude.
Upper bound for the amplitude.
Default: Infinity, which means no upper bound is imposed on the amplitude.
Lower bound for the scale.
Default: -Infinity, which means no lower bound is imposed on the scale.
Upper bound for the scale.
Default: Infinity, which means no upper bound is imposed on the scale.
Fitting method.
Name | Value | Description |
---|---|---|
Least Square | 0 | Uses the least square method. |
Least Absolute Residual | 1 | Uses the least absolute residual method. |
Bisquare | 2 | Uses the bisquare method. |
Algorithm for the Least Square Method
The least square method of fitting finds the amplitude and scale of the logarithmic model by minimizing residue according to the following equation:
where
Algorithm for the Least Absolute Residual Method
The least absolute residual method finds the amplitude and scale of the logarithmic model by minimizing residue according to the following equation:
where
Algorithm for the Bisquare Method
The bisquare method of fitting finds the amplitude and scale of the logarithmic model using an iterative process, as shown in the following illustration.
The node calculates residue according to the following equation:
where
Default: Least Square
Y-values of the fitted model.
Amplitude of the fitted model.
Scale of the fitted model.
Error information.
The node produces this output according to standard error behavior.
Standard Error Behavior
Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.
Weighted mean error of the fitted model.
If method is Least Absolute Residual, residue is the weighted mean absolute error. Otherwise, residue is the weighted mean square error.
This node uses the iterative general least square method and the Levenberg-Marquardt method to fit data to a logarithmic function of the general form described by the following equation:
where
This node finds the values of a and b that best fit the observations (x, y).
The following equation describes the logarithm function resulting from the logarithm fit algorithm:
Where This Node Can Run:
Desktop OS: Windows
FPGA: Not supported
Web Server: Not supported in VIs that run in a web application