Exponential Fit Coefficients VI

Owning Palette: Advanced Curve Fitting VIs

Requires: Full Development System

Returns the amplitude and damping of the exponential fit for a data set (X, Y) using the Least Square, Least Absolute Residual, or Bisquare method.

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	Y is the array of dependent values. Y must contain at least two points.
	X is the array of independent values. X must be the same size as Y.
	Weight is the array of weights for the observations (X, Y). Weight must be the same size as Y. Weight also must contain non-zero elements. If an element in Weight is less than 0, the VI uses the absolute value of the element. If you do not wire an input to Weight, the VI sets all elements of Weight to 1.
	tolerance determines when to stop the iterative adjustment of amplitude and damping when you use the Least Square or Bisquare method. If the relative difference of the weighted mean error of the exponential fit in two successive iterations is less than tolerance, this VI returns the resulting amplitude and damping. If tolerance is less than or equal to 0, this VI sets tolerance to 0.0001. tolerance is invalid if method is Least Square and refine? is FALSE.
	method specifies the fitting method. 0 Least Square (default) 1 Least Absolute Residual 2 Bisquare
	refine? specifies whether to further refine amplitude and damping. The computation time for this VI increases if refine? is TRUE. The default is FALSE.
	amplitude returns the amplitude of the fitted model.
	damping returns the damping of the fitted model.
	error returns any error or warning from the VI. You can wire error to the Error Cluster From Error Code VI to convert the error code or warning into an error cluster.

Exponential Fit Coefficients Details

This VI is similar to the Exponential Fit VI but does not return the y-values or weighted mean error of the fitted model.