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#Curve fitting methods igor pro pro#

Chi-square defines a surface in a multidimensional error space. Initial Guessesįor non-linear least-squares data fitting, Igor uses the Levenberg-Marquardt algorithm to search for the minimum value of chisquare. For each try, it computes chisquare searching for the coefficient values that yield the minimum value of chi-square. Igor tries various values for the unknown coefficients. Iterative Data Fitting (non-linear least-squares / non-linear regression)įor the other built-in data fitting functions and for user-defined functions, the operation must be iterative. Igor uses the singular value decomposition algorithm. For curve fitting to a straight line or polynomial function, we can find the best-fit coefficients in one step. We want to find the coefficients a and b that best match our data. Suppose we have a theoretical reason to believe that our data should fall on a straight line. The simplest case is data fitting to a straight line: y = ax + b, also called "linear regression". Where y is a fitted value (model value) for a given point, y i is the measured data value for the point and σ i is an estimate of the standard deviation for y i. The best values of the coefficients are the ones that minimize the value of Chi-square. We want to find values for the coefficients such that the function matches the raw data as well as possible. In curve fitting we have raw data and a function with unknown coefficients. Some people try to use curve fitting to find which of thousands of functions fit their data. The curve fit finds the specific coefficients (parameters) which make that function match your data as closely as possible. We assume that you have theoretical reasons for picking a function of a certain form. The idea of curve fitting is to find a mathematical model that fits your data. Packages built on Igor's basic curve fitting capability add functionality:

Programmer support for simultaneous fits using multiple processors.

User-defined fits take advantage of multiple processors.

Fully programmable for repetitive or unusual curve fitting tasks.

Follow fit progress with automatic graph updates during iterative fits.

For simple fits to built-in functions, fit with a single menu selection.

Implicit fits, when your fitting function is in the form f(x,y)=0.

Errors-in-variables fitting (when you have measurment errors in both X and Y).

Set and hold the value of any fit coefficient.

Optional automatic calculation of confidence limits for fit coefficients.

These curves can be automatically added to a graph of your data.

Automatic calculation of the model curve, curve fit residuals, and confidence and prediction bands.

Curve fits to data with linear constraints on the fit parameters.

Virtually unlimited number of independent variables in a Multivariate curve fit (multiple regression).Virtually unlimited number of fit coefficients in user-defined fitting functions.Do linear, polynomial and non-linear regression.Fit data to built-in and user-defined fitting functions.

#Curve fitting methods igor pro pro#

Igor Pro includes powerful curve fitting features: Wide-Angle Neutron Spin Echo Spectroscopy.