2.4.15 nlbegino

1 Menu Information
2 Brief Information
3 Additional Information
4 Command Line Usage
5 X-Function Execution Options
6 Variables
7 Description
8 Algorithm
9 Reference
10 Examples
11 Related X-Functions

Menu Information

Analysis: Fitting: Nonlinear Implicit Curve Fit...

Brief Information

Start a nonlinear implicit curve fitting session

Additional Information

Minimum Origin Version Required: 9.0 SR0 (Pro Only)

X-Function not designed for Auto GetN Dialog.

Command Line Usage


nlbegino iy:=1 func:=ellipse; //initialize fitting the active plot using ellipse function
nlbegino iy:=(1,2) func:=circle nltree:=mytree;//initialize to fit data with column 1 as X and column 2 as Y with circle function
nlbegino iy:=(1,2,3,4) func:=hyperbola weight:=1;//initialize to fit data with column 1 as X, column 2 as Y, column 3 as Y error and column 4 as X error with user-defined function "hypobola", this would require you to construct a fitting function named hyperbola under implicit category first

X-Function Execution Options

Please refer to the page for additional option switches when accessing the x-function from script

Variables

Display Name	Variable Name	I/O and Type	Default Value	Description
Input	iy	Input XYRange	<active>	The data to be fitted
Fitting Function Name	func	Input string	<unassigned>	The fitting function used to fit data
NLFit Tree	nltree	Input/Output TreeNode	nlt	Tree containing the information of fitting such as parameter values, standard error, etc.
Fitting Mode	mode	Input int	0	The method to treat the input data for fitting. Option list: auto:Auto The input dataset is fitted use the model concat:Concatenate Fit All input datasets are concatenated and fitted as one curve. global:Global Fit All datasets are fitted globally. This mode should be used when you want to fit one model to multiple datasets with shared parameters. cons:Independent Fit - Consolidated Report All datasets are fitted independently. This mode should be used when you want to fit one model to multiple datasets without shared parameters and create the consolidated reports.
Dialog Theme	theme	Input string	<optional>	The theme is used to store the special settings that user often use.
Notation of Parameters	pnotation	Input int	0	Notation of parameters in the output tree. Option list: para:ParaName Use name of parameter as notation. abbr:Abbreviation Use abbreviation of parameter as notation. both:Both Use both name and abbreviation of parameter as notation.
Parameter Initialization	init	Input int	0	Specify the status of Parameter Initialization. Option list: auto:Auto Origin automatically decides whether to enable parameter initialization according to the FDF file of the fitting function. enable:Enable Enable parameter initialization. disable:Disable Disable parameter initialization.
Global Weight Method	weight	Input int	-1	Option list: -1=auto Weight is Instrumental if error data are co-selected. 0=none: No Weights No weight is used. 1=ins:Instrumental (=1/ei^2) Use instrumental weight method. 2=stat:Statistical (=1/yi) Use statistical weight method.
How to Initialize the NLFit Object	option	Input int	0	Option list: init_all:Init All Parameters Initialize all parameters and then do the fitting with these parameters. change_data:Only Change Data Keep the former settings and only change dataset, then do the fitting to the new dataset.

Description

Start a nonlinear implicit fitting session, including option settings and parameter initialization.

The nltree is a TreeNode variable, which can control many fitting settings and allows you to read fitting results such as the parameter values, standard error, etc.

For details please refer to this.

Note that only the fitting functions under the Implicit category in Fitting Function Organizer are available with this x-function.

Algorithm

The nonlinear implicit fitting uses the orthogonal distance regression (ODR) iteration algorithm.

Reference

"J. W. Zwolak, P.T. Boggs, and L.T. Watson, ``Algorithm 869: ODRPACK95: A weighted orthogonal distance regression code with bound constraints, ACM Transactions on Mathematical Software Vol. 33, Issue 4, August 2007."

Examples

This example shows to fit an implicit ellipse function with X Y error.

string fn$=system.path.program$ + "Samples\Curve Fitting\ellipse.dat";//prepare data
newbook;
impasc fname:=fn$;//import data into new workbook 
nlbegino iy:=(1,2,4,3) func:=Ellipse weight:=1;//Start the implicit fit with x y instrumental error, column 4 is the y error and column 3 is the x error.
nlfit;
nlend 1 1;