2.4.15 nlbegino
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
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Variable Name
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I/O and Type
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Default Value
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Description
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Input
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iy
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Input
XYRange
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<active>
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The data to be fitted
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Fitting Function Name
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func
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Input
string
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<unassigned>
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The fitting function used to fit data
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NLFit Tree
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nltree
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Input/Output
TreeNode
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nlt
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Tree containing the information of fitting such as parameter values, standard error, etc.
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Fitting Mode
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mode
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Input
int
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0
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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.
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Dialog Theme
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theme
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Input
string
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<optional>
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The theme is used to store the special settings that user often use.
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Notation of Parameters
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pnotation
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Input
int
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0
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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.
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Parameter Initialization
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init
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Input
int
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0
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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.
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Global Weight Method
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weight
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Input
int
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-1
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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.
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How to Initialize the NLFit Object
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option
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Input
int
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0
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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.
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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;
The parameter control in nlbegino is similar to nlbegin, you could refer to more examples.
Related X-Functions
nlbegin nlfit nlend nlpara
Keywords:regression, NLFit
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