Curve Fitting in MATLAB: Error Diagnosis

In summary, when trying to fit Y to a model asin(bx), an error message may occur if the independent variable x is not specified in the fittype expression. To fix this, the variable name 'x' should be included in the expression or the independent variable should be indicated as 'X' in the fittype function. This information can be found in the documentation for fittype and an example is also provided.
  • #1
ergospherical
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I have two 1D matrices X(1,j) and Y(1,j) of equal length. To fit Y to a model asin(bx) I tried:
Matlab:
fit = fittype(@(a,b,X), a*sin(b*X));
[fitted, gof] = fit(X, Y, fit)
coefficients = coeffvalues(fitted)
this gives the message: Error using fittype>iDeduceCoefficients
The independent variable x does not appear in the equation expression.
Use x in the expression or indicate another variable as the independent variable.


What's wrong?
 
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  • #3
sweet, thanks
 
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Related to Curve Fitting in MATLAB: Error Diagnosis

1. What is curve fitting in MATLAB and why is it important?

Curve fitting in MATLAB is the process of finding a mathematical model that best represents a set of data points. It is important because it allows scientists and researchers to analyze and understand complex data sets, make predictions, and draw conclusions about relationships between variables.

2. How do I perform curve fitting in MATLAB?

To perform curve fitting in MATLAB, you can use the built-in function "fit" or the Curve Fitting app. First, import your data into MATLAB and then choose the appropriate model for your data. Next, use the "fit" function or the app to fit the model to your data and visualize the results.

3. What is the difference between linear and nonlinear curve fitting?

Linear curve fitting involves finding a linear relationship between variables, such as a straight line. Nonlinear curve fitting, on the other hand, involves finding a curve or function that best fits the data, which may not follow a linear pattern. Nonlinear curve fitting is typically used for more complex data sets with more than one independent variable.

4. How do I diagnose errors in curve fitting in MATLAB?

There are several ways to diagnose errors in curve fitting in MATLAB. First, you can visually inspect the fit of the model to the data. If there are obvious discrepancies, you may need to adjust the model or data. Additionally, you can use statistical measures, such as the coefficient of determination (R-squared) and the root-mean-square error (RMSE), to evaluate the accuracy of the fit.

5. Can I automate the curve fitting process in MATLAB?

Yes, you can automate the curve fitting process in MATLAB by using the "fit" function in a script or by writing a custom function for your specific data and model. You can also use the Curve Fitting app to save and reuse your fitting settings for future analyses.

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