MATLAB: Fitnlm either runs perfectly or crashes with nearly identical data

curve fittingfitnlm

I am using fitnlm to do a simple curve fit. Most of the time it runs perfectly, but every now and then nearly identical data will cause it to crash, and I am not sure why. Here is an example that runs perfectly:

Here is an example where it doesn't work. Both example codes are attached, along with the function "weibull.m" which defines the fit.

Thank you very much for your help with this. It really has be baffled.

Best Answer

Hi Clay,

The roots returned in the latter case (fitnlm_broken.m) are imaginary, hence, you receive the error "JW must be scalar or real matrix". To know this, you can put a breakpoint on the 23rd line (x50 = ...) and replace fitnlm by nlinfit, and check the variable mdl (returns the model parameters) which are not real in your case.

Since the parameters are not full rank matrix, the model can not fit the x- and y- parameters.

A solution to this could be that you can try making your data full rank using PCA.

Related Solutions

MATLAB: Non-Linear Regression Methods

READ THE HELP.

For example, a fragment of the help for fitnlm:

NLM = fitnlm(X,Y,MODELFUN,BETA0) fits a nonlinear regression model
    using the column vector Y as a response variable and the columns of the
    matrix X as predictor variables.

So if you have TWO independent variables, they would be passed in as two columns of the ARRAY X.

As far as LSQNONLIN goes, it can handle any number of independent variables, because it only cares about the residual vector. You don't even pass in data to LSQNONLIN, just a function.

GLMFIT does not accept nonlinear models. But as long as the model is linear in the coefficients, there is no problem. It can still be nonlinear in the independent variable, since you pass in an array of data. And it does handle the case of multiple independent variables, since you must create the array X.

Of course, there is also NLINFIT, which is able to handle an array of multiple independent variables.

So basically, you need to read the help. Different tools handle a variety of problems.

MATLAB: Error in nonlinear regression

You are not extracting your table variables correctly.

This works (extracting the data from your table):

X = [data.sst, data.at];
y = data.sea;
modelfun = @(b,x)b(1) + b(2)*x(:,1).^b(3) + ...
  b(4)*x(:,2).^b(5)
beta0 = [100 100 100 100 100];
mdl = fitnlm(X,y,modelfun,beta0)

and so does this (using your table, and simply rearranging its columns to be compatible with what fitnlm expects):

new_table = data(:, [2 3 1]);
modelfun = @(b,x)b(1) + b(2).*x(:,1).^b(3) + ...
  b(4).*x(:,2).^b(5)
beta0 = [100 100 100 100 100];
mdl = fitnlm(new_table,modelfun,beta0)

The ‘carbig’ model is likely not appropriate for your sea level and temperature data, so I am not surprised that the model fails, even though the code now runs without error.

Best Answer

Related Solutions

MATLAB: Non-Linear Regression Methods

MATLAB: Error in nonlinear regression

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