i have 17 observation (x and y)
the relation between them as follows
y = 0.392 * (1 - (x / J)) ^ i
i want to use nonlinear least square regression to know J and i
Thanks in advance
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Do you have the statistics toolbox installed? It supports nonlinear regression - look for NonLinearModel.fit() in the docs.
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i have it installed
mdl = NonLinearModel.fit(X,y,modelfun,beta0)
i think i can use this one
so i have 17 x and 17 y
how can i put my modelfun and beta0 ??
For modelfun: either write a function or use 'y ~ .392*(1-x/b1)^b2'. The function will be happier if you use names b1/b2 in place of J/I.
For beta0: give a two-element vector with your best guess at b1 and b2. This depends on your data. A good guess gives the function a better shot at solving the problem. If you have real data, you might consider something like b1=2*max(x) to avoid problems with complex numbers.
thank you for your kind reply
i put my x values, then my y values, and put beta0=[0.65,1.1]
then i write
mdl=nonlinearmodel.fit(x ,y ,'y=0.392*(1-x/b1)^b2',beta0)
and i received error message ' Undefined variable "nonlinearmodel" or class "nonlinearmodel.fit".'
Try NonLinearModel.fit capitalized just that way. This should work in the most recent releases of MATLAB. Otherwise try nlinfit.
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