[R] Effect of each term in the accuracy of Nonlinear multivariate regression fitting equation

[dsfakianakis]

Dear all,

I have a set of data with 4 inputs (independent variables) and one output
(dependent variable). I want to perform a regression analysis in order to
fit these data to a regression model, however due to the non-linearity of
the model I do not have a clue which equation to use. I am thinking of
starting with a very general equation including ^3 terms and interactions
between the variables however this will lead to a very long equation. Is
there a way to assess the effect of each term to the accuracy of the
regression model in order to discard the terms with the least importance?
Something like a sensitivity analysis of the effect of each term to the
accuracy regression model. I know one possible solution to my problem is
simply 'trial and error' however before going down that road I want to check
if there is an easier way.

e.g. Let's say I have four input variables A B C and D, one output 'JIM' and
let z1, z2, ...  be the coefficients of the terms of the equation.  The
regression will be something like that:

Result = nls(JIM ~ z1*A + z2*B + z3*A*B^2 + z4*C*D^3 + z5*A^2*B^2 ... )

Is there a way to assess the contribution of each term (z1*A, z3*A*B^3 etc)
to the accuracy of the regression model?

Thanks a lot



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