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 -- View this message in context: http://r.789695.n4.nabble.com/Effect-of-each-term-in-the-accuracy-of-Nonlinear-multivariate-regression-fitting-equation-tp4650949.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
