R help - Nov 2012 - Effect of each term in the accuracy of Nonlinear multivariate regression fitting equation

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.

In this context, "linear model" means linear in the _coefficients_ not
(necessarily) linear in the predictors, so your model:
    JIM ~ z1*A + z2*B + z3*A*B^2 + z4*C*D^3 + z5*A^2*B^2 ...
is a linear model (in z1, z2, ...).

So you don't need to use nls, lm is probably favourite. You can use all 
the techniques around for evaluating linear models; anova.lm might give 
you a start.

KJ

On 27/11/2012 11:40, dsfakianakis wrote:
> 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.
>