> -----Original Message-----
> I am doing a polynomial linear regression with 2 independent variables
> such as :
>
> lm(A ~ B + I(B^2) + I(lB^3) + C, data=Dataset))
>
> R return me a coefficient per independent variable, and I would need
> the coefficient of the C parameter to equal 1.
Leaving aside the question of fitting simple polynomial coefficients instead of
orthogonal polynomials - generally frowned upon, but not always serious - the
problem you describe is one in which you are not fitting C at all; you're
assuming C adds exactly. What you're really fitting is the difference between A
and C.
Try fitting
A-C ~ B + I(B^2) + I(lB^3)
to obtain the coefficients you're looking for. But be aware that you will still
have a constant intercept, so the model you will have fitted is
A = b0 + b1.B +b2.B^2 +b3.B^3 + C + error
S Ellison
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