On 12/08/2010 10:35 AM, szisziszilvi wrote:
I've tried lm, but something is wrong.
I've made a test dataset of 599 data points, my original equation is
zz = 1 +0.5*xx -3.2*xx*xx -1*yy +4.2*yy*yy
but the R gives this result:
-------------------------------------------------------
> mp <- read.csv(file="sample.csv",sep=";",header=TRUE)
> lm(zz ~ poly(xx,2) + poly(yy,2), data=mp)
Call:
lm(formula = zz ~ poly(xx, 2) + poly(yy, 2), data = mp)
Coefficients:
(Intercept) poly(xx, 2)1 poly(xx, 2)2 poly(yy, 2)1 poly(yy, 2)2
25.86 -2239.86 -595.01 2875.54 776.84
-----------------------------------------------------------
which is definitely not the original. :(
I don't think you are interpreting the coefficients properly. The basis
functions are orthogonal polynomials, not xx and xx^2, so the
coefficients won't match the ones you used in your definition. You
should compare the predictions of the model, e.g. by looking at
range(predict(lm(zz ~ poly(xx,2) + poly(yy,2), data=mp)) - zz)
If you insist on the power basis, just fit the model as
lm(zz ~ xx + I(xx^2) + yy + I(yy^2), data=mp)
but you might get less accurate predictions due to increased collinearity.
Duncan Murdoch
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