On 14/01/2015 14:20, Stanislav Aggerwal wrote:
This method of finding yhat as x %*% b works when I use raw polynomials:
x<-1:8
y<- 1+ 1*x + .5*x^2
fit<-lm(y~poly(x,2,raw=T))
b<-coef(fit)
xfit<-seq(min(x),max(x),length=20)
yfit<-b[1] + poly(xfit,2,raw=T) %*% b[-1]
plot(x,y)
lines(xfit,yfit)
But it doesn't work when I use orthogonal polynomials:
fit<-lm(y~poly(x,2))
b<-coef(fit)
yfit<-b[1] + poly(xfit,2) %*% b[-1]
plot(x,y)
lines(xfit,yfit,col='red')
I have a feeling that the second version needs to incorporate poly() coefs
(alpha and norm2) somehow. If so, please tell me how.
I do know how to use predict() for this. I just want to understand how
poly() works.
What matters is how lm() and predict() use poly(): see ?makepredictcall
and its code.
str(fit) might also help.
Thanks very much for any help
Stan
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Brian D. Ripley, rip...@stats.ox.ac.uk
Emeritus Professor of Applied Statistics, University of Oxford
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