Re: [R] automated polynomial regression

Also coef(mylm) where you can deal with coef(mylm) [1], coef(mylm)[2]... And plot(y~x, data=mydf) curve(coef(mylm)[1]+coef(mylm)[2]*x) could produce interesiting visual results :-) milton On Thu, May 14, 2009 at 2:30 PM, Dieter Wirz wrote: > Thanks Bert and Luc! > Sometimes the solutio

Re: [R] automated polynomial regression

Thanks Bert and Luc! Sometimes the solution is close, but I did not find it I always tried mylm$Coefficients... Stupid /me. -didi BTW: many thanks to all developers of R. IMHO R is one of the most outstanding free projects! On Thu, May 14, 2009 at 7:07 PM, Bert Gunter wrote: > > -- but it i

Re: [R] automated polynomial regression

-- but it is preferable to use the appropriate access functions: coef(mylm) ?coef Bert Gunter Nonclinical Biostatistics 467-7374 ## Now, what I believe you're looking for ; mylm$coefficients ; Cheers, -- *Luc Villandré* /Biostatistician McGill University Health Center - Montreal Child

Re: [R] automated polynomial regression

Dieter Wirz wrote: Dear all - We perform some measurements with a machine that needs to be recalibrated. The best calibration we get with polynomial regression. The data might look like follows: true_y <- c(1:50)*.8 # the real values m_y <- c((1:21)*1.1, 21.1, 22.2, 23.3 ,c(25:50)*.9)/0.3-5.

[R] automated polynomial regression

Dear all - We perform some measurements with a machine that needs to be recalibrated. The best calibration we get with polynomial regression. The data might look like follows: > true_y <- c(1:50)*.8 > # the real values > m_y <- c((1:21)*1.1, 21.1, 22.2, 23.3 ,c(25:50)*.9)/0.3-5.2 > # the measured