Charles Annis, P.E. wrote: > Jarek: > > Although it is not universally agreed on, I believe the first step in any > data analysis is to PLOT YOUR DATA. > > dd <- data.frame(a=c(1, 2, 3, 4, 5, 6), b=c(3, 5, 6, 7, 9, 10)) > plot(b ~ a, data=dd) > simple.model <- lm(b~a,data=dd) > abline(simple.model) > > Why to you think you need a cubic model to describe 6 observations? > > Your model is overparameterized - it has two more parameters than the number > of observations can reasonably justify, something that would be obvious from > your plot. > > The summary of the simple.linear model shows both the intercept and the > slope are statistically meaningful. (That's what the asterisks mean.) > > Call: > lm(formula = b ~ a, data = dd) > > Residuals: > 1 2 3 4 5 6 > -0.23810 0.39048 0.01905 -0.35238 0.27619 -0.09524 > > Coefficients: > Estimate Std. Error t value Pr(>|t|) > (Intercept) 1.86667 0.30132 6.195 0.00345 ** > a 1.37143 0.07737 17.725 5.95e-05 *** > --- > Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > Residual standard error: 0.3237 on 4 degrees of freedom > Multiple R-Squared: 0.9874, Adjusted R-squared: 0.9843 > F-statistic: 314.2 on 1 and 4 DF, p-value: 5.952e-05 > > I think you should invest a small amount of your time, and an even smaller > amount of your money to purchase and read - cover-to-cover - one of the > several very good books on elementary statistics and R. My recommendation > is _Introductory Statistics with R_ by Peter Dalgaard (Paperback - Jan 9, > 2004). Amazon.com carries it. > > Best wishes. > > > > Charles Annis, P.E. > > [EMAIL PROTECTED] > phone: 561-352-9699 > eFax: 614-455-3265 > http://www.StatisticalEngineering.com > > > -----Original Message----- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On > Behalf Of Jarek Jasiewicz > Sent: Saturday, January 12, 2008 2:06 PM > To: [EMAIL PROTECTED] > Cc: R-help@r-project.org > Subject: Re: [R] glm expand model to more values > > Charles Annis, P.E. wrote: > >> How many parameters are you trying to estimate? How many observations do >> you have? >> >> What is wrong is that half of your parameter estimates are statistically >> meaningless: >> >> dd <- data.frame(a=c(1, 2, 3, 4, 5, 6), b=c(3, 5, 6, 7, 9, 10)) >> >> overparameterized.model <- glm(b~poly(a,3),data=dd) >> >> summary(overparameterized.model) >> >> >> Coefficients: >> Estimate Std. Error t value Pr(>|t|) >> >> (Intercept) 6.6667 0.1725 38.644 0.000669 *** >> >> poly(a, 3)1 5.7371 0.4226 13.576 0.005382 ** >> >> poly(a, 3)2 -0.1091 0.4226 -0.258 0.820395 >> >> poly(a, 3)3 0.2236 0.4226 0.529 0.649562 >> >> >> >> >> Charles Annis, P.E. >> >> [EMAIL PROTECTED] >> phone: 561-352-9699 >> eFax: 614-455-3265 >> http://www.StatisticalEngineering.com >> >> >> -----Original Message----- >> From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] >> > On > >> Behalf Of Jarek Jasiewicz >> Sent: Saturday, January 12, 2008 11:50 AM >> To: R-help@r-project.org >> Subject: [R] glm expand model to more values >> >> Hi >> >> I have the problem with fitting curve to data with lm and glm. When I >> use polynominal dependiency, fitted values from model are OK, but I >> cannot recive proper values when I use coefficents to caltulate this. >> Let me present simple example: >> >> I have simple data.frame: (dd) >> a: 1 2 3 4 5 6 >> b: 3 5 6 7 9 10 >> >> I try to fit it to model: >> >> model=glm(b~poly(a,3),data=dd) >> I have following data fitted to model (as I expected) >> > fitted(model) >> 1 2 3 4 5 6 >> 3.095238 4.738095 6.095238 7.333333 8.619048 10.119048 >> >> and coef(model) >> (Intercept) poly(a, 3)1 poly(a, 3)2 poly(a, 3)3 >> 6.6666667 5.7370973 -0.1091089 0.2236068 >> >> so when I try to expand the model to other data (simple extrapolation), >> let say: s=seq(1:10,by=1) >> >> I do: >> extra=sapply(s,function(x) coef(model) %*% x^(0:3)) >> and here is result: >> [1] 12.51826 19.49328 28.93336 42.18015 60.57528 85.46040 118.17714 >> [8] 160.06715 212.47207 276.73354 >> >> the data form expanding coefs are completly differnd from fitted >> >> What's going wrong? >> >> Jarek >> >> ______________________________________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >> > http://www.R-project.org/posting-guide.html > >> and provide commented, minimal, self-contained, reproducible code. >> >> >> > sorry but I cannot understand. What does it means data are statistically > meanningless? > > It is examle with very simple data which I use according to simpleR > manual example to check why I cannot recive expected result. I need > simple model y~x^3+x^2....+z to extrapolate data > Jarek > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > I understand that data are not well example. But I try to find rather general solution. Original data are list 98 dataframes and are calculated by over 100 lines R script I thought that it is too much to attach them, so I typed few digits to ilustrate problem.
The question was asked wrong. It shoud be: if formulas: pol3_model=lm(b~poly(a,3)) p3_model=lm(b~a+I(a^2)+I(a^3)) are the same? according R documetation - Yes both gives the same fitted() values, but completly different coef() ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.