Jarek Jasiewicz wrote: > 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
They're not using the same parameterization. You need to read `?poly'. > both gives the same fitted() values, but completly different coef() `poly' returns *orthogonal* polynomials unless told otherwise -- see the `raw' argument. Try setting `raw = TRUE', but for numerical reasons you may prefer the default. HTH, Henric > > ______________________________________________ > 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. > ______________________________________________ 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.