From: Jarek Jasiewicz <jarekj_at_amu.edu.pl>

Date: Sat, 12 Jan 2008 21:48:22 +0100

R-help_at_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. Received on Sat 12 Jan 2008 - 20:52:34 GMT

Date: Sat, 12 Jan 2008 21:48:22 +0100

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.
**>
**> Charles.Annis_at_StatisticalEngineering.com
**> phone: 561-352-9699
**> eFax: 614-455-3265
**> http://www.StatisticalEngineering.com
**>
**>
**> -----Original Message-----
**> From: r-help-bounces_at_r-project.org [mailto:r-help-bounces_at_r-project.org] On
**> Behalf Of Jarek Jasiewicz
**> Sent: Saturday, January 12, 2008 2:06 PM
**> To: Charles.Annis_at_statisticalengineering.com
**> Cc: R-help_at_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.
**>>
**>> Charles.Annis_at_StatisticalEngineering.com
**>> phone: 561-352-9699
**>> eFax: 614-455-3265
**>> http://www.StatisticalEngineering.com
**>>
**>>
**>> -----Original Message-----
**>> From: r-help-bounces_at_r-project.org [mailto:r-help-bounces_at_r-project.org]
**>>
**> On
**>
**>> Behalf Of Jarek Jasiewicz
**>> Sent: Saturday, January 12, 2008 11:50 AM
**>> To: R-help_at_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_at_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_at_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_at_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. Received on Sat 12 Jan 2008 - 20:52:34 GMT

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