[R] Polynomial fitting
I wonder how one in R can fit a 3rd degree polynomial to some data? Say the data is: y <- c(15.51, 12.44, 31.5, 21.5, 17.89, 27.09, 15.02, 13.43, 18.18, 11.32) x <- seq(3.75, 6, 0.25) And resulting degrees of polynomial are: 5.8007 -91.6339 472.1726 -774.2584 THanks in advance! -- Jonas Malmros Stockholm University Stockholm, Sweden __ 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.
Re: [R] Polynomial fitting
try this: y <- c(15.51, 12.44, 31.5, 21.5, 17.89, 27.09, 15.02, 13.43, 18.18, 11.32) x <- seq(3.75, 6, 0.25) coef(lm(y ~ x + I(x^2) + I(x^3))) I hope it helps. Best, Dimitris Dimitris Rizopoulos Ph.D. Student Biostatistical Centre School of Public Health Catholic University of Leuven Address: Kapucijnenvoer 35, Leuven, Belgium Tel: +32/(0)16/336899 Fax: +32/(0)16/337015 Web: http://med.kuleuven.be/biostat/ http://www.student.kuleuven.be/~m0390867/dimitris.htm - Original Message - From: "Jonas Malmros" <[EMAIL PROTECTED]> To: Sent: Monday, January 07, 2008 4:15 PM Subject: [R] Polynomial fitting >I wonder how one in R can fit a 3rd degree polynomial to some data? > > Say the data is: > > y <- c(15.51, 12.44, 31.5, 21.5, 17.89, 27.09, 15.02, 13.43, 18.18, > 11.32) > x <- seq(3.75, 6, 0.25) > > And resulting degrees of polynomial are: > > 5.8007 -91.6339 472.1726 -774.2584 > > THanks in advance! > > > > -- > Jonas Malmros > Stockholm University > Stockholm, Sweden > > __ > 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. > Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm __ 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.
Re: [R] Polynomial fitting
Dimitris Rizopoulos wrote: > try this: > > y <- c(15.51, 12.44, 31.5, 21.5, 17.89, 27.09, 15.02, 13.43, 18.18, > 11.32) > x <- seq(3.75, 6, 0.25) > coef(lm(y ~ x + I(x^2) + I(x^3))) Or use the 'poly' function: > coef(lm(x~poly(y,3))) (Intercept) poly(y, 3)1 poly(y, 3)2 poly(y, 3)3 4.875 -0.6233293 0.0312415 -0.6464533 But hmmm those aren't the same coefficients? Because you need to use 'raw=TRUE' if you really want to fit the raw powers rather than orthogonal polynomials (which are better behaved than fitting the raw powers): > coef(lm(y~poly(x,3,raw=TRUE))) (Intercept) poly(x, 3, raw = TRUE)1 poly(x, 3, raw = TRUE)2 -774.258364 472.172611 -91.633939 poly(x, 3, raw = TRUE)3 5.800653 And there's your coefficients. See help(poly) for more. Barry __ 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.
Re: [R] Polynomial fitting
Jonas,
In statistical sense polynomial is a linear regression fit. The function
that handles linear fitting is called lm. Here is how you can reproduce
your results:
lm(y ~ x + I(x^2) + I(x^3))
Unless you are really after the polynomial coefficients it is probably
better to use orthogonal polynomials. You can get this fit by doing
lm(y ~ poly(x, 3))
Check out help pages for lm and poly. Hope this helps,
Andy
__
Andy Jaworski
518-1-01
Process Laboratory
3M Corporate Research Laboratory
-
E-mail: [EMAIL PROTECTED]
Tel: (651) 733-6092
Fax: (651) 736-3122
"Jonas Malmros"
<[EMAIL PROTECTED]
ail.com> To
Sent by: r-help@r-project.org
[EMAIL PROTECTED] cc
project.org
Subject
[R] Polynomial fitting
01/07/2008 09:16
AM
I wonder how one in R can fit a 3rd degree polynomial to some data?
Say the data is:
y <- c(15.51, 12.44, 31.5, 21.5, 17.89, 27.09, 15.02, 13.43, 18.18, 11.32)
x <- seq(3.75, 6, 0.25)
And resulting degrees of polynomial are:
5.8007 -91.6339 472.1726 -774.2584
THanks in advance!
--
Jonas Malmros
Stockholm University
Stockholm, Sweden
__
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.
Re: [R] Polynomial fitting
On Mon, 7 Jan 2008, [EMAIL PROTECTED] wrote: > Jonas, > > In statistical sense polynomial is a linear regression fit. The function > that handles linear fitting is called lm. Here is how you can reproduce > your results: > > lm(y ~ x + I(x^2) + I(x^3)) > > Unless you are really after the polynomial coefficients it is probably > better to use orthogonal polynomials. You can get this fit by doing > > lm(y ~ poly(x, 3)) And if you are, y ~ poly(x, 3, raw=TRUE) is simpler to type and comprehend. > > Check out help pages for lm and poly. Hope this helps, > > Andy > > __ > Andy Jaworski > 518-1-01 > Process Laboratory > 3M Corporate Research Laboratory > - > E-mail: [EMAIL PROTECTED] > Tel: (651) 733-6092 > Fax: (651) 736-3122 > > > > "Jonas Malmros" > <[EMAIL PROTECTED] > ail.com> To > Sent by: r-help@r-project.org > [EMAIL PROTECTED] cc > project.org > Subject > [R] Polynomial fitting > 01/07/2008 09:16 > AM > > > > > > > > > I wonder how one in R can fit a 3rd degree polynomial to some data? > > Say the data is: > > y <- c(15.51, 12.44, 31.5, 21.5, 17.89, 27.09, 15.02, 13.43, 18.18, 11.32) > x <- seq(3.75, 6, 0.25) > > And resulting degrees of polynomial are: > > 5.8007 -91.6339 472.1726 -774.2584 > > THanks in advance! > > > > -- > Jonas Malmros > Stockholm University > Stockholm, Sweden > > __ > 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. > -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UKFax: +44 1865 272595 __ 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.
Re: [R] Polynomial fitting
Dear Mr. Rowlingson, Rizopoulos, Jaworski, and Ripley Thank you for your help with the polynomial. Regards, Jonas On Jan 7, 2008 5:18 PM, Barry Rowlingson <[EMAIL PROTECTED]> wrote: > Dimitris Rizopoulos wrote: > > try this: > > > > y <- c(15.51, 12.44, 31.5, 21.5, 17.89, 27.09, 15.02, 13.43, 18.18, > > 11.32) > > x <- seq(3.75, 6, 0.25) > > coef(lm(y ~ x + I(x^2) + I(x^3))) > > Or use the 'poly' function: > > > coef(lm(x~poly(y,3))) > (Intercept) poly(y, 3)1 poly(y, 3)2 poly(y, 3)3 > 4.875 -0.6233293 0.0312415 -0.6464533 > > But hmmm those aren't the same coefficients? > > Because you need to use 'raw=TRUE' if you really want to fit the raw > powers rather than orthogonal polynomials (which are better behaved than > fitting the raw powers): > > > coef(lm(y~poly(x,3,raw=TRUE))) > (Intercept) poly(x, 3, raw = TRUE)1 poly(x, 3, raw = TRUE)2 > -774.258364 472.172611 -91.633939 > poly(x, 3, raw = TRUE)3 >5.800653 > > And there's your coefficients. See help(poly) for more. > > Barry > -- Jonas Malmros Stockholm University Stockholm, Sweden __ 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.
