I(a^2) + a)
You decide what's more elegant...
Hope this helps,
Berry
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Berry Boessenkool
Potsdam
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Date: Fri, 8 Jul 2011 21:12:46 +0300
From: matti.joki...@gmail.com
To: gerrit.eich...@math.uni-giessen.de
CC: r-help@r-project.org
Subject: Re: [R] Polynomia
ide what's more elegant...
Hope this helps,
Berry
-
Berry Boessenkool
Potsdam
-
> Date: Fri, 8 Jul 2011 21:12:46 +0300
> From: matti.joki...@gmail.com
> To: gerrit.eich...@math.uni-giessen.de
> CC: r-he
Thank you Gerrit for the quick reply! And yes, i'm Matti.
I can get the coeffs now, though i'm not sure whether i'm doing
something wrong or whether poly is just not the right method for what
i'm trying to find. I will look into this more closely and give it
another try.
Is poly best for fit
Hello, mfa (Matti?),
if x and y contain the coordinates of your data points and k is the wanted
polynomial degree, then
fit <- lm( y ~ poly( x, k))
fits orthonormal polynomials up to degree k to your data. Using
dummy.coef( fit)
should give the coefficients you are interested in.
Hth --
Hello,
i'm fairly familiar with R and use it every now and then for math related
tasks.
I have a simple non polynomial function that i would like to approximate
with a polynomial. I already looked into poly, but was unable to understand
what to do with it. So my problem is this. I can generate vi
Dear R helpers
Suppose I have a following data
y <- c(9.21, 9.51, 9.73, 9.88, 10.12. 10.21)
t <- c(0, 0.25, 1, 3, 6, 12)
I want to find out the polynomial which fits y in terms of t i.e. y = f(t) some
function of t.
e.g. y = bo + b1*t + (b2 * t^2) + (b3 * t^3) + .. and so on.
ject.org
> From: r.tur...@auckland.ac.nz
> Subject: Re: [R] Polynomial Fitting
> Date: Wed, 30 Sep 2009 08:30:15 +1300
> To: w_chris_carle...@hotmail.com
>
>
> On 30/09/2009, at 5:34 AM, chris carleton wrote:
>
> >
> > Thanks for the response. I'm sorry I didn
On 30/09/2009, at 5:34 AM, chris carleton wrote:
Thanks for the response. I'm sorry I didn't provide the code or
data example earlier. I was using the polynomial fitting technique
of this form;
test <- lm(x[,34] ~ I(x[,1]) + I(x[,1]^2) + I(x[,1]^3))
for the original fitting operation. I
sponding x values from the data before
fitting the poly and the result was the same coefficients. Thanks very much to
anyone who is willing to provide information.
Chris Carleton
> CC: r-help@r-project.org
> From: r.tur...@auckland.ac.nz
> Subject: Re: [R] Polynomial Fitting
> Date: T
On 29/09/2009, at 10:52 AM, chris carleton wrote:
Hello All,
This might seem elementary to everyone, but please bear with me. I've
just spent some time fitting poly functions to time series data in R
using lm() and predict(). I want to analyze the functions once I've
fit them to the vario
Hello All,
This might seem elementary to everyone, but please bear with me. I've
just spent some time fitting poly functions to time series data in R
using lm() and predict(). I want to analyze the functions once I've
fit them to the various data I'm studying. However, after pulling the
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
cc
> project.org
> Subject
> [R] Polynomial fitting
> 01/07/2008 09:16
> AM
>
>
>
>
>
>
>
>
> I wonder how one in R can fit a 3rd degre
project.org
Subject
[R] Polynomial fitting
01/07/2008 09:16
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
4.8750
nuary 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 re
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
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