Displaying 20 results from an estimated 2000 matches similar to: "Orthogonal polynomials and poly"
2010 Dec 08
2
Legendre polynomials
Hello everyone,
I would like to find out if there are already implemented function for legendre
polynomials. I tried google but returns nothing. How do you suggest me to search
for that?
Regards
Alex
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2013 Oct 11
3
Gaussian Quadrature for arbitrary PDF
Hi all,
We know that Hermite polynomial is for
Gaussian, Laguerre polynomial for Exponential
distribution, Legendre polynomial for uniform
distribution, Jacobi polynomial for Beta distribution. Does anyone know
which kind of polynomial deals with the log-normal, Student抯 t, Inverse
gamma and Fisher抯 F distribution?
Thank you in advance!
David
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2008 Oct 16
3
defining a function using strings
Hi All,
I need to evaluate a series expansion using Legendre polynomials.
Using the 'orthopolinom' package I can get a list of the first n
Legendre polynomials as character strings.
> library(orthopolynom)
> l<-legendre.polynomials(4)
> l
[[1]]
1
[[2]]
x
[[3]]
-0.5 + 1.5*x^2
[[4]]
-1.5*x + 2.5*x^3
[[5]]
0.375 - 3.75*x^2 + 4.375*x^4
But I can't figure out how to
2009 Feb 08
0
recursive derivative a list of polynomials
Dear list,
This is quite a specific question requiring the package orthopolynom.
This package provides a nice implementation of the Legendre
polynomials, however I need the associated Legendre polynomial which
can be readily expressed in terms of the mth order derivative of the
corresponding Legendre polynomial. (For the curious, I'm trying to
calculate spherical harmonics [*]).
2007 Feb 12
1
How to get the polynomials out of poly()
Hi Folks!
Im using the function poly to generate orthogonal polynomials, but Id like
to see the actual polynomials so that I could convert it to a polynomial
in my original variable. Is that possible and if so how do I do it?
/E
2002 Oct 08
2
Orthogonal Polynomials
Looking to the wonderful statistical advice that this group can offer.
In behavioral science applications of stats, we are often introduced to
coefficients for orthogonal polynomials that are nice integers. For
instance, Kirk's experimental design book presents the following
coefficients for p=4:
Linear -3 -1 1 3
Quadratic 1 -1 -1 1
Cubic -1 3 -3 1
In R orthogonal
2011 Jan 25
0
Multivariate polynomials Howto
Good Evening,
I would like to work with multivariate polynomials (x and y variables).
I know that there is a package called multipol but I am not sure that supports my needs.
I use a function (in reality legendre.polynomials) which creates me the polynomials I want.
For example the following returns
> legendre.polynomials(2)[[2]]
x (first order polynomial)
I would like to calculate the
2006 Jan 26
2
Prediction when using orthogonal polynomials in regression
Folks,
I'm doing fine with using orthogonal polynomials in a regression context:
# We will deal with noisy data from the d.g.p. y = sin(x) + e
x <- seq(0, 3.141592654, length.out=20)
y <- sin(x) + 0.1*rnorm(10)
d <- lm(y ~ poly(x, 4))
plot(x, y, type="l"); lines(x, d$fitted.values, col="blue") # Fits great!
all.equal(as.numeric(d$coefficients[1] + m
2011 Jan 26
0
Bivariate polynomials in R
Have you ever worked in R with bivariate polynomials? How did you implement simple operators like addition/multiplication?
I found a package called multipol that seems to support these kinds of operators but I do keep receiving error.
Check for example the following snippet of code (you can copy & paste)
require('orthopolynom')
require('polynom')
require('multipol')
2006 Apr 28
1
gauss.quad.prob
I've written a series of functions that evaluates an integral from -inf to a or b to +inf using equally spaced quadrature points along a normal distribution from -10 to +10 moving in increments of .01. These functions are working and give very good approximations, but I think they are computationally wasteful as I am evaluating the function at *many* points.
Instead, I would prefer to use
2002 Oct 09
1
Summary Orthogonal Polynomials
As usual, the R newsgroup set me straight (thanks to Douglas Bates, Robert
Balshaw and Albyn Jones).
There is really no difference between using orthogonal polynomials of the
form:
Linear -3 -1 1 3
Quadratic 1 -1 -1 1
Cubic -1 3 -3 1
Versus
> poly(c(1:4),3)
1 2 3
[1,] -0.6708204 0.5 -0.2236068
[2,] -0.2236068 -0.5 0.6708204
[3,] 0.2236068
2005 Jun 14
2
ordinary polynomial coefficients from orthogonal polynomials?
How can ordinary polynomial coefficients be calculated
from an orthogonal polynomial fit?
I'm trying to do something like find a,b,c,d from
lm(billions ~ a+b*decade+c*decade^2+d*decade^3)
but that gives: "Error in eval(expr, envir, enclos) :
Object "a" not found"
> decade <- c(1950, 1960, 1970, 1980, 1990)
> billions <- c(3.5, 5, 7.5, 13, 40)
> #
2009 Jan 02
1
R: numerical integration problems
hello all
happy new year and hope you r having a good holiday.
i would like to calculate the expectation of a particular random variable and would like to approximate it using a number of the functions contained in R. decided to do some experimentation on a trivial example.
example
========
suppose x(i)~N(0,s2) where s2 = the variance
the prior for s2 = p(s2)~IG(a,b)
so the posterior is
2001 Jul 09
1
polynomial regression and poly
When doing polynomial regression I believe it is a good idea to use the poly
function to generate orthogonal polynomials. When doing this in Splus there
is a handy function (transform.poly I think) to convert the coefficients
produced by regression with the poly function back to the original scale.
Has somebody written something similar for R ?
Robert
2006 Feb 22
1
Gram-Charlier series
Good day everyone,
I want to use the Gram-Charlier series expansion to model
some data. To do that, I need functions to:
1) Calculate 'n' moments from given data
2) Transform 'n' moments to 'n' central moments, or
3) Transform 'n' moments to 'n' cumulants
4) Calculate a number of Hermite polynomials
Are there R-functions to do any of the above?
2004 May 06
5
Orthogonal Polynomial Regression Parameter Estimation
Dear all,
Can any one tell me how can i perform Orthogonal
Polynomial Regression parameter estimation in R?
--------------------------------------------
Here is an "Orthogonal Polynomial" Regression problem
collected from Draper, Smith(1981), page 269. Note
that only value of alpha0 (intercept term) and signs
of each estimate match with the result obtained from
coef(orth.fit). What
2007 Apr 30
0
Intercept Coefficient in a Model with Orthogonal Polynomials
This very likely falls in the category of an unexpected result due to
user ignorance. I generated the following data:
time <- 0:10
set.seed(4302007)
y <- 268 + -9*time + .4*(time^2) + rnorm(11, 0, .1)
I then fit models using both orthogonal and raw polynomials:
fit1 <- lm(y ~ poly(time, 2))
fit2 <- lm(y ~ poly(time, degree=2, raw=TRUE))
> predict(fit1, data.frame(time =
2008 Mar 27
2
assistance with RDAtest beta version application
Pierre Legendre has developed a beta version of a new redundancy analysis package called RdaTest that is available on his web page at the Universit® de Montréal. The test example that is included with the package is based on the example provided in his book (Numerical Ecology, Chapter 11 (Legendre & Legendre 1998))
I have downloaded the package and am attempting to run it so that I might
2008 Sep 27
3
Double integration - Gauss Quadrature
Hi,
I would like to solve a double integral of the form
\int_0^1 \int_0^1 x*y dx dy
using Gauss Quadrature.
I know that I can use R's integrate function to calculate it:
integrate(function(y) {
sapply(y, function(y) {
integrate(function(x) x*y, 0, 1)$value
})
}, 0, 1)
but I would like to use Gauss Quadrature to do it.
I have written the following code (using R's statmod package)
2011 Feb 02
2
unequally spaced factor levels orthogonal polynomial contrasts coefficients trend analysis
Hello [R]-help
I am trying to find
> a package where you can do ANOVA based trend analysis on grouped data
> using orthogonal polynomial contrasts coefficients, for unequally
> spaced factor levels. The closest hit I've had is from this web site:
>(http://webcache.googleusercontent.com/search?q=cache:xN4K_KGuYGcJ:www.datavis.ca/sasmac/orpoly.html+Orthogonal+polynomial
>l
but I