similar to: Elementary Symmetric Polynomials

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

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

Dear All, I have found in the poly help this sentence: The orthogonal polynomial is summarized by the coefficients, which can be used to evaluate it via the three-term recursion given in Kennedy & Gentle (1980, pp. 343–4), and used in the predict part of the code. My question: which type of orthogonal polynomials are used by this function? Hrmite, legendre.. TIA Giovanni [[alternative HTML

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 [[alternative HTML version deleted]]

Dear All, Need some help in polynomials transformation to get the coefficients. I have tried "poly.transform" as applied in S-plus but it does not work. Thanks in advanced for any helps. Regards. Abd. Rahman Kassim (PhD) Head Forest Ecology Branch Forest Management & Ecology Program Forestry and Conservation Division Forest Research Institute Malaysia Kepong 52109 Selangor,

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

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

Hi: I am using the package "KernSmooth" to do the local polynomial regression. However, it seems the function "locpoly" can only deal with univariate covaraite. I wonder is there any kernel smoothing package in R can deal with bivariate covariates? I also checked the package "lcofit" in which function "lcofit" can indeed deal with bivariate case. The

I'm having trouble reshaping a data.frame from long to wide. (I think that's the right terminology; feel free to educate me.) I've looked at the reshape function and package and plyr package, but I can't quite figure out how to do this after a dozen variations. I have a data.frame with more levels than this, but similar to: > tst K1 K2 K3 V1 V2 V3 1 10 D a 0.08 99

I wanted to try out package distrMod, so I did > install.packages('distrMod') > library(distrMod) and played around, saved and quit. Now whenever I start up in this directory, I get distr and lots of other stuff loaded and lots of messages. How do I keep it from automatically loading, other than starting over in another directory? I read ?Startup, but I couldn't suss out

Hello everyone, I hope this is a fair enough question, but I don’t have access to a copy of Bates and Pinheiro. It is probably quite obvious but the answer might be of general interest. If I fit a fixed effect with an added quadratic term and then do it as an orthogonal polynomial using maximum likelihood I get the expected result- they have the same logLik.

Dear R Community, I am pleased to announce the beta-release of the prob package. The source code is now on CRAN, and binaries should be generated there before long. In the meantime, you can get it with install.packages("prob", repos = "http://r-forge.r-project.org") The prob package gives a framework for doing elementary probability on finite sample spaces in R. The

Dear R Community, I am pleased to announce the beta-release of the prob package. The source code is now on CRAN, and binaries should be generated there before long. In the meantime, you can get it with install.packages("prob", repos = "http://r-forge.r-project.org") The prob package gives a framework for doing elementary probability on finite sample spaces in R. The

I would have thought this to be relatively elementary, but I can't find it mentioned in any of my stats texts. Please consider the following: library(fitdistrplus) fp = fitdist(y,"exp"); rate = fp$estimate; sd = fp$sd fOneWeek = exp(-rate*7); #fraction that happens within a week - y is measured in days fr = exp(-rate*dt); #fraction remaining - dt = elapsed time from

Ajay, thank you very much for picking up that age-old habit of posting summaries. It existed years ago on s-help and I find it is still a great thing: I would not have bothered to read your original question nor the answers you got, but I did read the summary -- and I learned something quite interesting! Maybe some others who receive multiple non-elementary answers to their questions could

Hi, we are about to update some class definitions in our distrXXX family of packages, so I would be eager to know whether there are plans in R Core to implement some version management tools for S4 classes as described in section 7.4 in JMC's "Green Book". In his recent book (continuing the color scheme, is it to be called the "Yellow Book"? :-), this topic has not been

I am discovering sapply! :-) Could you please help me with a very elementary question? Here is what I know. The following two programs generate the same answer. --------------------------------+---------------------------------------- Loops version | sapply version --------------------------------+----------------------------------------

I am grateful to Andy Liaw, Douglas Grove, Brian Ripley, Tony Plate, Dirk Eddelbuettel and Sundar Dorai-Raj all of whom got together and drilled sense into my skull. I would like to take some effort into explaining what the question was, that I was grappling with, and the (nice) R way of solving the question. My apologies: I am still a victim of too many years of writing C, so I'm a bit dense

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 [*]).

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