similar to: Summary 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

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

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 =

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

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) > #

Platform: WIN2000 Version of R: 1.6.2 I'm interested in plotting fitted values in a trellis xyplot. I believe the following should work; however, I only get the points (not the fitted lines). library(lattice) trellis.device(bg="white") xyplot(MULTDV~TIME|SUBNUM,data=TEMP, panel=function(x,y){ panel.xyplot(x,y) lines(x,fitted(lm(y~poly(x,1),na.action=na.omit)))

I apologize if this is a FAQ -- I kind of recall seeing something along these lines before, but I couldn't find the message when I searched the archives. Problem: 1. I have hundreds of small files in a subdirectory ("c:\\temp") and I would like to combine the files into a single data frame. 2. Individually, it is easy to read each file

I'm trying to add a set of reference lines to a multipanel xyplot xyplot(y ~ x | Visit, panel = function(x, y, ...){ panel.xyplot(x, y, ...) abline(v = c(0.5, 1)) }) However, the reference lines are different for different visits. For example, for the first 2 visits, I'd like vertical lines at x = 0.5 and 1. For visits 3 and four, I'd like vertical lines at x = 1 and 1.5. I can

I'm using R V 2.2.1. When I try an example from the as.Date help page, I get an error. > x <- c("1jan1960", "2jan1960", "31mar1960", "30jul1960") > z <- as.Date(x, "%d%b%Y") Error in strptime(x, format) : 2 arguments passed to 'strptime' which requires 3 > Any suggestions would be appreciated. Thanks, Rob

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

hi im starting with R.....have no idea to start...plz help -- View this message in context: http://www.nabble.com/new-to-R-tp19342903p19342903.html Sent from the R help mailing list archive at Nabble.com.

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

R Version 2.2.0 Platform: Windows When I use barplot but select a ylim value greater than zero, the graph is distorted. The bars extend below the bottom of the graph. For instance the command produces a problematic graph. barplot(c(200,300,250,350),ylim=c(150,400)) Any help would be appreciated. Paul [[alternative HTML version deleted]]

We have been using Least Angle Regression (lars) to help identify predictors in models where the outcome is continuous. To do so we have been relying on the lars package. Theoretically, it should be possible to use the lars procedure within a general linear model (glm) framework - we are particular interested in a logistic regression model. Does anyone have examples of using lars with logistic

Hi R-Wizards: I've looking through the R docs and have yet to find what I'm looking for and have tried a few intermediate steps to now avail yet and rather than spend another few hours looking for the solution, I figured I would post a message. I have a matrix (actually a set of them) that I want to pull all the names of the non-zero columns into a vector/list for further processing:

sorry, I had a typo there, it's datadist(b) for the analysis of data frame "b". --- Robert Balshaw <Robert.Balshaw at syreon.com> wrote: > Not sure if this will help, but did you mean to use > datadist(a) for > the analysis of B? > > Rob > > > -----Original Message----- > > From: r-help-bounces at stat.math.ethz.ch > >

hi all i have a question that about the eigen analysis found in R and in eviews. i used the same data set in the two packages and found different answers. which is incorrect? the data is: aa ( a correlation matrix) 1 0.9801 0.9801 0.9801 0.9801 0.9801 1 0.9801 0.9801 0.9801 0.9801 0.9801 1 0.9801 0.9801 0.9801 0.9801 0.9801 1 0.9801 0.9801 0.9801 0.9801 0.9801 1 now > svd(aa) $d [1] 4.9204

Dear all using the orthogonal polymial on a set of data, I get an approximate density which basically is in the form: exp(-polynomial), as you know, the parameters are the converged coeeficients. obviously, It is hard, if not impossible, to use the inverse CDF method to get a sample and then plot density. then how can I plot the approximated density in order to have a graphical comparision

How do you remove one of the terms from an ordered polynomial contrast in your linear model. For example, I have significant terms for linear and cubic but not quadratic, how would i remove the quadratic term from lm(response~treatment) Cheers, Chris -- View this message in context: http://www.nabble.com/Orthogonal-polynomial-contrasts-tp16608353p16608353.html Sent from the R help mailing list

Looking for help with the following problem. Given a sample of zeros and ones, for example: > VECTOR1<-rep(c(1,0),c(15,10)) > VECTOR1 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 How would I create a new sample (VECTOR2) also containing zeros and ones, in which the phi-coefficient between the two sample vectors was drawn from a population with a known