similar to: Orthogonal polynomial contrasts

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

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

Dear R-experts, my goal is to visualize the following polynomial regression as a 3D-surface: Z = b0 + b1*X + b2*Y + b3*XY + b4*X^2 + b5*Y^2 I believe that a solution to this problem may be of interest to a wider range of scientists because the problem is a derivative of a more general problem, i.e.: how to describe the relationship between one dependent variable and the DIFFERENCE between two

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

Dear all, I have a question regarding the use of stepAIC and polynomial (quadratic to be specific) terms in a binary logistic regression model. I read in McCullagh and Nelder, (1989, p 89) and as far as I remember from my statistics cources, higher-degree polynomial effects should not be included without the main effects. If I understand this correctly, following a stepwise model selection based

Hi all! I'm getting a model fit from glm() (a binary logistic regression fit, but I don't think that's important) for a formula that contains powers of the explanatory variable up to fourth. So the fit looks something like this (typing into mail; the actual fit code is complicated because it involves step-down and so forth): x_sq <- x * x x_cb <- x * x * x x_qt <- x * x * x

Dear all, I do not seem to grasp how contrasts are set for ordered factors. Perhaps someone can elighten me? When I work with ordered factors, I would often like to be able to reduce the used polynomial to a simpler one (where possible). Thus, I would like to explicetly code the polynomial but ideally, the intial model (thus, the full polynomial) would be identical to one with an ordered factor.

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) > #

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

Hi, I am new-comer to statistics and R-Project. I would like to know if these features can be attained in R-Project.Please help. 1) beta 1 and Beta 2, or gamma one and gamma two for skewness and kurtosis, respectively, including standard errors and tests for significance (relative to values for a Gaussian distribution). 2) linear correlation 3) quadratic regression 4) polynomial regression

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

Dear Thomas, Unfortunately, I do not know if any packages implement this functionality. Though, it is a topic that interests me. Unlike the "classic discriminant", I prefer to work with the reduced polynomial. This "discriminant" is generalizable to a superset of Chebysev polynomials (which I called Cardano-polynomials). x^3 - 3*c*x - 2*d = 0 x^5 - 5*c*x^3 + 5*c^2*x - 2*d =

Dear all, I am quite new to R and I am having trouble writing the polynomial contrasts for an ordinal factor in MANOVA. # I have a model such as this fit<-manova(cbind(Y1,Y2,Y3)~Groups,data=Events) # where groups is an ordinal factor with 4 levels # how to set polynomial contrasts for the "Groups" factor ? Thank you very much in advance for any help! Best regards, Mauro -- Dr.

Hi R experts, Could you please help me to fit a linear, cubic and quadratic curve in a figure? I was trying to show all these three fitting curves with different colour in one figure. I spent substantial time to figure it out, but I could not. I have given here a example and what I did for linear, but no idea for cubic and quadratic fitting curve > dput(test) structure(list(sp = c(4L, 5L,

Colleagues Given the coefficients of a cubic polynomial, a,b,c,d and using discriminant_cubic <- function(a, b, c, d) { D <- 18 * a * b * c * d - 4 * b^3 * d + b^2 * c^2 - 4 * a * c^2 - 27 * a^2 * d^2 return(D) } I can find the discriminant of a cubic polynomial. Is there an R package which can do this? Thomas Subia

I have a variable which is roughly age categories in decades. In the original data, it came in coded: > str(xxx) 'data.frame': 58271 obs. of 29 variables: $ issuecat : Factor w/ 5 levels "0 - 39","40 - 49",..: 1 1 1 1... snip I then defined issuecat as ordered: > xxx$issuecat<-as.ordered(xxx$issuecat) When I include issuecat in a glm model, the result

Hi, using polynomial contrasts for the ordered factors in an experiment leads to much nicer covariance structure than using treatment contrasts. It is easy to assess the mean effect for each of the experimental groups. However, standard errors are provided only for the components of the orthogonal contrasts. I wonder how to assess the standard errors not of the components, but of the respective

?? If I haven't misunderstood, they are completely different! 1) NIR must be a matrix, or poly(NIR,...) will fail. 2) Due to the previously identified bug in poly, degree must be explicitly given as poly(NIR, degree =2,raw = TRUE). Now consider the following example: > df <-matrix(runif(60),ncol=3) > y <- runif(20) > mdl1 <-lm(y~df*I(df^2)) > mdl2

Hello R users, Given a linear (in the parameters) regression model where one predictor x interacts with time and time*time (ie, a quadratic effect of time t): y = b0 + b1(x) + b2(t) + b3(t^2) + b4(x*t) + b5(x*t^2) + e, I would like to construct 95% confidence bands (optimally, shaded) around this function: *dy* = b1 + b4(t) + b5(t^2) *dx* That is, the partial effect of x on y changing over

In S-Plus, I can obtain polynomial contrasts for an ordered factor with contr.poly(). The function also exists in R, however is limited to factors where the levels are equally spaced. In S-Plus, one can obtain the contrasts for a set of numeric values representing unequally spaced ordered factors. Has anyone implemented this in R? I see that the S-Plus function calls another function (poly.raw())