similar to: poly(*,*) in lm() (PR#8972)

Hi all, I try to interpolate a data set in the form: time Erg 0.000000 48.650000 1.500000 56.080000 3.000000 38.330000 4.500000 49.650000 6.000000 61.390000 7.500000 51.250000 9.000000 50.450000 10.500000 55.110000 12.000000 61.120000 18.000000 61.260000 24.000000 62.670000 36.000000 63.670000 48.000000 74.880000 I want to get smoothed splines by using the class gam The first way I tried , was

Does anyone know of a function that turns the coefficients of lm( y~ poly(x1 , x2, degree=2)) into something that can be interpreted easily. I was think along the lines of the matrix representation of quadratic forms: (x-mu)'A(x-mu) +k , and finding the eigenvectors/values of A, and the vector mu, but anything that allows me to visualise a contour plot would be great. Thanks Simon Bond

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

Hello List, I am trying to do the following: 1. Use a BandPassFilter h[n] on a series x[n] 2. Then Decimate the series by a factor D such that y[k]=x[k*D] The decimation factor is considerable (10,000) so that filtering before the decimation, using convolve(), seems foolish. Can you think of an R implementation for a polyphase filter, that uses the fact that most samples will be thrown away...?

Hi Tobi, http://lab.llvm.org:8011/builders/perf-x86_64-penryn-O3-polly Looks like one of your boxes are bad and the other is good, but since they're both on the same bot, it looks to use as if it's unstable, spamming people every other build. :) Can you have a look, please? cheers, --renato

Full_Name: David Clifford Version: Version 1.5.1 (2002-06-17) OS: Red Hat 7.3 Submission from: (NULL) (128.135.149.55) For n values above 100 there appears to be a bug in contr.poly(n). The contrast matrix should have rank n-1. Running the code below gives output (ie errors) at n=98, 100 and every value greater than 102. for(n in 2:150) { K <- contr.poly(n) rnk <-

The poly() function can create more variables than can be fitted when there are replicated values. In the example below, 'x' has only 5 distinct values, but I can apparently fit a 12th-degree polynomial with no error messages or even nonzero coefficients: R> x = rep(1:5,3) R> y = rnorm(15) R> lm(y ~ poly(x, 12)) Call: lm(formula = y ~ poly(x, 12)) Coefficients:

DDear developeRs, about a year ago, Alex Stolpovsky posted an issue with predict.lm on a fit generated using poly with the raw=TRUE option and too few new data (slightly modified reproducible example below). Alex did not get any reply. I have just stumbled on the same problem, and I think that this is a bug of function poly, which arises from the check whether the polynomial degree is

Dear R People: Suppose I want to estimate the parameters of the following AR model: (1 - phi_1 B - phi_2 B^2 - phi_9 B^9) x_t = a_t and I want to use the arima0 command from the ts library. How would I use the order subcommand, please? R Version 1.4.1 for Windows. Thanks! Sincerely, Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston -

Dear all, I am trying to transform polynomial coefficients from orthogonal form to the standard power basis. There's poly.transform in S-plus. Does anybody know how to do that in R ? I've found question about that in the archives of R-help but no real answer. Example : I'm doing polynomial regression of percentage of one insect in a community on altitude, precipitations,

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 r-help, I'm fitting a model with lm() and using the orthogonal polynomials from poly() as my basis: dat <- read.csv("ConsolidatedData.csv", header=TRUE) attach(dat) nrows <- 1925 Rad <- poly(Radius, 2) ntheta <- 14 Theta <- poly(T.Angle..deg., ntheta) nbeta <- 4 Beta <- poly(B.Beta..deg., nbeta) model.1 <- lm( Measurement ~ Block + Rad + Theta + Beta

I have a list of polygons generated by the contourLines() command (each object of the list is a list in itself with two objects: a vector of x values, and a vector of y values for each vertex). I wish to convert that list into a gpc.poly object of multiple contours. How do I do this? gpclib apparently has no method of coercing lists into the gpc.poly object type. As well, when I have a

Dear R experts, I am observing undesired behavior of predict(fit, newdata), in case when fit object is produced by lm() involving a poly(). Here is how to reproduce: x <- c(1:10) y <- sin(c(1:10)) fit <- lm(formula=y~poly(x, 5, raw=TRUE)) predict(fit, newdata=data.frame(x=c(1:10))) ## this works predict(fit, newdata=data.frame(x=c(1:1))) ## this is broken, error below Error in poly(x,

Dear UseRs, I just learnt that the number of columns of a data frame is not always what I thought it to be, and I wonder where I should have learnt about this. Consider the following example: dat <- data.frame(X1=1:10, X2=LETTERS[1:10]) ncol(dat) ## evaluates to 2 (of course) dat$X1poly <- poly(dat$X1,3) dat ## five columns displayed ncol(dat) ##

By any chance is anyone aware of an R function that duplicates Octave's poly function? Here is a description of Octave's poly function: Function File: poly (A) If A is a square N-by-N matrix, `poly (A)' is the row vector of the coefficients of `det (z * eye (N) - a)', the characteristic polynomial of A. As an example we can use this to find the eigenvalues

Dear r-devel list members, I've recently encountered the following problem using predict() with a model that has raw-polynomial terms. (Actually, I encountered the problem using model.frame(), but the source of the error is the same.) The problem is technical and concerns the design of poly(), which is why I'm sending this message to r-devel rather than r-help. To illustrate:

hi all, On 2010-1-7 0:11, John Mosby wrote: > In LLVM we could add support for generalized CFG regions and > RegionPasses. A region is a part of the CFG. The only information we > have is, that it has one entry and one exit, this it can be optimized > separately. > I think this is the best way to add region analysis. I must admit > this approach > helps me on another,

How can I get the result of, e.g., poly(1:3. degree=2) to give me the unnormalized integer coefficients usually used to explain orthogonal polynomial contrasts, e.g, -1 1 0 -2 1 1 As I understand things, the columns of x^{1:degree} are first centered and then are normalized by 1/sqrt(col sum of squares), but I can't see how to relate this to what is returned by 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