similar to: Discriminant of a cubic polynomial

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 am looking for a "multiple discriminant analysis" function It seems to be called DISCR in Splus, but I can't find it in R, and R-packages. Thank for your help -- Anne BADEL-CHAGNON Email:badel at urbb.jussieu.fr Equipe de Bioinformatique Mol?culaire, Universite Paris 7 Tour 53, 1er etage, case 7113 Tel : 01.44.27.77.14 75251 Paris cedex 05 Fax :

Dear R Users, Are there any tools to extract the function names called by reverse-dependencies? I would like to group these functions using clustering methods based on the co-occurrence in the reverse-dependencies. Utility: It may be possible to split complex packages into modules with fewer reverse-dependencies. Package pkgdepR may offer some of the functionality; but I did not have time to

?.S3methods f <- function()(2) > length(.S3methods(f)) [1] 0 > length(.S3methods(print)) [1] 206 There may be better ways, but this is what came to my mind. -- Bert On Wed, Mar 8, 2023 at 11:09?AM Leonard Mada via R-help < r-help at r-project.org> wrote: > Dear R-Users, > > I want to change the args() function to return by default the arguments > of the default

Dear Konrad, I presume that the system can be written as follows, where h0, d0, ga0, kga and kd are given: err1 = h + hd + hga - h0; err2 = d + hd - d0; err3 = ga + hga - ga0; err4 = hga - kga*h*ga; err5 = hd - kd*h*d; All error terms should be zero. Do you need (a) the symbolic solution or (b) is a numeric solution fine? I do not have any experience with yacas or caracas. But see below. ###

Dear R-Users, I want to change the args() function to return by default the arguments of the default generic function: args = function(name, default = TRUE) { ?? ?# TODO: && is.function.generic(); ?? ?if(default) { ?? ???? fn = match.call()[[2]]; ?? ???? fn = paste0(as.character(fn), ".default"); ?? ???? name = fn; ?? ?} ?? ?.Internal(args(name)); } Is there a nice way

Dear Rich, It depends how the data is generated. Although I am not an expert in ecology, I can explain it based on a biomedical example. Certain variables are generated geometrically (exponentially), e.g. MIC or Titer. MIC = Minimum Inhibitory Concentration for bacterial resistance Titer = dilution which still has an effect, e.g. serially diluting blood samples; Obviously, diluting the

Here are the functions documented in the Blue Book that I found missing in R (ignoring the ones which are obviously outdated). aggregate allocated amatch axes chull clorder cutree cycle date debugger dget discr faces interp l1fit labclust lag loglin monthplot mstree mulbar napsack odometer persp plclust plotfit rep.int restore rreg sabl sablplot set.seed smooth sort.list Stable stars

Dear Gregg, Thank you for the fast response. I believe though that isGeneric works only for S4-functions: isGeneric("plot") # FALSE I still try to get it to work. Sincerely, Leonard On 3/8/2023 9:13 PM, Gregg Powell wrote: > Yes, there is a way to check if a function is generic. You can use the isGeneric function to check if a function is generic. Here's an updated version

>>>>> Ross Ihaka writes: > I have got a little side-tracked (from graphics) and am putting > together a little multivariate analysis library. This is just > intended to be a "core" library rather than anything exhaustive. > Mainly it is a matter of putting togther code which already exists at > StatLib. Here is my present list (only some of which is

hello list, when I use method lda of the MASS package I experience a warning: variables are collinear in: lda.default(data[train, ], classes[train]) Is there an easy way to recover from this issue within the MASS package? Or how can I tell how severe this issue is at all? I understand that I shouldn't use lda at all with collinear data and should use "quadratische" (squared?)

I'm looking for a way to solve a linear equation system where the factors are polynomials: Here is a simplified example (To solve my problem, I have to deal with dimensions larger than 2): ( s + 2) x1 + (s - 3) x2 = 2 ( s^2 + 2s - 1) x1 - 2 x2 = 1 Theoretically the solution is easy: By performing polynomial multiplications, divisions and sums. I found out, that R is able to

Hi list, This was asked a couple of years ago but I can't find a resolution. Is there any way to get the coefficients from one of the local polynomial fits in locfit. I realize that locfit only constructs polynomials at a handful of intelligently selected points and uses interpolation to predict any other points. I would like to know the terms of the polynomials at these points. It seems

On 05/28/15 15:27, Yuanfang Chen wrote: > #define GET_BIT(lll) \ > // blah blah > > #define G(label1,label2) \ > { \ > // decent amount code \ > ... > while (1) { \ > GET_BIT(label2); \ > }; \ > } > > void f() { > if (..)

I'm trying to find a way to fit a polynomial of degree n in x and y to a set of x, y, and z data that I have and obtain the coefficients for the terms of the fitted polynomial. However, when I try to use the surf.ls function I'm getting odd results. > x <- seq(0, 10, length=50) > y <- x > f <- function (x, y) {x^2 + y} > library(spatial) > test <-

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

Hi! Does anyone know how to do the test for goodness of fit of a logistic model (in rms package) after running fit.mult.impute? I am using the rms and Hmisc packages to do a multiple imputation followed by a logistic regression model using lrm. Everything works fine until I try to run the test for goodness of fit: residuals(type=c("gof")) One needs to specify y=T and x=T in the fit. But

Dear R users, The package 'mfp' that fits fractional polynomial terms to predictors. Example: data(GBSG) f <- mfp(Surv(rfst, cens) ~ fp(age, df = 4, select = 0.05) + fp(prm, df = 4, select = 0.05), family = cox, data = GBSG) print(f) To describe the association between the original predictor, eg. age and risk for different values of age I can plot it the polynomials

Full_Name: Tamas K Papp Version: 2.4.0 OS: linux Submission from: (NULL) (140.180.166.160) I was using the lines.polynomial method for plotting piecewise polynomials (parts of splines). I needed a feature to limit the range of plotting using a parameter given to the function (as opposed to par("usr")). I think that the following changes would be a nice addition: lines.polynomial

Are there implementations of, e.g. Neville's algorithm, for interpolating polynomials through some data points? Nevilles' is an improvement on Lagrange interpolation. And how about interpolating rational functions? I could not find anything at rseek.org or at crantastic.org. thanks -- View this message in context: