similar to: Symbolic differentiation ("D","deriv", etc.) (PR#1928)

I feel like a complete dolt, as I know this question has been asked by others on a fairly regular basis, but I'm going in circles trying to get the following to work: id.prob<-function (tt) { library(mvtnorm) #============================ Makeham<-function(tt) { a2=0.030386513 a3=0.006688287 b3=0.039047537 t<-tt-20 h.t<-a2+a3*exp(b3*t) S.t<-exp(-a2*t+a3/b3*(1-exp(b3*t)))

Dear R listers, I have a data file looks like the following: Testing marker: s_1 --------------------------------------------- Allele df(0) -LnLk(0) df(T) -LnLk(T) ChiSq p 3 7995 29320.30 7994 29311.85 16.90 4e-05 (2229/8000 probands) Testing marker: s_2 --------------------------------------------- Allele df(0)

Dear Sir/ Madam, I'm having trouble de-bugging the following - which works perfectly well with optim or optimx - but not with mle2. I'd be really grateful if someone could show me what is wrong. Many thanks in advance. JSC: gompertz<- function (x,t=data) { a3<-x[1] b3<-x[2] shift<-data[1] h.t<-a3*exp(b3*(t-shift))

Hi, all. I'm working with published paleodemographic data (counts of skeletons that have been assigned into an age-range category, based upon observed morphological characteristics). For example, the following is the age distribution from a prehistoric cemetery in Egypt: naga <-

Hi all, I wrote a symbollic differentiation function in R, which can be downloaded here: http://www.econ.upenn.edu/~clausen/computing/Deriv.R http://www.econ.upenn.edu/~clausen/computing/Simplify.R It is just a prototype. Of course, R already contains two differentiation functions: D and deriv. However, these functions have several limitations. They can probably be fixed, but

Ah... I searched for half an hour for this function... you know, the help function in R could really be a lot better... But wait a minute... looking at this, it appears you have to pass in an expression. What if it is an unknown function, where you only have a handle to the function, but you cannot see it's implementation ? Will this work then ? -----Original Message----- From: Berton Gunter

Hi, I have been trying to do numerical differentiation using R. I found some old S code using Richardson Extrapolation which I managed to get to work. I am posting it here in case anyone needs it. ######################################################################## richardson.grad <- function(func, x, d=0.01, eps=1e-4, r=6, show=F){ # This function calculates a numerical approximation

Hi, Could anyone tell me what is the command used in R to do 1. Differentiation 2. Newton Raphson method (Numerical Analysis in general...) Are there any packages separately for this? Thanks for your help! BR, Shubha [[alternative HTML version deleted]]

Hi R People: Is there a package for automatic differentiation, please? thanks in advance, Erin -- Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston - Downtown mailto: erinm.hodgess at gmail.com

Hi I recently gave a presentation about Automatic Differentiation (AD) and R at the Eighth Euro AD Workshop in Oxford (17/07/09). The presentation was intended as a general introduction to R and the desire for a generic AD interface for R. During the presentation I emphasised the need and the high level of interest that the R community has in developing such an interface and that input from the

Hi all, I am not used to using the computer to do calculus and have up to now done my differentiation "by hand" , calling on skills I learned many years ago and some standard cheat sheets. My interest at present is in getting the second derivative of a gaussian, which I did by hand and results in a somewhat messy result involving terms in sigma^5 .. I have done some spot checks

Dear everyone, the following is obviously used to compute the nth derivative, which seems to work (deriv(sqrt(1 - x^2),x,n)) However, before using this, I wanted to make sure it does what I think it does but can't figure it out when reading the ?deriv info or any other documentation on deriv for that matter: deriv(expr, namevec, function.arg = NULL, tag = ".expr", hessian = FALSE,

Dear list, Hi, I am trying to get the second derivative of a logistic formula, in R summary the model is given as : ### >$nls >Nonlinear regression model >model: data ~ logistic(time, A, mu, lambda, addpar) >data: parent.frame() > A mu lambda >0.53243 0.03741 6.94296 ### but I know the formula used is #

Martin (see below) gives a good explanation of the difference between AD and symbolic differentiations. I'm of the opinion we can use both. However, the real issue as far as I'm concerned (from an optimizer's point of view, which may also be that of ODE and PDE folk) is that right now none of the offerings that we have are easy to use. Indeed, usability is one of the key issues in my

Dear ExpeRts, I have trouble implementing a function which computes the k-th derivative of a specified function f and returns it as a function. I tried to adapt what I found under ?deriv but could not get it to work. Here is how it should look like: ## specify the function f <- function (x,alpha) x^alpha ## higher derivatives DD <- function(expr, variable, order = 1) { if(order <

Dear all, I am the author of an open-source package for mathematical optimization and automatic differentiation called CasADi (www.casadi.org) and have recently started realize the potential of the LLVM project. At the core of CasADi are two fast interpretors for mathematical expressions and I'm now planning to complement these with JIT-compilation using LLVM. Does anyone know if there is

Dear all, I'm wondering if there are some ongoing projects for interior point methods in R (e.g. linking ipopt from Coin written in C++ to R) and for automatic differentiation in R (e.g. linking openAD available in C++ and Fortran) ? Many thanks, David [[alternative HTML version deleted]]

Hi, I have a scalar valued function with several variables. One of the variables is restricted to be non-negative. For example, f(x,y)=sqrt(x)*exp(y), then x should be non-negative. I need the gradient and hessian for some vector (0,y), i.e., I need the gradient and hessian at the boudary of parameter space. The "numderiv" package does not work, even for f(x)=sqrt(x), if you do

Hi, no-one else has said anything more pertinent so here's my two-pence. I have been thinking for a while about LLVM in the context of simulating _small_ stochastic systems by which I mean very much non-trivial stochastic transition functions, but still small enough that if compiled carefully down to machine code via LLVM with a good chance that they'll be faster. (With even

Could somebody share some tips on implementing multivariate integration and partial differentiation in R? For example, for a trivariate joint distribution (cumulative density function) of F(x,y,z), how to differentiate with respect to x and get the bivariate distribution (probability density function) of f(y,z). Or integrate f(x,y,z) with respect to x to get bivariate distribution of (y,z). Your