similar to: finding roots of multivariate equation

I used the command below, but R gives me the error message--syntax error. can anyone see the mistakes I made? optimize(function(x,y) + ((327.727-(1-0.114^10)*y*(1-x)/x/(1-x^y))+(9517.336-327.727 *(1+(1-x)*(1+y)/x-327.727)))^2 + interval=c(0,1)) At the same time, I use nlm() but R gives me the code $code [1] 3 function(vals) { x <- vals[1] y <- vals[2]

i want to estimate parameters with maximum likelihood method with contraints (contant numbers). for example sum(Ai)=0 and sum(Bi)=0 i have done it without the constraints but i realised that i have to use the contraints. Without constraints(just a part-not complete): skellamreg_LL=function(parameters,z,design) { n=length(z); mu=parameters[1]; H=parameters[2]; Apar=parameters[3:10];

Hi, I would like to solve a system of coupled ordinary differential equations, where there is a delay (time lag) term. I would like to use the "lsoda" function "odesolve" package. However, I am not sure how to specify the delay term using the syntax allowed by odesolve. Here is an example of the kind of problem that I am trying to solve: > library(odesolve)

Hi, I have a data file, certain lines of which are character fields. I would like to skip these rows, and read the data file as a numeric data frame. I know that I can skip lines at the beginning with read.table and scan, but is there a way to skip a specified sequence of lines (e.g., 1, 2, 10, 11, 19, 20, 28, 29, etc.) ? If I read the entire data file, and then delete the character

Hi, I apologize for this repeat posting, which I first posted yesterday. I would appreciate any hints on solving this problem: I have two matrices A (m x 2) and B (n x 2), where m and n are large integers (on the order of 10^4). I am looking for an efficient way to create another matrix, W (m x n), which can be defined as follows: for (i in 1:m){ for (j in 1:n) { W[i,j] <-

Dear R-users I would like to integrate something like \int_k^\infty (1 - F(x)) dx, where F(.) is a cumulative distribution function. As mentioned in the "integrate" help-page: integrate(dnorm,0,20000) ## fails on many systems. This does not happen for an adaptive Simpson or Lobatto quadrature (cf. Matlab). Even though I am hardly familiar with numerical integration the implementation

> > >From: robert-mcfadden w o2.pl > > >Date: 2008/04/02 Wed AM 09:58:28 CDT > > >To: r-help w r-project.org > > >Subject: [R] Nonlinear equation > > > > hi: you need to give an example and details or > > you won't get much response, if any. Equation e.g. (A, B are known constants): 3log(gamma(x))-log(gamma(x)*gamma(2x))+(x-1)*A+B=0

Hi, I have a simple problem that I would appreciate getting some tips. I am using the "truehist" function within an "apply" call to plot multiple histograms. I can't figure out how to get truehist to use the column names of the matrix as the labels for the x-axis of the histograms. Here is a simple example: X <- matrix(runif(4000),ncol=4) colnames(X)

Hello. I have a script in which I repeatedly fit a nonlinear regression to a series of data sets using nls and the port algorithm from within a loop. The general structure of the loop is: for(i in 1:n){ … extract relevant vectors of dependent and independent variables … … estimate starting values for Amax and Q.LCP…

i'm trying to use the BB package to minimize the sum of the squared deviations for 2 vectors. The only thing am having trouble with is defining the project constraint. I got the upper and lower bounds to work but i am not sure how to create a constraint that the sum of x must be 1. Any help would be greatly appreciated. -- View this message in context:

Hey fellas: I would like to integrate the following function: integrand <- function (x,t) { exp(-2*t)*(2*t)^x/(10*factorial(x)) } with respect to the t variable, from 0 to 10. The variable x here works as a parameter: I would like to integrate the said function for each value of x in 0,1,..,44. I have tried Vectorize to no avail. Thanks in advance, jose romero

Hi, Suppose I have a multivariate response Y (n x k) obtained at a set of predictors X (n x p). I would like to perform a linear regression taking into consideration the covariance structure of Y within each unit - this would be represented by a specified matrix V (k x k), assumed to be the same across units. How do I use "lm" to do this? One approach that I was thinking of

Hi, I have noticed that there is a change in the use of ellipses or . in R versions 2.6.1 and later. In versions 2.5.1 and earlier, the . were always at the end of the argument list, but in 2.6.1 they are placed after the main arguments and before method control arguments. This results in the user having to specify the exact (complete) names of the control arguments, i.e. partial matching is

Hi, Given a positive integer N, and a real number \lambda such that 0 < \lambda < 1, I would like to generate an N by N stochastic matrix (a matrix with all the rows summing to 1), such that it has the second largest eigenvalue equal to \lambda (Note: the dominant eigenvalue of a stochastic matrix is 1). I don't care what the other eigenvalues are. The second eigenvalue is

Hi, I am trying to do the following computation: p <- rep(0, n) coef <- runif(K+1) U <- matrix(runif(n*(2*K+1)), n, 2*K+1) for (i in 0:K){ for (j in 0:K){ p <- p + coef[i+1]* coef[j+1] * U[,i+j+1] } } I would appreciate any suggestions on how to perform this computation efficiently without the "for" loops? Thank

Hi, We (Paul Gilbert and I) have just released a new R package on CRAN called "BB" (stands for Barzilai-Borwein) that provides functionality for solving large-scale (and small-scale) nonlinear system of equations. Until now, R didn't have any functionality for solving nonlinear systems. We hope that this package fills that need. We also have an implementation of the

At office I have been introduced by another company to new, complex energy forecasting models using gams as the basic software. I have been told by the company offering the models that gams is specialised in dealing with huge, hevy-weight linear and non-linear modelling (see an example in http://www.gams.com/modtype/index.htm) and they say it is almost the only option for doing it. I would

I'm solving the differential equation dy/dx = xy-1 with y(0) = sqrt(pi/2). This can be used in computing the tail of the normal distribution. (The actual solution is y(x) = exp(x^2/2) * Integral_x_inf {exp(-t^2/2) dt} = Integral_0_inf {exp (-xt - t^2/2) dt}. For large x, y ~ 1/x, starting around x~2.) I'm testing both lsoda and rk4 from the package odesolve. rk4 is accurate using step

Hi, I would like to know whether there exist algorithms to compute the coefficients or, at least, the degree of the minimal polynomial of a square matrix A (over the field of complex numbers)? I don't know whether this would require symbolic computation. If not, has any of the algorithms been implemented in R? Thanks very much, Ravi. P.S. Just for the sake of completeness, a

I am trying to maximise a complicated loglikelihood function with the "optim" command. Is there some way to get to know the estiamtes at each iteration? When I put "control=list(trace=TRUE)" as an option in "optim", I just got the initial and final values of the loglikelihood, number of iterations and whether the routine has converged or not. I need to know the