similar to: "adaptIntegrate" function

Dear all, I got an error message when running the following code. Can anyone give any suggestions on fixing this type of error? Thank you very much in advance. Hanna > integrand <- function(x, rho, a, b, z){ + x1 <- x[1] + x2 <- x[2] + Sigma <- matrix(c(1, rho, rho, 1), 2,2) + mu <- rep(0,2) + f <-

Dear list, [cross-posting from Stack Overflow where this question has remained unanswered for two weeks] I'd like to perform a numerical integration in one dimension, I = int_a^b f(x) dx where the integrand f: x in IR -> f(x) in IR^p is vector-valued. integrate() only allows scalar integrands, thus I would need to call it many (p=200 typically) times, which sounds suboptimal. The

Dear R-users, I am facing problem with integrating in R a likelihood function which is a function of four parameters. It's giving me the result at the end but taking more than half an hour to run. I'm wondering is there any other efficient way deal with. The following is my code. I am ready to provide any other description of my function if you need to move forward.

Hello, I am trying to use adaptIntegrate function but I need to pass on a few additional parameters to the integrand. However, this function seems not to have the flexibility of passing on such additional parameters. Am I missing something or this is a known limitation. Is there a good alternative to such restrictions, if there at all are? Many thanks for your time. HC -- View this message in

Hi, I am using adaptIntegrate from Cubature to do numerical integration on a double integral with a 1 x 2 vector x. Say the function is something simple to start like f(x)=x1*x2 and I wish to integrate x1 over (0,365-x2) and x2 over (0,365) f <- function(x) {(x[2])*(x[1])} # "x" is vector int1<-adaptIntegrate(f, lowerLimit = c(0, 0), upperLimit = c(365-x[2], 365)) I recieve

Hi R-users. I'm having difficulty with an integration in R via the package "cubature". I'm putting it with a simple example here. I wish to integrate a function like: f(x1,x2)=2/3*(x1+x2) in the interval 0<x1<x2<7. To be sure I tried it by hand and got 114.33, but the following R code is giving me 102.6667.

Hi everyone, I have been using adaptIntegrate from the cubature package for a multidimensional integral that has infinite variance (and so not appropriate for Monte Carlo techniques). Most of the time it works but sometimes (though not always) when I slightly increase the accuracy I want, or increase the bounds of integration I get the following error: REAL() can only be applied to a

Hi All, I get the message failed with message ‘Dimension out of range’ when using cuhre in package R2Cuba. Does anyone know what this mean? Or would I need to email the package author? The funny thing is it does give a result and comparing it to "adaptIntegrate" in package cubature, the two numbers are very close. Thanks, Sachin [[alternative HTML version deleted]]

Hello, I'm using dmnorm from the package {mnormt} and getting strange results. First, according to the documentation, dmnorm should return a vector of densities, and I'm only getting one value returned (which is what I would expect). I've been interpreting this as the joint density of all values in the x vector (which is what I want). Should a vector of densities be returned, and if

Dear list, I'm seeking some advice regarding a particular numerical integration I wish to perform. The integrand f takes two real arguments x and y and returns a vector of constant length N. The range of integration is [0, infty) for x and [a,b] (finite) for y. Since the integrand has values in R^N I did not find a built-in function to perform numerical quadrature, so I wrote my own after

Dear R-users, I'm wondering how to calculate this double integral in R: int_a^b int_c^y g(x, y) dx dy where g(x,y) = exp(- alpha (y - x)) * b Thanks for answering! Cheers, Alui [[alternative HTML version deleted]]

I want to measure the error in the estimation of a 2 dimensional density function that is calculated using an integral but run into problems trying to integrate with adaptIntegrate because the integrand also calls the function adaptIntegrate. In particular I want \int \hat{f}(x,y) - f(x,y) dx dy where \hat{f}(x,y) = \int K(a,b, x, y) da db and in this simulation study I know what the true value

hello I work on the probabilities of bivariate normal distribution. I need integrate  the following function. f (x, y) = exp [- (x ^ 2 + y ^ 2 + x * y)] with - ∞ ≤ x ≤ 7.44 and - ∞ ≤ y ≤ 1.44   , either software R or  matlab Version R 2009a Thank you for helping me Regards Mezouara hicham PhD in Metrology Hicham_dess @ yahoo.fr [[alternative HTML version deleted]]

Please suggest a way out to the following problem. I have a T by n data matrix (say Y) where coulmns are time series of length T. To do some analysis in WinBUGS I need to construct my data as follows. yy<-rep(Y,k) ## this will be a vector Yk<-array(yy,dim=c(T,n,k)) ## data array Here the definition of dim indices is first index: T rows second index: n columns third index: for kth T by n

Hello Dear List members, as you can see (and guess) from the code below adaptIntegrate(f,lowerLimit=c(-1,-1),upperLimit=c(.9999,.9999)) $integral [1] 9.997e-09 $error [1] 1.665168e-16 $functionEvaluations [1] 17 $returnCode [1] 0 > adaptIntegrate(f,lowerLimit=c(-1,-1),upperLimit=c(1,1)) the last command runs for 45 mins now. -this one takes only less than sec:

Dear all, I am a new user to R and I am using pracma and nloptr libraries to minimize a numerical integration subject to a single constraint . The integrand itself is somehow a complicated function of x and y that is computed through several steps. i formulated the integrand in a separate function called f which is a function of x &y. I want to find the optimal value of x such that the

Dear R helpers I am working on the Bi-variate Normal distribution probabilities. I need to double integrate the following function (actually simplified form of bivariate normal distribution) f(x, y) = exp [ - 0.549451 * (x^2 + y^2 - 0.6 * x * y) ] where 2.696 < x < 3.54 and -1.51 < y < 1.98 I need to solve something like INTEGRATE (2.696 to 3.54) dx INTEGRATE [(-1.51 to 1.98)]

Hi all, I'm trying to define and log-likelihood function to work with MLE. There will be parameters like mu_i, sigma_i, tau_i, ro_i, for i between 1 to 24. Instead of listing all the parameters, one by one in the function definition, is there a neat way to do it in R ? The example is as follows: ll<- function(mu1=-0.5,b=1.2,tau_1=0.5,sigma_1=0.5,ro_1=0.7) { if (tau1>0 &&

Dear all, I tried running the following syntax but it keeps running for about 4 hours and then i got the following errors: Error in if (is.na(f0$objective)) { : argument is of length zero In addition: Warning message: In is.na(f0$objective) : is.na() applied to non-(list or vector) of type 'NULL' Here is the syntax itself: library('nloptr') library('pracma') #

Hi all, Is there any other packages to do numerical integration other than the default 'integrate'? Basically, I am integrating: integrate(function(x) dnorm(x,mu,sigma)/(1+exp(-x)),-Inf,Inf)$value The integration is ok provided sigma is >0. However, when mu=-1.645074 and sigma=17535.26 It stopped working. On the other hand, Maple gives me a value of 0.5005299403. It is an