similar to: Multidimensional quadrature using "integrate"

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

Hello to all, I am having trouble with intregrating a complicated uni-dimensional function of the following form Phi(x-a_1)*Phi(x-a_2)*...*Phi(x-a_{n-1})*phi(x-a_n). Here n is about 5000, Phi is the cumulative distribution function of standard normal, phi is the density function of standard normal, and x ranges over (-infty,infty). My idea is to to use quadrature to handle this integral. But

Hi, I need to integrate a 2D function over range where the limits depend on the other e.g integrate f(x,y)=x*y over {x,0,1} and {y,x,1}. i.e \int_0^1 \int_x^1 xy dydx I checked adapt but it doesn't seem to help here. Are they any packages for this sort of thing? I tried RSitesearch but couldn't find the answer to this. Many thanks for you help. Regards Saptarshi Saptarshi Guha

Spencer Thanks for your thoughts on this. I did a bit of work and did end up with a method (more a trick), but it did work. I am certain there are better ways to do this, but here is how I resolved the issue. The integral I need to evaluate is \begin{equation} \frac{\int_c^{\infty} p(x|\theta)f(\theta)d\theta} {\int_{-\infty}^{\infty} p(x|\theta)f(\theta)d\theta} \end{equation} Where

Hi All, I am trying to use A Gaussian quadrature over the interval (-infty,infty) with weighting function W(x)=exp(-(x-mu)^2/sigma) to estimate an integral. Is there a way to do it in R? Is there a function already implemented which uses such weighting function. I have been searching in the statmode package and I found the function "gauss.quad(100, kind="hermite")" which uses

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

I am moving this to r-devel. The problem and solution below posted on r-help could have been a bit slicker if %*% worked with multidimensional arrays multiplying them so that if the first arg is a multidimensional array it is mulitplied along the last dimension (and first dimension for the second arg). Then one could have written: Tbar <- tarray %*% t(wt) / rep(wti, each = 9) which is a bit

Hi, I would like to solve a double integral of the form \int_0^1 \int_0^1 x*y dx dy using Gauss Quadrature. I know that I can use R's integrate function to calculate it: integrate(function(y) { sapply(y, function(y) { integrate(function(x) x*y, 0, 1)$value }) }, 0, 1) but I would like to use Gauss Quadrature to do it. I have written the following code (using R's statmod package)

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

Hello everyone, My data consists of marked points and several covariates, whereby the marks are the time points of the observations. The problem is, that one of the covariates is hard to handle as an image. This covariate represents the type of roads. As there aren't roads at every location of the map, one cannot specify the value of the covariate at any point on a grid, which is necessary to

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Hi: I have two matrices, A and B, where A is n x k, and B is m x k, where n >> m >> k. Is there a computationally fast way to count the number of times each row (a k-vector) of B occurs in A? Thanks for any suggestions. Best, Ravi. [[alternative HTML version deleted]]

Hi all, Does anybody know any function that performs gaussian adapative quadrature integration of univariate functions? Thanks in advance, Regards, Caio __________________________________________________ [[alternative HTML version deleted]]

Hi: In R, how do I display some intermediate results calculated in a "for" loop within a function? For example, in the attached code, how do I get it to print the intermediate variable "mh.new" for each simulation, when I call the function "MHsim.ind"? thanks for any help, Ravi. #################################################################### MHsim.ind

Can anyone help me on how to get the nodes and weights of the adaptive quadrature using R. Best wishes Boikanyo. ----- The University of Glasgow, charity number SC004401

Hi: I am using R 1.7.0 on Windows. I am having trouble getting "outer" to work on one of my functions. Here is a simple example illustrating my problem: > b1 <- c(1.2,2.3) > b2 <- c(0.5,0.6) > x <- c(3e+01, 1e+02, 3e+02, 5e+02, 1e+03, 1e+04, 1e+05, 1e+06) > y <- c(2,4,2,5,2,3,1,1) > n <- c(5,8,3,6,2,3,1,1) > outer(b1,b2,FUN=bpllkd,x,y,n)

Hi, I have a dataset with the binary outcome Y(0,1) and 4 covariates (X1,X@,X#,X$). I am trying to use MCMClogit to model logistic regression using MCMC. I am getting an error where it doesnt identify the covariates ,although its reading in correctly. The dataset is a sample of actual dataset. Below is my code: > ####################### > > > #retreive data > # considering four

Dear All, I need to perform a numerical integration of one dimensional fucntions. The extrems of integration are both finite and the functions I'm working on are quite complicated. I have already tried both area() and integrate(), but they do not perform well: area() is very slow and integrate() does not converge. Are in R other functions for numerical integration of one dimentional

A new version, 0.65-1, of glmmML is now on CRAN. It is a major rewrite of the inner structures, so frequent updates (bug fixes) may be expected for some time. News: * The Laplace and adaptive Gauss-Hermite approximations to the log likelihood function are fully implemented. The Laplace method is made the default. It should give results you can compare to the results from 'lmer' (for the

A new version, 0.65-1, of glmmML is now on CRAN. It is a major rewrite of the inner structures, so frequent updates (bug fixes) may be expected for some time. News: * The Laplace and adaptive Gauss-Hermite approximations to the log likelihood function are fully implemented. The Laplace method is made the default. It should give results you can compare to the results from 'lmer' (for the