similar to: multidimensional integration with adapt

Dear all, I'm performing RDA on plant presence/absence data, constrained by geographical locations. I'd like to constrain the RDA by the "extended matrix of geographical coordinates" -ie the matrix of geographical coordinates completed by adding all terms of a cubic trend surface regression- . This is the command I use (package vegan): >rda(Helling ~

Dear People, I'm trying to use the function adapt, from the adapt library package, which does multidimensional numerical integration. I think I must be using the wrong syntax or something, because even a simple example does not work. Consider foo <- function(x){x[1]*x[2]} and adapt(2, lo = c(-1,-1), up = c(1,1), functn = foo) This simply hangs. A more complicated example crashes R,

Dear All, There is one problem I encountered when I used ADAPT to compute some 2-D integral w.r.t beta density. For example, when I try to run the following comments: fun2<-function(theta){return(dbeta(theta[1],0.005,0.005)*dbeta(theta[2],0.005,0.005))} int.fun2<-adapt(ndim=2,lo = c(0,0), up = c(1,1),functn = fun2,eps = 1e-4) It seems it will take very long time to run. Acturally, I

I am trying calculate a probability using numerical integration. The first program I ran spit out an answer in a very short time. The program is below: ## START PROGRAM trial <- function(input) { pmvnorm(lower = c(0,0), upper = c(2, 2), mean = input, sigma = matrix(c(.1, 0, 0, .1), nrow = 2, ncol = 2, byrow = FALSE)) } require(mvtnorm) require(adapt) bottomB <- -5*sqrt(.1) topB <-

Dear All, I have a function f(x,y,z)=exp(x^3+y^4+x^2*y+x*z^2+y/z) over D, where is D={ (x,y,z)| 0 <z<Inf, 0<y<c1*z, 0<x<c2*/y}. x,y,z are all vectors and c1 and c2 are constants. I tried the "adapt" package and I get some error. This is the error message: "Error in function (z, y, x) : argument "x" is missing, with no default" I included my R

Hi all, I need to calculate a multidimensional integration on R. I am using the command "adapt" (from library adapt), although sometimes I get the following error message: Ifail=2, lenwrk was too small. -- fix adapt() ! Check the returned relerr! in: adapt(3, linf, lsup, functn = Integrando1) I guess it happens because the domain of integration is too small, although I tried a

The background: I'm trying to fit a Poisson-lognormal distrbutuion to some data. This is a way of modelling species abundances: N ~ Pois(lam) log(lam) ~ N(mu, sigma2) The number of individuals are Poisson distributed with an abundance drawn from a log-normal distrbution. To fit this to data, I need to integrate out lam. In principle, I can do it this way: PLN1 <- function(lam, Count,

In the following code I have created the posterior density for a Bayesian survival model with four parameters. However, when I try to use the adapt function to perform integration in four dimensions (on my old version of R I get an error message saying that I have applied a non-function, although the function does work when I type kernel2(param0, theta0), or on the newer version of R the computer

Hi. I am trying to analyze with BRugs the Box-Tiao variance components example in WinBUGS. The output from BRugs, mean sd MC_error val2.5pc median val97.5pc start sample sigma2.btw 681.9 1161 10.89 0.7016 253.8 4232 25001 100000 sigma2.with 4266.0 1246 4.92 2480.0000 4057.0 7262 25001 100000 doesn't match the output from WinBUGS, node mean

Hi All, This is not really an R question but a statistical one. If someone could either give me the brief explanation or point me to a reference that might help, I'd appreciate it. I want to estimate the mean of a log-normal distribution, given the (log scale normal) parameters mu and sigma squared (sigma2). I understood this should simply be: exp(mu + sigma2) ... but I the following code

Dear Bill, All makes perfect sense (including the late evaluation). I actually discovered the problem by looking at old code which used your proposed solution. Still I find it strange (and, hnestly, I don?t like R?s behavior in this respect), and I am wondering why u is not being copied to L just before u is assigned a new value. Of course, this would require the R interpreter to track all these

Hello, One way of preventing that is to use ?force. Just put force(l) right after the commented out print and before you change 'u'. Hope this helps, Rui Barradas Citando Matthias Gondan <matthias-gondan at gmx.de>: > Dear R developers, > > sessionInfo() below > > Please have a look at the following two versions of the same function: > > 1. Intended

Another way to avoid the problem is to not redefine variables that are arguments. E.g., > Su3 <- function(u=100, l=u, mu=0.53, sigma2=4.3^2, verbose) { if (verbose) { print(c(u, l, mu)) } uNormalized <- u/sqrt(sigma2) lNormalized <- l/sqrt(sigma2) muNormalized <- mu/sqrt(sigma2) c(uNormalized, lNormalized, muNormalized) } > Su3(verbose=TRUE)

Mathias, If it's any comfort, I appreciated the example; 'expected' behaviour maybe, but a very nice example for staff/student training! S Ellison > -----Original Message----- > From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Matthias > Gondan > Sent: 02 September 2017 18:22 > To: r-help at r-project.org > Subject: [R] Strange lazy evaluation of

Hi everyone, I am looking to do some manual maximum likelihood estimation in R. I have done a lot of work in Stata and so I have been using output comparisons to get a handle on what is happening. I estimated a simple linear model in R with lm() and also my own maximum likelihood program. I then compared the output with Stata. Two things jumped out at me. Firstly, in Stata my coefficient

When I saw the subject of the original message on R-help, I was 95% confident that I knew the answer (before I had seen the question). This made me think that perhaps for some functions there should be a 'Troubleshooting' section in the help file. The current help file for 'integrate' does say, as Sundar points out, what the requirements are. However, I think more people would

Moved to R-help because there's no obvious financial content. Michael On Sat, Apr 6, 2013 at 10:56 AM, Stat Tistician <statisticiangermany at gmail.com> wrote: > Hi, > I want to implement the EM algorithm manually, with my own loops and so. > Afterwards, I want to compare it to the normalmixEM output of mixtools > package. > > Since the notation is very advanced, I

Dear all, I am stuck on the following problem with integrate(). I have been out of luck using RSiteSearch().. My function is g2<-function(b,theta,xi,yi,sigma2){ xi<-cbind(1,xi) eta<-drop(xi%*%theta) num<-exp((eta + rep(b,length(eta)))*yi) den<- 1 + exp(eta + rep(b,length(eta))) result=(num/den)*exp((-b^2)/sigma2)/sqrt(2*pi*sigma2)

R-listers, In using the adapt function, I am getting the following warning: Ifail=2, lenwrk was too small. -- fix adapt() ! Check the returned relerr! in: adapt(ndim = 2, lower = lower.limit, upper = upper.limit, functn = pr.set, Would someone explain what the 'lenwrk' value indicates in order to help diagnose this issue. Also, what are the possible codes for Ifail, so I can set

Hi: I'm trying to estimate a model which involves the estimation of double integrals, so I'm using adapt procedure. I need to calculate the integrals trough my 2000 size database, so I do it using a loop. My code correctly estimates the integral for the first row, but for the second R crashes. I tried changing the order of the data but the result is the same so I guess there is something