similar to: Numerical Integration

Full_Name: Travis Porco Version: 1.5.1 (6/17/2002) OS: Suse Linux 7.1 Submission from: (NULL) (66.81.124.235) This happens: > integrate(function(x)1,0,1) Error in integrate(function(x) 1, 0, 1) : evaluation of function gave a result of wrong length > integrate(function(x)x^2,0,1) 0.3333333 with absolute error < 3.7e-15 > integrate(function(x){cat(x);1},0,1) 0.5 0.01304674 0.9869533

Full_Name: Travis Porco Version: 1.5.1 (6/17/2002) OS: Suse Linux 7.1 Submission from: (NULL) (66.81.124.235) This happens: > integrate(function(x)1,0,1) Error in integrate(function(x) 1, 0, 1) : evaluation of function gave a result of wrong length > integrate(function(x)x^2,0,1) 0.3333333 with absolute error < 3.7e-15 > integrate(function(x){cat(x);1},0,1) 0.5 0.01304674 0.9869533

Dear list, I have a question related to the correct interpretation of the relative convergence criterion used by 'optim'. In the help of the function is it written that: "reltol:Relative convergence tolerance. The algorithm stops if it is unable to reduce the value by a factor of reltol * (abs(val) + reltol) at a step." and I was wondering if the previous criterion is

i am using sarima() function as below ___________________________________________________________________________________________ sarima=function(data,p,d,q,P=0,D=0,Q=0,S=-1,tol=.001){ n=length(data) constant=1:n xmean=matrix(1,n,1) if (d>0 & D>0) fitit=arima(data, order=c(p,d,q), seasonal=list(order=c(P,D,Q), period=S),

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

Hi, I apologize for the long message but the problem I encountered can't be stated in a few lines. I am having some problems with the function constrOptim. My goal is to maximize the likelihood of product of K multinomials, each with four catagories under linear constraints on the parameter values. I have found that the function does not work for many data configurations. #The likelihood

Hello , I have estimated the following model, a sarima: p=9 d=1 q=2 P=0 D=1 Q=1 S=12 In R 2.12.2 Call: arima(x = xdata, order = c(p, d, q), seasonal = list(order = c(P, D, Q), period = S), optim.control = list(reltol = tol)) Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.3152 0.8762 -0.4413 0.0152 0.1500 0.0001 -0.0413 -0.1811

Hello to all, I have been using an optim with the following call: optim(param_ini,fun_errores2,Precio_mercado=Precio,anos_pagosE2=anos_pagos,control=list(maxit=10000,reltol=1e-16)) depending on the intial values I'm getting the same solution but once I get the convergence message=10 (no convergence) and for the others I get convergence message = 0 Solution1: $par beta1

Hello: The example "integrate(dnorm,0,20000)" says it "fails on many systems". I just got 0 from it, when I should have gotten either an error or something close to 0.5. I got this with R 2.12.0 under both Windows Vista_x64 and Linux (Fedora 13); see the results from Windows below. I thought you might want to know. Thanks for all your work in creating

Buenos dias R-help-es, Estoy interesado en estimar una curva dosis-respuesta para un conjunto de datos y para ello, estoy utilizando la libreria "drm". Hasta ahi todo bien. Me gustaria automatizar algunas cosas y el primer paso para ello es la estimacion del modelo. Si la estimacion funciona, todo lo demas funciona; de lo contrario, todo fallara. Tengo algunas lineas que mitigan un

Hello, I want to maximize a likelihood function expressed as an integral that can not be symbolically evaluated. I expose my problem in a reduced form. g<- function(x){ integrand<-function(y) {exp(-x^2)*y} g<-integrate(integrand,0,1) } h<-function(x) log((g(x))) g is an object of the class function, but g(2) is a integrate object, I can print(g(2))

I am trying to use the R API to call optim functions (nmmin, vmmin, lbfgsb, etc.) through a C program but I couldn't find the shared library to link under the R-2.6.0 build which is compiled under Linux (REL5). main.cpp:35: undefined reference to `Rf_initEmbeddedR(int, char**)' main.cpp:41: undefined reference to `nmmin' Thanks in advance for any help. ------------------------

Hi! I was wondering if there are any other functions for numerical integration, besides 'integrate' from the stats package, but which wouldn't require the integrand to be vectorized. Oh, and must be capable of integrating over (-inf,+inf). Thanks in advance, Eduardo Horta [[alternative HTML version deleted]]

Dear all, I have found some difference of the results between multinom() function in R and multinomial logistic regression in SPSS software. The input data, model and parameters are below: choles <- c(94, 158, 133, 164, 162, 182, 140, 157, 146, 182); sbp <- c(105, 121, 128, 149, 132, 103, 97, 128, 114, 129); case <- c(1, 3, 3, 2, 1, 2, 3, 1, 2, 2); result <- multinom(case ~ choles

Hi, I'm trying to get a toy program making use of nmmin to run successfully. I've gotten to the point of compiling. However, when I attempt to run my executable, I guess a bus error. I see that someone else has asked about using nmmin before <http://tolstoy.newcastle.edu.au/R/help/06/03/23944.html>, but I haven't come across any replies. Is there some documentation on how to

Estimada comunidad, nuevamente con algo que no se como hacer en R, pero aprendiendo .... El problema es el siguiente: 1. tengo un conjunto de 10 mil datos (n) cuyo valor va entre 0 y 10.000 a los que aplico una funcion sm.density() para obtener una especie de histograma pero con una curva que parece continua. 2. Una vez que obtengo esa curva necesito calcular el area bajo la curva en ciertos

Dear All, Hello! I have some questoins in R programming as follows: Question 1- How to take the integral of this function with respect to y, such that x would appear in the output after taking integral. f(x,y)=(0.1766*exp(-exp(y+lnx))*-exp(y+lnx))/(1-exp(-exp(y+lnx))) y in (-6.907,-1.246) It is doable in maple but not in R. At least I could not find the way. p.s: result from maple is:

I am attempting to fit a full zero-inflated Poisson model then use backward elimination to arrive at the best-fitting model. When I try to fit the model with zeroinfl() I get this error: Error in while (abs((ll_old - ll_new)/ll_old) > control$reltol) { : missing value where TRUE/FALSE needed Any suggestions? Thanks for your help! Paige Barlow MS Student Virginia Tech Dept Fish

In the documentation for 'optim' it gives the following function: fr <- function(x) { ## Rosenbrock Banana function x1 <- x[1] x2 <- x[2] 100 * (x2 - x1 * x1)^2 + (1 - x1)^2 } optim(c(-1.2,1), fr) When I run this code I get: $par [1] 1.000260 1.000506 I am sure I am missing something but why isn't 1,1 a better answer? If I plug 1,1 in the function it seems

Hi, I'm having a problem converting a Matlab program into R. The R code works almost all the time, but about 4% of the time R's optim function gets stuck on a local minimum whereas matlab's fminsearch function does not (or at least fminsearch finds a better minimum than optim). My understanding is that both functions default to Nelder-Mead optimization, but what's different about