search for: integration

...5/x)^5)*exp(-13.5/x)) } Numerically integrating this function, I observed a couple of things: A) Integrating the function f over the entire positive real line gives an error: > integrate(f,0,Inf) Error in integrate(f, 0, Inf) : the integral is probably divergent B) Increasing the interval of integration actually decreases the value of the integral: > integrate(f,0,10^5) 9.813968e-06 with absolute error < 1.9e-05 > integrate(f,0,10^6) 4.62233e-319 with absolute error < 4.6e-319 Since the function f is uniformly positive, B) can not occur. Also, the theory tells me that the infinite in...

Dear R useRs, i have the function f1(x, y, z) which i want to integrate for x and y. On the one hand i do this by first integrating for x and then for y, on the other hand i do this the other way round and i wondering why i doesn't get the same result each way? z <- c(80, 20, 40, 30) "f1" <- function(x, y, z) {dgamma(cumsum(z)[-length(z)], shape=x, rate=y)}

Dear R users, i try to integrate lgamma from 0 to Inf. But here i get the message "roundoff error is detected in the extrapolation table", if i use 1.0e120 instead of Inf the computation works, but this is against the suggestion of integrates help information to use Inf explicitly. Using stirlings approximation doesnt bring the solution too. ## Stirlings approximation lgammaApprox

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,

Oops, I meant to CC the list on this. -- Iain ----- Forwarded message from Iain Morgan <Iain.Morgan at nasa.gov> ----- Date: Thu, 17 Jan 2013 14:51:01 -0800 From: Iain Morgan <Iain.Morgan at nasa.gov> To: Damien Miller <djm at mindrot.org> Subject: Re: Inconsisten declaration of ssh_aes_ctr_iv() On Wed, Jan 16, 2013 at 21:26:39 -0600, Damien Miller wrote: > On Mon, 14 Jan

In the help for ?integrate: >When integrating over infinite intervals do so explicitly, rather than just using a large number as the endpoint. This increases the chance of a correct answer ? any function whose integral over an infinite interval is finite must be near zero for most of that interval. I understand that and there are examples such as: ## a slowly-convergent integral integrand

I'm trying to provide different parameters to the integrate function for various probability functions. I'm using dnorm as the simplest example here. For instance integrate(dnorm, -1.96, 1.96) produces the correct answer for a normal distribution with mean 0 and standard deviation 1. I've tried two ways to use mean=2.0 and standard deviation 1, but with no luck. The examples follow.

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 valu...

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:

Full_Name: Bert De Boeck Version: R 2.2.0 OS: Windows Submission from: (NULL) (157.193.193.152) I think there is a bug when using integrate for integrating a function which is 0 in a whole sub-interval. For example: #define uniform function f<-function(x){ifelse(x<1,0,ifelse(x<3,1,0))} #this is the correct integral integrate(f,-10,10) #here there is a problem integrate(f,-50,50)

I need to calcuate the cumulative probability for the Natural Exponential Family - Hyperbolic secant distribution with a parameter theta between -pi/2 and pi/2. The integration should be between 0 and 1 as it is a probability. The function "integrate" works fine when the absolute value of theta is not too large. That is, the NEF-HS distribution is not too skewed. However, once the theta gets large in absolute value, such as -1 as shown in the example below,...

In 'Writing R Extensions' section 6.9 there is the paragraph There are interfaces (defined in header R_ext/Applic.h) for definite and for indefinite integrals. ?Indefinite? means that at least one of the integration boundaries is not finite. An indefinite integral usually means an antiderivative, not an integral over an infinite spread. Should that first sentence end with 'for integrals over finite and infinite ranges' and the second sentence omitted? Bill Dunlap TIBCO Software wdunlap tibco.com [...

...r to estimate. Is that in the end possible to implement in R [Originally it was done in GAUSS]? I'm nearly into giving up. I constucted an example to showing the problems I face. I have three questions - related to three errors shown below: 1) How to make clear that in the integrand z is the integration variable and b1 is a parameter while x1 is a data variable 2) and 3) How to set up a correct estimation of the integral? library(stats) y <- c(2,15,24,21,5,6,) x1 <- c(2.21,5,3,5,2,1) x2 <- c(4.51,6,2,11,0.4,3) f <- function(z) {z + b1*x1} vf <- Vectorize(f) g <- function(z) {z +...

Hi guys im having a problem getting R to numerically integrate for some function, say f(bhat)*optimize(G(bhat)), over bhat. Where id like to integrate this over some finite range, so that here as we integrate over bhat optimize would return a different optimum. For instance consider this simple example for which I cannot get R to return the desired result: f <- function(bhat) exp(bhat) g

...ss I should interpolate the points of f_y over x and integrate both lines on these intervals. Even then I would miss points where the lines cross. There are functions to integrate below *one* line (I'm thinking about the trapz function in caTools). Do you know if there is a function to do this integration properly with two lines (and especially their absolute difference)? Regards, Xavier

Dear r-help, I need to draw some lattice plots and have stuck in strange problems. I have a data frame of 405 rows: > str(dframe) `data.frame': 405 obs. of 4 variables: $ year : num 1990 1990 1990 1990 1990 1990 1990 1990 1990 1990 ... $ month: num 1 2 3 4 5 6 7 8 9 10 ... $ V_A2 : num NA NA NA NA NA ... $ V_A3 : num NA NA NA NA NA ... Variable month changes from 1 to 27

I'm having a function of the form 1> f<-function(x){ 1+ 1+ return(x^p) 1+ 1+ } ,and I would like to integrate it with respect to x, where p should be any constant. One way would be to set a value for p globally and then call integrate function: p=2 integrate(f, lower = -1, upper = 1) However, I would like to use 'integrate' inside a function, so I could call it passing

...e with plots: https://stackoverflow.com/q/55639401). Suppose one wants integrate a function that is just a product of two density functions (like gamma). The support of the random variable is (-Inf, 0]. The scale parameter of the distribution is quite small (around 0.01), so often, the standard integration routine would fail to integrate a function that is non-zero on a very small section of the negative line (like [-0.02, -0.01], where it takes huge values, and almost 0 everywhere else). R?s integrate would often return the machine epsilon as a result. So I stretch the function around the zero b...

Hi R users, I have a couple of questions about some problems that I am facing with regard to numerical integration and optimization of likelihood functions. Let me provide a little background information: I am trying to do maximum likelihood estimation of an econometric model that I have developed recently. I estimate the parameters of the model using the monthly US unemployment rate series obtained from the Fe...

The integral of any probability density from -Inf to Inf should equal 1, correct? I don't understand last result below. > integrate(function(x) dnorm(x, 0,1), -Inf, Inf) 1 with absolute error < 9.4e-05 > integrate(function(x) dnorm(x, 100,10), -Inf, Inf) 1 with absolute error < 0.00012 > integrate(function(x) dnorm(x, 500,50), -Inf, Inf) 8.410947e-11 with absolute error <