similar to: Double integration using R

I'm working on a personal project which involves lowering a superset of LLVM to "vanilla" LLVM. Part of this involves rewriting types. I'm working off of the 2.6 code base, so this may have been addressed in a bug fix. I'm having trouble with refineAbstractType and recursive types. Specifically, I'm creating opaque types to serve as placeholders for the final

I have written test.ll as below and ran 'opt' on it as " opt -std-compile-opts test.ll -S -o -" . But the output shows that there is code motion around the barrier intrinsics. test.ll ------- ; ModuleID = 'test.bc' define void @test(i16* %I_0, i16* %I_1, i16* %I_2, i16* %I_3, i16* %O_0) { entry: %T_0 = load volatile i16* %I_0 %T_1 = load volatile i16* %I_1 %T_2 =

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

Hello, I am not sure how to do this in R. Any suggestion would be appreciated. I have a vector of values from where I build an empirical CDF. For example: > x <- seq(1,100) > x <- sample(x,1000,replace=T) > quantile(x,probs=seq(0,1,.05)) 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 1.00 5.00 10.00 16.00 20.00 25.00 31.00 36.00 41.00

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

Hi all, Does anybody have the experience of using optim to estimate variables with integral forms? here the code: trun.mean<- function(x) # t is the threshold { mu=x[1]; sigma=x[2]; t=x[3]; f <- function(x) (1/(sigma*sqrt(2*pi)))*exp(-(x-mu)^2/(2*sigma^2)); pdf.fun <- function(x) x*f(x); integrate(f,thre,upper=Inf)$value/integrate(pdf.fun,thre,upper=Inf)$value ; } when I

Dear friends i am trying to fit an Ornstein-Uhlenbeck process by MAXIMUM LIKELYHOOD method. i found these formulas on http://www.sitmo.com/article/calibrating-the-ornstein-uhlenbeck-model/ this is the mean-reverting process http://r.789695.n4.nabble.com/file/n4637271/process.txt process.txt and this is the script that i am using....... ouFit.ML=function(spread) { n=length(spread)

Hi all. I'm trying to numerically invert the characteristic function of a quadratic form following Imhof's (1961, Biometrika 48) procedure. The parameters are: lambda=c(.6,.3,.1) h=c(2,2,2) sigma=c(0,0,0) q=3 I've implemented Imhof's procedure two ways that, for me, should give the same answer: #more legible integral1 = function(u) {

Hi, Any help on this would be appreciated: I need to integrate where K is a 4x4 matrix, and SIGMA is a 4x4 matrix from say a to b, i.e. 0 to 5: integral MatrixExp(-K * s) %*% SIGMA %*% t(SIGMA) %*% MatrixExp(t(-K) s) ds t is tranpose , %*% : matrix mult , MatrixExp : matrix exponential I've use integrate before on univariate functions like f(x) = x^2 which is fine but when doing this on

Hello there Can anybody please tell me if there is any package in R to solve the following 4 nonlinear equations with 4 unknowns: alpha*exp(20/sigma)+ beta*exp(21/tau) = 2 alpha*exp(22/sigma)+ beta*exp(9/tau) = 4 alpha*exp(10/sigma)+ beta*exp(30/tau) = 6 alpha*exp(40/sigma)+ beta*exp(39/tau) = 5 where alpha = exp(lambda/sigma) beta= exp(delta/tau) I need to estimate lambda, sigma, delta, tau

Hi all, myint=function(mu,sigma){ integrate(function(x) dnorm(x,mu,sigma)/(1+exp(-x)),-Inf,Inf)$value } mymu=seq(-3,3,length(1000)) mysigma=seq(0,1,length(500))[-1] k=1 v=c() for (j in 1:length(mymu)) { for (i in 1:length(mysigma)) { v[k]=myint(mymu[j],mysigma[i]) k=k+1 } } Basically, I want to investigate for what values of mu and sigma, the integral is divergent. Is there another way

hello all happy new year and hope you r having a good holiday. i would like to calculate the expectation of a particular random variable and would like to approximate it using a number of the functions contained in R. decided to do some experimentation on a trivial example. example ======== suppose x(i)~N(0,s2) where s2 = the variance the prior for s2 = p(s2)~IG(a,b) so the posterior is

Hola! santes<-c(210,169,187,160,167,176,185,206,173,146,174,201,198,148,154) sdespues<-c(201,165,166,157,147,145,168,180,147,136,151,168,179,129,131) dantes<-c(130,122,124,104,112,101,121,124,115,102,98,119,106,107,100) ddespues<-c(125,121,121,106,101,85,98,105,103,98,90,98,110,103,82) presion<-data.frame( santes, sdespues, dantes, ddespues) logvero <-

I believe this may be more related to analysis than it is to R, per se. Suppose I have the following function that I wish to integrate: ff <- function(x) pnorm((x - m)/sigma) * dnorm(x, observed, sigma) Then, given the parameters: mu <- 300 sigma <- 50 m <- 250 target <- 200 sigma_i <- 50 I can use the function integrate as: > integrate(ff, lower= -Inf, upper=target)

I'm proposing to add something like this to the stats package : ---------------------------------------------------------- ### "The" sigma in lm/nls - "like" models: sigma <- function(object, ...) UseMethod("sigma") ## works whenever deviance(), nobs() and coef() do fine: sigma.default <- function (object, use.fallback=TRUE, ...)

Dear r-helpers, I can run the examples on the mcsamp help page. For example: **************************************** > M1 <- lmer (y1 ~ x + (1|group)) > (M1.sim <- mcsamp (M1)) fit using lmer, 3 chains, each with 1000 iterations (first 500 discarded) n.sims = 1500 iterations saved mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff beta.(Intercept)

Dear useRs, I have written a function that implements a Bayesian method to compare a patient's score on two tasks with that of a small control group, as described in Crawford, J. and Garthwaite, P. (2007). Comparison of a single case to a control or normative sample in neuropsychology: Development of a bayesian approach. Cognitive Neuropsychology, 24(4):343?372. The function (see

I color some area grey with polygon() (with a red border) and then I want to have the dashed red border in the legend as well. How do I manage it? And I want to mix (latex) expressions with text in my legend. Just execute my lines below and you know want I mean. Or pass by at http://de.wikipedia.org/wiki/Bild:GBM.png to see the picture online. Thomas bm <- function(n=500, from=0, to=1) {

Hello, I am a novice R user and am having difficulty retrieving the values from 21 iterations of the R function integrate. The only way I have found is to do a write.table and then a read.table as shown in the code below. I would rather capture the 21 values inside the braces ( sapply might work, but I can't set it up without getting an error in function) so I could compute 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)]