similar to: triple integral: adapt package question

Dear All, I am running a Monte Carlo simulation study and have some questions on how to manage data storage efficiently at the end of each 1000 replication loop. I have three conditions coded using the FOR {} loops and a FOR loop that generates data for each condition, performs analysis, and computes a statistic 1000 times. Therefore, for each condition, I will have 1000 statistic values. My

Hello, II used the logLik() function to get the log-likelihood estimate of an object. The function also prints the degrees of freedom. How can I extract the degrees of freedom and assign it to a variable. Below is the output: > logLik(fit2pl) 'log Lik.' -4842.912 (df=36) Thanks, Davood Tofighi [[alternative HTML version deleted]]

Hello All, I was wondering how I can get the overall Pearson chi^2 test of model fit with its df and p value in the LTM package for the 2PL models. Thanks, -- Davood Tofighi Department of Psychology Arizona State University [[alternative HTML version deleted]]

Hello, I'm using filehash on the windows XP and it has been working fine with the newest R version 2.6.2. However, on the windows vista, when I ran the same code, I got the following error: > dbCreate("simdb") #create simdb database [1] TRUE > db<-dbInit("simdb") #initiate an object of database Error in sprintf(gettext(fmt, domain = domain), ...) : object

Davood, I developed an MC simulation model for wind hazard analysis last year. I found three important issues to increase efficiency: 1) Reuse most variables in each loop 2) Write results (1000 stats) to external files, perhaps one file for each condition. (a good ID for each file can be implemented using "paste") 3) Develop a function to process the results stored in the external

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

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,

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 <-

Hard to follow data analysis without data. Try making your example reproducible [1][2][3] and post in plain text (a setting in your emailer). Read the Posting Guide mentioned in the footer to avoid other posting pitfalls. [1] http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example [2] http://adv-r.had.co.nz/Reproducibility.html [3]

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 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

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

Hello all, I am trying to minimize a function which contains a double integral, using "nlminb" for the minimization and "adapt" for the integral. The integral is over two variables (thita and radiusb) and the 3 free parameters I want to derive from the minimization are counts0, index and radius_eff. I have used both tasks in the past successfully but this is the first time

Dear r-users, Is there any command in R allowing to evaluate a double integral? for instance let say I want to evaluate the following integral: integrate[lo=(0,1),up=(2,3)] f(x,y)=x^2+y^2 where lo is the vector of lower bounds and up that of upper bounds. I thaught the function "adapt" would work but it did not. Many thanks, Dominique K.

I tried to integrate numerically a function wich is similar to the following: > dummy <- function(x) { exp(-1*x) * dnorm(x) } > dummy(-100) [1] 0 > dummy(-1000) [1] NaN > dummy(-10000) [1] NaN If I choose the lower boundary to be too small integrate causes a segmentation fault: > library(integrate) > integrate(dummy, -100, 0)$value [1] 1.387143 > integrate(dummy, -1000,

Dear all, May I do multiple integration using R? I was looking adapt but it is saying it integrates a scalar function over a multidimensional rectangle. I have integrand of several variable and upper, lower limit too variable. I wanted to see the result using adapt (though it is not for this purpose, I suppose) Func<-function(x){(x[1]*x[2])} adapt(2, lo=c(0,1), up=c(1,x[1]), functn=Func) it

hello, I have a graph and i use qgraph package to calculate centrality parameters. Now I want to know the maximum value of shortest path for each vertex with discarding the Inf value in short pathes. For this I use the ShortestPathLengths of centrality function in qgraph. but when I want to get the maximum the result is wrong. here is my code: cen<-centrality(Q)

I find the one-dimensional "integrate" very helpful, but often enough I stumble into problems that require two (or more)-dimensional integrals. I suppose there are no R functions that can do this for me, "directly"? The ideal thing would be to be able to define say f <- function(x) { x1 <- x[1] x2 <- x[2] sin(x1*x2)*exp(x1-x2) } and then write say integrate(f,

hello, i want to use igraph for drawing graph of my data. First time i try do it and it was successful. but now i want to test again i got this error: "edges must be numeric or character". this is my edge list(this is output from R): From To 1 US20100331312 2 US20100331341 3 US20100330919 US20100041339 4 US20100330919 US20100195590 5 US20100330919

Hi there. Thanks for your time in advance. I am using R 2.2.0 and OS: Windows XP. My final goal is to calculate 1/2*integral of (f1(x)^1/2-f2(x)^(1/2))^2dx (Latex codes: $\frac{1}{2}\int^{{\infty}}_{\infty} (\sqrt{f_1(x)}-\sqrt{f_2(x)})^2dx $.) where f1(x) and f2(x) are two marginal densities. My problem: I have the following R codes using "adapt" package. Although "adapt"