similar to: Bivariate lognormal distribution

Following the recommendation of Prof. Ripley, I have created the Makevars file with the line: PKG_LIBS="-L/usr/lib/libm -lm -L/usr/local/lib/libgsl -lgsl -L/usr/local/lib /libgslcblas -lgslcblas" in the working directory. Now I have the code file Example3.c which computes the Bessel function value (the example is taken from the GSL reference book). I am running: R CMD SHLIB Example3.c

Dear all, I think that my question is very simple but I failed to solve it. I have a list which elements are matrices like this: >mylist [[1]] [,1] [,2] [,3] [1,] 1 3 5 [2,] 2 4 6 [[2]] [,1] [,2] [,3] [1,] 7 9 11 [2,] 8 10 12 I'd like to create a matrix M<-mylist[[1]]+mylist[[2]] [,1] [,2] [,3] [1,] 8 12 16 [2,] 10 14 18

Dear experts! I have a minimization problem with non-linear constraint and Objective function(theta)=lambda*(Constr)^2-f(x,theta). Theta is a vector of parameters. I'd like to stop the optimization after the value of the constraint is less or equal some constant value, say d, and save the last computed value of the function. For this purpose, I thought to define the Objective function like

Dear all, I need an advice in the following problem. I have to maximize two functions of the form f1(x)=f(y1,x,alpha1,beta1) and f2(x)=(y2,x,alpha2,beta2), the maximization is with respect to alpha1, alpha2, beta1, beta2. I can maximize each function separately using nlm. The problem is that I have to add the constraint of the form g(alpha1)=g(alpha2). The total number of parameters is

Dear all, I'd like to find a minimum of (-loglik) function which is a function of k parameters. I'd like to run the minimization algorithm for the different subsets of the parameters and assign the fixed values to the complementary subset. How should I define my (-loglik) function such that it can be passed to the optim or other optimization function? Much thanks for any suggestions.

>From the `R Data Import/Export' manual, shipped with R, as well as available from the official R web site (last three paragraphs of Section 3.1, describing functions in the `foreign' package): Function read.S which can read binary objects produced by S-PLUS 3.x, 4.x or 2000 on (32-bit) Unix or Windows (and can read them on a di erent OS). This is able to read many but not all S

Couldn't you do this by subtracting 0.5 + x from your y values and checking for normality with mean 0 and sd = 1 (using ks.test or another test of normality). If you fail, you'll have to do additional work to find out whether pairs with some particular x value (or range of x values) is causing the problem, but I think this fits the question as stated. Of course, if you have discrete x

Dear all, I'd like to find the mle esttimates using the mle function mle(negloglik, start = list(), fixed=list(), method="..."). I am using the L-BGFS-B method and I don't supply the gradient function. Is there a way to print the gradients found at the solution value? I am using R-1.9.1 on Windows and on Unix. Thank you in advance, Victoria Landsman. [[alternative

Dear all, I need to compute (numerically) the two-dimensional integral: int(int f(x,y)dy)dx. What is the more efficient(fast) way to do it? Is adapt function appropriate for this problem? I will much appreciate your help and attention. Vicky. [[alternative HTML version deleted]]

I have found the use of save( ) and attach( ) when supported by a pair of functions written by my colleague John Miyamoto, move( ) and rm.sv( ) quite useful in managing (1) collections of useful homebrew functions, (2) project workspaces, and (3) "packages" under development. An .Rdata file containing these and other handy functions together with a brief supporting document can be

Deall all, We need to perform a statistical analysis of a large database (40,000 entries with approximately 500 fields in each entry) currently handled in Oracle. The data contains categorical variables only. At the current stage we suggest classification and clustering analysis. We are planning to perform the analysis in R and would be very grateful for any

This is not a direct answer to your question, but if all you want to do is integrate a function numerically, you don't need to be doing it through R. You should be able to find codes that you can use directly, such as quadpack or even GSL. You may want to search on GAMS (Guide to Available Mathematical Software, http://gams.nist.gov/). Andy > From: Victoria Landsman > > Dear all,

Dear R users,   I need to do a F test on the hypothesis that a 2 by 1 vector (X_1, X_2)' has the mean vector  (M_1, M_2)'. Specifically, I would like to assume the X vector comes from a bivariate Normal distribution (M, Sigma). Then, given 1000 observations on X, I wanted to test if the means of X agree with the means of the target Normal distribution. Any function or package in R could do

Hi all, I understand that it is simple to create data with a specific correlation (say, .5) using mvrnorm from the MASS library: > library(MASS) > set.seed(1) > > a=mvrnorm( + n=10 + ,mu=rep(0,2) + ,Sigma=matrix(c(1,.5,.5,1),2,2) + ,empirical=T + ) > a [,1] [,2] [1,] -1.0008380 -1.233467875 [2,] -0.1588633 -0.003410001 [3,] 1.2054727 -0.620558768

Dear R users I use simulated data to evaluate a model by sampling the parameters in my model from lognormal distributions. I would like these (lognormal distributed) parameters to be correlated, that is, I would like to have pairwise samples of 2 parameters with a given correlation coefficient. I have seen that a covariance matrix can be fixed when generating random variables from a

Hi, I need to generate two normal variables with covariance matrix: 0.25, 0.20 0.20, 0.25 but I have no idea how to do that. Can anyone help me? Thanks, Joaquim ---------------------------------------------------------------------------- ---------- Joaquim J. S. Ramalho University of Bristol Department of Economics 8 Woodland Road Bristol BS8 1TN United Kingdom

Hi, I conduct a panel data estimation and obtain estimators for two of the coefficients beta1 and beta2. R tells me the mean and covariance of the distribution of (beta1, beta2). Now I would like to find the distribution of the quotient beta1/beta2, and one way to do it is to simulate via the joint distribution (beta1, beta2), where both beta1 and beta2 follow t distribution. How could we

Just did some testing.. root at vicky:~# cat /var/log/auth.log | grep "Set" May 30 21:41:05 vicky sshd[1568]: Set /proc/self/oom_adj from -17 to -17 May 30 21:41:07 vicky sshd[1574]: Set /proc/self/oom_adj to -17 root at vicky:~# ps faux | grep 1574 root 1574 0.0 0.0 70488 3404 ? Ss 21:41 0:00 \_ sshd: root at pts/1 root at vicky:~# ps faux | grep

Dear expert, I have two coefficients and covariance matrix. My objective is sampling 1000 times from the mean and covariance matrix. In order to get that, what kind of commend should I use? If you do not mind, could you tell me the comment in detail about parameter used in that commend also? Thank you. Sung. [[alternative HTML version deleted]]

Dear R-list people, I asked a similar question a few hours before. I will try to be more specific. We like to add the GSL library to the file SHLIB in order to make it possible to run the C code using GSL functions from R. We read that the path to the libgsl.a should be added to the line shlib_libadd=' ' in the file SHLIB but it does not work on our system. Dyn.load fails with error