similar to: msm and pmatrix

Dear All, I would like to use the simprof function (clustsig package) but the available distances do not include Jaccard distance, which is the most appropriate for pres/abs community data. Here is the core of the function: > simprof function (data, num.expected = 1000, num.simulated = 999, method.cluster = "average", method.distance = "euclidean", method.transform =

All, We are researching approaches to parallel R with the end goal of running R in a distributed manner on a Linux cluster. We expect of course to do some work decomposing our problems to be task-parallel or data-parallel, but wouldn't mind getting an initial boost working with "embarrassingly parallel" code sections and one of the approaches below. Incidentally our environment

Hello I'm a population ecologist and use R for all my stats and modelling. Recently I have been using R to numerically solve integral projection models. This involves constructing several large matrices. The current code by Easterling (Size-specific sensitivity: Applying a new structured population model. Ecology, 2000, 81, 694-708) uses nested loops to construct the matrices. To speed up the

Dear R Users, I have the following glm, which I am running several times in a loop (I am not including the full code): reduced_model <- NULL; full_model <- NULL; reduced_model <- try(glm.fit(X4,n,family=poisson(link="log"))) full_model <- try(glm.fit(X5,n,family=poisson(link="log"))); On some occasions, an error is produced, which is why I have attempted to work

Dear all, Currently I am trying to integrate a function which depends on four variables, two of which are given, one is given in the integrate function, so there is one variable to integrate on. The code is as follows: Pmatrix = function(th) { P = matrix(nrow=6, ncol=6, data=0) P[1,1] = P[2,1]=P[3,2]=P[4,3]=P[5,4]=P[6,5]= exp(-th) P[,6] = 1-exp(-th) return(P)} lim.verd =

Hi, I use nnet for my classification problem and have a problem concerning the calculation of the final value for my validation data.(nnet only calculates the final value for the training data). I made my own final value formula (for the training data I get the same value as nnet): # prob-matrix pmatrix <- cat*fittedValues tmp <- rowSums(pmatrix) # -log likelihood

Dear R Programmers, I am trying to run a Poisson regression on all pairs of variables in a data set and obtain the permutation distribution. The number of pairs is around 100000. It seems my code will take weeks to run, unless I try something else. Could you give me any suggestions on how to improve the speed of the code below, or any general suggestions on how I may accomplish this task. Thanks

I have code and documentation that don't match, but R CMD check didn't flag it. in mspath.R mspath <- function(formula, # formula with observed Markov states ~ observation times (required) qmatrix, # matrix of 1s and 0s with indices of allowed transitions (diagonal is ignored) (required) misc = FALSE, ematrix = NULL, # matrix

Dear R-experts, this is not a direct R-problem but I hope you can help me anyway. I would like to minimize || PG-I || over P, where P is a p x p permutation matrix (obtained by permuting the rows and/or columns of the identity matrix), G is a given p x p matrix with full rank and I the identity matrix. ||.|| is the frobenius norm. Does anyone know an algorithm to solve such a problem? And if

[this message needed manual improvement by the mailing list administrator since it was `HTMLified' .. ``please do not''] Apologies for bothering you about a fairly trivial matter. I have been getting some inconsistencies with the display digits in R V1.5. I have been using the hypergeometric distribution function, and have found that when printing out the results from this

For the vcdExtra package, I'm exploring methods to generate and fit collections of glm models, and handling lists of such model objects, of class "glmlist". The simplest example is fitting all k-way models, from k=0 (null model) to the model with the highest-order interaction. I'm having trouble writing a function, Kway (below) to do what is done in the example below >

There is a bug in Matrix package, please check it, thanks! Matlab result: x = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 >> lu(x) ans = 21.0000 22.0000 23.0000 24.0000 25.0000 0.0476 0.9524 1.9048 2.8571 3.8095 0.7619