similar to: multivariate numerical integration.

Does anybody know of a function that implements the derivative (gradient) of the multivariate normal density with respect to the *parameters*? It?s easy enough to implement myself, but I?d like to avoid reinventing the wheel (with some bugs) if possible. Here?s a simple example of the result I?d like, using numerical differentiation: library(mvtnorm) library(numDeriv) f=function(pars, xx, yy)

Hi All, I have been reading about general purpose GPU (graphical processing units) computing for computational statistics. I know very little about this, but I read that GPUs currently cannot handle double-precision floating points and also that they are not necessarily IEEE compliant. However, I am not sure what the practical impact of this limitation is likely to be on computational

Hello, I have a bigger problem in calculating the Maximum Likelihood Estimator regarding the degree of freedom of a multivariate skew-t copula. First of all I would like to describe what this is all about, so that you can understand my problem: I have 2 time series with more than 3000 entries each. I would like to calculate a multivariate skew-t Copula that fits this time series. Notice:

I am using numerical optimization to fit a 1 parameter model, in which the input parameter is bounded. I am currently using optimize(), however, the problem turns out to have local minima, and optimize does not always seem to find the global minimum. I could to write a wrapping function that tries multiple intervals or starting values, but I would prefer a package that has built-in methods to make

Dear R users, i try to use function dmvnorm(x, mean, sigma, log=FALSE) from R package mvtnorm to calculate the probability of x under the multivariate normal distribution with mean equal to mean and covariance matrix sigma. I become the following Error in solve.default(cov, ...) : system is computationally singular: reciprocal condition number = 1.81093e-19 What could be the reason of it?

Hi, Given a positive integer N, and a real number \lambda such that 0 < \lambda < 1, I would like to generate an N by N stochastic matrix (a matrix with all the rows summing to 1), such that it has the second largest eigenvalue equal to \lambda (Note: the dominant eigenvalue of a stochastic matrix is 1). I don't care what the other eigenvalues are. The second eigenvalue is

i want to estimate parameters with maximum likelihood method with contraints (contant numbers). for example sum(Ai)=0 and sum(Bi)=0 i have done it without the constraints but i realised that i have to use the contraints. Without constraints(just a part-not complete): skellamreg_LL=function(parameters,z,design) { n=length(z); mu=parameters[1]; H=parameters[2]; Apar=parameters[3:10];

I have a list object, in which I have stored n lme4-models. For example: library(lme4); myModels <- list(); myModels[1] <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) myModels[2] <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy) Now I would like to perform an anova over all models in the list. However, the anova function requires that every model is inserted

Hi there I'm trying to construct a model of mortality risk in 2D space that requires numerical integration of a hazard function, for which I'm using the integrate function. I'm occasionally encountering parameter combinations that cause integrate to terminate with error "Error in integrate... the integral is probably divergent", which I'm not sure how to interpret. The

I would like to print some tables and figures to a PDF device on a CentOS 5 vps. However, I cannot seem to get the latex function from Hmisc working. I followed the example, and got an error: sh: xdvi: command not found. I tried installing the 'tetex-xdvi' linux package, and now it returns: Error: Can't open display. I guess the reason for this is that the machine is a VPS terminal, so

I just grabbed the new EL5 binary's for my x64 VPS from CRAN. However, where updates usually go smoothly, I now get these errors: -bash-3.2# rpm -i R-2.9.1-1.el5.x86_64.rpm error: Failed dependencies: R-devel = 2.9.1-1.el5 is needed by R-2.9.1-1.el5.x86_64 -bash-3.2# rpm -i R-core-2.9.1-1.el5.x86_64.rpm error: Failed dependencies: perl(File::Copy::Recursive) is needed by

I would like to convert a dataframe to an array of lists, one for every record. A natural choide is apply as.list to the rows. However, as it seems, as.list() automatically converts all list elements to the same datatype. Eg: myData <- data.frame(a="foo",b=as.logical(rbinom(10,1,.5))); apply(myData,1,as.list); In this output, all boolean values have been converted to character

Hello, I want to find the roots of an equation in two variables. I am aware of the uniroot function, which can do this for a function with a single variable (as I understand it...) but cannot find a function that does this for an equation with more than one variable. I am looking for something implementing similar to a Newton-Raphson algorithm. Thanks. -- Bill Shipley North American Editor for

I am making a little GUI for lme4, and I was wondering if there is a function that automatically detects on which level every variable exists. Furtheremore I got kind of confused about what a random effects model actually calculates. I have some experience with commercial software packages for multilevel analysis, like HLM6, and I was surprised that lme4 does not require the user to specify the

Hi, Suppose I have a multivariate response Y (n x k) obtained at a set of predictors X (n x p). I would like to perform a linear regression taking into consideration the covariance structure of Y within each unit - this would be represented by a specified matrix V (k x k), assumed to be the same across units. How do I use "lm" to do this? One approach that I was thinking of

Could somebody share some tips on implementing multivariate integration and partial differentiation in R? For example, for a trivariate joint distribution (cumulative density function) of F(x,y,z), how to differentiate with respect to x and get the bivariate distribution (probability density function) of f(y,z). Or integrate f(x,y,z) with respect to x to get bivariate distribution of (y,z). Your

I would like to get some idea of which R-packages are popular, and what R is used for in general. Are there any statistics available on which R packages are downloaded often, or is there something like a package-survey? Something similar to http://popcon.debian.org/ maybe? Any tips are welcome! ----- Jeroen Ooms * Dept. of Methodology and Statistics * Utrecht University Visit

I think that this is because `[<-` dispatches on S4 methods only if the first argument is S4. ?"[<-" says: "These operators are also implicit S4 generics, but as primitives, S4 methods will be dispatched only on S4 objects ?x?." Georgi Boshnakov -----Original Message----- Message: 19 Date: Tue, 8 Sep 2020 22:04:44 -0400 From: Ben Bolker <bbolker at

Dear Martin, I have been playing a lot with the glkerns() function in the "lokern" package for "automatic" smoothing of time-series data. This kernel smoothing approach of Gasser and Mueller seems to perform quite well for estimating the function and its derivatives (first and second derivatives). In fact, this is one of the best methods based on my simulation studies for

Hi, I am currently trying to execute the following command: f<-factanal(factors=k$Components$nparallel,covmat=m,n.obs=2287,rotation="varimax",control=list(opt=list(method=c("BFGS")))) but keep getting the error: L-BFGS-B needs finite values of 'fn' I can't figure out what I am doing wrong here, why isn't optim being told to use BFGS instead of L-BFGS-B...