similar to: Error "system is computationally singular" by using function dmvnorm

Dear R users, I would like to align two protein sequences using BLAST (bl2seq). The question is whether this programm have been implemented in R. Thank you for your help, Alla.

Summary: + I am writing an R extension that needs to call dmvnorm more than 10,000 times during a model fitting computation. + My extension uses openmp for parallel execution. + As of R 3.0, it is no longer permitted for threads to call the R interpreter because there is a stack overflow check that always trips because the thread's stack is different from what R is expecting. +

Dear R community, I was trying to estimate density at point zero of a multivariate distribution (9 dimensions) and for this I was using a multinormal approximation and the function dmvnorm , gtools package. To have a sense of the error I tried to look the mismatch between a unidimensional version of my distribution and estimate density at point zero with function density, dmvnorm and dnorm. At

I am using curve3d in the emdbook package to graph a gaussian copula density function generated via the copula package. Unfortunately, it appears that emdbook masks dmvnorm from the package mvtnorm in a way that prohibits copula from generating the gaussian copula. (Sounds very confusing!) For example, > library(copula) > f<-function(x,y) dcopula(normalCopula(0),c(x,y)) >

I am experiencing strange results using dmvnorm. I define a scaled distance matrix from the coordinates bellow and then calculate a covariance matrix using a spherical correlation function. Then with certain combinations of range and sill parameters dmvnorm is returning values greater than 1. Surely the results of dmvnorm should be in the interval 0:1 (or do I just nead a holiday?). In addition

Dear R users, i try to produce the fold change versus fold change plot where i have the values for x and y ranging from 0.01 to 100. So i start with plot(x,y,xlim=c(0.01,100),ylim=c(0.01,100), axes=F). Then i would like both axes to have tick marks as c(0.01,0.1,1,10,100) but they should appear equidistant. How should i manage this? Thank you for your help, Alla.

Hi All, Why would calls to dnorm and dmvnorm return values that are above 1? For example, > dnorm(0.00003,mean=0, sd=0.1) [1] 3.989423 This is happening on two different installations of R that I have. Thank you. Hailu [[alternative HTML version deleted]]

I have the following function, a MSE calc based on some Multivariate normals: MV.MSE<-function(n,EP,X,S){ (dmvnorm(X,mean=rep(0,2),I+S+EP)-dmvnorm(X,mean=rep(0,2),I+S))^2 + 1/n*(dmvnorm(X,mean=rep(0,2),1+S+EP/2)*det(4*pi*EP)^-.5- (dmvnorm(X,mean=rep(0,2),I+S+EP ))^2)} I can get the MV.MSE for given values of the function e.g

Hi, All: Is there a way to identify whether any users are using a particular package in a shared network R installation? I ask, because we have such a multiple-user installation and when I tried to install a package using Rgui that was in use by Rterm on a single-user installation, 'install.packages' deleted the existing package but failed to install the new version;

sorry for cross-posting Dear all, I have tow (several) bivariate distributions with a known mean and variance-covariance structure (hence a known density function) that I would like to compare in order to get an intersect that tells me something about "how different" these distributions are (as t-statistics for univariate distributions). In order to visualize what I mean hear a little

Package mvtnorm provides dmvnorm, pmvnorm that can be used to compute Pr(X=x,Y=y) and Pr(X<x,Y<y) for a bivariate normal. Are there functions that would compute Pr(X<x,Y=y)? I'm currently using "integrate" with dmvnorm but it is too slow.

Hello, everyone, I have a question about integration of product of two densities. Here is the sample code; however the mean of first density is a function of another random variable, which is to be integrated. ## f=function(x) {dmvnorm(c(0.6, 0.8), mean=c(0.75, 0.75/x))*dnorm(x, mean=0.6, sd=0.15)} integrate(f, lower=-Inf, upper=Inf) ## error message Error in dmvnorm(c(0.6, 0.8), mean = c(0.75,

Dear R users, I'm running R version 2.10.1. I tried installing the Cairo package and I got the following message: * installing *source* package ?Cairo? ... checking for gcc... gcc -std=gnu99 checking for C compiler default output file name... a.out checking whether the C compiler works... yes checking whether we are cross compiling... no checking for suffix of executables... checking for

Hi uRsers, when inverting a 2 by 2 matrix using solve, I encountered a error message: solve.default(sigma, tol = 1e-07) : system is computationally singular: reciprocal condition number = 1.7671e-017 and then I test the determinant of this matrix: 6.341393e-06. In my program, I have a condition block that whether a matrix is invertible like this: if(det(sigma)<1e-7) return NULL;

Hi folks, I know, this is a fairly common question and I am really disappointed that I could not find a solution. I am trying to calculate Mahanalobis distances in a data frame, where I have several hundreds groups and several hundreds of variables. Whatever I do, however I subset it I get the "system is computationally singular: reciprocal condition number" error. I know what it means

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)

I get this error while computing partial correlation. *Error in solve.default(Szz) : system is computationally singular: reciprocal condition number = 4.90109e-18* Why is it?Can anyone give me some idea ,how do i get rid it it? This is the function i use for calculating partial correlation. pcor.mat <- function(x,y,z,method="p",na.rm=T){ x <- c(x) y <- c(y)

Hi all, I have been looking for means of add a contour around some points in a scatterplot as a means of representing the center of density for of the data. I'm imagining something like a 95% confidence estimate drawn around the data. So far I have found some code for drawing polygons around the data. These look nice, but in some cases the polygons are strongly influenced by outlying points.

Hi all, I was using the Newton-Raphson method to estimate paremeters in the model developed by my supervisor. However, when I interatively computed theta(t+1)=theta(t) - solve(H)*s (where the Hessian matrix and score vector were explicitely derived), I got the error message: Error in solve.default(H) : system is computationally singular: reciprocal condition number = 1.70568e-032. Assume my score

Hi, I have a dataset which has around 138 variables and 30,000 cases. I am trying to calculate a mahalanobis distance matrix for them and my procedure is like this: Suppose my data is stored in mymatrix > S<-cov(mymatrix) # this is fine > D<-sapply(1:nrow(mymatrix), function(i) mahalanobis(mymatrix, mymatrix[i,], S)) Error in solve.default(cov, ...) : system is computationally