similar to: Error: the leading minor of order 6 is not positive definite

Dear friends - I'm having troubles with nlme fitting a simplified model as shown below eliciting the error Error in chol.default((value + t(value))/2) : ? the leading minor of order 1 is not positive definite - I have seen the threads on this error but it didn't help me solve the problem. The model runs well in brms and identifies the used parameters even with fixed effects for TRT?

This thread reveals that R has some holes in the solution of some of the linear algebra problems that may arise. It looks like Jim Ramsay used a quick and dirty approach to the generalized eigenproblem by using B^(-1) %*% A, which is usually not too successful due to issues with condition of B and making a symmetric/Hermitian problem unsymmetric. In short, the problem is stated as follows:

Performing CCA in R I know they say don't say please... or plead...but I'm sorry but I really need some help with this problem. I have tried to perform CCA in R and I can never do this successfully. Can someone please tell me what I'm doing wrong. I can't attach any file...so Please email me and I'll attach the necessary files. (there's only two) the files will be my CCA R

Hello I am trying to determine wether a given matrix is symmetric and positive matrix. The matrix has real valued elements. I have been reading about the cholesky method and another method is to find the eigenvalues. I cant understand how to implement either of the two. Can someone point me to the right direction. I have used ?chol to see the help but if the matrix is not positive definite it

I do have some trouble with matrices. I want to build up a covariance matrix with a hierarchical structure). For instance, in dimension n=10, I have two subgroups (called REGION). NR=2; n=10 CORRELATION=matrix(c(0.4,-0.25, -0.25,0.3),NR,NR) REGION=sample(1:NR,size=n,replace=TRUE) R1=REGION%*%t(rep(1,n)) R2=rep(1,n)%*%t(REGION) SIGMA=matrix(NA,n,n) for(i in 1:NR){ for(j in

Hello, Could somebody please tell me what the following is about? I try to run BMS function for quite a small dataset (400 rows and 50 columns) to get model suggestions, but I end up getting following error message: > bms<-bms(dat) Error in chol.default(XtX.start) : the leading minor of order 36 is not positive definite I've done it before, it worked for some other data set

Hi, I recently tried to estimate a linear unconditional latent growth curve on 7 repeated measures using lavaan (most recent version): modspec=' alpha =~ 1*read_g0 + 1*read_g1 + 1*read_g2 + 1*read_g3 + 1*read_g4 + 1*read_g5 + 1*read_g6 beta =~ 0*read_g0 + 1*read_g1 + 2*read_g2 + 3*read_g3 + 4*read_g4 + 5*read_g5 + 6*read_g6 ' gmod=lavaan(modspec, data=math, meanstructure=T,

printCoefmat(signif.legend =FALSE) does not work properly. The option "signif.legend = FALSE" is ignored as shown in the example below. cmat <- cbind(rnorm(3, 10), sqrt(rchisq(3, 12))) cmat <- cbind(cmat, cmat[,1]/cmat[,2]) cmat <- cbind(cmat, 2*pnorm(-cmat[,3])) colnames(cmat) <- c("Estimate", "Std.Err", "Z value", "Pr(>z)") #

Hello I can construct a correlation matrix from an (ordered) vector of correlation coefficients as follows: x <- c(0.1,0.2,0.3,0.4,0.5) n <- length(x) cmat <- diag(rep(0.5,n)) cmat[lower.tri(cmat,diag=0)] <- x cmat <- cmat+t(cmat) But how to do the reverse operation, i.e. produce x from cmat? Thanks for help, Serguei Kaniovski [[alternative HTML version deleted]]

In a package I've been developing for manipulating genetic data I discovered a problem when indexing into character arrays using NA's: Create a character matrix and a numeric matrix > cmat <- matrix( letters[1:4], ncol=2, nrow=2) > nmat <- matrix( 1:4, ncol=2, nrow=2) Create an index vector containing an NA value > indvec <- c(1,2,NA) Indexing works fine for both

Hello all R-helpers, I've performed an experiment to test for differential effects of elevated temperatures on three different groups of corals. I'm currently performing a cox proportional hazards regression with censoring on the survivorship (days to mortality) of each individual in the experiment with two factors: Temperature Treatment (2 levels: ambient and elevated) and

Hi, In the data below, "case" represents cases, "x" binary states. Each "case" has exactly 9 "x", ie is a binary vector of length 9. There are 2^9=512 possible combinations of binary states in a given "case", ie 512 possible vectors. I generate these in the order of the decimals the vectors represent, as:

I am creating covariance matrices from sets of points, and I am having frequent problems where I create matrices that are non-positive definite. I've started using the corpcor package, which was specifically designed to address these types of problems. It has solved many of my problems, but I still have one left. One of the matrices I need to calculate is a cross-covariance matrix. In other

Hey, all Given a square matrix, how can I check if this matrix is positive definite or not? Thanks. Fred

Dear list, I fitted the same GAM model using directly the function gam(mgcv) ... then as a parameter of another function that capture the warnings messages (see below). In the first case, there is no warning message printed, but in the last one, the function find two warning messages stating "matrix not positive definite" So my question is: Do I have to worry about those warnings and

Hello to everybody, I have a quadratic programming problem that I am trying to solve by various methods. One of them is to use the quadprog package in R. When I check positive definiteness of the D matrix, I get that one of the eigenvalues is negative of order 10^(-8). All the others are positive. When I set this particular eigenvalue to 0.0 and I recheck the eigenvalues in R, the last

I have a covariance matrix that is not positive semi-definite matrix and I need it to be via some sort of adjustment. Is there any R routine or package to help me do this? Thanks, Roger [[alternative HTML version deleted]]

Is there a function in r that let's you efficiently invert a positive definite symmetric Block Toeplitz matrix? My matrices are the covariance matrices of observations of a multivariate time series and can be 1000*1000 or larger. I know the package 'ltsa' which seems to use the Trench algorithm to compute the inverse of a Toeplitz matrix. I am looking for a so to say

I want to compute B=A^{1/2} such that B*B=A. For example a=matrix(c(1,.2,.2,.2,1,.2,.2,.2,1),ncol=3) so > a [,1] [,2] [,3] [1,] 1.0 0.2 0.2 [2,] 0.2 1.0 0.2 [3,] 0.2 0.2 1.0 > a%*%a [,1] [,2] [,3] [1,] 1.08 0.44 0.44 [2,] 0.44 1.08 0.44 [3,] 0.44 0.44 1.08 > b=a%*%a i have tried to use singular value decomposion > c=svd(b) > c$u%*%diag(sqrt(c$d))

Dear fellow R Users: I am doing a Cholesky decomposition on a correlation matrix and get error message the matrix is not semi-definite. Does anyone know: 1- a work around to this issue? 2- Is there any approach to try and figure out what vector might be co-linear with another in thr Matrix? 3- any way to perturb the data to work around this? Thanks for any suggestions.