similar to: Inverse Matrices

Hello I've stumbled upon a problem for inversion of a matrix with large values, and I haven't found a solution yet... I wondered if someone could give a hand. (It is about automatic optimisation of a calibration process, which involves the inverse of the information matrix) code: ********************* > macht=0.8698965 > coeff=1.106836*10^(-8) >

General goal: Write R code to find the inverse matrix of an nxn positive definite symmetric matrix. Use solve() to verify your code works. Started with a 3x3 matrix example to build the code, but something dosen't seem to be working. I just don't know where I am going wrong. ##Example matrix I found online A<-c(4,1,-1,1,2,1,-1,1,2) m<-matrix(A,nrow=3,ncol=3) ##Caculate the eigen

Dear List I have installed the 'lattice' package by using 'install.package("lattice") It appears to have created a lattice folder in my R library as I would expect. I have tried to call the package by library(lattice) but I am getting the following error message: Error in parse(file, n, text, prompt) : syntax error on line 2018 Could anybody tell me why this is

Hi I am wanting to fit some multinomial logit models (multinom command in package nnet) Is it possible to do any model checking techniques on these models e.g. residual, leverage etc. I cannot seem to find any commands that will allow me to do this. Many thanks ---------------------- L.E.Gross L.E.Gross at maths.hull.ac.uk

Dear list I have what is probably a very simple question that I can't seem to solve. I basically want to create a vector at the end of a 'for' loop that will store the output for each person and which I can recall after the loop has run. This will require some sort of indexing. X and Y are matrices defined earlier for each person (within the for loop but I'm not writing it here

Hi, I've been trying to figure out a special set of covariance matrices that causes some symmetric zero elements in the inverse covariance matrix but am having trouble figuring out if that is possible. Say, for example, matrix a is a 4x4 covariance matrix with equal variance and zero covariance elements, i.e. [,1] [,2] [,3] [,4] [1,] 4 0 0 0 [2,] 0 4

Hi, I am using the nlme package to fit random effects models, however, I have many missing values in my response variable which I cannot just omit. I can't replace the missing values with the mean or median either as this is too simple and would need to use something like imputation or the EM Algorithm. Is there any way of including these missing values since the model will not run when

Hello, I'm using the following for loop to find regression curves using a list of functions (formList), a list of starting values (startList), uppervalues (upperList) and lower values (lowerList). A sample of the list of function I use in the loop is the following: FormList <- list(PTG.P ~ fz1(Portata, a, b), PTG.P ~ fz2(Portata, a, b), PTG.P ~ fz3(Portata,a, b, d, e), PTG.P ~

Hello, I requested help a couple of weeks ago creating a dipole field in R but receieved no responses. Eventually I opted to create a 3d sinusoidal plot and concatenate this with its inverse as a means for a "next best" situation. It seems that this isn't sufficient for my needs and I'm really after creating a continuous 3d gaussian mesh with a "positive" and

Dear R-listers, I have a dxr matrix Z, where d > r. And the product Z*Z' is a singular square matrix. The problem is how to get the left inverse U of this singular matrix Z*Z', such that U*(Z*Z') = I? Is there any to figure it out using matrix decomposition method? Thanks a lot for your help. Fred

>I'm rusty, but not *that* rusty here, I hope. > >If W (=Z*Z' in your case) is singular, it can not have >inverse, which by >definition also mean that nothing multiply by it will >produce the identity >matrix (for otherwise it would have an inverse and >thus nonsingular). > >The definition of a generalized inverse is something >like: If A is a >non-null

Dear R-users, I have one question about using SVD to get an inverse matrix of covariance matrix Sometimes I met many singular values d are close to 0: look this example $d [1] 4.178853e+00 2.722005e+00 2.139863e+00 1.867628e+00 1.588967e+00 [6] 1.401554e+00 1.256964e+00 1.185750e+00 1.060692e+00 9.932592e-01 [11] 9.412768e-01 8.530497e-01 8.211395e-01 8.077817e-01 7.706618e-01 [16]

Hi, Suppose I have a matrix, A. Is there an easy way to find A^{n}? I mean, I can do something like: A %*% A %*% A %*% A for A^4, but if I want A^{10} it would be kind of annoying... Cheers, Kevin ------------------------------------------------------------------------------ Ko-Kang Kevin Wang Postgraduate PGDipSci Student Department of Statistics University of Auckland New Zealand

If so, I''ve had trouble finding it. Can anyone help?

Dear all, I am using R 2.9.2 on Windows XP. I am undertaking a simulation study to consider methods of external validation in the context of missing covariates in the validation data set. I would like to use Royston's measure of prognostic separation as a method of external validation. Although there is no R code for this, the author informs me that it should be easy: 1. Calculate the

Thorsten is right. There is a direct formula for computing the Moore-Penrose inverse using the singular value composition of a matrix. This is incorporated in the following: mpinv <- function(A, eps = 1e-13) { s <- svd(A) e <- s$d e[e > eps] <- 1/e[e > eps] return(s$v %*% diag(e) %*% t(s$u)) } Hope it helps. Dietrich

I haven't found a function that directly calculates the matrix inverse, does it exist? Otherwise what would be the fastest way to do it? Patrik Waldmann -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the

I am looking fpr a algo to find matrix inverse. Till time I am aware of Gauss-Jordan Elimination procedure to find the same. Are there any other algo. as well? What does R use to find the inverse? -- View this message in context: http://www.nabble.com/Algo.-for-matrix-inverse-tp20182285p20182285.html Sent from the R help mailing list archive at Nabble.com.

Dear All, My question is simple but I need someone to help me out. Suppose I have a positive definite matrix A. The funtion chol() gives matrix L, such that A = L'L. The inverse of A, say A.inv, is also positive definite and can be factorized as A.inv = M'M. Then A = inverse of (A.inv) = inverse of (M'M) = (inverse of M) %*% (inverse of M)' = ((inverse of

I have a one-variable data set in R. The plot of histogram of my numerical variable suggests an inverse gaussian distribution. How can I obtain best estimation for the two parameters of inverse gaussian based on my data? Thanks. -- View this message in context: http://n4.nabble.com/estimate-inverse-gaussian-in-R-tp949692p949692.html Sent from the R help mailing list archive at Nabble.com.