similar to: How to check a matrix is positive definite?

Hey, All In principal component analysis (PCA), we want to know how many percentage the first principal component explain the total variances among the data. Assume the data matrix X is zero-meaned, and I used the following procedures: C = covriance(X) %% calculate the covariance matrix; [EVector,EValues]=eig(C) %% L = diag(EValues) %%L is a column vector with eigenvalues as the elements percent

Hi, I have a question for my simulation problem: I would like to generate a positive (or semi def positive) covariance matrix, non singular, in wich the spectral decomposition returns me the same values for all dimensions but differs only in eigenvectors. Ex. sigma [,1] [,2] [1,] 5.05 4.95 [2,] 4.95 5.05 > eigen(sigma) $values [1] 10.0 0.1 $vectors [,1]

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

1. Martin Maechler's comments should be taken as replacements for anything I wrote where appropriate. Any apparent conflict is a result of his superior knowledge. 2. 'eigen' returns the eigenvalue decomposition assuming the matrix is symmetric, ignoring anything in m[upper.tri(m)]. 3. The basic idea behind both posdefify and nearPD is to compute the

Hey, R-listers I was recommended to try using smooth.spline function for estimating 2-Dimensinal curve given a data set. So will you please tell me where to get this R function? Or which package provides this function? Thanks for your point. Fred

Hey, I want to draw several plots sequently, but have to make them dispaly in one figure. So how to achieve this? Thanks. Fred

Hi, R-listers I tried to plot several graphs in a sigle x-y coordinate settings, like the following: |(y) s | ****** s | ***** s | sssssssssssssssssss |_______________________________(x) where "*" and "s" denote two diffrent plots. However, when I used plot(data1); % data1 is the data points of "*"

Hi, If a matrix is not positive definite, make.positive.definite() function in corpcor library finds the nearest positive definite matrix by the method proposed by Higham (1988). However, when I deal with correlation matrices whose diagonals have to be 1 by definition, how do I do it? The above-mentioned function seem to mess up the diagonal entries. [I haven't seen this complication, but

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

Hey, all Now I am using EM algorithm to do some optimization. Within that E-step, I have to calculate some multivariate integration given some parameter values Theta and function form of a probability density function f. So I want to know if there are some package in R to do such numerical integration given such function form and parameter values? Thanks for your kind support on this. Have a

$ R --version R 1.6.1 (2002-11-01). So I would like to perform principal components analysis on a 16X16 correlation matrix, [princomp(cov.mat=x) where x is correlation matrix], the problem is princomp complains that it is not non-negative definite. I called eigen() on the correlation matrix and found that one of the eigenvectors is close to zero & negative (-0.001832311). Is there any way

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

Dear all, I've used the 'prcomp' command to calculate the eigenvalues and eigenvectors of a matrix(gg). Using the command 'principal' from the 'psych' package  I've performed the same exercise. I got the same eigenvalues but different eigenvectors. Is there any reason for that difference? Below are the steps I've followed: 1. PRCOMP #defining the matrix

Hey, all Do you how to calculate the trigamma function, that is d**2(log(gamma(x))) / dx**2. The second-order derivative of log(Gamma(x))? I cannot find it in the R package, and somebody knows who or where to get such one? Thanks. Fred -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html

Dear R-users, Is there a preferred method for testing whether a real symmetric matrix is positive definite? [modulo machine rounding errors.] The obvious way of computing eigenvalues via "E <- eigen(A, symmetric=T, only.values=T)$values" and returning the result of "!any(E <= 0)" seems less efficient than going through the LU decomposition invoked in

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

Assume F(x) is the cdf of stardard normal c.d.f, and want to get the integration of F(x)^2 over (-infinite, +infinite). So whats the value of this integration? And is there some function to achieve this? Thanks. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

Hi, I was wondering if function eigen() does something different from the function call eigen() in SAS. I'm in the process of translating a SAS code into a R code and the values of the eigenvectors and eigenvalues of a square matrix came out to be different from the values in SAS. I would also appreciate it if someone can explain the difference in simple terms. I'm pretty new to both

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]]

Am I correct to understand from the previous discussions on this topic (a few years back) that if I have a matrix with missing values my PCA options seem dismal if: (1) I don’t want to impute the missing values. (2) I don’t want to completely remove cases with missing values. (3) I do cov() with use=”pairwise.complete.obs”, as this produces negative eigenvalues (which it has in