Displaying 20 results from an estimated 2000 matches similar to: "Determinant and inverse using cholsky parameter"
2012 May 18
2
Covariance matrix in R with non-numeric variables
Dear R help forum members,
I am modeling a gaussian distribution for a computational biology application and I am working in the statistical package "R". In this regard, my problem is that I have to construct a covariance matrix with variables (non-numeric) and the covariance matrix is to be used in an maximizers of the likelihood function to predict the variables in the matrix. I am
2012 Dec 05
1
Understanding svd usage and its necessity in generalized inverse calculation
Dear R-devel:
I could use some advice about matrix calculations and steps that might
make for faster computation of generalized inverses. It appears in
some projects there is a bottleneck at the use of svd in calculation
of generalized inverses.
Here's some Rprof output I need to understand.
> summaryRprof("Amelia.out")
$by.self
self.time self.pct
2010 Apr 13
1
Lapack, determinant, multivariate normal density, solution to linear system, C language
r-devel list,
I have recently written an R package that solves a linear least squares
problem, and computes the multivariate normal density function. The bulk
of the code is written in C, with interfacing code to the BLAS and
Lapack libraries. The motivation here is speed. I ran into a problem
computing the determinant of a symmetric matrix in packed storage.
Apparently, there are no explicit
2005 Jan 21
1
Cholesky Decomposition
Can we do Cholesky Decompositon in R for any matrix
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2004 Feb 28
2
matrix inverse in C
Hi,
I'm writing an R package using the C code i've written. I'm wondering if
anyone knows an easy way to calculate an inverse and cholesky factor of a
matrix using the Fortran/C library of R: and how to call them from C. My
code is based on the Numerical Reciepe code, and I'm trying to use
something that is already in R.
Thanks for your help in advance,
Kosuke
2007 Jul 02
2
how to use mle with a defined function
Hi all,
I am trying to use mle() to find a self-defined function. Here is my
function:
test <- function(a=0.1, b=0.1, c=0.001, e=0.2){
# omega is the known covariance matrix, Y is the response vector, X is the
explanatory matrix
odet = unlist(determinant(omega))[1]
# do cholesky decomposition
C = chol(omega)
# transform data
U = t(C)%*%Y
WW=t(C)%*%X
beta = lm(U~W)$coef
Z=Y-X%*%beta
2012 Nov 29
3
Help
Help Please
Hello,
I want to find the whole hat matrix not only the hat values. Is there anyway that could be extracted from lm function ?. If not, please suggest something else.
Direct computations using chol2inv or solve are not stable if t(X)%*%X has high determinant. In this case lm is still able to produce correct fitted values, hat_value, residuals...etc but direct computations fail to do
2004 Jan 12
1
question about how summary.lm works
Hi,
While exploring how summary.lm generated its output I came across a section
that left me puzzled.
at around line 57
R <- chol2inv(Qr$qr[p1, p1, drop = FALSE])
se <- sqrt(diag(R) * resvar)
I'm hoping somebody could explain the logic of these to steps or
alternatively point me in the direction of a text that will explain these
steps.
In particular I'm puzzled
2012 Feb 21
1
System is computationally singular error when using cholesky decompostion in MCMC
Hello Everyone
I have a MCMC loop to calculate a time varying hierarchical Bayesian
structure.
This requires me to use around 5-6 matrix inversions in the loop.
I use cholesky and chol2inv for the matrix decomposition.
Because of the data I am working with I am required to invert a 167 by 167
matrix twice in one iteration.
I need to run the iteration for 10000 times, but I get the error
2009 Mar 10
5
Cholesky Decomposition in R
Hi everyone:
I try to use r to do the Cholesky Decomposition,which is A=LDL',so far I
only found how to decomposite A in to LL' by using chol(A),the function
Cholesky(A) doesnt work,any one know other command to decomposte A in to
LDL'
My r code is:
library(Matrix)
A=matrix(c(1,1,1,1,5,5,1,5,14),nrow=3)
> chol(A)
[,1] [,2] [,3]
[1,] 1 1 1
[2,] 0 2 2
2013 Feb 05
1
impossible to invert a spam-object, but possible when it's a matrix-object
Dear R-users,
a question concerning sparse matrices in package "spam" (spam_0.29-2).
On one hand I have a spam object (n X n) from which I cannot compute the inverse. On the other hand, if I convert this object in a plain matrix, I can find the inverse without any problem.
Specifically I get the following error message:
Error in chol.spam(a, ...) :
Singularity problem when
2015 Nov 23
3
MKL Acceleration encouraging; need adjust package builds?
Dear R-devel:
The Cluster administrators at KU got enthusiastic about testing
R-3.2.2 with Intel MKL when I asked for some BLAS integration. Below
I forward a performance report, which is encouraging, and thought you
would like to know the numbers. Appears to my untrained eye there are
some extraordinary speedups on Cholesky decomposition, determinants,
and matrix inversion.
They had
2002 Dec 17
1
lme invocation
Hi Folks,
I'm trying to understand the model specification formalities
for 'lme', and the documentation is leaving me a bit confused.
Specifically, using the example dataset 'Orthodont' in the
'nlme' package, first I use the invocation given in the example
shown by "?lme":
> fm1 <- lme(distance ~ age, data = Orthodont) # random is ~ age
Despite the
2012 May 23
2
Special characters in an R package manual
Dear all,
I have some trouble with special characters while building my R package. I
tried to follow the usual LATEX format, but could not fix the problem:
For instance, for "greater than or equal", I tried "\geq", but R says that
this is an unknown macro.
Could anyone direct me how to solve this issue?
Best
Ozgur
-----
************************************
Ozgur ASAR
2011 Dec 29
1
Cholesky update/downdate
Dear R-devel members,
I am looking for a fast Cholesky update/downdate. The matrix A being
symmetric positive definite (n, n) and factorized as
A = L %*% t(L), the goal is to factor the new matrix A +- C %*% t(C)
where C is (n, r). For instance, C is 1-column when adding/removing an
observation in a linear regression. Of special interest is the case
where A is sparse.
Looking at the
2012 May 27
7
Customized R Regression Output?
Hello R-Experts,
I am facing the problem that I have to estimate several parameters for a lot
of different dependent variables.
One single regression looks something like this:
y = beta0 + beta1 * x1 + beta2 * x2 + beta3 * x1 * x2 + beta4 * x4 + beta5 *
lag(x4,-1)
where y is the dependent variable and xi are the independent ones. Important
to me are the different estimates of betai and their
2012 May 31
2
Loop question
Hello,
I have a dataframe (Lx) with 5 Lb, and 5 Lw variables. I want to
create several variables according to the loop presented below (data
attached).
Lx <- read.csv("Lx.csv", header=T, sep=",")
for (i in 1:20) {
Lx$sb1[i] <- Lx$Lb1[i+1]/Lx$Lb1[i]
Lx$sb2[i] <- Lx$Lb2[i+1]/Lx$Lb2[i]
Lx$sb3[i] <- Lx$Lb3[i+1]/Lx$Lb3[i]
Lx$sb4[i] <-
2007 Apr 24
1
Matrix: how to re-use the symbolic Cholesky factorization?
I have been playing around with sparse matrices in the Matrix
package, in particularly with the Cholesky factorization of matrices
of class dsCMatrix. And BTW, what a fantastic package.
My problem is that I have to carry out repeated Cholesky
factorization of a spares symmetric matrices, say Q_1, Q_2, ...,Q_n,
where the Q's have the same non-zero pattern. I know in this case one
does
2011 Jan 29
1
Regularization of a matrix that has some tiny negative eigenvalues
Dear all:
In what I am doing I sometimes get a (Hessian) matrix that has a
couple of tiny negative eigenvalues (e.g. -6 * 10^-17). So, I can't
run a Cholesky decomp on it - but I need to.
Is there an established way to regularize my (Hessian) matrix (e.g.,
via some transformation) that would allow me to get a semi-positive
definite matrix to be used in Cholesky decomp?
Or should I try some
2012 May 31
1
Warning message: numerical expression has 1000 elements: only the first used
Hi,
Your mistake seems to be in
sum(v[1:x])
You create "x" as a vector but your treat it as a single number.
v[1:x] expects "x" to be a single number and only considers its first
element which is 1.
If I understand your query correctly, the following might handle your
problem:
sum.vec <-NULL
for (x in 1:1000){
t <- rbinom(1000, 1, 0.5)
v <- replace(t,t==0,-1)