Displaying 4 results from an estimated 4 matches for "simualtion".
2008 Jun 26
2
constructing arbitrary (positive definite) covariance matrix
...from [-1, 1])
that will always be positive definite?
I have noticed that covraince matrices created using the following COV
function are positive definite for -0.5 < r <1. However, for r <
-0.5, the matrix is not positive definite.
Does anyone have any idea why this is the case? For my simualtion, I
need to generate multivariate data for the whole range of r, [-1, 1]
for a give value of sd.
Any help/ suggestion would be greatly appreciated.
Examples
########
COV<-function (p = 3, sd = 1, r= 0.5){
cov <- diag(sd^2, ncol=p, nrow=p)
for (i in 1:p) {
for (j in 1:p) {...
2003 Sep 03
1
glmmPQL probelm
...e.
Those are the two error messages I usually get:
Error in logLik.reStruct(object, conLin) :
NA/NaN/Inf in foreign function call (arg 3)
Error in solve.default(pdMatrix(a, fact = TRUE)) :
Lapack routine dgesv: system is exactly singular
The trick is that the model is a part of a simualtion run, which uses the
same starting variance covariance matrix as a source for a mutlivariate
normal simulated 2 level dataset. So the variations in the data set are just
a part of the stochastic process. In the majority of the cases the model
runs fine, while in some cases I get either of the two er...
2003 Oct 25
0
memory optimization and use of recursion
Hi listers,
In light with the recent discussion on the optimizing the use of memory in
straneous proceudres i present you m problem, and hope to some additional
ideas.
I'm running a simualtion that in each step uses quite an amount of memory
(but not exceedingly) - just to give you an idea - I create a pseudo
population (n=1000, m=3) run lme and lm model and multiply impute (M=5) and
do the pooling and final staistics calculation. Each simulation has 1000
cycles. I repeat the simulation...
2003 Mar 03
0
R-devel RNG change
...version.
I also suggest the addition of a small function to query and set all
aspects of random number generation. I find it is very useful to program with a
construct like:
simulate <- function(model, rng=NULL, ...)
{if(is.null(rng)) rng <- set.RNG() # current settings to return with
simualtion object
else {old.rng <- set.RNG(rng); on.exit(set.RNG(old.rng)) }
...
}
and know that I have all the information for reproducibility. Below is a version
of this that I have been using for a few years. Its main shortcoming is that it
only considers the uniform generator and n...