p-boun...@r-project.org] On
> Behalf
> Of Michael Dewey
> Sent: Sunday, August 12, 2012 6:54 AM
> To: Boel Brynedal; R-help Mailing List
> Subject: Re: [R] Problem when creating matrix of values based on covariance
> matrix
>
> At 15:17 11/08/2012, Boel Brynedal wrote:
>
On Sun, Aug 12, 2012 at 7:52 PM, R. Michael Weylandt
wrote:
> I am not sure if this is a general/fixed bias in the spearman
> estimator or if it's just a function of the covMat I randomly chose.
> Prof. Dalgaard and many others on this list must know.
To somewhat answer myself, I restricted my at
On Sun, Aug 12, 2012 at 1:46 PM, Boel Brynedal wrote:
> A clarification - yes, calculating the pearson covariance does give
> the expected results. I dont fully understand why yet, but many thanks
> for this help!
I'm not sure that the spearman correlation is an appropriate estimator
for the cova
A clarification - yes, calculating the pearson covariance does give
the expected results. I dont fully understand why yet, but many thanks
for this help!
2012/8/12 Boel Brynedal :
> Thanks for these replies.
> @Peter - are these methods only suitable for pearson covariances? That
> would def expla
Thanks for these replies.
@Peter - are these methods only suitable for pearson covariances? That
would def explain my issues. Sorry for my ignorance, but I would
highly appreciate an explanation. My original covariance matrix is
calculated using spearman as well (which is suitable for the data).
@M
On Aug 11, 2012, at 16:17 , Boel Brynedal wrote:
> cov8=cov(sample8,method='spearman')
There's your problem. I'm surprised that nobody seems to have picked up on
this, but Spearman covariances are of the ranks, not of the data. Try
method="pearson".
--
Peter Dalgaard, Professor,
Center for S
At 15:17 11/08/2012, Boel Brynedal wrote:
Hi,
I want to simulate a data set with similar covariance structure as my
observed data, and have calculated a covariance matrix (dimensions
8368*8368). So far I've tried two approaches to simulating data:
rmvnorm from the mvtnorm package, and by using t
Hi, thanks for the reply.
I am not assuming that the supplied covariance vector in any way
captures the 'true' covariance matrix of the population, but thats not
what I am after either. I just want to simulate data that has a
similar covariance as that covariance matrix. And the numbers are so
huge
Sampling error? Do you realize how large a sample size you would
need to precisely estimate an 8000 x 8000 covariance matrix? Probably
exceeds the number of stars in our galaxy...
Numerical issues may also play a role, but I am too ignorant on this
aspect to offer advice.
Finally, this is reall
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