On 07/15/2010 05:07 AM, Peter Dalgaard wrote:
paul s wrote:
On 07/14/2010 06:15 PM, Peter Dalgaard wrote:
A quick calculation reveals that a matrix of that size requires about
2.7 TERAbytes of storage, so I'm a bit confused as to how you might
expect to fit it into 16GB of RAM...
However, even with terabytes of memory, you would be running into the
(current) limitation that a single vector in R can have at most 2^31-1 =
ca. 2 trillion elements.
thank you for also confirming what Douglas had written.
(except that it should of course be billions not trillions; I got the
DK/UK difference of whether a billion is a thousand or a million
millions bass ackward there.)
Yes, you could be doing it wrong, but what is "it"?
we are trying to create a hedonic index: http://tinyurl.com/2fnl3jf
That link is to a description of Linear Regression. I'm sure you didn't
intend to instruct an audience of statisticioan about that....
definitely not instructing and do not mean to offend this audience...
just trying to understand all of the information that is coming at me
and keep it together...
the link i sent was my original intent. i am calculating the beta
coefficients using a linear regression and the professor keeps
addressing it as a hedonic index. so i guessing i am still trying to
grasp what it is i am still doing.
Hmm, do you really have only 2 nonzero entries per row? If so, then a
no there will be just a few more, switched on and off, but more then two
and less then a hundred would be a conservative guess..
sparse matrix representation would involve "only" 4 million entries and
it's beginning to look like the matrices involved in mixed-effects
models with crossed random factors, so that techniques like minimal
i will try to understand this.
fill-in Choleski decomposition would apply.
i will read about this.
cheers
paul
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