[R] Network validation (of sorts) using granger Causality in R

[Bert Gunter]

Have you searched?!

"Granger causality" at rseek.org brought up what appeared to be many
relevant hits.

-- Bert


Bert Gunter

"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )


On Mon, Jan 2, 2017 at 10:20 PM, PWD7052 via R-help
<r-help at r-project.org> wrote:
> Hi Everyone,
>
> We have a question about whether one can to do a particular type of Granger Causality (GC) network validation in R. We hope you'll agree it's an interesting problem and that someone's figured out how to solve it.
>
> We have a cellular network with n nodes (proteins).  We have two different n x s x k time series matrices that describe the network activity under two mutually exclusive conditions, C (cancerous cell) and H (healthy cell), where s is the length of the time series data, and k is the number of observations.
> Using the time series matrices, we calculated two different n x n GC matrices, one for healthy cells and one for cancerous cells, so that ij th element in each matrix represents the GC influence of node i on node j.  Using the various standard tests, we know that many of the GC values are extremely significant.
> Now we’re given a brand-new observation in the form of a n x s x 1 time series matrix Y that represents the activity of the same n nodes (we don’t know a priori whether the new data come from a healthy cell or a cancerous cell).
> Given this matrix Y :
> (1) How can we go about determining if Y comes from a cancerous cell (condition C) or a healthy cell (condition H)?
> (2) Is there a package in R that we can use for this purpose?
>
> Thank you very much!
> Pat
>
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>
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