[R] Network validation (of sorts) using granger Causality in R
bgunter.4567 at gmail.com
Tue Jan 3 14:56:08 CET 2017
Have you searched?!
"Granger causality" at rseek.org brought up what appeared to be many
"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!
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