Thanks for the response. That doesn't seem to do it. It's able to identify
if one edge connects back into itself, but isn't able to identify whether
an edge eventually connects back into itself (after passing through
multiple variables). For example, the following should fail, because the
path goes from 1-2-3, then back into 1:
is.simple(graph(c(1,2,2,3,3,1,3,4)))
But, it returns TRUE.
On Tue, Jan 29, 2013 at 10:21 AM, Duncan Murdoch
<murdoch.dun...@gmail.com>wrote:
> On 29/01/2013 11:12 AM, Dustin Fife wrote:
>
>> Hi,
>>
>> I'm working on a project that will generate RAM matrices at random. What I
>> want to do is to be able to automatically identify if the model is
>> non-recursive. For example, the following RAM matrix has a non-recursive
>> loop (going from A to B to C to A):
>>
>
> I'm not familiar with your terms, but your description sounds like you
> want a test for a simple graph. I believe the igraph package has that in
> the "is.simple" function (and a lot of other tests of graph properties in
> case that's not the one you want).
>
> Duncan Murdoch
>
>
>> n.recursive <- data.frame(matrix(c("A", "B", 1,
>> "B", "C", 1,
>> "C", "A", 1,
>> "B", "D", 1), nrow=4, byrow=TRUE))
>> names(n.recursive) <- c("From", "To", "Arrows")
>>
>> What I want to be able to do is have a function that automatically checks
>> whether there is a non-recursive path. Here's what I've thought of so far:
>>
>> 1. Find all variables that both send and receive an arrow. (in this case,
>> A
>> and B both fit that criteria).
>>
>> vars <- LETTERS[1:5]
>> double.arrow.vars <- vars[which(vars %in% n.recursive$From & vars %in%
>> n.recursive$To)]
>>
>> 2. For all variables found in #1, follow all paths exiting that variable
>> to
>> other variables, then follow all paths exiting that next variable
>> variable,
>> etc. and continue tracing the path.
>>
>> ##### insert complicated code here
>>
>> 3. If a variable is repeated, identify it as non-recursive.
>>
>> The problem with #2 is that, for large models, the number of paths to be
>> traced could be really large. (Also, I'm having trouble thinking of how to
>> code it so it's not really awkward).
>>
>> So, my question is this: is there a better way to approach the problem? Is
>> there a more efficient way?
>>
>> I know that I could probably identify which models are non-recursive after
>> estimation (via convergence failures or negative parameter estimates). But
>> I want to be able to identify them before estimation. Any help would be
>> appreciated.
>>
>> Dustin
>>
>>
>>
>>
>>
>
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
