I did have it loaded with the newest version of igraph, but apparently it requires the newest version of R. I now have R-2.15.2 loaded and it works! Thanks for all the help!
Dustin On Tue, Jan 29, 2013 at 11:21 AM, William Dunlap <wdun...@tibco.com> wrote: > Is package:igraph loaded in your R session on the mac? Is it up to > date? **** > > (I would doubt that is.dag would be a recently written function.)**** > > ** ** > > Bill Dunlap**** > > Spotfire, TIBCO Software**** > > wdunlap tibco.com**** > > ** ** > > *From:* Dustin Fife [mailto:fife.dus...@gmail.com] > *Sent:* Tuesday, January 29, 2013 9:16 AM > *To:* William Dunlap > *Cc:* Duncan Murdoch; r-help > > *Subject:* Re: [R] identify non-recursive models**** > > ** ** > > That looks like exactly what I need. I tested it on my PC and it ran, but > my mac couldn't find the function "is.dag." Any ideas? **** > > On Tue, Jan 29, 2013 at 11:03 AM, William Dunlap <wdun...@tibco.com> > wrote:**** > > is.dag()? > > Bill Dunlap > Spotfire, TIBCO Software > wdunlap tibco.com**** > > > > > -----Original Message----- > > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] > On Behalf > > Of Dustin Fife > > Sent: Tuesday, January 29, 2013 8:52 AM > > To: Duncan Murdoch > > Cc: r-help > > Subject: Re: [R] identify non-recursive models > > > > 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.**** > > > > > -- > Dustin Fife > PhD Student > Quantitative Psychology > University of Oklahoma **** > -- Dustin Fife PhD Student Quantitative Psychology University of Oklahoma [[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.
