Questions about interpreting ERGM goodness of fit
Thank you Philip, I have played around with introducing gwesp and it does not seem to shift the GOF at all. I feared it may have something to do fundamentally with the network. I think I may be losing information (i.e. weights) converting formats. I start with a phyloseq imported from a biom file and then build an igraph network. For the ERGM I am converting the igraph using as.network() from the Intergraph package. I think here I may be losing weights or other information. Loss of information at the data level may also be the answer as the networks are related in that one data set is indirectly derived from the other and I see the edge wise shift get worse between the two. I will revisit it. I appreciate the assist! Erick Erick LeBrun PhD Student Kang Microbial Ecology Lab Erick_leBrun at baylor.edu -----Original Message----- From: Philip Leifeld [mailto:philip.leifeld at ipw.unibe.ch] Sent: Wednesday, July 27, 2016 5:12 PM To: r-sig-networks at r-project.org Cc: LeBrun, Erick <Erick_LeBrun at baylor.edu>; philip.leifeld at glasgow.ac.uk Subject: Re: [R-sig-networks] Questions about interpreting ERGM goodness of fit Hi Erick, This indicates a lack of theoretical understanding of what is creating your network topology. Most likely, the interplay of endogenous network statistics you have chosen collectively produces networks that do not look like the network you observe. It is less likely, but may be the case, that there is an omitted exogenous variable the inclusion of which would solve the problem. I would think more carefully about what theoretical mechanisms may be at work with regard to the endogenous dependencies. In the absence of a good theory, GWESP or transitiveties may be candidate model terms that can potentially solve the issue. The weird distribution with peaks at 3 and 7 may indicate that the problem may have occurred at the data collection stage, but I am not familiar with your field or application. Perhaps because you binarized the distance matrix and incurred a severe loss of information? In that case, you may want to try the GERGM package for weighted ERGMs, or perhaps model the original bipartite network before applying the distance measure (in case it was binary). You may also want to look at the xergm package, which has a replacement for statnet's gof function that adds a couple of interesting extra functions, like more auxiliary statistics for comparison, ROC and PR curves for assessing predictive performance, and out-of-sample GOF. Take care, Philip
On 27/07/16 22:56, Erick LeBrun wrote:
Hello all, I apologize in advance as I am fairly new to networks. I have two networks generated in R with sample sites as nodes representing microbial community relatedness between sites (as bray-curtis distance from an OTU counts table). I am interested in looking at two categorical groupings as factors in network design and have built a few ERGMs to explore this. Everything looks good in mcmc.diagnostics() and summary() and the anova.ergm() looks good as well. My problem occurs in testing goodness of fit (gof()). The degree graph is not too bad but the edge-wise shared partners graph for the data peaks far to the right of the simulated (for example simulated will peak at~3 and actual data has a small peak at ~3 and then a huge one at ~7. I know that there are probably important factors missing from these models and I am wondering if that may be what is causing the issue(?) My main interest is in demonstrating importance in my categorical variables. Or is this indicative of some larger problem? I have looked through many resources explaining how to generate goodness of fit metrics but have not found anything to help me interpret this issue. Thank you in advance! Erick
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