as.one.mode and web2edges are convenience functions to morph bipartite networks into one-mode webs. Furthermore, some indices build on one-mode networks and are called from bipartite.)Before you start with the network, you have to get the data into the right shape. The function frame2webs aims to facilitate this process. Arranging a web, e.g. by size, is supported by sortweb.
The typical first step is to visualise the network. Two functions are on offer here: one (visweb) simply plots the matrix in colours depicting the strength of an interaction and options for re-arranging columns and rows (e.g. to identify compartments or nesting). The other function (plotweb) plots the actual web with participants (as two rows of rectangles) connected by lines (proportional to interaction strength). Both can be customised by many options.
The second step is to calculate indices describing network topography. There are three different levels this can be achieved at: the entire web (using function networklevel), at the level of each group (also using function networklevel) or the individual species (using function specieslevel). Most other functions in the package are helpers, although some can be called on their own and return the respective result (dfun, H2fun and second.extinct with slope.bipartite).
The third step is to compare results to null models. Many interaction matrices are very incomplete snapshots of the true underlying network (e.g. a one-week sampling of a pollination network on a patch of 4 x 4 meters). As a consequence, many species were rarely observed, many are singletons (only one recording). To make analyses comparable across networks with different sampling intensity and number of species per group, we need a common yardstick. We suggest that users should use a null model, i.e. an algorithm that randomises entries while constraining some web properties (such as dimensions, marginal totals or connectance). The function nullmodel provides a few such null models, but this is a wide field of research and we make no recommendations (actually, we do: see Dormann et al. 2009 and Dormann 2011, both shipping in the doc-folder of this package). You can also simulate networks using genweb or null.distr.
Finally, bipartite comes with 23 quantitative pollination network data sets taken from the NCEAS interaction webs data base (use data(package="bipartite") to show their names) and it has a few miscellaneous functions looking at some special features of bipartite networks (such as modularity: computeModules or apparent competition: PAC).
Speed: The code of bipartite is almost exclusively written in R. You can increase the speed a bit (by 30 to 50 %, depending on the functions you use) by compiling functions on-the-fly. To do so, you need to load the enableJIT(3). The first time you call a function, it will be compiled to bytecode (just-in-time: jit), which takes a few seconds, but the second call will be substantially faster than without compilation. In the few tests we have run, this improvement was NOT substantial (i.e. a few tens of percent), indicating, I guess, that our R code wasn't too bad. See
See help pages for details and examples.
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