predictimportance: predictimportance: a package containing code and data...

In elsander/PredictImportance: Generate Networks, Simulate Abundance Data, and Analyze Importance Correlates

Description

The predictimportance package contains functions to randomly parameterize empirical networks, generate and parameterize model food webs, simulate community dynamics, and predict species importance using several covariates using a hierarchical modelling approach. The highest-level and most user-friendly functions are runScriptsEmpirical() and runScriptsModel(), which can be used to replicate all or part of the data simulation and analysis in a single function call. For users who wish to replicate all results, the simplest approach is to run the scripts in inst/scripts, FullSimulationEmpirical.R (for main text results) and FullSimulationModels.R (for supplemental results). These scripts run perform all data simulation and analysis, and plot violin plots to summarize the results. For users who wish to analyze their own empirical dataset, it is recommended to use the Step1_Empirical_Parameterization(), Step2_Discrete_LV(), and Step3_Hierarchical_Model() functions in order. Note that it is worthwhile to examine output from Step 2 before determining if the specific hierarchical model in Step 3 is appropriate for your data. Note that Step 2 (simulation of community dynamics) may produce non-finite values for large networks, due to precision issues.

References

Wootton, J.T., Sander, E.L., Barab\'as, Gy\"orgy, and A. Henry. In prep.

elsander/PredictImportance documentation built on May 5, 2019, 3:49 a.m.