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Dispersal kernels

Source: R/params.R
Dispersal-kernels.Rd

-[inv_powerlaw()] Get parameters and values pertaining to the inverse power law model. -[neg_exp()] Get parameters and values pertaining to the negative exponential model.

Usage

inv_powerlaw(
  params = load_parameters(),
  betas = NULL,
  mets = NULL,
  we = NULL,
  linkcutoff = NULL
)

neg_expo(
  params = load_parameters(),
  gammas = NULL,
  mets = NULL,
  we = NULL,
  linkcutoff = NULL
)

Arguments

params

Object. This function uses the parameter values defined in load_parameters() by default. If load_parameters() is not provided, the function will require the user to specify all arguments as listed below. If both load_parameters() and values for the arguments listed below are provided, load_parameters() takes precedence over the specified arguments.

betas

Numeric. Beta is the dispersal parameter used in the inverse power law to estimate a species' dispersal gradient. Please refer to Mundt et al (2009) for details on how to calculate this parameter. Any beta values should be positive. Smaller beta values indicate a higher likelihood of dispersal between nodes.

mets

Character. There are seven network metrics supported by geohabnet: "node_strength", "sum_of_nearest_neighbors", "eigenvector_centrality", "closeness", "betweeness", "degree", and "page_rank". Each specified network metric is calculated for each location in the target region, based on the link weights between each pair of locations. Run, for example, pagerank() for details of each network metric.

we

Numeric. This parameter indicates the weight(s) of each specified network metric, representing the importance of the network metric in the analysis. Since these weights represent percentages, any weight(s) should be between 0 and 100, and the sum of all specified weights should be 100.

linkcutoff

Numeric. This parameter is only used to calculate betweeness() and closeness(), and is equivalent to cutoff in these functions in the igraph package.

gammas

Numeric. Gamma is the dispersal parameter used in the negative exponential to estimate a species' dispersal gradient. Any gamma values should be positive. Smaller gamma values indicate a higher likelihood of dispersal between nodes.

Value

List with parameters and values. See details.

Details

Refer to Esker et al (2007) for a discussion on the characteristics of each dispersal gradient or kernel model (i.e., inverse power law and negative exponential). The resulting object produced by load_parameters() provides the following values used when running the analysis -beta is a dispersal parameter for calculating the inverse power law model. -gamma is a dispersal parameter for calculating the negative exponential model. -metrics Each network metric is applied to the adjacency matrix produced in the intermediate step. -weights The link weights that is used in the network analysis. -cutoff Currently used as a parameter to calculate centrality in the network - betweeness() and closeness(). As defined in igraph::betweenness(), it's the maximum length to consider when calculating centrality. If zero or negative, then there is no such limit.

References

Esker PD, Sparks AH, Antony G, Bates M, Dall' Acqua W, Frank EE, Huebel L, Segovia V, Garrett KA (2007). “Ecology and Epidemiology in R: Modeling dispersal gradients.” The Plant Health Instructor. doi:10.1094/PHI-A-200​8-0129-03 .

Mundt CC, Sackett KE, Wallace LD, Cowger C, Dudley JP (2009). “Aerial Dispersal and Multiple-Scale Spread of Epidemic Disease.” Ecohealth. doi:10.1007/s10393-009-0251-z .

Csardi G, Nepusz T (2006). “The igraph software package for complex network research.” InterJournal, Complex Systems, 1695. https://igraph.org.

Csárdi G, Nepusz T, Traag V, Horvát Sz, Zanini F, Noom D, Müller K (2024). igraph: Network Analysis and Visualization in R. doi:10.5281/zenodo.7682609 , R package version 1.5.1, https://CRAN.R-project.org/package=igraph.

See also

supported_metrics()