envi-package: The envi Package: Environmental Interpolation using Spatial...
In envi: Environmental Interpolation using Spatial Kernel Density Estimation
envi-package R Documentation
The envi Package: Environmental Interpolation using Spatial Kernel Density Estimation
Description
Estimates an ecological niche model using occurrence data, covariates, and kernel density-based estimation methods.
Details
For a single species with presence and absence data, the 'envi' package uses the spatial relative risk function estimated using the sparr package. Details about the sparr package methods can be found in the tutorial: Davies et al. (2018) doi: 10.1002/sim.7577. Details about kernel density estimation can be found in J. F. Bithell (1990) doi: 10.1002/sim.4780090616. More information about relative risk functions using kernel density estimation (KDE) can be found in J. F. Bithell (1991) doi: 10.1002/sim.4780101112.
This package provides a function to estimate the ecological niche for a single species with presence and absence data. The 'envi' package also provides some sensitivity and visualization tools for the estimated ecological niche, its predicted spatial distribution, and prediction diagnostics. Various options for the correction of multiple testing are available.
Key content of the 'envi' package include:
Ecological Niche Model
lrren Estimates the ecological niche for a single species with presence/absence data, two covariates, and the spatial relative risk function. Provide functionality to predict the spatial distribution of the estimated ecological niche in geographic space and prepare internal k-fold cross-validation data.
Sensitivity Analysis
perlrren Iteratively estimates the ecological niche for a single species with spatially perturbed ("jittered") presence/absence data, two covariates, and the spatial relative risk function. Various radii for the spatial perturbation can be specified.
Data Visualization
plot_obs Visualizes the lrren output, specifically the estimated ecological niche in a space with dimensions as the two specified covariates in the model.
plot_predict Visualizes the lrren output, specifically the predicted spatial distribution of the ecological niche.
plot_cv Visualizes the lrren output, specifically two prediction diagnostics (area under the receiver operating characteristic curve and precision-recall curve).
plot_perturb Visualizes the perlrren output, specifically four summary statistics of the iterations, including mean log relative risk, standard deviation of the log relative risk, mean p-value, and proportion of iterations the p-value was significant based on an alpha-level threshold. It also can predict the spatial distribution of the summary statistics.
Dependencies
The 'envi' package relies heavily upon sparr, spatstat.geom, sf, and terra. For a single species (presence/absence data), the spatial relative risk function uses the risk function. Cross-validation is can be performed in parallel using the future, doFuture, doRNG, and foreach packages. Spatial perturbation is performed using the rjitter function. Basic visualizations rely on the plot.ppp and image.plot functions.
Author(s)
Ian D. Buller
Social & Scientific Systems, Inc., a division of DLH Corporation, Silver Spring, Maryland, USA (current); Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA (former); Environmental Health Sciences, James T. Laney School of Graduate Studies, Emory University, Atlanta, Georgia, USA (original)
Maintainer: I.D.B. ian.buller@alumni.emory.edu
envi documentation built on Feb. 16, 2023, 7:38 p.m.
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| envi-package | R Documentation |
The envi Package: Environmental Interpolation using Spatial Kernel Density Estimation
Description
Estimates an ecological niche model using occurrence data, covariates, and kernel density-based estimation methods.
Details
For a single species with presence and absence data, the 'envi' package uses the spatial relative risk function estimated using the sparr package. Details about the sparr package methods can be found in the tutorial: Davies et al. (2018) doi: 10.1002/sim.7577. Details about kernel density estimation can be found in J. F. Bithell (1990) doi: 10.1002/sim.4780090616. More information about relative risk functions using kernel density estimation (KDE) can be found in J. F. Bithell (1991) doi: 10.1002/sim.4780101112.
This package provides a function to estimate the ecological niche for a single species with presence and absence data. The 'envi' package also provides some sensitivity and visualization tools for the estimated ecological niche, its predicted spatial distribution, and prediction diagnostics. Various options for the correction of multiple testing are available.
Key content of the 'envi' package include:
Ecological Niche Model
lrren Estimates the ecological niche for a single species with presence/absence data, two covariates, and the spatial relative risk function. Provide functionality to predict the spatial distribution of the estimated ecological niche in geographic space and prepare internal k-fold cross-validation data.
Sensitivity Analysis
perlrren Iteratively estimates the ecological niche for a single species with spatially perturbed ("jittered") presence/absence data, two covariates, and the spatial relative risk function. Various radii for the spatial perturbation can be specified.
Data Visualization
plot_obs Visualizes the lrren output, specifically the estimated ecological niche in a space with dimensions as the two specified covariates in the model.
plot_predict Visualizes the lrren output, specifically the predicted spatial distribution of the ecological niche.
plot_cv Visualizes the lrren output, specifically two prediction diagnostics (area under the receiver operating characteristic curve and precision-recall curve).
plot_perturb Visualizes the perlrren output, specifically four summary statistics of the iterations, including mean log relative risk, standard deviation of the log relative risk, mean p-value, and proportion of iterations the p-value was significant based on an alpha-level threshold. It also can predict the spatial distribution of the summary statistics.
Dependencies
The 'envi' package relies heavily upon sparr, spatstat.geom, sf, and terra. For a single species (presence/absence data), the spatial relative risk function uses the risk function. Cross-validation is can be performed in parallel using the future, doFuture, doRNG, and foreach packages. Spatial perturbation is performed using the rjitter function. Basic visualizations rely on the plot.ppp and image.plot functions.
Author(s)
Ian D. Buller
Social & Scientific Systems, Inc., a division of DLH Corporation, Silver Spring, Maryland, USA (current); Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA (former); Environmental Health Sciences, James T. Laney School of Graduate Studies, Emory University, Atlanta, Georgia, USA (original)
Maintainer: I.D.B. ian.buller@alumni.emory.edu
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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