cu_sf_kde: Spatial kernel density estimate
In dsjohnson/crawlUtils: Enhance And Integrate the {crawl} Package For Spatial Analysis Of Telemetry Output
cu_sf_kde R Documentation
Spatial kernel density estimate
Description
A weighted or unweighted Gaussian Kernel Density estimate
for point spatial data
Usage
cu_sf_kde(x, w = NULL, bw = NULL, ref, ess = NULL, mask = FALSE)
Arguments
x
sf POINT object
w
Optional values, associated with x coordinates,
to be used as weights
bw
Standard deviation scale bandwidth of Gaussian Kernel, must be units
of x projection.
ref
A terra SpatRaster
ess
A effective sample size to use instead of nrow(x) for determining the default bandwidth.
mask
(TRUE/FALSE) mask resulting raster if ref is provided
as a SpatRaster
Details
Please note that ks methods for estimation has been reverted to the Gussian method proposed
in Venables & Ripley (2002). There was not enought evendence that the Chacon & Duong (2018)
multivariate method(s) for bandwidth selection and kernal estimation were suitable for
spatial random fields.
Value
a terra SpatRaster class object containing kernel density estimate
Author(s)
Jeffrey S. Evans jeffrey_evans@tnc.org and Devin S. Johnson devin.johnson@noaa.gov
References
Duong, T. & Hazelton, M.L. (2005) Cross-validation bandwidth matrices for multivariate
kernel density estimation. Scandinavian Journal of Statistics, 32, 485-506.
Wand, M.P. & Jones, M.C. (1994) Multivariate plug-in bandwidth selection. Computational
Statistics, 9, 97-116.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S.
Fourth edition. Springer.
dsjohnson/crawlUtils documentation built on Sept. 13, 2024, 1:34 p.m.
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| cu_sf_kde | R Documentation |
Spatial kernel density estimate
Description
A weighted or unweighted Gaussian Kernel Density estimate for point spatial data
Usage
cu_sf_kde(x, w = NULL, bw = NULL, ref, ess = NULL, mask = FALSE)
Arguments
x |
sf POINT object |
w |
Optional values, associated with |
bw |
Standard deviation scale bandwidth of Gaussian Kernel, must be units
of |
ref |
A terra SpatRaster |
ess |
A effective sample size to use instead of |
mask |
(TRUE/FALSE) mask resulting raster if ref is provided as a SpatRaster |
Details
Please note that ks methods for estimation has been reverted to the Gussian method proposed in Venables & Ripley (2002). There was not enought evendence that the Chacon & Duong (2018) multivariate method(s) for bandwidth selection and kernal estimation were suitable for spatial random fields.
Value
a terra SpatRaster class object containing kernel density estimate
Author(s)
Jeffrey S. Evans jeffrey_evans@tnc.org and Devin S. Johnson devin.johnson@noaa.gov
References
Duong, T. & Hazelton, M.L. (2005) Cross-validation bandwidth matrices for multivariate kernel density estimation. Scandinavian Journal of Statistics, 32, 485-506.
Wand, M.P. & Jones, M.C. (1994) Multivariate plug-in bandwidth selection. Computational Statistics, 9, 97-116.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
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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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