npsp-package: npsp: Nonparametric spatial (geo)statistics
In npsp: Nonparametric Spatial Statistics
npsp-package R Documentation
npsp: Nonparametric spatial (geo)statistics
Description
This package implements nonparametric methods
for inference on multidimensional spatial (or spatio-temporal) processes,
which may be (especially) useful in (automatic) geostatistical modeling and interpolation.
Main functions
Nonparametric methods for inference on both spatial trend
and variogram functions:
np.fitgeo (automatically) fits an isotropic nonparametric geostatistical model
by estimating the trend and the variogram (using a bias-corrected estimator) iteratively
(by calling h.cv, locpol, np.svariso.corr and
fitsvar.sb.iso at each iteration).
locpol, np.den and np.svar
use local polynomial kernel methods to compute
nonparametric estimates of a multidimensional regression function,
a probability density function or a semivariogram (or their first
derivatives), respectively.
Estimates of these functions can be constructed for any dimension
(the amount of available memory is the only limitation).
To speed up computations, linear binning is used to discretize the data.
A full bandwidth matrix and a multiplicative triweight kernel is used
to compute the weights. Main calculations are performed in FORTRAN
using the LAPACK library.
np.svariso.corr computes a bias-corrected nonparametric semivariogram
estimate using an iterative algorithm similar to that described in
Fernandez-Casal and Francisco-Fernandez (2014). This procedure tries to correct
the bias due to the direct use of residuals, obtained from a
nonparametric estimation of the trend function, in semivariogram estimation.
fitsvar.sb.iso fits a ‘nonparametric’ isotropic Shapiro-Botha variogram
model by WLS. Currently, only isotropic semivariogram estimation is supported.
Nonparametric residual kriging (sometimes called external drift kriging):
np.kriging computes residual kriging predictions
(and the corresponding simple kriging standard errors).
kriging.simple computes simple kriging predictions, standard errors
Currently, only global simple kriging is implemented in this package.
Users are encouraged to use krige (or krige.cv)
utilities in gstat package together with as.vgm for local kriging.
Other functions
Among the other functions intended for direct access by the user, the following
(methods for multidimensional linear binning, local polynomial kernel regression,
density or variogram estimation) could be emphasized: binning, bin.den,
svar.bin, h.cv and interp.
There are functions for plotting data joint with a legend representing a
continuous color scale. splot allows to combine a standard R plot
with a legend. spoints, simage and spersp
draw the corresponding high-level plot with a legend strip for the color scale.
These functions are based on image.plot of package fields.
There are also some functions which can be used to interact with other packages.
For instance, as.variogram (geoR) or as.vgm (gstat).
Acknowledgments
Important suggestions and contributions to some techniques included here were
made by Sergio Castillo-Paez (Universidad de las Fuerzas Armadas ESPE, Ecuador)
and Tomas Cotos-Yañez (Dep. Statistics, University of Vigo, Spain).
Author(s)
Ruben Fernandez-Casal (Dep. Mathematics, University of A Coruña, Spain).
Please send comments, error reports or suggestions to rubenfcasal@gmail.com.
References
Castillo-Páez S., Fernández-Casal R. and García-Soidán P. (2019)
A nonparametric bootstrap method for spatial data, 137,
Comput. Stat. Data Anal., 1-15, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.csda.2019.01.017")}.
Fernandez-Casal R., Castillo-Paez S. and Francisco-Fernandez M. (2018)
Nonparametric geostatistical risk mapping, Stoch. Environ. Res. Ris. Assess.,
32, 675-684, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s00477-017-1407-y")}.
Fernandez-Casal R., Castillo-Paez S. and Garcia-Soidan P. (2017)
Nonparametric estimation of the small-scale variability of heteroscedastic spatial processes,
Spa. Sta., 22, 358-370, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.spasta.2017.04.001")}.
Fernandez-Casal R. and Francisco-Fernandez M. (2014)
Nonparametric bias-corrected variogram estimation under non-constant trend,
Stoch. Environ. Res. Ris. Assess., 28, 1247-1259, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s00477-013-0817-8")}.
Fernandez-Casal R., Gonzalez-Manteiga W. and Febrero-Bande M. (2003)
Flexible Spatio-Temporal Stationary Variogram Models,
Statistics and Computing, 13, 127-136, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1023/A:1023204525046")}.
Rupert D. and Wand M.P. (1994) Multivariate locally weighted least squares regression.
The Annals of Statistics, 22, 1346-1370.
Shapiro A. and Botha J.D. (1991) Variogram fitting with a general class of
conditionally non-negative definite functions. Computational Statistics
and Data Analysis, 11, 87-96.
Wand M.P. (1994) Fast Computation of Multivariate Kernel Estimators.
Journal of Computational and Graphical Statistics, 3, 433-445.
Wand M.P. and Jones M.C. (1995) Kernel Smoothing. Chapman and Hall, London.
npsp documentation built on May 29, 2024, 5:31 a.m.
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| npsp-package | R Documentation |
npsp: Nonparametric spatial (geo)statistics
Description
This package implements nonparametric methods for inference on multidimensional spatial (or spatio-temporal) processes, which may be (especially) useful in (automatic) geostatistical modeling and interpolation.
Main functions
Nonparametric methods for inference on both spatial trend and variogram functions:
np.fitgeo (automatically) fits an isotropic nonparametric geostatistical model
by estimating the trend and the variogram (using a bias-corrected estimator) iteratively
(by calling h.cv, locpol, np.svariso.corr and
fitsvar.sb.iso at each iteration).
locpol, np.den and np.svar
use local polynomial kernel methods to compute
nonparametric estimates of a multidimensional regression function,
a probability density function or a semivariogram (or their first
derivatives), respectively.
Estimates of these functions can be constructed for any dimension
(the amount of available memory is the only limitation).
To speed up computations, linear binning is used to discretize the data.
A full bandwidth matrix and a multiplicative triweight kernel is used
to compute the weights. Main calculations are performed in FORTRAN
using the LAPACK library.
np.svariso.corr computes a bias-corrected nonparametric semivariogram
estimate using an iterative algorithm similar to that described in
Fernandez-Casal and Francisco-Fernandez (2014). This procedure tries to correct
the bias due to the direct use of residuals, obtained from a
nonparametric estimation of the trend function, in semivariogram estimation.
fitsvar.sb.iso fits a ‘nonparametric’ isotropic Shapiro-Botha variogram
model by WLS. Currently, only isotropic semivariogram estimation is supported.
Nonparametric residual kriging (sometimes called external drift kriging):
np.kriging computes residual kriging predictions
(and the corresponding simple kriging standard errors).
kriging.simple computes simple kriging predictions, standard errors
Currently, only global simple kriging is implemented in this package.
Users are encouraged to use krige (or krige.cv)
utilities in gstat package together with as.vgm for local kriging.
Other functions
Among the other functions intended for direct access by the user, the following
(methods for multidimensional linear binning, local polynomial kernel regression,
density or variogram estimation) could be emphasized: binning, bin.den,
svar.bin, h.cv and interp.
There are functions for plotting data joint with a legend representing a
continuous color scale. splot allows to combine a standard R plot
with a legend. spoints, simage and spersp
draw the corresponding high-level plot with a legend strip for the color scale.
These functions are based on image.plot of package fields.
There are also some functions which can be used to interact with other packages.
For instance, as.variogram (geoR) or as.vgm (gstat).
Acknowledgments
Important suggestions and contributions to some techniques included here were made by Sergio Castillo-Paez (Universidad de las Fuerzas Armadas ESPE, Ecuador) and Tomas Cotos-Yañez (Dep. Statistics, University of Vigo, Spain).
Author(s)
Ruben Fernandez-Casal (Dep. Mathematics, University of A Coruña, Spain). Please send comments, error reports or suggestions to rubenfcasal@gmail.com.
References
Castillo-Páez S., Fernández-Casal R. and García-Soidán P. (2019) A nonparametric bootstrap method for spatial data, 137, Comput. Stat. Data Anal., 1-15, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.csda.2019.01.017")}.
Fernandez-Casal R., Castillo-Paez S. and Francisco-Fernandez M. (2018) Nonparametric geostatistical risk mapping, Stoch. Environ. Res. Ris. Assess., 32, 675-684, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s00477-017-1407-y")}.
Fernandez-Casal R., Castillo-Paez S. and Garcia-Soidan P. (2017) Nonparametric estimation of the small-scale variability of heteroscedastic spatial processes, Spa. Sta., 22, 358-370, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.spasta.2017.04.001")}.
Fernandez-Casal R. and Francisco-Fernandez M. (2014) Nonparametric bias-corrected variogram estimation under non-constant trend, Stoch. Environ. Res. Ris. Assess., 28, 1247-1259, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s00477-013-0817-8")}.
Fernandez-Casal R., Gonzalez-Manteiga W. and Febrero-Bande M. (2003) Flexible Spatio-Temporal Stationary Variogram Models, Statistics and Computing, 13, 127-136, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1023/A:1023204525046")}.
Rupert D. and Wand M.P. (1994) Multivariate locally weighted least squares regression. The Annals of Statistics, 22, 1346-1370.
Shapiro A. and Botha J.D. (1991) Variogram fitting with a general class of conditionally non-negative definite functions. Computational Statistics and Data Analysis, 11, 87-96.
Wand M.P. (1994) Fast Computation of Multivariate Kernel Estimators. Journal of Computational and Graphical Statistics, 3, 433-445.
Wand M.P. and Jones M.C. (1995) Kernel Smoothing. Chapman and Hall, London.
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