| Type: | Package |
| Title: | Spatial Regression Models with Compositional Data |
| Version: | 1.0 |
| Date: | 2025-10-17 |
| Author: | Michail Tsagris [aut, cre] |
| Maintainer: | Michail Tsagris <mtsagris@uoc.gr> |
| Depends: | R (≥ 4.0) |
| Imports: | blockCV, Compositional, doParallel, foreach, minpack.lm, parallel, Rfast, sf, stats |
| Suggests: | Rfast2 |
| Description: | Spatial regression models with compositional responses using the alpha–transformation. Relevant papers include: Tsagris M. (2025), <doi:10.48550/arXiv.2510.12663>, Tsagris M. (2015), https://soche.cl/chjs/volumes/06/02/Tsagris(2015).pdf, Tsagris M.T., Preston S. and Wood A.T.A. (2011), <doi:10.48550/arXiv.1106.1451>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| LazyData: | true |
| NeedsCompilation: | no |
| Packaged: | 2025-10-20 15:58:50 UTC; mtsag |
| Repository: | CRAN |
| Date/Publication: | 2025-10-23 13:50:08 UTC |
Spatial Regression Models with Compositional Data
Description
Spatial regression models with compositional responses using the \alpha–transformation.
The models includes are the \alpha-regression (not spatial), the \alpha-spatially lagged X (\alpha-SLX) model and the geographically weighted \alpha-regression (GW\alphaR) model.
Details
| Package: | CompositionalSR |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2025-10-17 |
| License: | GPL-2 |
Maintainers
Michail Tsagris <mtsagris@uoc.gr>
Author(s)
Michail Tsagris mtsagris@uoc.gr.
References
Tsagris M. (2025). The \alpha–regression for compositional data: a unified framework for standard, spatially-lagged, and geographically-weighted regression models.
https://arxiv.org/pdf/2510.12663
Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2): 47-57. https://arxiv.org/pdf/1508.01913v1.pdf
Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf
Computation of the contiguity matrix W
Description
Computation of the contiguity matrix W.
Usage
contiguity(coords, k = 10)
Arguments
coords |
A matrix with the coordinates of the locations. The first column is the latitude and the second is the longitude. |
k |
The number of nearest neighbours to consider for the contiguity matrix. |
Value
The contiguity matrix W. A square matrix with row standardised values (the elements of each row sum to 1).
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
See Also
Examples
data(fadn)
W <- contiguity(fadn[, 1:2])
Leave-one-out cross-validation for the GW\alphaR model
Description
Leave-one-out cross-validation for the GW\alphaR model
Usage
cv.gwar(y, x, a = c(0.1, 0.25, 0.5, 0.75, 1), coords, h, nfolds = 10, folds = NULL)
Arguments
y |
A matrix with compositional data. zero values are allowed. |
x |
A matrix with the continuous predictor variables or a data frame including categorical predictor variables. |
a |
The value of the power transformation, it has to be between -1 and 1. If zero values are present it has to be greater than 0. If |
coords |
A matrix with the coordinates of the locations. The first column is the latitude and the second is the longitude. |
h |
A vector with bandwith values. |
nfolds |
The number of folds to split the data. |
folds |
If you have the list with the folds supply it here. You can also leave it NULL and it will create folds. |
Details
The \alpha-transformation is applied to the compositional data and the numerical optimisation is performed for the regression, unless \alpha=0, where the coefficients are available in closed form.
Value
A list including:
runtime |
The runtime required by the cross-validation. |
perf |
A matrix with the Kullback-Leibler divergence of the observed from the fitted values. Each row corresponds to a value of |
opt |
A vector with the minimum Kullback-Leibler divergance, the optimal value of |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Tsagris M. (2025). The \alpha–regression for compositional data: a unified framework for standard, spatially-lagged, and geographically-weighted regression models.
https://arxiv.org/pdf/2510.12663
Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2): 47-57. https://arxiv.org/pdf/1508.01913v1.pdf
Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf
See Also
Examples
data(fadn)
coords <- fadn[, 1:2]
y <- fadn[, 3:7]
x <- fadn[, 8]
mod <- gwar(y, x, a = 1, coords, h = 0.001)
Leave-one-out cross-validation for the \alpha-SLX model
Description
Leave-one-out cross-validation for the \alpha-SLX model
Usage
cv.alfaslx(y, x, a = seq(0.1, 1, by = 0.1), coords, k = 2:15, nfolds = 10, folds = NULL)
Arguments
y |
A matrix with compositional data. zero values are allowed. |
x |
A matrix with the continuous predictor variables or a data frame including categorical predictor variables. |
a |
The value of the power transformation, it has to be between -1 and 1. If zero values are present it has to be greater than 0. If |
coords |
A matrix with the coordinates of the locations. The first column is the latitude and the second is the longitude. |
k |
A vector with the nearest neighbours to consider for the contiguity matrix. |
nfolds |
The number of folds to split the data. |
folds |
If you have the list with the folds supply it here. You can also leave it NULL and it will create folds. |
Details
The \alpha-transformation is applied to the compositional data and the numerical optimisation is performed for the regression, unless \alpha=0, where the coefficients are available in closed form.
Value
A list including:
runtime |
The runtime required by the cross-validation. |
perf |
A vector with the Kullback-Leibler divergence of the observed from the fitted values. Every value corresponds to a value of |
opt |
A vector with the minimum Kullback-Leibler divergence, the optimal value of |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Tsagris M. (2025). The \alpha–regression for compositional data: a unified framework for standard, spatially-lagged, and geographically-weighted regression models.
https://arxiv.org/pdf/2510.12663
Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2): 47-57. https://arxiv.org/pdf/1508.01913v1.pdf
Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf
See Also
Examples
data(fadn)
coords <- fadn[1:100, 1:2]
y <- fadn[1:100, 3:7]
x <- fadn[1:100, 8]
mod <- cv.alfaslx(y, x, a = 0.5, coords, k = 2)
Marginal effects for the GW\alphaR model
Description
Marginal effects for the GW\alphaR model.
Usage
me.gwar(be, mu, x)
Arguments
be |
A matrix with the beta regression coefficients of the |
mu |
The fitted values of the |
x |
A matrix with the continuous predictor variables or a data frame. Categorical predictor variables are not suited here. |
Details
The location-specific marginal effects for the GW\alphaR model are computed.
Value
A list including:
me |
An array with the location-specific marginal effects of each component for each predictor variable. |
ame |
The average location-specific marginal effects of each component for each predictor variable. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Tsagris M. (2025). The \alpha–regression for compositional data: a unified framework for standard, spatially-lagged, and geographically-weighted regression models.
https://arxiv.org/pdf/2510.12663
Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2): 47-57. https://arxiv.org/pdf/1508.01913v1.pdf
Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf
See Also
Examples
data(fadn)
coords <- fadn[, 1:2]
y <- fadn[, 3:7]
x <- fadn[, 8]
mod <- gwar(y, x, a = 1, coords, h = 0.001)
me <- me.gwar(mod$be, mod$est, x)
Marginal effects for the \alpha-SLX model
Description
Marginal effects for the \alpha-SLX model.
Usage
me.aslx(be, gama, mu, x, coords, k = 10, cov_theta = NULL)
Arguments
be |
A matrix with the beta coefficients of the |
gama |
A matrix with the gamma coefficients of the |
mu |
The fitted values of the |
x |
A matrix with the continuous predictor variables or a data frame. Categorical predictor variables are not suited here. |
coords |
A matrix with the coordinates of the locations. The first column is the latitude and the second is the longitude. |
k |
The number of nearest neighbours to consider for the contiguity matrix. |
cov_theta |
The covariance matrix of the beta and gamma regression coefficients. If you pass this argument, then the standard error of the average marginal effects will be returned. |
Details
The \alpha-transformation is applied to the compositional data first and then the \alpha-SLX model is applied.
Value
A list including:
me.dir |
An array with the direct marginal effects of each component for each predictor variable. |
me.indir |
An array with the indirect marginal effects of each component for each predictor variable. |
me.total |
An array with the total marginal effects of each component for each predictor variable. |
ame.dir |
An array with the average direct marginal effects of each component for each predictor variable. |
ame.indir |
An array with the average indirect marginal effects of each component for each predictor variable. |
ame.total |
An array with the aerage total marginal effects of each component for each predictor variable. |
se.amedir |
An array with the standard errors of the average direct marginal effects of each component for each predictor variable. This is returned if you supply the covariance matrix cov_theta. |
se.ameindir |
An array with the standard errors of the average indirect marginal effects of each component for each predictor variable. This is returned if you supply the covariance matrix cov_theta. |
se.ametotal |
An array with the standard errors of the average total marginal effects of each component for each predictor variable. This is returned if you supply the covariance matrix cov_theta. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Tsagris M. (2025). The \alpha–regression for compositional data: a unified framework for standard, spatially-lagged, and geographically-weighted regression models.
https://arxiv.org/pdf/2510.12663
Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2): 47-57. https://arxiv.org/pdf/1508.01913v1.pdf
Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf
See Also
Examples
data(fadn)
coords <- fadn[, 1:2]
y <- fadn[, 3:7]
x <- fadn[, 8]
mod <- alfa.slx(y, x, a = 0.5, coords, k = 10, xnew = x, coordsnew = coords)
me <- me.aslx(mod$be, mod$gama, mod$est, x, coords, k = 10)
Marginal effects for the \alpha-regression model
Description
Marginal effects for the \alpha-regression model.
Usage
me.ar(be, mu, x, cov_be = NULL)
Arguments
be |
A matrix with the beta regression coefficients of the |
mu |
The fitted values of the |
x |
A matrix with the continuous predictor variables or a data frame. Categorical predictor variables are not suited here. |
cov_be |
The covariance matrix of the beta regression coefficients. If you pass this argument, then the standard error of the average marginal effects will be returned. |
Details
The \alpha-transformation is applied to the compositional data first and then the \alpha-regression model is applied.
Value
A list including:
me |
An array with the marginal effects of each component for each predictor variable. |
ame |
The average marginal effects of each component for each predictor variable. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2): 47-57. https://arxiv.org/pdf/1508.01913v1.pdf
Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf
See Also
Examples
data(fadn)
y <- fadn[, 3:7]
x <- fadn[, 8]
mod <- alfa.reg(y, x, 0.2, xnew = x)
me <- me.ar(mod$be, mod$est, x)
Prediction with the GW\alphaR model
Description
Prediction with GW\alphaR model.
Usage
gwar.pred(y, x, a, coords, h, xnew, coordsnew)
Arguments
y |
A matrix with the compositional data. |
x |
A matrix with the continuous predictor variables or a data frame including categorical predictor variables. |
a |
A vector with values for the power transformation, it has to be between -1 and 1. |
coords |
A matrix with the coordinates of the locations. The first column is the latitude and the second is the longitude. |
h |
A vector with bandwith values. |
xnew |
The new data. |
coordsnew |
A matrix with the coordinates of the new locations. The first column is the latitude and the second is the longitude. |
Details
The \alpha-transformation is applied to the compositional data first and then the GW\alphaR model is applied and predictions are given for each observation.
Value
A list including:
runtime |
The time required by the regression. |
est |
A list with the fitted values, for each combination of |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Tsagris M. (2025). The \alpha–regression for compositional data: a unified framework for standard, spatially-lagged, and geographically-weighted regression models.
https://arxiv.org/pdf/2510.12663
Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2): 47-57. https://arxiv.org/pdf/1508.01913v1.pdf
Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf
See Also
cv.gwar, me.gwar, alfa.slx, alfa.reg
Examples
data(fadn)
coords <- fadn[-c(1:10), 1:2]
y <- fadn[-c(1:10), 3:7]
x <- fadn[-c(1:10), 8]
xnew <- fadn[1:10, 8]
coordsnew <- fadn[1:10, 1:2]
mod <- gwar.pred(y, x, a = c(0.25, 0.5, 1), coords,
h = c(0.002, 0.006), xnew = xnew, coordsnew = coordsnew)
Regression with compositional data using the \alpha-transformation
Description
Regression with compositional data using the \alpha-transformation.
Usage
alfa.reg(y, x, a, covb = FALSE, xnew = NULL, yb = NULL)
alfa.reg2(y, x, a, xnew = NULL)
Arguments
y |
A matrix with the compositional data. |
x |
A matrix with the continuous predictor variables or a data frame including categorical predictor variables. |
a |
The value of the power transformation, it has to be between -1 and 1. If zero values are present it has to be greater than 0. If |
covb |
If this is FALSE, the covariance matrix of the coefficients will not be returned. If however you set it equal to TRUE and the covariance matrix is not returned it means it was singular. |
xnew |
If you have new data use it, otherwise leave it NULL. |
yb |
If you have already transformed the data using the This is intended to be used in the function |
Details
The \alpha-transformation is applied to the compositional data first and then multivariate regression is applied. This involves numerical optimisation. The alfa.reg2() function accepts a vector with many values of \alpha, while the the alfa.reg3() function searches for the value of \alpha that minimizes the Kulback-Leibler divergence between the observed and the fitted compositional values. The functions are highly optimized.
Value
For the alfa.reg() function a list including:
runtime |
The time required by the regression. |
be |
The beta coefficients. |
covb |
The covariance matrix of the beta coefficients, or NULL if it is singular. |
est |
The fitted values for xnew if xnew is not NULL. |
For the alfa.reg2() function a list with as many sublists as the number of values of \alpha. Each element (sublist) of the list contains the beta coefficients and the fitted values.
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2): 47-57. https://arxiv.org/pdf/1508.01913v1.pdf
Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf
Mardia K.V., Kent J.T., and Bibby J.M. (1979). Multivariate analysis. Academic press.
Aitchison J. (1986). The statistical analysis of compositional data. Chapman & Hall.
See Also
Examples
data(fadn)
y <- fadn[, 3:7]
x <- fadn[, 8]
mod <- alfa.reg(y, x, 0.2)
Spatial k-folds
Description
Spatial k-folds.
Usage
spat.folds(coords, nfolds = 10)
Arguments
coords |
A matrix with the coordinates of the locations. The first column is the latitude and the second is the longitude. |
nfolds |
The number of spatial folds to create. |
Details
Folds of data are created based on their coordinates. For more information see the package blockCV.
Value
A list with nfolds elements. Each elements contains a list with two elements, the first is the indices of the training set and the second contains the indices of the test set.
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
See Also
cv.alfaslx, me.aslx, gwar, alfa.reg
Examples
data(fadn)
coords <- fadn[, 1:2]
folds <- spat.folds(coords, nfolds = 10)
The GW\alphaR model
Description
The GW\alphaR model.
Usage
gwar(y, x, a, coords, h, yb = NULL, nc = 1)
Arguments
y |
A matrix with the compositional data. |
x |
A matrix with the continuous predictor variables or a data frame including categorical predictor variables. |
a |
The value of the power transformation, it has to be between -1 and 1. |
coords |
A matrix with the coordinates of the locations. The first column is the latitude and the second is the longitude. |
h |
The bandwith value. |
yb |
If you have already transformed the data using the |
nc |
The number of cores to use. IF you have a multicore computer it is advisable to use more than 1. It makes the procedure faster. It is advisable to use it if you have many observations and or many variables, otherwise it will slow down th process. |
Details
The \alpha-transformation is applied to the compositional data first and then the GW\alphaR model is applied.
Value
A list including:
runtime |
The time required by the regression. |
be |
The beta coefficients. |
est |
The fitted values. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Tsagris M. (2025). The \alpha–regression for compositional data: a unified framework for standard, spatially-lagged, and geographically-weighted regression models.
https://arxiv.org/pdf/2510.12663
Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2): 47-57. https://arxiv.org/pdf/1508.01913v1.pdf
Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf
See Also
cv.gwar, me.gwar, alfa.slx, alfa.reg
Examples
data(fadn)
coords <- fadn[, 1:2]
y <- fadn[, 3:7]
x <- fadn[, 8]
mod <- gwar(y, x, a = 1, coords, h = 0.001)
The \alpha-SLX model
Description
The \alpha-SLX model.
Usage
alfa.slx(y, x, a, coords, k = 10, covb = FALSE, xnew = NULL, coordsnew, yb = NULL)
Arguments
y |
A matrix with the compositional data. |
x |
A matrix with the continuous predictor variables or a data frame including categorical predictor variables. |
a |
The value of the power transformation, it has to be between -1 and 1. If zero values are present it has to be greater than 0. If |
coords |
A matrix with the coordinates of the locations. The first column is the latitude and the second is the longitude. |
k |
The number of nearest neighbours to consider for the contiguity matrix. |
covb |
If this is FALSE, the covariance matrix of the coefficients will not be returned. If however you set it equal to TRUE and the covariance matrix is not returned it means it was singular. |
xnew |
If you have new data use it, otherwise leave it NULL. |
coordsnew |
A matrix with the coordinates of the new locations. The first column is the latitude and the second is the longitude. If you do not have new data to make predictions leave this NULL. |
yb |
If you have already transformed the data using the This is intended to be used in the function |
Details
The \alpha-transformation is applied to the compositional data first and then the spatially lagged X (SLX) model is applied.
Value
A list including:
runtime |
The time required by the regression. |
be |
The beta coefficients. |
gama |
The gamma coefficients. |
covb |
The covariance matrix of the beta coefficients, or NULL if it is singular. If it is returned, the upper left block is the covariance matrix of the beta coefficients and the lower right block is the covariance matrix of the gama coefficients. It is in this way so as to pass it on to the marginal effects function
|
est |
The fitted values for xnew if xnew is not NULL. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Tsagris M. (2025). The \alpha–regression for compositional data: a unified framework for standard, spatially-lagged, and geographically-weighted regression models.
https://arxiv.org/pdf/2510.12663
Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2): 47-57. https://arxiv.org/pdf/1508.01913v1.pdf
Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf
See Also
cv.alfaslx, me.aslx, gwar, alfa.reg
Examples
data(fadn)
coords <- fadn[, 1:2]
y <- fadn[, 3:7]
x <- fadn[, 8]
mod <- alfa.slx(y, x, a = 0.5, coords, k = 10)
Tuning the value of \alpha in the \alpha-regression
Description
Tuning the value of \alpha in the \alpha-regression.
Usage
cv.alfareg(y, x, a = seq(0.1, 1, by = 0.1), nfolds = 10,
folds = NULL, nc = 1, seed = NULL)
Arguments
y |
A matrix with compositional data. zero values are allowed. |
x |
A matrix with the continuous predictor variables or a data frame including categorical predictor variables. |
a |
The value of the power transformation, it has to be between -1 and 1. If zero values are present it has to be greater than 0. If |
nfolds |
The number of folds to split the data. |
folds |
If you have the list with the folds supply it here. You can also leave it NULL and it will create folds. |
nc |
The number of cores to use. IF you have a multicore computer it is advisable to use more than 1. It makes the procedure faster. It is advisable to use it if you have many observations and or many variables, otherwise it will slow down th process. |
seed |
You can specify your own seed number here or leave it NULL. |
Details
Tuning the value of \alpha in the \alpha-regression takes place using k-fold cross-validation.
Value
A list including:
runtime |
The runtime required by the cross-validation. |
perf |
A vector with the Kullback-Leibler divergence of the observed from the fitted values. Every value corresponds to a value of |
opt |
A vector with the minimum Kullback-Leibler divergence and the optimal value of |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Tsagris M. (2025). The \alpha–regression for compositional data: a unified framework for standard, spatially-lagged, and geographically-weighted regression models.
https://arxiv.org/pdf/2510.12663
Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2): 47-57. https://arxiv.org/pdf/1508.01913v1.pdf
Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf
See Also
alfa.reg, cv.alfaslx, cv.gwar, me.ar
Examples
data(fadn)
y <- fadn[, 3:7]
x <- fadn[, 8]
mod <- cv.alfareg(y, x, a = c(0.5, 1))
FADN dataset
Description
A matrix with 11 columns. The first two are the locations (latitude and longitude), the next five contain the compositional data (percentages of cultivated area of five crops), Y1.1: cereals, Y2.1: cotton, Y3.1: tree crops, Y4.1: other annual crops and pasture and Y5.1: grapes and wine. The next four columns contain the covariates, G1: Human Influence Index, G2: soil pH, G3: topsoil organic carbon content and G7: erosion.
Usage
fadnFormat
A matrix with 168 rows and 11 columns.
Source
Clark and Dixon (2021), available at https://github.com/nick3703/Chicago-Data.
References
Clark, N. J. and P. M. Dixon (2021). A class of spatially correlated self-exciting statistical models. Spatial Statistics, 43, 1–18.
See Also
Examples
data(fadn)
y <- fadn[, 3:7]
x <- fadn[, 8:11]
mod <- alfa.reg(y, x, a = 0.1)