GeoDosocores: Computation of drop-one predictive scores

In GeoModels: Procedures for Gaussian and Non Gaussian Geostatistical (Large) Data Analysis

Computation of drop-one predictive scores

Description

The function computes RMSE, MAE, LSCORE, CRPS predictive scores based on drop-one prediction for a spatial, spatiotemporal and bivariate Gaussian RFs

Usage

GeoDoScores(data,  method="cholesky", matrix)

Arguments

data	A d-dimensional vector (a single spatial realisation) or a a(t \times d)-matrix (a single spatial-temporal realisation). or a a(2 \times d)-matrix (a single bivariate realisation).
method	String; the type of matrix decomposition used in the computation of the predictive scores. Default is cholesky. The other possible choices is svd.
matrix	An object of class GeoCovmatrix. See the Section Details.

Details

For a given covariance matrix object (GeoCovmatrix) and a given spatial, spatiotemporal or bivariare realization from a Gaussian random field, the function computes four predictive scores based on drop-one prediction.

Value

Returns a list containing the following informations:

RMSE	Root-mean-square error predictive score
MAE	Mean absolute error predictive score
LSCORE	Logarithmic predictive score
CRPS	Continuous ranked probability predictive score

Author(s)

Moreno Bevilacqua, [e-mail],https://sites.google.com/view/moreno-bevilacqua/home, Víctor Morales Oñate, [e-mail], https://sites.google.com/site/moralesonatevictor/, Christian", Caamaño-Carrillo, [e-mail],https://www.researchgate.net/profile/Christian-Caamano

References

Zhang H. and Wang Y. (2010). Kriging and cross-validation for massive spatial data. Environmetrics, 21, 290–304. Gneiting T. and Raftery A. Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association, 102

See Also

GeoCovmatrix

Examples

library(GeoModels)

################################################################
######### Examples of predictive score computation  ############
################################################################
set.seed(8)
  # Define the spatial-coordinates of the points:
x <- runif(500, 0, 2)
y <- runif(500, 0, 2)
coords=cbind(x,y)
matrix1 <- GeoCovmatrix(coordx=coords, corrmodel="Matern", param=list(smooth=0.5,
                      sill=1,scale=0.2,nugget=0))
 
data <- GeoSim(coordx=coords, corrmodel="Matern", param=list(mean=0,smooth=0.5,
                      sill=1,scale=0.2,nugget=0))$data

Pr_scores <- GeoDoScores(data,matrix=matrix1)

Pr_scores

GeoModels documentation built on April 13, 2025, 5:09 p.m.