GeoModels (version 2.2.5)

GeoDoScores: 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 random fields

Usage

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

Value

Returns a list containing the following information:

RMSE

Root-mean-square error predictive score

MAE

Mean absolute error predictive score

LSCORE

Logarithmic predictive score

CRPS

Continuous ranked probability predictive score

Arguments

data

A \(d\)-dimensional vector (a single spatial realisation) or a a(\(t \times d\))-matrix (a single spatio-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 returned by GeoCovmatrix.

Author

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

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.

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