GeoDosocores function

Computation of drop-one predictive scores

The function computes RMSE, MAE, LSCORE, CRPS predictive scores based on drop-one prediction for a spatial, spatiotemporal and bivariate Gaussian RFs UTF-8

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

Arguments

• data: A $d$-dimensional vector (a single spatial realisation) or a a($t x d$)-matrix (a single spatial-temporal realisation). or a a($2 x d$)-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.

Returns

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

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

Author(s)

Moreno Bevilacqua, moreno.bevilacqua89@gmail.com ,https://sites.google.com/view/moreno-bevilacqua/home, Víctor Morales Oñate, victor.morales@uv.cl , https://sites.google.com/site/moralesonatevictor/, Christian", Caamaño-Carrillo, chcaaman@ubiobio.cl ,https://www.researchgate.net/profile/Christian-Caamano

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