GeoCV {GeoModels}

n-fold kriging Cross-validation

Description

The procedure use the GeoKrig or GeoKrigloc function to compute n-fold kriging cross-validation using informations from a GeoFit object. The function returns some prediction scores.

Usage

GeoCV(fit, K=100, estimation=TRUE, optimizer=NULL,
     lower=NULL, upper=NULL, n.fold=0.05,local=FALSE,
    neighb=NULL, maxdist=NULL,maxtime=NULL,sparse=FALSE,
    type_krig="Simple", which=1, parallel=FALSE, ncores=NULL)

Arguments

fit

An object of class GeoFit.

K

The number of iterations in cross-validation.

estimation

Logical; if TRUE then an estimation is performed at each iteration and the estimates are used in the prediction. Otherwise the estimates in the object fit are used.

optimizer

The type of optimization algorithm if estimation is TRUE. See GeoFit for details. If NULL then the optimization algorithm of the object fit is chosen.

lower

An optional named list giving the values for the lower bound of the space parameter when the optimizer is L-BFGS-B or nlminb or optimize if estimation is TRUE.

upper

An optional named list giving the values for the upper bound of the space parameter when the optimizer is L-BFGS-B or nlminb or optimize if estimation is TRUE.

n.fold

Numeric; the percentage of data to be deleted (and predicted) in the cross-validation procedure.

local

Logical; If local is TRUE, then local kriging is performed. The default is FALSE.

neighb

Numeric; an optional positive integer indicating the order of neighborhood if local kriging is performed.

maxdist

Numeric; an optional positive value indicating the distance in the spatial neighborhood if local kriging is performed.

maxtime

Numeric; an optional positive value indicating the distance in the temporal neighborhood if local kriging is performed.

sparse

Logical; if TRUE kriging and simulation are computed with sparse matrices algorithms using spam package. Default is FALSE. It should be used with compactly supported covariances.

type_krig

String; the type of kriging. If Simple (the default) then simple kriging is performed. If Optim then optimal kriging is performed for some non-Gaussian RFs

which

Numeric; In the case of bivariate cokriging it indicates which variable to predict. It can be 1 or 2

parallel

Logical; if TRUE then the estimation step is parallelized

ncores

Numeric; number of cores involved in parallelization.

Value

Returns an object containing the following informations:

predicted

A list of the predicted values in the CV procedure;

data_to_pred

A list of the data to predict in the CV procedure;

mae

The vector of mean absolute error in the CV procedure;

mad

The vector of median absolute error in the CV procedure;

brie

The vector of brie score in the CV procedure;

rmse

The vector of root mean squared error in the CV procedure;

lscore

The vector of log-score in the CV procedure;

crps

The vector of continuous ranked probability score in the CV procedure;

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

See Also

GeoKrig.

Examples

  
 library(GeoModels)

################################################################
########### Examples of spatial kriging ############
################################################################

model="Gaussian"
set.seed(79)
x = runif(400, 0, 1)
y = runif(400, 0, 1)
coords=cbind(x,y)
# Set the exponential cov parameters:
corrmodel = "GenWend"
mean=0; sill=5; nugget=0
scale=0.2;smooth=0;power2=4

param=list(mean=mean,sill=sill,nugget=nugget,scale=scale,smooth=smooth,power2=power2)

# Simulation of the spatial Gaussian random field:
data = GeoSim(coordx=coords, corrmodel=corrmodel,
              param=param)$data

## estimation with pairwise likelihood
fixed=list(nugget=nugget,smooth=0,power2=power2)
start=list(mean=0,scale=scale,sill=1)
I=Inf
lower=list(mean=-I,scale=0,sill=0)
upper=list(mean= I,scale=I,sill=I)
# Maximum pairwise likelihood fitting :
fit = GeoFit(data, coordx=coords, corrmodel=corrmodel,model=model,
                    likelihood='Marginal', type='Pairwise',neighb=3,
                    optimizer="nlminb", lower=lower,upper=upper,
                    start=start,fixed=fixed)

#a=GeoCV(fit,K=100,estimation=TRUE) 
#a$rmse