recons_fd {SpatFD}

Linear combinations for functional kriging

Description

This is an internal function for functional kriging and cokriging. To perform the linear combinations to obtain functional kriging and cokriging. Once optimization process is finished and scores or lambda are obtained, this function builds the prediction, performing the linear combination between coefficients and basis functions.

Usage

recons_fd(X,name = NULL)

Arguments

X

A 'KS_pred' or 'COKS_pred' object obtained with function KS_scores_lambdas or COKS_scores_lambdas

name

Name of the variable of interest. Default 1, the first variable.

Value

Spatial functional predictions. The fd object based on the two functional kriging methods described in Bohorquez, M., Giraldo, R., & Mateu, J. (2016). List of

fd_scores

fd_lambda

Author(s)

Diego Sandoval [e-mail].

References

Bohorquez, M., Giraldo, R., & Mateu, J. (2016). Optimal sampling for spatial prediction of functional data. Statistical Methods & Applications, 25(1), 39-54.

Bohorquez, M., Giraldo, R., & Mateu, J. (2016). Multivariate functional random fields: prediction and optimal sampling. Stochastic Environmental Research and Risk Assessment, 31, pages53–70 (2017).

Bohorquez M.; Giraldo R. and Mateu J. Spatial prediction and optimal sampling of functional data in Geostatistical Functional Data Analysis: Theory and Methods (2021). John Wiley Sons, Chichester, UK. ISBN: 978-1-119-38784-8. https://www.wiley.com/en-us/Geostatistical+Functional+Data+Analysis-p-9781119387848. ~

See Also

KS_scores_lambdas

Examples

library(gstat)
data(AirQualityBogota)
newcoorden=data.frame(X=110000,Y=125000)
SFD_PM10 <- SpatFD(PM10, coords = coord[,2:3], basis = "Bsplines", nbasis = 17,
norder=5, lambda = 0.00002, nharm=3)
modelos <- list(vgm(psill = 2634000, "Exp", range = 2103.25, nugget =  0),
                vgm(psill = 101494.96, "Exp", range = 1484.57, nugget = 0),
                vgm(psill =53673, "Exp", range = 42406, nugget =  0))

#method = "lambda"
KS_SFD_PM10_l <- KS_scores_lambdas(SFD_PM10, newcoorden ,method = "lambda",
model = modelos)
curves_PM10_l <- recons_fd(KS_SFD_PM10_l)
plot(curves_PM10_l)

#method = "scores"
KS_SFD_PM10_sc <- KS_scores_lambdas(SFD_PM10, newcoorden, method = "scores",
model = modelos)
curves_PM10_sc <- recons_fd(KS_SFD_PM10_sc)
plot(curves_PM10_sc)