| CrossSpatFD {SpatFD} | R Documentation |
Creates univariate and multivariate CrossSpatFD object to perform crossed validation for functional spatial prediction.
Description
Creates univariate and multivariate CrossSpatFD object considering the base od a SpatFD or FD object to perform crossed validation for functional spatial prediction.
Usage
CrossSpatFD(data,coords,basis,lambda=0,nharm=NULL,name=NULL,add=NULL,...)
Arguments
data |
Data must be provided in a data-frame or a matrix where each column corresponds to a location, and the rows are a sequence of data points, that is, the rows are ordered according to time, frequency, depth, …. Data can also be an fd-object from the fda package. |
coords |
A data-frame or a matrix with spatial coordinates (x,y). The number of columns in data must coincide with the number of rows in coords for each variable. |
basis |
The basis from the SpatFD or FD object. |
lambda |
The value of the smoothing parameter. |
nharm |
The number of harmonics or eigenfunctions to be reported in the Functional Principal Components results if vp is not given. |
name |
A new name for data can be assigned. |
add |
Other variables can be added for spatial multivariate functional prediction, that is, for functional cokriging. It is not necessary that all variables are observed at the same spatial locations. |
... |
arguments from fda create.bspline.basis or create.fourier.basis. |
Details
The CrossSpatFD-objects storage the functional data, its parameters, the functional principal component analysis results, and the spatial coordinates for each variable. Each variable has its own functional data, data-frame or matrix and its spatial coordinates file.
Value
For each variable: The functional data and functional principal components linked with spatial coordinates.
Note
1. This function is for internal use and should not be implemented directly
Author(s)
Diego Sandoval diasandovalsk@unal.edu.co & Angie Villamil acvillamils@unal.edu.co.
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).