okfd.cv: Function for doing Cross-Validation analysis for Ordinary...

Description Usage Arguments Details Value Author(s) References

View source: R/okfd.cv.R

Description

Unreviewed

Usage

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okfd.cv(coords, data, argnames=c("argument", "sites", "values"),
        one.model=TRUE, smooth.type=NULL,
        array.nbasis=max(50,dim(data)[1]),
        argvals=seq(0,1,len=dim(data)[1]), array.lambda=0, cov.model=NULL,
        fix.nugget=FALSE, nugget=0, fix.kappa=TRUE, kappa=0.5,
        max.dist.variogram=NULL)

Arguments

coords

coordinates of the sites where functional data are observed (dim: s by 2)

data

matrix with values for the observed functions (dim: m by s)

argnames

a character vector of length three containing: the name of the argument (argvals), a description of the sites (coord), the name of the observed function values.

one.model

logical, indicates whether the cross validation is going to be done just one model or one model for each site. Deafult is TRUE. See details below.

smooth.type

a string with the name of smoothing method to be applied to data. Available choices are: "bsplines" and "fourier".

array.nbasis

array with values for the number of elements in the cubic B-spline basis.

argvals

a set of argument values. (length: m)

array.lambda

array of penalization parameters for smoothing the observed functions.

cov.model

a string with the name of the correlation function. Default is NULL, see DETAILS below.

fix.nugget

logical, indicating whether the nugget parameter should be estimated or not.

nugget

value for the nugget parameter.

fix.kappa

logical, indicating whether the kappa parameter should be estimated or not.

kappa

value of the smoothness parameter.

max.dist.variogram

a numerical value defining the maximum distance considered when fitting the variogram.

Details

Validation models

The parameter one.model is used to define the models used in the cross validation:

Value

A list with the following components:

k.opt

unreviewed

l.opt

unreviewed

krig.cv

unreviewed

mse.cv

unreviewed

mse.cv.opt

unreviewed

fd.models

unreviewed

Author(s)

Ramon Giraldo rgiraldoh@unal.edu.co,
Pedro Delicado pedro.delicado@upc.edu,
Jorge Mateu mateu@mat.uji.es.

References

Giraldo, R. (2009) Geostatistical Analysis of Functional Data. Ph.D. thesis. Universitat Politecnica de Catalunya.

Giraldo, R., Delicado, P. and Mateu, J. (2012) geofd: An R package for function-valued geostatistical prediction. Revista Colombiana de Estadistica. 35, 385-407.


geofd documentation built on Jan. 29, 2020, 5:08 p.m.