idw.cv: idw cross validation leave-one-out

In amsantac/geospt: Geostatistical Analysis and Design of Optimal Spatial Sampling Networks

idw cross validation leave-one-out

Description

Generate the RMSPE value, which is given by the idw function with p smoothing parameter.

Usage

idw.cv(formula, locations, data, nmax = Inf, nmin = 0, p = 2, progress=FALSE, ...)

Arguments

formula	formula that defines the dependent variable as a linear model of independent variables; suppose the dependent variable has name z, for a idw detrended use z~1
data	SpatialPointsDataFrame: should contain the dependent variable, independent variables, and coordinates.
locations	object of class Spatial, or (deprecated) formula defines the spatial data locations (coordinates) such as ~x+y
nmax	number of nearest observations that should be used for a idw prediction, where nearest is defined in terms of the spatial locations. By default, all observations are used.
nmin	if the number of nearest observations within distance maxdist is less than nmin, a missing value will be generated; see maxdist.
p	value of smoothing parameter; we recommend using the parameter found by minimizing the root-mean-square prediction errors using cross-validation. Default is 2.
progress	logical. Use TRUE to see the percentage of progress of the process and FALSE otherwise). Default progress=FALSE.
...	Other arguments passed to idw

Value

returns the RMSPE value

See Also

idw

Examples

data(preci)
idw.cv(prec~1, ~x+y, preci, nmax=9, nmin=9, p=2, progress=TRUE)

amsantac/geospt documentation built on Feb. 21, 2024, 12:23 p.m.