IDW: IDW Optimizing inverse distance weighting power
In AybarCL/Dorado: Easy Rainfall and Temperature Interpolation

Description Usage Arguments Details Value Author(s) See Also Examples

This function use gstat packages for interpolate spatial point data (SpatialPoints) and RasterLayer data (see raster).

1	IDW(gauge, cov, formula, idpR = seq(0.8, 3.5, 0.1))

`gauge`	Is an object of SpatialPointsDataFrame class.
`cov`	Is An object of RasterLayer.
`formula`	that defines the dependent variable as a linear model of independent variables; suppose the dependent variable has name 'z', for Regression Inverse Distance Weigthing (RIDW) use the formula 'z~x+y+....', you do not need define.
`idpR`	Is vector numeric of the power coeficient to evaluate.

R_IDW use crossvalidation Leave-p-out cross-validation (LpO CV) and force brute (optimize MSE) for estimate the best idp power coeficient.

a List that contains: Interpol is the RIDW result in Raster, params being bestp is the best distance weighting power, MSE is the Residual Mean squared error of the residuals and finally linear_Model is the adjusted linear Model.

Cesar Aybar <aybar1994@gmail.com>

idw

library(raster)
library(Dorado)
data("Dorado")
gauge <- mean_doble_Station(gauge = Dorado$gauge,cov = Dorado$TRMM)
sat <- Dorado$TRMM
x <- IDW(gauge = gauge,cov = sat,formula = PP_anual~1)