spatialpred: Spatial Predictions Based on the Circular Variable-Radius...
In biotools: Tools for Biometry and Applied Statistics in Agricultural Science

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

A heuristic method to perform spatial predictions. The method consists of a local interpolator with stochastic features. It allows to build effective detailed maps and to estimate the spatial dependence without any assumptions on the spatial process.

1	spatialpred(coords, data, grid)

`coords`	a data frame or numeric matrix containing columns with geographic coordinates
`data`	a numeric vector of compatible dimension with `coords` containing the response variable data to be predicted at each `grid` point
`grid`	a data frame or numeric matrix containing columns with geographic coordinates where `data` is to be predicted

If grid receives the same input as coords, spatialpred will calculate the Percenntual Absolute Mean Error (PAME) of predictions.

A data.frame containing spatial predictions, standard errors, the radius and the number of observations used in each prediction over the grid.

Depending on the dimension of coords and/or grid, spatialpred() can be time demanding.

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

Da Silva, A.R., Silva, A.P.A., Tiago-Neto, L.J. (2020) A new local stochastic method for predicting data with spatial heterogeneity. ACTA SCIENTIARUM-AGRONOMY, 43:e49947.

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# data(moco)
# p <- spatialpred(coords = moco[, 1:2], data = rnorm(206), grid = moco[, 1:2]) 
# note: using coords as grid to calculate PAME
# head(p)
# lattice::levelplot(pred ~ Lat*Lon, data = p)

# End (not run)