ataKriging {atakrig}

Area-to-area, area-to-point ordinary Kriging prediciton, cross-validation.

Description

Area-to-area, area-to-point ordinary Kriging prediciton, cross-validation.

Usage

ataKriging(x, unknown, ptVgm, nmax = 10, longlat = FALSE,
    showProgress = FALSE, nopar = FALSE, clarkAntiLog = FALSE)
atpKriging(x, unknown0, ptVgm, nmax = 10, longlat=FALSE,
    showProgress = FALSE, nopar = FALSE)
ataKriging.cv(x, nfold = 10, ptVgm, nmax=10, longlat = FALSE,
    showProgress = FALSE, nopar = FALSE, clarkAntiLog = FALSE)

Arguments

x

a discreteArea object: list(areaValues, discretePoints), where areaValues: data.frame(areaId,centx,centy,value) discretePoints: data.frame(areaId,ptx,pty,weight)

unknown

a discreted discreteArea object, or just data.frame(areaId,ptx,pty,weight).

unknown0

for points prediction, data.frame(ptx,pty), one point per row.

nfold

number of fold for cross-validation. for leave-one-out cross-validation, nfold = nrow(x$areaValues).

ptVgm

point scale variogram, ataKrigVgm.

nmax

max number of neighborhoods used for interpolation.

longlat

coordinates are longitude/latitude or not.

showProgress

show progress bar for batch interpolation (multi destination areas).

nopar

disable parallel process in the function even if ataStartCluster() has been called, mainly for internal use.

clarkAntiLog

for log-transformed input data, whether the estimated value should be adjusted(i.e. exponentiation).

Value

estimated value of destination area and its variance.

References

Clark, I., 1998. Geostatistical estimation and the lognormal distribution. Geocongress. Pretoria, RSA., [online] Available from: http://kriging.com/publications/Geocongress1998.pdf. Goovaerts, P., 2008. Kriging and semivariogram deconvolution in the presence of irregular geographical units. Mathematical Geosciences 40 (1): 101-128. Isaaks, E. H., Srivastava, R. M., 1989. An introduction to applied geostatistics. New York, Oxford University Press. Skøien, J. O. and G. Blöschl, et al., 2014. rtop: an R package for interpolation of data with a variable spatial support, with an example from river networks. Computers & Geosciences 67: 180-190.

See Also

deconvPointVgm, ataCoKriging

Examples

library(atakrig)
library(sf)

## load demo data from rtop package ----
if (!require("rtop", quietly = TRUE)) message("rtop library is required for demo data.")
rpath <- system.file("extdata", package="rtop")
observations <- read_sf(rpath, "observations")
observations$obs <- observations$QSUMMER_OB/observations$AREASQKM

## point-scale variogram ----
obs.discrete <- discretizePolygon(observations, cellsize=1500, id="ID", value="obs")
pointsv <- deconvPointVgm(obs.discrete, model="Exp", ngroup=12, rd=0.75, fig=TRUE)

## cross validation ----
pred.cv <- ataKriging.cv(obs.discrete, nfold=length(observations), pointsv)
names(pred.cv)[6] <- "obs"

summary(pred.cv[,c("obs","pred","var")])
cor(pred.cv$obs, pred.cv$pred)			# Pearson correlation
mean(abs(pred.cv$obs - pred.cv$pred))	# MAE
sqrt(mean((pred.cv$obs - pred.cv$pred)^2))	# RMSE

## prediction ----
predictionLocations <- read_sf(rpath, "predictionLocations")
pred.discrete <- discretizePolygon(predictionLocations, cellsize = 1500, id = "ID")
pred <- ataKriging(obs.discrete, pred.discrete, pointsv$pointVariogram)