ataCoKriging {atakrig}

Area-to-area, area-to-point coKriging prediciton, cross-validation.

Description

Area-to-area, area-to-point coKriging prediciton, cross-validation.

Usage

ataCoKriging(x, unknownVarId, unknown, ptVgms, nmax = 10, longlat = FALSE,
    oneCondition = FALSE, meanVal = NULL, auxRatioAdj = TRUE,
    showProgress = FALSE, nopar = FALSE, clarkAntiLog = FALSE)

atpCoKriging(x, unknownVarId, unknown0, ptVgms, nmax = 10, longlat = FALSE,
    oneCondition = FALSE, meanVal = NULL, auxRatioAdj = TRUE,
    showProgress = FALSE, nopar = FALSE)

ataCoKriging.cv(x, unknownVarId, nfold = 10, ptVgms, nmax = 10, longlat = FALSE,
    oneCondition = FALSE, meanVal = NULL, auxRatioAdj = TRUE,
    showProgress = FALSE, nopar = FALSE, clarkAntiLog = FALSE)

Arguments

x

discretized areas of all variables, each is a discreteArea object.

unknownVarId

variable name (charaster) defined in x for prediction.

unknown

a discreted discreteArea object or data.frame[areaId,ptx,pty,weight] to be predicted.

unknown0

for points prediction or data.frame[ptx,pty] (one point per row) to be predicted.

nfold

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

ptVgms

point-scale direct and cross variograms, ataKrigVgm object.

nmax

max number of neighborhoods used for interpolation.

longlat

coordinates are longitude/latitude or not.

oneCondition

only one contrained condition for all points and all variables, \sum_i=1^n\lambda_i +\sum_j=1^m\beta_j =1, assuming expected means of variables known and constant with the study area.

meanVal

expected means of variables for oneCondition coKriging, data.frame(varId,value). If missing, simple mean values of areas from x will be used instead.

auxRatioAdj

for oneCondition Kriging, adjusting the auxiliary variable residue by a ratio between the primary variable mean and auxiliary variable mean.

showProgress

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

nopar

disable parallel process in the function even if ataEnableCluster() 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.

See Also

deconvPointVgmForCoKriging, deconvPointCrossVgm, ataKriging

Examples

library(atakrig)
library(terra)

## demo data ----
rpath <- system.file("extdata", package="atakrig")
aod3k <- rast(file.path(rpath, "MOD04_3K_A2017042.tif"))
aod10 <- rast(file.path(rpath, "MOD04_L2_A2017042.tif"))

aod3k.d <- discretizeRaster(aod3k, 1500)
aod10.d <- discretizeRaster(aod10, 1500)
grid.pred <- discretizeRaster(aod3k, 1500, type = "all")

aod3k.d$areaValues$value <- log(aod3k.d$areaValues$value)
aod10.d$areaValues$value <- log(aod10.d$areaValues$value)

## area-to-area Kriging ----
# point-scale variogram from combined AOD-3k and AOD-10
aod.combine <- rbindDiscreteArea(x = aod3k.d, y = aod10.d)
vgm.ok_combine <- deconvPointVgm(aod.combine, model="Exp", ngroup=12, rd=0.75)

# point-scale cross-variogram
aod.list <- list(aod3k=aod3k.d, aod10=aod10.d)
aod.list <- list(aod3k=aod3k.d, aod10=aod10.d)
vgm.ck <- deconvPointVgmForCoKriging(aod.list, model="Exp", ngroup=12, rd=0.75,
                                    fixed.range = 9e4)

# prediction
ataStartCluster(2) # parallel with 2 nodes
pred.ataok <- ataKriging(aod10.d, grid.pred, vgm.ck$aod10, showProgress = TRUE)
pred.ataok_combine <- ataKriging(aod.combine, grid.pred, vgm.ok_combine, showProgress = TRUE)
pred.atack <- ataCoKriging(aod.list, unknownVarId="aod3k", unknown=grid.pred,
                           ptVgms=vgm.ck, oneCondition=TRUE, auxRatioAdj=TRUE, showProgress = TRUE)
ataStopCluster()

# reverse log transform
pred.ataok$pred <- exp(pred.ataok$pred)
pred.ataok$var <- exp(pred.ataok$var)
pred.ataok_combine$pred <- exp(pred.ataok_combine$pred)
pred.ataok_combine$var <- exp(pred.ataok_combine$var)

pred.atack$pred <- exp(pred.atack$pred)
pred.atack$var <- exp(pred.atack$var)

# convert result to raster
pred.ataok.r <- rast(pred.ataok[,2:4])
pred.ataok_combine.r <- rast(pred.ataok_combine[,2:4])
pred.atack.r <- rast(pred.atack[,2:4])

# display
pred <- rast(list(aod3k, pred.ataok_combine.r$pred, pred.ataok.r$pred, pred.atack.r$pred))
names(pred) <- c("aod3k","ok_combine","ataok","atack")
plot(pred)