okcv: Cross validation, n-fold for ordinary kriging (OK)

In spm: Spatial Predictive Modeling

Cross validation, n-fold for ordinary kriging (OK)

Description

This function is a cross validation function for ordinary kriging.

Usage

okcv(
  longlat,
  trainy,
  cv.fold = 10,
  nmax = 12,
  transformation = "none",
  delta = 1,
  vgm.args = ("Sph"),
  anis = c(0, 1),
  alpha = 0,
  block = 0,
  predacc = "VEcv",
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples.
trainy	a vector of response, must have length equal to the number of rows in longlat.
cv.fold	integer; number of folds in the cross-validation. if > 1, then apply n-fold cross validation; the default is 10, i.e., 10-fold cross validation that is recommended.
nmax	for local kriging: the number of nearest observations that should be used for a kriging prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used.
transformation	transform the response variable to normalise the data; can be "sqrt" for square root, "arcsine" for arcsine, "log" or "none" for non transformation. By default, "none" is used.
delta	numeric; to avoid log(0) in the log transformation.
vgm.args	arguments for vgm, e.g. variogram model of response variable and anisotropy parameters. see notes vgm in gstat for details. By default, "Sph" is used.
anis	anisotropy parameters: see notes vgm in gstat for details.
alpha	direction in plane (x,y). see variogram in gstat for details.
block	block size. see krige in gstat for details.
predacc	can be either "VEcv" for vecv or "ALL" for all measures in function pred.acc.
...	other arguments passed on to gstat.

Value

A list with the following components: me, rme, mae, rmae, mse, rmse, rrmse, vecv and e1; or vecv only

Note

This function is largely based on rfcv in randomForest and some functions in library(gstat). When 'A zero or negative range was fitted to variogram' occurs, to allow gstat running, the range was set to be positive by using min(vgm1$dist). In this case, caution should be taken in applying this method. If it still occur for okpred function, different method should be used.

Author(s)

Jin Li

References

Li, J., 2013. Predictive Modelling Using Random Forest and Its Hybrid Methods with Geostatistical Techniques in Marine Environmental Geosciences, In: Christen, P., Kennedy, P., Liu, L., Ong, K.-L., Stranieri, A., Zhao, Y. (Eds.), The proceedings of the Eleventh Australasian Data Mining Conference (AusDM 2013), Canberra, Australia, 13-15 November 2013. Conferences in Research and Practice in Information Technology, Vol. 146.

A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3), 18-22.

Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30: 683-691.

Examples

## Not run: 
library(sp)
data(swmud)
data(petrel)

okcv1 <- okcv(swmud[, c(1,2)], swmud[, 3], nmax = 7, transformation =
"arcsine", vgm.args = ("Sph"), predacc = "VEcv")
okcv1

n <- 20 # number of iterations,60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
okcv1 <- okcv(petrel[, c(1,2)], petrel[, 5], nmax = 12,
transformation = "arcsine", predacc = "VEcv")
VEcv [i] <- okcv1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for OK", ylab = "VEcv (%)")
points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(VEcv), col = 'blue', lwd = 2)

n <- 20 # number of iterations, 60 to 100 is recommended.
measures <- NULL
for (i in 1:n) {
okcv1 <- okcv(petrel[, c(1,2)], petrel[, 3], nmax = 12, transformation =
"arcsine", predacc = "ALL")
measures <- rbind(measures, okcv1$vecv)
}
plot(measures ~ c(1:n), xlab = "Iteration for OK", ylab = "VEcv (%)")
points(cumsum(measures) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(measures), col = 'blue', lwd = 2)

## End(Not run)

spm documentation built on May 6, 2022, 9:06 a.m.