spm (version 1.2.2)

idwcv: Cross validation, n-fold for inverse distance weighting (IDW)

Description

This function is a cross validation function for inverse distance weighting.

Usage

idwcv(longlat, trainy, cv.fold = 10, nmax = 12, idp = 2, 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 a local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used.

idp

numeric; specify the inverse distance weighting power.

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.

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)

idwcv1 <- idwcv(swmud[, c(1,2)], swmud[, 3], nmax = 12, idp = 2)
idwcv1

n <- 20 # number of iterations, 60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
idwcv1 <- idwcv(petrel[, c(1,2)], petrel[, 3], nmax = 12, predacc = "VEcv")
VEcv [i] <- idwcv1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for IDW", 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) {
idwcv1 <- idwcv(swmud[, c(1,2)], swmud[, 3], predacc = "ALL")
measures <- rbind(measures, idwcv1$vecv)
}
plot(measures ~ c(1:n), xlab = "Iteration for IDW", ylab="VEcv (%)")
points(cumsum(measures) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(measures), col = 'blue', lwd = 2)
# }
# NOT RUN {
# }