idwcv {spm}

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.

Note

This function is largely based on rfcv in randomForest and some functions in library(gstat).

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)

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)

## End(Not run)