# 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)
# }
# NOT RUN {
# }
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