# NOT RUN {
data(hard)
data(petrel)
rgcv1 <- rgcv(petrel[, c(1,2, 6:9)], petrel[, 5], predacc = "ALL")
rgcv1
n <- 20 # number of iterations, 60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
rgcv1 <- rgcv(petrel[, c(1,2,6:9)], petrel[, 5], predacc = "VEcv")
VEcv [i] <- rgcv1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for RF", 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) {
rgcv1 <- rgcv(hard[, c(4:6)], hard[, 17])
measures <- rbind(measures, rgcv1$ccr) # for kappa, replace ccr with kappa
}
plot(measures ~ c(1:n), xlab = "Iteration for RF", ylab = "Correct
classification rate (%)")
points(cumsum(measures) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(measures), col = 'blue', lwd = 2)
# }
# NOT RUN {
# }
Run the code above in your browser using DataLab