I can't access dropbox, as its blocked on my corporate LAN.
However using your orginal code, modified to have adjustible sample size
library(dismo)
sizeparm <- 42720
HasRes <- rbinom(n = sizeparm, size = 1, prob = 0.5217)
use <- rnorm(n = sizeparm, mean = 8.735, sd = 1.158753)
acc <- rnorm(n = sizeparm, mean = 1.637 , sd = 0.6275683)
tmp <- rnorm(n = sizeparm, mean = 2.450 , sd = 0.01050098)
irg <- rnorm(n = sizeparm, mean = 1.0245 , sd = 0.6520517)
pgExt <- rnorm(n = sizeparm, mean = 3.039 , sd = 1.126698)
pgInt <- rnorm(n = sizeparm, mean = 2.594 , sd = 1.534927)
ChExt <- rnorm(n = sizeparm, mean = 3.569 , sd = 1.169632)
ChInt <- rnorm(n = sizeparm, mean = 4.158 , sd = 1.447912)
Ca <- rnorm(n = sizeparm, mean = 2.383 , sd = 1.189579)
veg <- rnorm(n = sizeparm, mean = 0.5522 , sd = 0.6824301)
Region <- sample(1:4, sizeparm, replace = T )
DFbrt_df2 <-
data.frame(
HasRes = HasRes,
use = use,
acc = acc,
tmp = tmp,
irg = irg,
pgExt = pgExt,
pgInt = pgInt,
ChExt = ChExt,
ChInt = ChInt,
Ca = Ca,
veg = veg,
Region.num = Region
)
ColIndexCov <- 2:(ncol(DFbrt_df2)-1)
ColIndexResp <- 1
myBRT2<- gbm.step(data = DFbrt_df2,
gbm.x = ColIndexCov,
gbm.y = ColIndexResp,
tree.complexity = 3,
learning.rate = 0.000005,
n.trees = 50,
family = "bernoulli",
n.folds = 4,
fold.vector = DFbrt_df2$Region.num,
step.size = 5,
verbose = F,
silent = FALSE )
I received an error advising to adjust learning rate and/or step.size.
I adjusted step size to 1 and it worked without error.