How can I modify default parameters of a gbm.step plot? I am using the function gbm.step() from the dismo package  to assess the optimal number of boosting trees using k-fold cross validation for a clasification problem. This function ﬁts a model with the optimal number of trees, returning it as a gbm model along with additional
information from the cross-validation selection process. And also shows a plot with default parameters.
This is my plot:

How can I change the default settings? That is, I want a different title and different  x and y labels. After googling and reading the dismo package information I didn't find anything.
Thanks.
 A: If you check the source (tar.gz), you can see how the plot is made by gbm.step. Most of the settings, like the labels and colors, are hard-coded. But it's possible to suppress the generated plot and make your own from the result.
    y.bar <- min(cv.loss.values) 
    ...

    y.min <- min(cv.loss.values - cv.loss.ses)
    y.max <- max(cv.loss.values + cv.loss.ses)

    if (plot.folds) {
      y.min <- min(cv.loss.matrix)
      y.max <- max(cv.loss.matrix) }

      plot(trees.fitted, cv.loss.values, type = 'l', ylab = "holdout deviance", xlab = "no. of trees", ylim = c(y.min,y.max), ...)
      abline(h = y.bar, col = 2)

      lines(trees.fitted, cv.loss.values + cv.loss.ses, lty=2)  
      lines(trees.fitted, cv.loss.values - cv.loss.ses, lty=2)  

      if (plot.folds) {
        for (i in 1:n.folds) {
          lines(trees.fitted, cv.loss.matrix[i,],lty = 3)
      }
    }
  }
  target.trees <- trees.fitted[match(TRUE,cv.loss.values == y.bar)]

  if(plot.main) {
    abline(v = target.trees, col=3)
    title(paste(sp.name,", d - ",tree.complexity,", lr - ",learning.rate, sep=""))
  }

Fortunately, most of the variables in the above code are returned as members of the result object, sometimes with slightly different names (notably, cv.loss.values -> cv.values).
Here's an example of calling gbm.step with main.plot=FALSE to suppress the built-in plot and creating the plot from the result object.
data(Anguilla_train)
m <- gbm.step(data=Anguilla_train, gbm.x = 3:14, gbm.y = 2, family = "bernoulli",tree.complexity = 5, learning.rate = 0.01, bag.fraction = 0.5, plot.main=F)

y.bar <- min(m$cv.values) 
y.min <- min(m$cv.values - m$cv.loss.ses)
y.max <- max(m$cv.values + m$cv.loss.ses)

plot(m$trees.fitted, m$cv.values, type = 'l', ylab = "My Dev", xlab = "My Count", ylim = c(y.min,y.max))
abline(h = y.bar, col = 3)

lines(m$trees.fitted, m$cv.values + m$cv.loss.ses, lty=2)  
lines(m$trees.fitted, m$cv.values - m$cv.loss.ses, lty=2)  

target.trees <- m$trees.fitted[match(TRUE,m$cv.values == y.bar)]
abline(v = target.trees, col=4)
title("My Title")


