I'm using the gbm.step
package in R to look at the influence of three continuous variables on my continuous response variable. I have 234 observations. The model:
poa.tc2.lr005.bg0.5 <- gbm.step(data=poa,
gbm.x = 8:10,
gbm.y = 7,
tree.complexity = 2,
family = "gaussian",
#n.trees = 50,
#n.folds = 10,
#step.size = 25,
max.trees = 10000,
prev.stratify = FALSE,
learning.rate = 0.005,
bag.fraction = 0.5)
After settling on some initial parameters of tc, lr and bag fraction, I would like to produce partial dependency plots for my predictor variables using gbm.plot
:
gbm.plot(poa.tc2.lr005.bg0.5, n.plots=3,
write.title = F,
show.contrib=T,
y.label="Marginal effect on gs")
The general trend of the dependencies makes sense given the data, however the y-axis scale is throwing me off.
The y-axis range is between -0.04 to 0.06, however, my response variable range is between 0.02 and 0.38. With the new range, it's hard to accurately interpret the results.
My questions:
Why doesn't the y-axis in the dependency plot reflect the range of my dependent variable? Are the values normalized to something?
How do I extract the values used to construct these graphs? I would like to reconstruct the dependency plots in a different program. I have tried
names(poa.tc2.lr005.bg0.5) poa.fitted <- (poa.tc2.lr005.bg0.5$fitted)
but those values are not the same used in the dependency plots generated by the gbm.plot
code above. Is there a different output for these values that I should be looking for?