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(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.
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?