What if explained deviance is greater than 1.0 (or 100%)?

I am using explained deviance (sometimes referred to as percent deviance, or deviance explained by the model) as a goodness-of-fit measure for my species distribution model. Explained deviance is calculated as: (Null Deviance - Residual Deviance) / Null Deviance , and the greater the explained deviance, the greater the explanatory power of the model. One of my deviance values is greater than 1.0 (when multiplied, greater than 100%)....why is that?

Edit: Here is my R code & output for the boosted regression tree:

CODE

spurge10.tc5.lr001 <- gbm.step(data=model.data10, gbm.x = 6:34, gbm.y = 4, family = "bernoulli", tree.complexity = 5, learning.rate = 0.001, bag.fraction = 0.5)

OUTPUT: I am using "estimated cv deviance"

fitting final gbm model with a fixed number of 1400 trees for PresOrAbs

mean total deviance = 1.386 mean residual deviance = 0.718

estimated cv deviance = 1.079 ; se = 0.046

training data correlation = 0.861 cv correlation = 0.58 ; se = 0.038

training data ROC score = 0.982 cv ROC score = 0.819 ; se = 0.021

Thanks!

• This question would be much easier to answer if you could edit it and paste in your model output. – Silverfish Mar 12 '16 at 20:50