The Hosmer-Lemeshow test is not my favorite test; it has low power in smaller samples and can show significance for important deviations in vary large ones.  I much prefer to look at the observed-predicted plots themselves. If the HL test is significant, it doesn't say that the model you have is "wrong," it says that it can be "improved."  Sometimes "improvement" means a different model, additional variables, or data transformations.  However, if the model seems adequate, I may just "declare victory" and move on. One caution (that doesn't seem to matter here), if the HL test is significant, then it would be inappropriate to claim that nothing is going on (e.g. to "accept the null"). Doc Muhlbaier Duke ... View more