Interpret this observed-vs-fitted plot I have this OvF plot with the outcome being a ln-transformed continuous variable (length of stay in days).
This plot is the result of a survey-adjusted weighted mixed-level (1 level random intercept), linear regression done in stata 14.
I don't know how to interpret this plot. There seems to be a weird horizontal pattern accross the o=f line that i cannot understand. Please advise.
Also if you know another method of assessing the model fit for generalized linear regression (continuous outcomes), please let me know (stata commands --> extra kudos!)

 A: Your variable is measured in days, so the lowest value is $1$ day or $\ ln 1=0$, the second lowest value is $2$ days or $\ln 2\approx0.69$, the third is $3$ days or $\ln 3\approx1.10$, etc. That is what you see in the graph; the bottom three horizontal lines of points happen at about the values $0$, $0.69$, and $1.10$. So the graph accurately represents what is happening in your data.
You mentioned you wanted to treat length of stay as continuous. However, what would that mean in terms of your length of stay variable? In principle we could have measured length of stay in hours. This would not solve the problem completely, as the variable will still be granular to some extent, but the graph would look much prettier. More importantly, does that extra detail contain information you care about? Probably not, as the hour of the day that you leave a hospital is largely determined by organizational concerns, e.g. when is the doctor who does the final check-up available? So a finer measure of length of stay would probably only add random noise to your variable.
Having said all that, I probably would not use a linear model for such duration data. There is a whole set of statistical models that are explicitly designed for such data, typically referred to as survival analysis. See here, here, here, or here.
