# Interpret residual vs fitted values for negative binomial GLM

I have a residual vs fitted values plot for the following negative binomial model:

glm.nb(formula = Numberpertow ~ as.factor(CruiseID) + as.factor(Stratum) +
offset(log((TowDist * Subsampling_fraction)/1850)), data = news2,link = log)


Numberpertow is discrete count data Cruise and Stratum are catergorical covariates. Cruise has 3 levels and stratum has 23 levels.

The plot below is of the deviance resiuals against the log of the fitted values. I am trying to assess model fit. Based on this plot the residuals dont appear to be centered around zero for larger fitted values and I can see a pattern of decreasing residuals for larger fitted values. I am wondering if there is enough deviation from expectations to not continue with this model. The scale location plot does not indicate heteroskedasticity.
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• You can see quite noticeable heteroskedasticity in this plot. The scale location plot is only any use if your fit to the mean is good, but you don't have that here, so the scale-location plot is misleading you -- when the mean is not well-fitted, it's better to judge the heteroskedacticity from this plot. – Glen_b Nov 21 '16 at 23:36