  Zero adjusted regression models are used to fit variables that are discrete
at zero and continuous at some interval of the positive real numbers.
Diagnostic analysis in these models is usually performed using the randomized
quantile residual, which is useful for checking the overall adequacy of a zero
adjusted regression model. However, it may fail to identify some outliers. In
this work, we introduce a residual for outlier identification in zero adjusted
regression models. Monte Carlo simulation studies and an application suggest
that the residual introduced here has good properties and detects outliers that
are not identified by the randomized quantile residual.
