Timeline for Interpreting residual diagnostic plots for glm models?
Current License: CC BY-SA 3.0
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| Jan 2, 2017 at 20:42 | history | edited | kjetil b halvorsen♦ | CC BY-SA 3.0 |
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| Aug 31, 2012 at 18:55 | comment | added | Greg Snow | @AndyW, I think that we are interpreting the original question differently. My answer gets the researcher started by letting them know if there is something more that they need to look for, or if the residual plot is reasonable. What to do if it does not look reasonable is then the next step and beyond my answer (though some additional assumptions could be compared using a new set of simulations). | |
| Aug 31, 2012 at 18:26 | comment | added | Andy W | I think that is a fine suggestion to seeing patterns that deviate from random in scatter or other plots, but that isn't the only goal when viewing residuals. Frequently we are interested in particular deviations from random (e.g. hetereoscedasticity, misspecified non-linearity in model, omitted variables, outliers or high leverage values, etc.). Comparisons to randomly generated data don't really help any in identifying why the residuals are not random nor the remedy. | |
| Aug 31, 2012 at 18:15 | history | answered | Greg Snow | CC BY-SA 3.0 |