Timeline for Chi Square vs F Tests for GLM Model Comparisons
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
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| May 28, 2019 at 13:26 | comment | added | Ben Bolker | Huh. For your first question, I'm surprised too. Can you post a reproducible example? For your second question: there's an unanswered question about this on SO ... (oh, never mind, I see that it's you ...) I don't have Microsoft R installed and don't really plan to, so it's going to be hard for me to find a solution for you ...) | |
| May 28, 2019 at 11:39 | comment | added | Alan | Also for fitting GLMs I often have to use the rxGlm() function in the revoScaleR package because it performs so much better with large data sets. However the objects that the rxGlm() function returns don't seem to work with the anova() function, so that: anova(rxModel1, rxModel2, test = "whatever") and anova(as.glm(rxModel1), as.glm(rxModel2), test = "whatever") both fail. Is there a way around this, I wonder? Apologies - I haven't managed to format this comment properly (not sure how!) | |
| May 28, 2019 at 11:38 | comment | added | Alan | Thank you. For the Tweedie application I'm using a fixed a priori shape parameter for the time being although I know that techniques exist for estimating it. I've done some anova() calls using test = "F" instead of test = "Chisq" and I'm getting identical p-values (for a number of different GLM error structures - Poisson, gaussian, gamma, tweedie). I didn't expect this - should I have? | |
| May 28, 2019 at 2:21 | history | answered | Ben Bolker | CC BY-SA 4.0 |