Timeline for Why do my cross validation delta values and MSE calculation conclude very different model fits?
Current License: CC BY-SA 4.0
9 events
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| May 12, 2021 at 20:43 | answer | added | Demetri Pananos | timeline score: 0 | |
| May 12, 2021 at 19:43 | comment | added | user303287 | One more question though - As I wasnt sure what to do with my contradicting (wrong) MSE value and CV outputs, I started looking into the information I can get from residual deviance which is provided for glm's. It seems that there is a rule of thumb (for binomial data like mine) that if (Residual deviance /( n(observation) - (n(regressors)) >>1, then the fit is inadequate. In my case, the value is 4.4. Are you familiar with residual deviance, and is 4.4 quite bad? Apologies if this should rather be posted as a separate question! I am new to Stack Exchange so not familiar with the Dos & Dont's! | |
| May 12, 2021 at 19:37 | comment | added | user303287 | oh wooops. I misread the example given here statology.org/how-to-calculate-mse-in-r. Embarrassing. Thank you very much. My MSE is now 0.2, which is a bit more acceptable ! | |
| May 12, 2021 at 19:28 | comment | added | Demetri Pananos |
Your actual should not be Var1 since it is the predictor. The actual should be df$y2/df$y1
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| May 12, 2021 at 19:26 | comment | added | user303287 | It gives the exact same output | |
| May 12, 2021 at 19:22 | comment | added | Demetri Pananos | Yes, my mistake | |
| May 12, 2021 at 19:14 | comment | added | user303287 | @DemetriPananos, do you mean mod = glm(cbind(y2, y1-y2)~v1, family=binomial, data = d)? | |
| May 12, 2021 at 19:08 | comment | added | Demetri Pananos |
Try calling model as mod = glm(cbind(y2, y2+y1)~v1, family=binomial, data = d)
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| May 12, 2021 at 18:06 | history | asked | user303287 | CC BY-SA 4.0 |