Timeline for How to interpret BIC
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
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| Jan 19, 2015 at 0:57 | vote | accept | Jeff | ||
| Dec 17, 2013 at 21:27 | comment | added | marbel | Can´t you use the average square error for predicting numerical targets? If your target is binary, then you can use the cumulative lift or the ROC curve. This is more in line with the SAS output, but i´m sure you can do this with R. | |
| Oct 29, 2013 at 19:24 | comment | added | Peter Flom |
Whether higher or lower is better depends on the implementation. In SAS I believe lower is better (it says which is better on the printout). In R, as well, a lower BIC is better. From the R documentation for AIC: "When comparing fitted objects, the smaller the AIC or BIC, the better the fit. "
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| Oct 29, 2013 at 19:10 | comment | added | Jeff | so you are saying that, indeed, i have justification for saying that the model with fewer parameters (higher BIC for all participants) is a "better" model, correct? this was my interpretation, but it seemed strange that the increase in BIC was nearly constant across participants. | |
| Oct 29, 2013 at 18:42 | history | answered | Peter Flom | CC BY-SA 3.0 |