Timeline for R- regression diagnostics for big data
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
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| Jul 17, 2021 at 2:43 | comment | added | Glen_b | If you're just interested in inference on the conditional mean (such as producing a confidence interval for the regression coefficients), near-normality of residuals isn't particularly consequential; correctness of the model for the conditional mean, heteroskedasticity and dependence are the main issues. | |
| Jul 17, 2021 at 0:33 | history | edited | kjetil b halvorsen♦ |
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| Jul 16, 2021 at 10:51 | comment | added | Frank Harrell | It is better to have a model fitting strategy than to posit a simple model and try to find out what went wrong. Unless you know things are linear from previous datasets allow continuous predictors to be nonlinear then there is one less thing to check. See RMS for how to use regression splines, etc. | |
| Jul 16, 2021 at 10:27 | comment | added | Irene | Thank you for your suggestion. I manage to use histograms to check for residuals normality. For heteroscedasticity I use the ncvTest(fit) , and for non- independence of errors the durbinWatsonTest(fit). However, I am not sure how to check for non- linearity. | |
| Jul 16, 2021 at 10:21 | comment | added | Roland | What kind of diagnostic plots are you tring to create? E.g., you should never try to plot 3.7 M residuals. You should sample about 1 % of the residuals and plot these. Or you can summarize the diagnostic data (histograms, boxplots, ...). | |
| Jul 16, 2021 at 9:36 | history | asked | Irene | CC BY-SA 4.0 |