Timeline for Scaled Odds Ratio and Interpretation
Current License: CC BY-SA 3.0
9 events
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| Dec 10, 2015 at 17:08 | comment | added | Frank Harrell |
If you use the R rms package lrm and summary.rms functions you get this quite easily without recoding variables. The functions default to giving you very useful interquartile-range odds ratios.
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| Feb 12, 2015 at 11:06 | comment | added | Buck2079 | Great! Thank you for your input! Due to model convergence issues on another modelling procedure, I need to re-scale my variables (or standardize by subtracting the mean and dividing by the standard deviation. How would you interpret the odds ratios on scaled data like this? | |
| Feb 12, 2015 at 8:27 | comment | added | Maarten Buis | thanks, I corrected the answer. It won't affect the model fit at all. The two models are mathematically equivalent, as it just involves a linear transformation of variables. | |
| Feb 12, 2015 at 8:25 | history | edited | Maarten Buis | CC BY-SA 3.0 |
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| Feb 11, 2015 at 16:09 | comment | added | Buck2079 | I assume you meant roads_eucl / 100 and not roads_eucl / 1000? Would this affect the model fit at all if a number of your road_eucl distances range from 0-200-meters? Thanks! | |
| Feb 11, 2015 at 8:31 | comment | added | Maarten Buis | I updated the answer to answer that question too. | |
| Feb 11, 2015 at 8:29 | history | edited | Maarten Buis | CC BY-SA 3.0 |
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| Feb 10, 2015 at 15:47 | comment | added | Buck2079 | ...is there a way to calculate scaled odds ratios in R rather than by hand? I would like to also include a confidence interval for the scaled odds ratio. Thanks again for the help! Current R code: #odds ratios and 95% CI exp(cbind(OR = coef(glm.catfinal), confint(glm.catfinal))) | |
| Feb 10, 2015 at 14:52 | history | answered | Maarten Buis | CC BY-SA 3.0 |