Timeline for Transforming the independent variables is NOT improving fit for conditional logistic regression
Current License: CC BY-SA 3.0
5 events
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| Mar 30, 2017 at 19:58 | comment | added | Michael Grogan | Thank you Matthew, I misunderstood the question somewhat. To your point, since a logistic regression is not looking at distribution of independent variables as a factor - as you stated, transforming variables in order to improve fit is likely not the best way to go about this. | |
| Mar 30, 2017 at 19:39 | comment | added | Matthew Drury | But a logistic regression does not care about the distribution of the independent variables, there is no model fit reason to transform one to normality, or anything else. What is important in the conditional distribution of $Y \mid X$. | |
| Mar 30, 2017 at 19:07 | comment | added | Michael Grogan | @Scortchi The distribution of the independent variables in the study. e.g. if the OP is trying to normalise a variable that already follows a normal distribution, then clearly the results will be inaccurate. Therefore, one needs to know the distribution of the variable before attempting to modify the same. | |
| Mar 30, 2017 at 19:03 | comment | added | Scortchi♦ | What exactly are you checking the distribution of, & why, in (1)? | |
| Mar 30, 2017 at 18:39 | history | answered | Michael Grogan | CC BY-SA 3.0 |