Timeline for How to use ordinal logistic regression with random effects?
Current License: CC BY-SA 4.0
18 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 7, 2019 at 3:56 | history | edited | Ben Bolker | CC BY-SA 4.0 | small typo |
| S Feb 6, 2018 at 15:30 | history | suggested | Qaswed | CC BY-SA 3.0 | changed "random factors" to "random effects" for consistency as discussed in the comments by a user and the OP |
| Feb 6, 2018 at 12:56 | review | Suggested edits | |||
| S Feb 6, 2018 at 15:30 | |||||
| May 9, 2017 at 16:26 | comment | added | Weiwen Ng | @RobinKramer My bad, I failed to note the date! That said, I still think there is some confusion here. Do you have repeated measures on the individuals? If so, then you should probably include a random intercept by person. If you're interested in the effect of gender on the DV, then you would probably only need to model it as a normal covariate. Some would say model it as a fixed effect (because you're treating its effect on the DV as fixed). Treating gender as a random effect would really be ontologically confusing. | |
| May 9, 2017 at 15:55 | comment | added | Robin Kramer-ten Have | @WeiwenNg the question is rather old, but I was used to use LME regressions in which I placed variables, in which I was not interested (but did have an effect on the DV), as random effects. I attempted to do the same with this project. | |
| May 9, 2017 at 15:24 | comment | added | Weiwen Ng | @RobinKramer Please clarify what you think you mean by random effects. When statisticians say random effects, they usually want to account for clustering among different observations. For example, say you had repeated measures on the same individuals, so each obs is one person at a certain time, and you had 4 observations per person. You arguably should fit a random effects model; each person has a person-specific random effect (usually assumed to be from a normal distribution). When you say gender, smoking, etc, those can usually be modeled as fixed effects. So, what do you mean? | |
| May 9, 2017 at 14:52 | history | edited | mdewey | Replaced ordinal with ordered-logit | |
| Apr 13, 2017 at 12:44 | history | edited | CommunityBot | replaced http://stats.stackexchange.com/ with https://stats.stackexchange.com/ | |
| Nov 22, 2016 at 13:15 | vote | accept | Robin Kramer-ten Have | ||
| Oct 5, 2016 at 21:37 | answer | added | Ben Bolker | timeline score: 49 | |
| Oct 5, 2016 at 15:45 | answer | added | gung - Reinstate Monica | timeline score: 7 | |
| Oct 5, 2016 at 12:54 | comment | added | mdewey | You are not obliged to use proportional odds for this sort of outcome, you can use continuation ratio models and others. You could investigate the ordinal package available from CRAN. | |
| Oct 5, 2016 at 12:37 | history | edited | Robin Kramer-ten Have | CC BY-SA 3.0 | edited title |
| S Oct 5, 2016 at 12:29 | history | edited | Robin Kramer-ten Have | CC BY-SA 3.0 | changed "random factor" to "random effect" |
| S Oct 5, 2016 at 12:29 | history | suggested | Ferdi | CC BY-SA 3.0 | Reformating. Marking code as code |
| Oct 5, 2016 at 12:21 | review | Suggested edits | |||
| S Oct 5, 2016 at 12:29 | |||||
| Oct 5, 2016 at 12:08 | review | First posts | |||
| Oct 5, 2016 at 12:19 | |||||
| Oct 5, 2016 at 12:03 | history | asked | Robin Kramer-ten Have | CC BY-SA 3.0 |