Timeline for Calculating ICC for random-effects logistic regression
Current License: CC BY-SA 3.0
16 events
| when toggle format | what | by | license | comment | |
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| Dec 19, 2017 at 21:27 | comment | added | John Flournoy | @Randel, I'm tempted to use the variant you (err, Zeger et al) suggest, but the paper is somewhat over my head. Can you point me to any more material about this? | |
| Jul 21, 2017 at 17:15 | vote | accept | Megan | ||
| Jun 10, 2017 at 5:46 | history | protected | CommunityBot | ||
| Dec 15, 2016 at 9:07 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Nov 14, 2016 at 14:19 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Oct 5, 2016 at 18:59 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Sep 5, 2016 at 12:45 | answer | added | Daniel | timeline score: 9 | |
| Jul 20, 2015 at 19:31 | comment | added | Wolfgang |
@Megan: It is intercept_variance / (intercept_variance + pi^2/3) -- so don't square the variance.
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| Apr 10, 2014 at 17:07 | history | tweeted | twitter.com/#!/StackStats/status/454304591786831872 | ||
| Jun 27, 2013 at 23:50 | comment | added | Randel | @Megan: You are right. In practice, Zeger et al. (1988) suggests $(15/16)^2\pi^2/3$ works better than $\pi^2/3$ as residual variance for logistic regression models, though the two are very close. See S. L. Zeger, K. Y. Liang, and P. S. Albert. Models for longitudinal data: a generalized estimating equation approach. Biometrics, 44: 1049-1060 1988. | |
| Jun 27, 2013 at 20:13 | comment | added | AdamO | You're using the full maximum likelihood approach. Can't you do a likelihood ratio test with 1 degree of freedom against the fixed effects model? | |
| Jun 27, 2013 at 19:15 | comment | added | Megan | In order to test the assumption that ordinary logistic regression is not valid for these data, as evidence that I should be using GLMM. I found an equation: ICClogit=intercept variance^2/(intercept variance^2+pi^2/3). Does this seem reasonable? | |
| Jun 27, 2013 at 19:08 | comment | added | AdamO | Why are you calculating the ICC? | |
| Jun 27, 2013 at 19:07 | history | edited | gung - Reinstate Monica | CC BY-SA 3.0 |
added tag; light editing & formatting
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| Jun 27, 2013 at 18:55 | review | First posts | |||
| Jun 27, 2013 at 19:08 | |||||
| Jun 27, 2013 at 18:38 | history | asked | Megan | CC BY-SA 3.0 |