Timeline for Time Series of Wound Healing percentages / proportions
Current License: CC BY-SA 4.0
10 events
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| Feb 1, 2019 at 13:59 | comment | added | mb5572 | @ Stephan Note.. The back transformed fitted values look almost identical to the original so I suppose I can back-transform with no distortion. My question is what does that 51.40% value actually mean in this context? | |
| Feb 1, 2019 at 13:54 | comment | added | mb5572 |
@Stephan Last question re interpretation. My fixed effects (minimum adequate) output gives an estimate of 0.05610 for Days. When I convert this back into a percentage from the logit transform it equals: 51.40213. Is this a useful/useable value? Can I use it in my write up as I don't have a unit increase in days or has the transform distorted my data?
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| Jan 30, 2019 at 23:57 | comment | added | Stefan | @FreyaWomersley Yes that should be fine as long as the residuals in your diagnostic plots don't show any patterns that would violate the assumptions of linear modeling. Two useful papers are here and here. Also you have to be careful now with interpretation since you data is transformed, see here: stats.stackexchange.com/questions/19069/… | |
| Jan 30, 2019 at 18:13 | comment | added | mb5572 | @Stephen. I cant seem to get along with beta regression. So I went for an LME with the observed value 'Logit' transformed. This actually seems ok. Do you think this is an appropriate choice in your experience? | |
| Jan 28, 2019 at 17:25 | comment | added | Stefan |
No need to thank me @FreyaWomersley ! If this answer is useful consider upvoting/accepting it. Since you have Injury and Shark ID as random intercepts, the varying number of days should be accounted for by the model. You could also try and model random slopes for Severity, i.e. + (Severity | Shark ID / Injury). But again for all those possibilities have a look at the GLMM FAQ linked above. Unfortunately, I won't have the time available to look more closely into your analysis, although it seems like an interesting study!
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| Jan 28, 2019 at 15:25 | comment | added | mb5572 |
Thank you for your reply Stefan, very much appreciated. I will take the steps you suggested. Injury is a running number, so I will stick with the original code. One point that just occurred to me is about the reliance of the response on the number of days. The response alone does not mean anything unless coupled with the number of days that it took to reach that given percentage. When I want to explore the effects of Severity etc., will the model take this into account? Does that make sense? Happy to send over a .csv if you're interested in exploring this in detail. Thanks again.
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| Jan 28, 2019 at 14:06 | history | edited | Stefan | CC BY-SA 4.0 |
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| Jan 28, 2019 at 1:31 | history | edited | Stefan | CC BY-SA 4.0 |
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| Jan 28, 2019 at 1:23 | history | edited | Stefan | CC BY-SA 4.0 |
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| Jan 28, 2019 at 1:16 | history | answered | Stefan | CC BY-SA 4.0 |