Timeline for Is the linear mixed-effects model the right choice for analysing my data?
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| Apr 23, 2021 at 0:55 | comment | added | jérémy Gelb | Well, your model has 7 parameters to estimate (one intercept, 4 coefficients, one population variance, and one variance for the random effect), so you would need at least around 140 - 210 (7*20 - 7*30) observations to be at least a bit confident with the results (or even for the model to converge) according to this rule of thumbs. Moreover, you use a mixed model which requires generally even more observations. If you can not gather more data, you will have to use a less complicated model. | |
| Apr 23, 2021 at 0:21 | comment | added | Ala Czesik | Hello Jeremy, thank you for your reply! Yes, I believe that your understanding sounds exactly right. Do I understand correctly that since I have 52 observations it does fulfill the rule as that's above 20-30? However, since the difference between the measurements is relatively small or virtually none (as almost all recorded were: 0.3, 0.4 and 0.5 mm), it means that there is not enough precision and that dataset is also not big enough to fit it into such model? | |
| Apr 22, 2021 at 23:56 | history | answered | jérémy Gelb | CC BY-SA 4.0 |