high shrinkage solutions
#1
Posted 22 October 2021 - 11:30 PM
#2
Posted 23 October 2021 - 07:03 AM
Hi Amashehri,
I'm not sure if there is anything you can to decrease the shrinkage; it is a diagnostic that tells you the data does not have enough information to estimate random effects on this parameter. I think with the second compartment parameters identifiability can be an issue, since even when you remove random effects for v2, shrinkage is still a little high for CL2
I don't think considering covariates would help, but I'd be interested to hear others experiences.
#4
Posted 27 February 2022 - 02:58 AM
amashehri, 在 05 十一月 2021 - 10:49 下午, 说:谢谢西蒙的回复
How did you end up dealing with high shrinkage values? The shrinkage value of one of my parameters was not high before the addition of the covariable model, but after the addition of the covariable model, the shrinkage value of the parameter was close to 0.98. Do you have any suggestions?
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