[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a1

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ 1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 1:10
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
   level # NA % NA
y lvlone    0    0

   level # NA % NA
id    id    0    0


$m0a2

Bayesian linear mixed model fitted with JointAI

Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "identity"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 1:10
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
   level # NA % NA
y lvlone    0    0

   level # NA % NA
id    id    0    0


$m0a3

Bayesian linear mixed model fitted with JointAI

Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "log"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
   level # NA % NA
y lvlone    0    0

   level # NA % NA
id    id    0    0


$m0a4

Bayesian linear mixed model fitted with JointAI

Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "inverse"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
   level # NA % NA
y lvlone    0    0

   level # NA % NA
id    id    0    0


$m0b1

Bayesian binomial mixed model fitted with JointAI

Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "logit"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
    level # NA % NA
b1 lvlone    0    0

   level # NA % NA
id    id    0    0


$m0b2

Bayesian binomial mixed model fitted with JointAI

Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "probit"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
    level # NA % NA
b1 lvlone    0    0

   level # NA % NA
id    id    0    0


$m0b3

Bayesian binomial mixed model fitted with JointAI

Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "log"), 
    n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 51:60
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
    level # NA % NA
b1 lvlone    0    0

   level # NA % NA
id    id    0    0


$m0b4

Bayesian binomial mixed model fitted with JointAI

Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "cloglog"), 
    n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 51:60
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
    level # NA % NA
b1 lvlone    0    0

   level # NA % NA
id    id    0    0


$m0c1

Bayesian Gamma mixed model fitted with JointAI

Call:
glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "inverse"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
         Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
    level # NA % NA
L1 lvlone    0    0

   level # NA % NA
id    id    0    0


$m0c2

Bayesian Gamma mixed model fitted with JointAI

Call:
glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "log"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
         Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
    level # NA % NA
L1 lvlone    0    0

   level # NA % NA
id    id    0    0


$m0d1

Bayesian poisson mixed model fitted with JointAI

Call:
glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "log"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_p1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
    level # NA % NA
p1 lvlone    0    0

   level # NA % NA
id    id    0    0


$m0d2

Bayesian poisson mixed model fitted with JointAI

Call:
glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "identity"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_p1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
    level # NA % NA
p1 lvlone    0    0

   level # NA % NA
id    id    0    0


$m0e1

Bayesian log-normal mixed model fitted with JointAI

Call:
lognormmm_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
         Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
    level # NA % NA
L1 lvlone    0    0

   level # NA % NA
id    id    0    0


$m0f1

Bayesian beta mixed model fitted with JointAI

Call:
betamm_imp(fixed = Be1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
              Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Be1_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of other parameters:
        Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
tau_Be1    0  0    0     0          0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
     level # NA % NA
Be1 lvlone    0    0

   level # NA % NA
id    id    0    0


$m1a

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ C1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
C1             0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 1:10
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
   level # NA % NA
y lvlone    0    0

   level # NA % NA
C1    id    0    0
id    id    0    0


$m1b

Bayesian binomial mixed model fitted with JointAI

Call:
glme_imp(fixed = b1 ~ C1 + (1 | id), data = longDF, family = binomial(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
C1             0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
    level # NA % NA
b1 lvlone    0    0

   level # NA % NA
C1    id    0    0
id    id    0    0


$m1c

Bayesian Gamma mixed model fitted with JointAI

Call:
glme_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, family = Gamma(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
C1             0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
         Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
    level # NA % NA
L1 lvlone    0    0

   level # NA % NA
C1    id    0    0
id    id    0    0


$m1d

Bayesian poisson mixed model fitted with JointAI

Call:
glme_imp(fixed = p1 ~ C1 + (1 | id), data = longDF, family = poisson(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
C1             0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_p1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
    level # NA % NA
p1 lvlone    0    0

   level # NA % NA
C1    id    0    0
id    id    0    0


$m1e

Bayesian log-normal mixed model fitted with JointAI

Call:
lognormmm_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
C1             0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
         Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
    level # NA % NA
L1 lvlone    0    0

   level # NA % NA
C1    id    0    0
id    id    0    0


$m1f

Bayesian beta mixed model fitted with JointAI

Call:
betamm_imp(fixed = Be1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
C1             0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
              Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Be1_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of other parameters:
        Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
tau_Be1    0  0    0     0          0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
     level # NA % NA
Be1 lvlone    0    0

   level # NA % NA
C1    id    0    0
id    id    0    0


$m2a

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ c2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 1:10
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #     %
id         id 100 100.0
lvlone lvlone 263  79.9

Number and proportion of missing values:
    level # NA % NA
y  lvlone    0  0.0
c2 lvlone   66 20.1

   level # NA % NA
id    id    0    0


$m2b

Bayesian binomial mixed model fitted with JointAI

Call:
glme_imp(fixed = b2 ~ c2 + (1 | id), data = longDF, family = binomial(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b2_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #     %
id         id 100 100.0
lvlone lvlone 189  57.4

Number and proportion of missing values:
    level # NA % NA
c2 lvlone   66 20.1
b2 lvlone   99 30.1

   level # NA % NA
id    id    0    0


$m2c

Bayesian Gamma mixed model fitted with JointAI

Call:
glme_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, family = Gamma(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
                Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1mis_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
            Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1mis    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #     %
id         id 100 100.0
lvlone lvlone 246  74.8

Number and proportion of missing values:
       level # NA  % NA
L1mis lvlone   20  6.08
c2    lvlone   66 20.06

   level # NA % NA
id    id    0    0


$m2d

Bayesian poisson mixed model fitted with JointAI

Call:
glme_imp(fixed = p2 ~ c2 + (1 | id), data = longDF, family = poisson(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_p2_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #     %
id         id 100 100.0
lvlone lvlone 142  43.2

Number and proportion of missing values:
    level # NA % NA
c2 lvlone   66 20.1
p2 lvlone  162 49.2

   level # NA % NA
id    id    0    0


$m2e

Bayesian log-normal mixed model fitted with JointAI

Call:
lognormmm_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
                Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1mis_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
            Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1mis    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #     %
id         id 100 100.0
lvlone lvlone 246  74.8

Number and proportion of missing values:
       level # NA  % NA
L1mis lvlone   20  6.08
c2    lvlone   66 20.06

   level # NA % NA
id    id    0    0


$m2f

Bayesian beta mixed model fitted with JointAI

Call:
betamm_imp(fixed = Be2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
              Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Be2_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of other parameters:
        Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
tau_Be2    0  0    0     0          0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #     %
id         id 100 100.0
lvlone lvlone 246  74.8

Number and proportion of missing values:
     level # NA  % NA
Be2 lvlone   20  6.08
c2  lvlone   66 20.06

   level # NA % NA
id    id    0    0


$m3a

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
   Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 1:10
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id  58  58
lvlone lvlone 329 100

Number and proportion of missing values:
   level # NA % NA
y lvlone    0    0

   level # NA % NA
id    id    0    0
C2    id   42   42


$m3b

Bayesian binomial mixed model fitted with JointAI

Call:
glme_imp(fixed = b2 ~ 0 + C2 + (1 | id), data = longDF, family = binomial(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
   Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b2_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #    %
id         id  58 58.0
lvlone lvlone 230 69.9

Number and proportion of missing values:
    level # NA % NA
b2 lvlone   99 30.1

   level # NA % NA
id    id    0    0
C2    id   42   42


$m3c

Bayesian Gamma mixed model fitted with JointAI

Call:
glme_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, family = Gamma(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
   Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
                Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1mis_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
            Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1mis    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #    %
id         id  58 58.0
lvlone lvlone 309 93.9

Number and proportion of missing values:
       level # NA % NA
L1mis lvlone   20 6.08

   level # NA % NA
id    id    0    0
C2    id   42   42


$m3d

Bayesian poisson mixed model fitted with JointAI

Call:
glme_imp(fixed = p2 ~ 0 + C2 + (1 | id), data = longDF, family = poisson(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
   Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_p2_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #    %
id         id  58 58.0
lvlone lvlone 167 50.8

Number and proportion of missing values:
    level # NA % NA
p2 lvlone  162 49.2

   level # NA % NA
id    id    0    0
C2    id   42   42


$m3e

Bayesian log-normal mixed model fitted with JointAI

Call:
lognormmm_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
   Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
                Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1mis_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
            Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1mis    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #    %
id         id  58 58.0
lvlone lvlone 309 93.9

Number and proportion of missing values:
       level # NA % NA
L1mis lvlone   20 6.08

   level # NA % NA
id    id    0    0
C2    id   42   42


$m3f

Bayesian beta mixed model fitted with JointAI

Call:
betamm_imp(fixed = Be2 ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
   Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
              Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Be2_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of other parameters:
        Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
tau_Be2    0  0    0     0          0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #    %
id         id  58 58.0
lvlone lvlone 309 93.9

Number and proportion of missing values:
     level # NA % NA
Be2 lvlone   20 6.08

   level # NA % NA
id    id    0    0
C2    id   42   42


$m4a

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = c1 ~ c2 + B2 + p2 + L1mis + Be2 + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, models = c(p2 = "glmm_poisson_log", 
        L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), seed = 2020, 
    warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
B21            0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN
p2             0  0    0     0          0     NaN    NaN
L1mis          0  0    0     0          0     NaN    NaN
Be2            0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_c1_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
         Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_c1    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #  %
id         id  90 90
lvlone lvlone 125 38

Number and proportion of missing values:
       level # NA  % NA
c1    lvlone    0  0.00
L1mis lvlone   20  6.08
Be2   lvlone   20  6.08
c2    lvlone   66 20.06
p2    lvlone  162 49.24

   level # NA % NA
id    id    0    0
B2    id   10   10


$m4b

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_inverse", 
        p2 = "glmm_poisson_identity", b2 = "glmm_binomial_probit", 
        L1mis = "glmm_lognorm"), seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN
b21            0  0    0     0          0     NaN    NaN
p2             0  0    0     0          0     NaN    NaN
L1mis          0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_c1_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
         Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_c1    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #     %
id         id 100 100.0
lvlone lvlone  96  29.2

Number and proportion of missing values:
       level # NA  % NA
c1    lvlone    0  0.00
L1mis lvlone   20  6.08
c2    lvlone   66 20.06
b2    lvlone   99 30.09
p2    lvlone  162 49.24

   level # NA % NA
id    id    0    0


$m4c

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log", 
        p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log", 
        b2 = "glmm_binomial_log"), no_model = "time", seed = 2020, 
    warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN
b21            0  0    0     0          0     NaN    NaN
p2             0  0    0     0          0     NaN    NaN
L1mis          0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_c1_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
         Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_c1    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #     %
id         id 100 100.0
lvlone lvlone  96  29.2

Number and proportion of missing values:
       level # NA  % NA
c1    lvlone    0  0.00
L1mis lvlone   20  6.08
c2    lvlone   66 20.06
b2    lvlone   99 30.09
p2    lvlone  162 49.24

   level # NA % NA
id    id    0    0


$m4d

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + Be2 + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log", 
        p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log", 
        b2 = "glmm_binomial_log"), shrinkage = "ridge", seed = 2020, 
    warn = FALSE, mess = FALSE, trunc = list(Be2 = c(0, 1)))


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN
b21            0  0    0     0          0     NaN    NaN
p2             0  0    0     0          0     NaN    NaN
L1mis          0  0    0     0          0     NaN    NaN
Be2            0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_c1_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
         Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_c1    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone  92  28

Number and proportion of missing values:
       level # NA  % NA
c1    lvlone    0  0.00
L1mis lvlone   20  6.08
Be2   lvlone   20  6.08
c2    lvlone   66 20.06
b2    lvlone   99 30.09
p2    lvlone  162 49.24

   level # NA % NA
id    id    0    0


$m5a

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ M2 + o2 * abs(C1 - c2) + log(C1) + time + 
    I(time^2) + (time | id), data = longDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
                 Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)         0  0    0     0          0     NaN    NaN
M22                 0  0    0     0          0     NaN    NaN
M23                 0  0    0     0          0     NaN    NaN
M24                 0  0    0     0          0     NaN    NaN
log(C1)             0  0    0     0          0     NaN    NaN
o22                 0  0    0     0          0     NaN    NaN
o23                 0  0    0     0          0     NaN    NaN
o24                 0  0    0     0          0     NaN    NaN
abs(C1 - c2)        0  0    0     0          0     NaN    NaN
time                0  0    0     0          0     NaN    NaN
I(time^2)           0  0    0     0          0     NaN    NaN
o22:abs(C1 - c2)    0  0    0     0          0     NaN    NaN
o23:abs(C1 - c2)    0  0    0     0          0     NaN    NaN
o24:abs(C1 - c2)    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #  %
id         id  56 56
lvlone lvlone 217 66

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0  0.0
time lvlone    0  0.0
o2   lvlone   59 17.9
c2   lvlone   66 20.1

   level # NA % NA
C1    id    0    0
id    id    0    0
M2    id   44   44


$m5b

Bayesian binomial mixed model fitted with JointAI

Call:
glme_imp(fixed = b1 ~ L1mis + abs(c1 - C2) + log(Be2) + time + 
    (time + I(time^2) | id), data = longDF, family = binomial(), 
    n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log", 
        L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), shrinkage = "ridge", 
    seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)     0  0    0     0          0     NaN    NaN
L1mis           0  0    0     0          0     NaN    NaN
abs(c1 - C2)    0  0    0     0          0     NaN    NaN
log(Be2)        0  0    0     0          0     NaN    NaN
time            0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1]    0  0    0     0                NaN    NaN
D_b1_id[1,2]    0  0    0     0          0     NaN    NaN
D_b1_id[2,2]    0  0    0     0                NaN    NaN
D_b1_id[1,3]    0  0    0     0          0     NaN    NaN
D_b1_id[2,3]    0  0    0     0          0     NaN    NaN
D_b1_id[3,3]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #    %
id         id  58 58.0
lvlone lvlone 291 88.4

Number and proportion of missing values:
       level # NA % NA
b1    lvlone    0 0.00
c1    lvlone    0 0.00
time  lvlone    0 0.00
L1mis lvlone   20 6.08
Be2   lvlone   20 6.08

   level # NA % NA
id    id    0    0
C2    id   42   42


$m6a

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ b2 + C1 + C2 + time + (0 + time | id), data = longDF, 
    n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, 
    warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
C1             0  0    0     0          0     NaN    NaN
C2             0  0    0     0          0     NaN    NaN
b21            0  0    0     0          0     NaN    NaN
time           0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #    %
id         id  58 58.0
lvlone lvlone 230 69.9

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0  0.0
time lvlone    0  0.0
b2   lvlone   99 30.1

   level # NA % NA
C1    id    0    0
id    id    0    0
C2    id   42   42


$m6b

Bayesian binomial mixed model fitted with JointAI

Call:
glme_imp(fixed = b1 ~ c1 + C2 + B1 + time + (0 + time + I(time^2) | 
    id), data = longDF, family = binomial(), n.adapt = 5, n.iter = 10, 
    shrinkage = "ridge", seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
C2             0  0    0     0          0     NaN    NaN
B11            0  0    0     0          0     NaN    NaN
c1             0  0    0     0          0     NaN    NaN
time           0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1]    0  0    0     0                NaN    NaN
D_b1_id[1,2]    0  0    0     0          0     NaN    NaN
D_b1_id[2,2]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id  58  58
lvlone lvlone 329 100

Number and proportion of missing values:
      level # NA % NA
b1   lvlone    0    0
c1   lvlone    0    0
time lvlone    0    0

   level # NA % NA
B1    id    0    0
id    id    0    0
C2    id   42   42


$m7a

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ ns(time, df = 2), data = longDF, random = ~ns(time, 
    df = 2) | id, n.iter = 10, seed = 2020, adapt = 5)


Posterior summary:
                  Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)          0  0    0     0          0     NaN    NaN
ns(time, df = 2)1    0  0    0     0          0     NaN    NaN
ns(time, df = 2)2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN
D_y_id[1,3]    0  0    0     0          0     NaN    NaN
D_y_id[2,3]    0  0    0     0          0     NaN    NaN
D_y_id[3,3]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 101:110
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0    0
time lvlone    0    0

   level # NA % NA
id    id    0    0


$m7b

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ bs(time, df = 3), data = longDF, random = ~bs(time, 
    df = 3) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)


Posterior summary:
                  Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)          0  0    0     0          0     NaN    NaN
bs(time, df = 3)1    0  0    0     0          0     NaN    NaN
bs(time, df = 3)2    0  0    0     0          0     NaN    NaN
bs(time, df = 3)3    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN
D_y_id[1,3]    0  0    0     0          0     NaN    NaN
D_y_id[2,3]    0  0    0     0          0     NaN    NaN
D_y_id[3,3]    0  0    0     0                NaN    NaN
D_y_id[1,4]    0  0    0     0          0     NaN    NaN
D_y_id[2,4]    0  0    0     0          0     NaN    NaN
D_y_id[3,4]    0  0    0     0          0     NaN    NaN
D_y_id[4,4]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0    0
time lvlone    0    0

   level # NA % NA
id    id    0    0


$m7c

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ C1 + c1 + ns(time, df = 3), data = longDF, 
    random = ~ns(time, df = 3) | id, n.iter = 10, seed = 2020, 
    nadapt = 5)


Posterior summary:
                  Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)          0  0    0     0          0     NaN    NaN
C1                   0  0    0     0          0     NaN    NaN
c1                   0  0    0     0          0     NaN    NaN
ns(time, df = 3)1    0  0    0     0          0     NaN    NaN
ns(time, df = 3)2    0  0    0     0          0     NaN    NaN
ns(time, df = 3)3    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN
D_y_id[1,3]    0  0    0     0          0     NaN    NaN
D_y_id[2,3]    0  0    0     0          0     NaN    NaN
D_y_id[3,3]    0  0    0     0                NaN    NaN
D_y_id[1,4]    0  0    0     0          0     NaN    NaN
D_y_id[2,4]    0  0    0     0          0     NaN    NaN
D_y_id[3,4]    0  0    0     0          0     NaN    NaN
D_y_id[4,4]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 101:110
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0    0
c1   lvlone    0    0
time lvlone    0    0

   level # NA % NA
C1    id    0    0
id    id    0    0


$m7d

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, 
    random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)


Posterior summary:
                  Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)          0  0    0     0          0     NaN    NaN
C1                   0  0    0     0          0     NaN    NaN
C2                   0  0    0     0          0     NaN    NaN
c1                   0  0    0     0          0     NaN    NaN
ns(time, df = 3)1    0  0    0     0          0     NaN    NaN
ns(time, df = 3)2    0  0    0     0          0     NaN    NaN
ns(time, df = 3)3    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id  58  58
lvlone lvlone 329 100

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0    0
c1   lvlone    0    0
time lvlone    0    0

   level # NA % NA
C1    id    0    0
id    id    0    0
C2    id   42   42


$m7e

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, 
    random = ~ns(time, df = 3) | id, n.adapt = 5, n.iter = 10, 
    no_model = "time", seed = 2020)


Posterior summary:
                  Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)          0  0    0     0          0     NaN    NaN
C1                   0  0    0     0          0     NaN    NaN
C2                   0  0    0     0          0     NaN    NaN
c1                   0  0    0     0          0     NaN    NaN
ns(time, df = 3)1    0  0    0     0          0     NaN    NaN
ns(time, df = 3)2    0  0    0     0          0     NaN    NaN
ns(time, df = 3)3    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN
D_y_id[1,3]    0  0    0     0          0     NaN    NaN
D_y_id[2,3]    0  0    0     0          0     NaN    NaN
D_y_id[3,3]    0  0    0     0                NaN    NaN
D_y_id[1,4]    0  0    0     0          0     NaN    NaN
D_y_id[2,4]    0  0    0     0          0     NaN    NaN
D_y_id[3,4]    0  0    0     0          0     NaN    NaN
D_y_id[4,4]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id  58  58
lvlone lvlone 329 100

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0    0
c1   lvlone    0    0
time lvlone    0    0

   level # NA % NA
C1    id    0    0
id    id    0    0
C2    id   42   42


$m7f

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, 
    random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)


Posterior summary:
                  Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)          0  0    0     0          0     NaN    NaN
C1                   0  0    0     0          0     NaN    NaN
C2                   0  0    0     0          0     NaN    NaN
c1                   0  0    0     0          0     NaN    NaN
ns(time, df = 3)1    0  0    0     0          0     NaN    NaN
ns(time, df = 3)2    0  0    0     0          0     NaN    NaN
ns(time, df = 3)3    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id  58  58
lvlone lvlone 329 100

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0    0
c1   lvlone    0    0
time lvlone    0    0

   level # NA % NA
C1    id    0    0
id    id    0    0
C2    id   42   42


$m8a

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time + 
    c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, 
    warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
c1             0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN
time           0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN
D_y_id[1,3]    0  0    0     0          0     NaN    NaN
D_y_id[2,3]    0  0    0     0          0     NaN    NaN
D_y_id[3,3]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #     %
id         id 100 100.0
lvlone lvlone 263  79.9

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0  0.0
c1   lvlone    0  0.0
time lvlone    0  0.0
c2   lvlone   66 20.1

   level # NA % NA
id    id    0    0


$m8b

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time + 
    c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
c1             0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN
time           0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN
D_y_id[1,3]    0  0    0     0          0     NaN    NaN
D_y_id[2,3]    0  0    0     0          0     NaN    NaN
D_y_id[3,3]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #     %
id         id 100 100.0
lvlone lvlone 263  79.9

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0  0.0
c1   lvlone    0  0.0
time lvlone    0  0.0
c2   lvlone   66 20.1

   level # NA % NA
id    id    0    0


$m8c

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time + 
    c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, 
    warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
B21            0  0    0     0          0     NaN    NaN
c1             0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN
time           0  0    0     0          0     NaN    NaN
B21:c1         0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN
D_y_id[1,3]    0  0    0     0          0     NaN    NaN
D_y_id[2,3]    0  0    0     0          0     NaN    NaN
D_y_id[3,3]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #    %
id         id  90 90.0
lvlone lvlone 263 79.9

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0  0.0
c1   lvlone    0  0.0
time lvlone    0  0.0
c2   lvlone   66 20.1

   level # NA % NA
id    id    0    0
B2    id   10   10


$m8d

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time + 
    c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
B21            0  0    0     0          0     NaN    NaN
c1             0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN
time           0  0    0     0          0     NaN    NaN
B21:c1         0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN
D_y_id[1,3]    0  0    0     0          0     NaN    NaN
D_y_id[2,3]    0  0    0     0          0     NaN    NaN
D_y_id[3,3]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #    %
id         id  90 90.0
lvlone lvlone 263 79.9

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0  0.0
c1   lvlone    0  0.0
time lvlone    0  0.0
c2   lvlone   66 20.1

   level # NA % NA
id    id    0    0
B2    id   10   10


$m8e

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, 
    random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
C1             0  0    0     0          0     NaN    NaN
B21            0  0    0     0          0     NaN    NaN
c1             0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN
time           0  0    0     0          0     NaN    NaN
B21:c1         0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN
D_y_id[1,3]    0  0    0     0          0     NaN    NaN
D_y_id[2,3]    0  0    0     0          0     NaN    NaN
D_y_id[3,3]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #    %
id         id  90 90.0
lvlone lvlone 263 79.9

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0  0.0
c1   lvlone    0  0.0
time lvlone    0  0.0
c2   lvlone   66 20.1

   level # NA % NA
C1    id    0    0
id    id    0    0
B2    id   10   10


$m8f

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, 
    random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", 
    seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
C1             0  0    0     0          0     NaN    NaN
B21            0  0    0     0          0     NaN    NaN
c1             0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN
time           0  0    0     0          0     NaN    NaN
B21:c1         0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN
D_y_id[1,3]    0  0    0     0          0     NaN    NaN
D_y_id[2,3]    0  0    0     0          0     NaN    NaN
D_y_id[3,3]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #    %
id         id  90 90.0
lvlone lvlone 263 79.9

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0  0.0
c1   lvlone    0  0.0
time lvlone    0  0.0
c2   lvlone   66 20.1

   level # NA % NA
C1    id    0    0
id    id    0    0
B2    id   10   10


$m8g

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, 
    random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = c("time", 
        "c1"), seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
C1             0  0    0     0          0     NaN    NaN
B21            0  0    0     0          0     NaN    NaN
c1             0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN
time           0  0    0     0          0     NaN    NaN
B21:c1         0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN
D_y_id[1,3]    0  0    0     0          0     NaN    NaN
D_y_id[2,3]    0  0    0     0          0     NaN    NaN
D_y_id[3,3]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #    %
id         id  90 90.0
lvlone lvlone 263 79.9

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0  0.0
c1   lvlone    0  0.0
time lvlone    0  0.0
c2   lvlone   66 20.1

   level # NA % NA
C1    id    0    0
id    id    0    0
B2    id   10   10


$m8h

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, 
    random = ~time + c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
C1             0  0    0     0          0     NaN    NaN
B21            0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN
c1             0  0    0     0          0     NaN    NaN
time           0  0    0     0          0     NaN    NaN
B21:c2         0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN
D_y_id[1,3]    0  0    0     0          0     NaN    NaN
D_y_id[2,3]    0  0    0     0          0     NaN    NaN
D_y_id[3,3]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #    %
id         id  90 90.0
lvlone lvlone 263 79.9

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0  0.0
c1   lvlone    0  0.0
time lvlone    0  0.0
c2   lvlone   66 20.1

   level # NA % NA
C1    id    0    0
id    id    0    0
B2    id   10   10


$m8i

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, 
    random = ~time + c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", 
    seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
C1             0  0    0     0          0     NaN    NaN
B21            0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN
c1             0  0    0     0          0     NaN    NaN
time           0  0    0     0          0     NaN    NaN
B21:c2         0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN
D_y_id[1,3]    0  0    0     0          0     NaN    NaN
D_y_id[2,3]    0  0    0     0          0     NaN    NaN
D_y_id[3,3]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #    %
id         id  90 90.0
lvlone lvlone 263 79.9

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0  0.0
c1   lvlone    0  0.0
time lvlone    0  0.0
c2   lvlone   66 20.1

   level # NA % NA
C1    id    0    0
id    id    0    0
B2    id   10   10


$m8j

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, 
    random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
C1             0  0    0     0          0     NaN    NaN
B21            0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN
c1             0  0    0     0          0     NaN    NaN
time           0  0    0     0          0     NaN    NaN
B21:c2         0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN
D_y_id[1,3]    0  0    0     0          0     NaN    NaN
D_y_id[2,3]    0  0    0     0          0     NaN    NaN
D_y_id[3,3]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #    %
id         id  90 90.0
lvlone lvlone 263 79.9

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0  0.0
c1   lvlone    0  0.0
time lvlone    0  0.0
c2   lvlone   66 20.1

   level # NA % NA
C1    id    0    0
id    id    0    0
B2    id   10   10


$m8k

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, 
    random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
C1             0  0    0     0          0     NaN    NaN
B21            0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN
c1             0  0    0     0          0     NaN    NaN
time           0  0    0     0          0     NaN    NaN
B21:c2         0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN
D_y_id[1,3]    0  0    0     0          0     NaN    NaN
D_y_id[2,3]    0  0    0     0          0     NaN    NaN
D_y_id[3,3]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #    %
id         id  90 90.0
lvlone lvlone 263 79.9

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0  0.0
c1   lvlone    0  0.0
time lvlone    0  0.0
c2   lvlone   66 20.1

   level # NA % NA
C1    id    0    0
id    id    0    0
B2    id   10   10


$m8l

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ C1 + B2 * c1 * time, data = longDF, random = ~time + 
    I(time^2) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
C1             0  0    0     0          0     NaN    NaN
B21            0  0    0     0          0     NaN    NaN
c1             0  0    0     0          0     NaN    NaN
time           0  0    0     0          0     NaN    NaN
B21:c1         0  0    0     0          0     NaN    NaN
B21:time       0  0    0     0          0     NaN    NaN
c1:time        0  0    0     0          0     NaN    NaN
B21:c1:time    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN
D_y_id[1,3]    0  0    0     0          0     NaN    NaN
D_y_id[2,3]    0  0    0     0          0     NaN    NaN
D_y_id[3,3]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id  90  90
lvlone lvlone 329 100

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0    0
c1   lvlone    0    0
time lvlone    0    0

   level # NA % NA
C1    id    0    0
id    id    0    0
B2    id   10   10


$m8m

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ c1 * b1 + o1, data = longDF, random = ~b1 | 
    id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
c1             0  0    0     0          0     NaN    NaN
b11            0  0    0     0          0     NaN    NaN
o1.L           0  0    0     0          0     NaN    NaN
o1.Q           0  0    0     0          0     NaN    NaN
c1:b11         0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id 100 100
lvlone lvlone 329 100

Number and proportion of missing values:
    level # NA % NA
y  lvlone    0    0
c1 lvlone    0    0
b1 lvlone    0    0
o1 lvlone    0    0

   level # NA % NA
id    id    0    0


$m8n

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ c1 + C1 * time + b1 + B2, data = longDF, 
    random = ~C1 * time | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
C1             0  0    0     0          0     NaN    NaN
B21            0  0    0     0          0     NaN    NaN
c1             0  0    0     0          0     NaN    NaN
time           0  0    0     0          0     NaN    NaN
b11            0  0    0     0          0     NaN    NaN
C1:time        0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN
D_y_id[1,3]    0  0    0     0          0     NaN    NaN
D_y_id[2,3]    0  0    0     0          0     NaN    NaN
D_y_id[3,3]    0  0    0     0                NaN    NaN
D_y_id[1,4]    0  0    0     0          0     NaN    NaN
D_y_id[2,4]    0  0    0     0          0     NaN    NaN
D_y_id[3,4]    0  0    0     0          0     NaN    NaN
D_y_id[4,4]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id  90  90
lvlone lvlone 329 100

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0    0
c1   lvlone    0    0
time lvlone    0    0
b1   lvlone    0    0

   level # NA % NA
C1    id    0    0
id    id    0    0
B2    id   10   10


$m9a

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ c1 + b1 + time + (1 | id) + (1 | o1), data = longDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
c1             0  0    0     0          0     NaN    NaN
b11            0  0    0     0          0     NaN    NaN
time           0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:

* For level "id":
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN

* For level "o1":
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_o1[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 1:10
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100
 - o1: 3


Number and proportion of complete cases:
        level   #   %
id         id 100 100
o1         o1   3 100
lvlone lvlone 329 100

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0    0
c1   lvlone    0    0
b1   lvlone    0    0
time lvlone    0    0

   level # NA % NA
id    id    0    0

   level # NA % NA
o1    o1    0    0


$m9b

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ C1 + C2 + B1 + time + (time | id), data = longDF, 
    n.adapt = 5, n.iter = 10, monitor_params = c(analysis_random = TRUE), 
    seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
C1             0  0    0     0          0     NaN    NaN
C2             0  0    0     0          0     NaN    NaN
B11            0  0    0     0          0     NaN    NaN
time           0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN
D_y_id[1,2]    0  0    0     0          0     NaN    NaN
D_y_id[2,2]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id  58  58
lvlone lvlone 329 100

Number and proportion of missing values:
      level # NA % NA
y    lvlone    0    0
time lvlone    0    0

   level # NA % NA
C1    id    0    0
B1    id    0    0
id    id    0    0
C2    id   42   42


$m9c

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ C1 + C2 + B1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, monitor_params = c(analysis_random = TRUE), 
    seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
C1             0  0    0     0          0     NaN    NaN
C2             0  0    0     0          0     NaN    NaN
B11            0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 1:10
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100


Number and proportion of complete cases:
        level   #   %
id         id  58  58
lvlone lvlone 329 100

Number and proportion of missing values:
   level # NA % NA
y lvlone    0    0

   level # NA % NA
C1    id    0    0
B1    id    0    0
id    id    0    0
C2    id   42   42


