I had a great lunch companion today, a sleepy wood mouse.
Phone photo, obviously, because I don't carry a real camera when cycling. As an aside, it was great to see so many people of all ages out cycling today.
This is gross. NZ Food Safety say Eastgate Countdown did NOT dispose of the deli food where a mouse was seen running riot.
“Our investigations into the Christchurch incident are continuing, however it appears proper procedures were not followed immediately after the mouse was spotted.”
“We would expect the food in the deli counter to have been disposed of immediately.”
What model #statistics should one report after using multiple #imputation and #multilevel regressions, and how are they obtained? I'm using the #mice package in #rstats, and #lme4 on each imputed dataset. When pooling results, summary() yields what I need for each model term, but nothing for the whole model. If I didn't impute but deleted listwise, I would normally report AIC, BIC, Loglik. These are all in the mipo object, for each result for each imputed dataset, but they're not pooled. I'm sure I'm missing something here. Does anyone know an example article where such results are presented neatly?
Fedizens! Now in this darkest time of the year it is the time I urge you to give any keyboards, mice, and other input devices you have laying around a bit of a clean if you feel they need it. We use them every day to communicate with the wider world and we deserve to have the nicest user experience we can give ourselves and that includes nice clean boards and mice :)