Error: number of levels of each grouping factor must be < number of observations

Question

Problem :

I have my data as this the ticks feed on three of the groups of treated mouse’s and compare whether my treatment has the effect on ticks the bacteria load.

When I tried to make the mixed model like below:

lmer.model01 = lmer(log10.load ~ treat + (1|mouse.id/tick.id))

My screen shows me the error as below :

Error: number of levels of each grouping factor must be < number of observations

What must have happened and how can fix this error?

Answer 1

If you have only the single measurement per tick then you must leave your explicit mouse.id:tick.id grouping variable out of your model, i.e. use the log10.load ~ treat + (1|mouse.id). Alternatively, if this is the nested design then each mouse gets one of the treatment, each tick is fed on the single mouse and multiple ticks per mice you could also follow the Murtaugh 2007 "Simplicity and complexity in ecological data analysis" Ecology 88 and simply you need to compute the average load per mouse, then you can use the simple lm(log10.load~treat,data=aggregated_data). If you are insisting on fitting your model the way it is then you can use the  control=lmerControl(check.nobs.vs.nlev="ignore"), but if you ask me then I don't advise it.