Mixed Model Fit I am having a hard time understanding how lme4 is fitting my data and am unsure if I am setting up my model correctly. I have very limited experience with mixed effect models.
My data is monthly sales records of a number of salespeople in various regions. I do not have the same amount of observations for each salespersons, and most salespersons' sales by month seem to be somewhat consistent.
What I am trying to do is determine what indicates a good salesperson. My independent variables are mostly factors and remain unchanged throughout all observations of each salesperson.
I fit a model with
lmer(sales ~ factor1 + ... + factorn + (1|id), data)

This does give me a response. However, I'm not sure how to interpret the coefficients. Especially since each salesperson is getting their own intercept and with very minimal changes in the sales each month and with factors remaining constant how is it estimating the factors effect?
I have a few other things I'm uncertain about as well.
Are uneven sample sizes an issue?
Should the salespeople be nested in their regions? So instead of (1|id) I'd put (1|region/id)?
How are these parameters being estimated?
Is there a better way to evaluate which factors indicate a good salesperson?
 A: This is a lot of questions - ideally you should ask only one question per question that you post on StackExchange (including CrossValidated). However ...

Especially since each salesperson is getting their own intercept and with very minimal changes in the sales each month and with factors remaining constant how is it estimating the factors effect?

The covariates measure properties that are generalizable across salespersons and/or across time. The random effects try to capture the among-salesperson variability that is not explained by the covariates. It's not clear whether all your factors vary across subjects, across time, or both (you do say "with factors remaining constant", which suggests that all your factors are variable across subjects and constant over time). Factors that vary across time but are constant across all salespersons obviously can't give you any information about what makes a good salesperson - but they can help explain some of the variation in the data set, which makes your overall results stronger.

Are uneven sample sizes an issue?

No, except in extreme cases.

Should the salespeople be nested in their regions? So instead of (1|id) I'd put (1|region/id)?

As long as salespeople have unique IDs (i.e., there is only one "John Smith", not a "John Smith" from region 1 and another one from region 2) then you don't need to nest (see the glmm FAQ for more details); however, you might want to include region as a random effect, in which case (1|region/id) would make sense.  (More ambitiously, you could allow for the effects of some covariates to vary across regions, e.g. (factor1|region) + (1|id) ....)

How are these parameters being estimated?

That's a long story. 

Is there a better way to evaluate which factors indicate a good salesperson?

Maybe?  Hard to say.  Something simpler you could try is just to compute the average success over time of each salesperson, reducing the data set to a single observation per salesperson, then run a regular (lm) linear model ...
