random effects with very large variance I'm getting really very large variance for my random effects
Random effects:
 Groups   Name        Variance  Std.Dev.
 sub      (Intercept) 8.429e+07 34201   
 Residual             9.983e+09 48821   
Number of obs: 17128, groups:  sub, 497

what could be the reason for this? Does this mean that there must be something wrong with my data or model? What kind of diagnostics would you recommend I do?
 A: Your results are uncommon. Generally, we record our research data in a format with  units <10000. For example, when we get the height of a tree, we will use meters as the unit, instead of mm as the unit. If your data follow our common practice (<10000), it is hard to have large residual variance estimates. So it is possible the program is having a problem with analyzing your data.
$SST=\sum(Y_i-\bar Y)^2$ is the upper bound of that estimated variance. It is equivalent to the simplest model, i.e., a model with only an intercept. When you add the fixed effects into the model, some of SST will be explained by the fixed effects and the residual variance estimate (called SSE) should decrease. When the random effects are added, the variance is split between the residuals and the random effects. If this does not happen, something is wrong with the program, special values, etc. But it seems your situation is OK. If you think $Var(Y)$ should not be $> 9.983e+09$, then the values of Y variable in the dataset have a problem.
