The DRAFT r-sig-mixed-models FAQ details (in the "Predictions and/or confidence (or prediction) intervals on predictions" section) how to obtain predictions and confidence intervals for cells in the design of a mixed effects model. The ezPredict() function in the ez package wraps the code for the lme4 case (well, obtaining predictions and variances, leaving the user to decide their own CI).
I had to think hard about what you were asking. At first I thought along the lines of @user11852, that you were wanting every observation to have its own unique random effect. That would make the model hopelessly unidentified, as there would be no conceivable way to distinguish random effect variation from the model error.
But I believe that in the scope of your intended question, all random effects are actually continuous, and probably normally distributed. However, your allusion to "categorical" is not off the wall, because the design matrix for a random intercept (typically called Z) would look like a design matrix for a categorical variable.
Let's add a bit of concreteness and say that the linear predictor is $$(\bar{\alpha} + \alpha_i) + (\bar{\beta} + \beta_i) x_{ij},$$ where $\bar{\alpha}$ and $\bar{\beta}$ are the fixed effects and $\alpha_i$ and $\beta_i$ are the $i$-specific random effects. I think that by "continuous," you mean a random effect like $\beta_i$ rather than $\alpha_i$. Note that both of these are still constant within a subject $i$.
Now let's think of your proposed situation:
different levels of the fixed effect came from far ends of the random effect continuum
If we consider $\bar{\beta}$ to be the fixed effect, then it couldn't have different levels, but $x_{ij}$ could. Let's assume that for small values of $x_{ij}$, the slope is smaller; $\beta_i$ is negative for subjects $i$ with mostly small values of $x_{ij}$. Now by construction, the extremes of the $x_{ij}$ correspond to the extremes in $\beta_i$.
That leaves us with what happens with vs without the random effect. My thoughts are, if there were only a few extreme cases of the situation above, adding a random effect would tend to pull the estimate of $\beta$ upwards. But I'm not totally sure. In traditional linear mixed modeling, the estimates of the fixed effects are really just weighted least squares estimates. While those weights are directly related to the random effects distribution, their impact will diminish as your sample size increases. In a realistic setting with even moderate sample sizes, I wouldn't expect anything too extreme to happen to your fixed effect estimates when you add in a random effect.
Best Answer
Two ways are apparently possible: Function
MCMCglmm()in packageMCMCglmmaccepts both a (var-covar) matrix as well as a phylogenetic tree as an argument and functionlmekin()in packagecoxmealso accepts a var-covar matrix.