Can't find p-values in the output from lmer() in the lm4 package in R I'm a new user of mixed models and through the material I've been reading there are always probability values (p>t) or (p>z) that estimate the importance of a level of a factor in the model. However, when using the lmer() function in R, which supposedly gives you those probabilities, I simply don't find them. Here is the output: 
Linear mixed model fit by REML 
Formula: Temp ~ depth + (1 | locality) 
   Data: qminmatrix 
   AIC   BIC logLik deviance REMLdev
 561.3 581.3 -273.7    551.5   547.3
Random effects:
 Groups   Name        Variance Std.Dev.
 locality (Intercept) 4.7998   2.1909  
 Residual             4.0433   2.0108  
Number of obs: 128, groups: locality, 4

Fixed effects:
            Estimate Std. Error t value
(Intercept)  22.0103     1.1500  19.140
depth1        1.9564     0.6832   2.864
depth10       2.6624     0.5756   4.625
depth5        3.0209     0.4932   6.125
depthWS      -2.2585     0.5444  -4.149

Correlation of Fixed Effects:
        (Intr) depth1 dpth10 depth5
depth1  -0.157                     
depth10 -0.175  0.189              
depth5  -0.213  0.313  0.458       
depthWS -0.191  0.334  0.373  0.441

 A: use pvals.fnc() function the pMCMC here works like p-value which should be less than 0.05 to reject the null hypothesis.
A: The lmer package's author made a conscious choice not to create p-values for the fixed effects. Some packages do, but he feels that they are doing simplistic calculations that are misleading. (Many statisticians feel that there's a p-value obsession that causes confusion in and of itself, but that's a separate matter.)
He addresses the question in: this post. I believe the summary paragraph is:

Most of the research on tests for the fixed-effects specification in a
  mixed model begin with the assumption that these statistics will have
  an F distribution with a known numerator degrees of freedom and the
  only purpose of the research is to decide how to obtain an approximate
  denominator degrees of freedom.  I don't agree.

I don't understand the issue well enough to paraphrase it.
A: Install the coda and languageR package and run the pvals.fnc as follows for p-value:
Model.pval<-pvals.fnc(Model, nsim = n, withMCMC = TRUE)
Note that this will not work for level 3 or above in nested random effects models.
