Plotting and interpreting fixed effects using lmer I want to understand more about the values of fixed effects 
drawn using sjp.glmer function
  library(lme4)
  library(sjPlot)
  data("sleepstudy")


  fm1 <- lmer(Reaction ~ Days + (1 | Subject), sleepstudy)
  summary(fm1)
  sjp.glmer(fm1,type = "fe", sort = TRUE)


Question 1: What is this value of 35146.70 that is being shown on the plot?
It is nowhere present in the model summary of fm1
Second case:    
  fm2 <- lmer(log(Reaction) ~ Days + (1 | Subject), sleepstudy)
  summary(fm2)
  sjp.glmer(fm2,type = "fe", sort = TRUE)

  Fixed effects:
    Estimate Std. Error t value
  (Intercept) 5.530065   0.033060  167.27
  Days        0.033668   0.002521   13.36

Question 2: Is my interpretation correct: change in one unit in Days change
the Reaction by 0.03 units on log scale? 
Question 3: Also why the value again is different shown in the plot then in the model summary.

Interestingly, if I log the value 
      log(1.03)
      0.0295588

It matches the estimate shown in the mdoel summary of fm2
 A: The problem here is that the function you are using, `sjp.glmer" is for generalized linear mixed models. If you give the function such a model, you will get a sensible plot:
require(lme4)
require(sjPlot)
data("cbpp")
m1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp)
summary(m1)
sjp.glmer(m1,type = "fe", sort = TRUE)

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -1.3983     0.2312  -6.048 1.47e-09 ***
period2      -0.9919     0.3032  -3.272 0.001068 ** 
period3      -1.1282     0.3228  -3.495 0.000474 ***
period4      -1.5797     0.4220  -3.743 0.000182 ***


Note that each point on the plot corresponds to the odds ratio of each level of the fixed effect period relative to period=1. The odds ratios is simply the exponentials of the regression coefficients.For example:
> exp(-0.9919)
[1] 0.3708714

So when you use sjp.glmer, the function thinks you are giving it a generalized linear model, where the regression coefficients are on the log-odds scale (hence the need to expontiate them to get the odds ratios), but if you actually pass a linear model to it, it will just exponentiate the estimate anyway.
So to answer your first question:
35146.70 comes from:
> exp(10.4673)
[1] 35147.19

Question 2: Your interpretation is correct
Question 3: This is the same as question 1. The function is simply doing:
> exp(0.033668)
[1] 1.034241

Finally, using the correct functionin your first example:
sjp.lmer(fm1,type = "fe", sort = TRUE


