Lmer output in R: Why is the variable name changing? I am running an lmer function in R as follows, and I notice that the condition variable in the output changes from "condition" to "conditionE". In the data, condition has two values, "E" and "C". Does anyone know why this is happening or how to explain this behavior? What does this mean for the result (Estimate, -0.41434), namely is the result for the condition or for the conditionE?
> var7 <- lmer(grade.Post1 ~ 1 + condition + CPre5 + (1 | teacherID), data=data)
> summary(var7)
Linear mixed model fit by REML ['lmerMod']
Formula: grade.Post1 ~ 1 + condition + CPre5 + (1 | teacherID)
   Data: data

REML criterion at convergence: 867.2

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.1345 -0.6262 -0.0416  0.6375  4.4576 

Random effects:
 Groups    Name        Variance  Std.Dev.
 teacherID (Intercept) 0.0005629 0.02372 
 Residual              2.0504858 1.43195 
Number of obs: 242, groups:  teacherID, 5

Fixed effects:
            Estimate Std. Error t value
(Intercept)  5.61535    0.13089   42.90
conditionE  -0.41434    0.18633   -2.22
CPre5        0.86071    0.03029   28.41

Correlation of Fixed Effects:
           (Intr) cndtnE
conditionE -0.706       
CPre5      -0.108  0.153

 A: To illustrate my explanation (in comments), here's an example with ANOVA example. Displaying below is the summary table (not ANOVA table) with contrast variable names.
  > fit = lm(wt ~ as.factor(cyl), data = mtcars)
  > summary(fit)

  Call:
  lm(formula = wt ~ as.factor(cyl), data = mtcars)

  Residuals:
      Min      1Q  Median      3Q     Max 
  -0.8292 -0.4346 -0.1567  0.3229  1.4248 

  Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
  (Intercept)       2.2857     0.1899  12.037 8.41e-13 ***
  as.factor(cyl)6   0.8314     0.3045   2.730   0.0106 *  
  as.factor(cyl)8   1.7135     0.2538   6.753 2.07e-07 ***
  ---
  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

  Residual standard error: 0.6298 on 29 degrees of freedom
  Multiple R-squared:  0.6124,  Adjusted R-squared:  0.5857 
  F-statistic: 22.91 on 2 and 29 DF,  p-value: 1.075e-06

  > unique(mtcars$cyl)
  [1] 6 4 8

Note that the unique factors are 4, 6, 8 in that order. R moves 4's effect to the mean, and keeps the remaining as factors. Take note of the concatenated names in the output. The R manuals explain this somewhere, unfortunately, I don't remember where.
I believe SAS does something similar as well, except with the last factor not the first.
Faraway has a nice guide for linear regression and ANOVA. Review Ch 15.2 to get an understanding of what/why R is doing in the background with categorical variables.
ftp://cran.r-project.org/pub/R/doc/contrib/Faraway-PRA.pdf
