I´ve run a GLMM model with the following variables:
Response variable: continuous (with positive, negative and zero values**;**
Explicatory variable: 1 factor with 6 levels (DB, DF, NDB, NDF, BB, BF)**;**individual as a random effect variable


After running a lmer function, and checking for assumptions, I want to perform *a posteriori* specific comparisons. How do I do that?
I want to test for example if there is any difference between DF vs. DB, how do I write that in the script in R?? 

THANKS!

**CODE IS AS FOLLOWS**
    #Model:
    m1 <- lmer(Vueltasmin ~ Condicion + (1 | Bicho), Datos)
    summary(m1)
    
    e1<-resid(m1) 
    pre1<-predict(m1) 

    windows()
    par(mfrow = c(1, 2))
    plot(pre1, e1, xlab="Predichos", ylab="Residuos de pearson",main="Gráfico de                     
    dispersión de RE vs PRED",cex.main=.8 ) 
    abline(0,0)
    qqnorm(e1, cex.main=.9)   #QQ plot
    qqline(e1)
    par(mfrow = c(1, 1))
    shapiro.test(e1)    

    #Comparisons:
    model.matrix.gls <- function(object, ...) {
      model.matrix(terms(object), data = getData(object), ...)
    }
    model.frame.gls <- function(object, ...) {
    model.frame(formula(object), data = getData(object), ...)
    }
    terms.gls <- function(object, ...) {
      terms(model.frame(object), ...)
    }


    #Comparisons desired:
    #DB-DF
    #NDB-NDF
    #BB-BF
    #DB-BB


Here I show how the data plot looks like, and the comparisons desired. 
[![enter image description here][1]][1]


  [1]: https://i.sstatic.net/niAEJ.png