From the right hand side of a formula for a mixed-effects model, expand terms with the double vertical bar operator into separate, independent random effect terms.

expandDoubleVerts(term)

Arguments

term

a mixed-model formula

Value

the modified term

Note

Because || works at the level of formula parsing, it has no way of knowing whether a variable is a factor. It just takes the terms within a random-effects term and literally splits them into the intercept and separate no-intercept terms, e.g. (1+x+y|f) would be split into (1|f) + (0+x|f) + (0+y|f). However, || will fail to break up factors into separate terms; the dummy function can be useful in this case, although it is not as convenient as ||.

Examples

  m <- ~ x + (x || g)
  expandDoubleVerts(m)
#> ~x + ((1 | g) + (0 + x | g))
#> <environment: 0x55a2f90f25a8>
  set.seed(101)
  dd <- expand.grid(f=factor(letters[1:3]),g=factor(1:200),rep=1:3)
  dd$y <- simulate(~f + (1|g) + (0+dummy(f,"b")|g) + (0+dummy(f,"c")|g),
          newdata=dd,
          newparams=list(beta=rep(0,3),
                         theta=c(1,2,1),
                         sigma=1),
          family=gaussian)[[1]]
  m1 <- lmer(y~f+(f|g),data=dd)
  VarCorr(m1)
#>  Groups   Name        Std.Dev. Corr         
#>  g        (Intercept) 0.95687               
#>           fb          1.97293   0.106       
#>           fc          0.96425   0.109 -0.086
#>  Residual             1.02172               
  m2 <- lmer(y~f+(1|g) + (0+dummy(f,"b")|g) + (0+dummy(f,"c")|g),
               data=dd)
  VarCorr(m2)
#>  Groups   Name          Std.Dev.
#>  g        (Intercept)   0.98657 
#>  g.1      dummy(f, "b") 2.00636 
#>  g.2      dummy(f, "c") 0.99616 
#>  Residual               1.01771