relist() is an S3 generic function with a few methods in order
  to allow easy inversion of unlist(obj) when that is used
  with an object obj of (S3) class "relistable".
relist(flesh, skeleton)
# S3 method for default
relist(flesh, skeleton = attr(flesh, "skeleton"))
# S3 method for factor
relist(flesh, skeleton = attr(flesh, "skeleton"))
# S3 method for list
relist(flesh, skeleton = attr(flesh, "skeleton"))
# S3 method for matrix
relist(flesh, skeleton = attr(flesh, "skeleton"))as.relistable(x)
is.relistable(x)
# S3 method for relistable
unlist(x, recursive = TRUE, use.names = TRUE)
a vector to be relisted
a list, the structure of which determines the structure of the result
an R object, typically a list (or vector).
logical.  Should unlisting be applied to list
    components of x?
logical. Should names be preserved?
an object of (S3) class "relistable" (and "list").
Some functions need many parameters, which are most easily represented in
  complex structures, e.g., nested lists.  Unfortunately, many
  mathematical functions in R, including optim and
  nlm can only operate on functions whose domain is
  a vector.  R has unlist() to convert nested list
  objects into a vector representation.  relist(), its methods and
  the functionality mentioned here provide the inverse operation to convert
  vectors back to the convenient structural representation.
  This allows structured functions (such as optim()) to have simple
  mathematical interfaces.
For example, a likelihood function for a multivariate normal model needs a variance-covariance matrix and a mean vector. It would be most convenient to represent it as a list containing a vector and a matrix. A typical parameter might look like
list(mean = c(0, 1), vcov = cbind(c(1, 1), c(1, 0))).
However, optim cannot operate on functions that take
  lists as input; it only likes numeric vectors.  The solution is
  conversion. Given a function mvdnorm(x, mean, vcov, log = FALSE)
  which computes the required probability density, then
        ipar <- list(mean = c(0, 1), vcov = c bind(c(1, 1), c(1, 0)))
        initial.param <- as.relistable(ipar)ll <- function(param.vector)
        {
           param <- relist(param.vector, skeleton = ipar)
           -sum(mvdnorm(x, mean = param$mean, vcov = param$vcov,
                        log = TRUE))
        }
optim(unlist(initial.param), ll)
relist takes two parameters: skeleton and flesh.  Skeleton is a sample
  object that has the right shape but the wrong content.  flesh
  is a vector with the right content but the wrong shape.  Invoking
relist(flesh, skeleton)
will put the content of flesh on the skeleton.  You don't need to specify
  skeleton explicitly if the skeleton is stored as an attribute inside flesh.
  In particular, if flesh was created from some object obj with
  unlist(as.relistable(obj))
  then the skeleton attribute is automatically set.  (Note that this
  does not apply to the example here, as optim is creating
  a new vector to pass to ll and not its par argument.)
As long as skeleton has the right shape, it should be a precise inverse
  of unlist.  These equalities hold:
relist(unlist(x), x) == x unlist(relist(y, skeleton)) == yx <- as.relistable(x) relist(unlist(x)) == x
# NOT RUN {
 ipar <- list(mean = c(0, 1), vcov = cbind(c(1, 1), c(1, 0)))
 initial.param <- as.relistable(ipar)
 ul <- unlist(initial.param)
 relist(ul)
 stopifnot(identical(relist(ul), initial.param))
# }
Run the code above in your browser using DataLab