Objective functions giving the likelihoods computed by
likeLTD.
These functions do not exist per se. They are created by
create.likelihood and similar function.
Details
The general workflow of likeLTD will generally result in the creation
and maximization of one or more objective function. These functions will take
the arguments listed above. They can also be accept any number of other
arguments. This makes it easy both to extend likeLTD and to use
objective functions with penalty functions.
Some of the arguments are verified for size and/or value.
locusAdjustmentA scalar (single locus objective) or a list of values
giving the locus adjustment to dropout for each locus. Must be
positive.
powerTvedebrink exponent. Must be a scalar and negative.
dropoutDropout rate for each replicate. Must be between 0 and 1.
degradationDegradation parameters to determine likeliness of
dropout. Should be of length k + u.
rcontRelative contribution. Must be of length k + u or k
+ u - 1, where k is the number of contributors with
known profiles, and u is the number of unknown
contributors. Since the input is for relative
contributions, there are two possible sizes. In the first
case, the contribution of a reference individual is kept to 1.
In general, the reference individual will be the queried
individual (if subjecto dropout and in prosecution hypothesis)
or the first individual subject to dropout.
In the second case, the contributions are used for all
known and unknown contributors as given. It is for the user
to know what she is doing. rcont should always be
positive.
dropinA scalar giving the dropin rate. Ignored in models without
dropin. Otherwise it should be positive (unless given in
exponential form).
... Any other parameter. Mostly ignored by objective functions,
but it makes it easier to pass on parameters to the penalty
function
providing 
.