| fixedPoint | R Documentation |
A fixed-point function supplied by the user is iteratively
evaluated. addAverageDamp can be used to add average damping to the
function - this may have a positive effect on the speed of convergence.
fixedPoint(fun, x0, convCrit)
addAverageDamp(fun)
addConstraintMin(fun, value)
addConstraintMax(fun, value)
convCritAbsolute(tolerance = 1e-06)
convCritRelative(tolerance = 1e-06)
addMaxIter(fun, maxIter)
addCounter(fun)
addHistory(fun)
addStorage(fun)
newtonRaphson(funList, ...)
newtonRaphsonFunction(funList)
fun |
the function to be evaluated in the algorithm |
x0 |
starting value |
convCrit |
a function returning a logical scalar. Is called with two arguments; the first is the value from iteration n; the second is the value from iteration n-1 |
value |
(numeric) |
tolerance |
a numeric value > 0 |
maxIter |
maximum number of iterations |
funList |
(list) the functions to be evaluated in the algorithm. First element is typically the score function, second is the derivative of the score. |
... |
arguments passed to |
addAverageDamp adds average damping to an arbitrary fixed point
function.
addConstraintMin takes care that values are not below a
minimum value.
addConstraintMax takes care that values are not larger than
maximum value.
convCritAbsolute absolute difference as convergence criterion.
convCritRelative relative (to previous iteration) absolute
difference as convergence criterion. If value is smaller than 1, absolute
difference is used.
addMaxIter can be used to modify convergence criterion functions.
addCounter can be used to count the number of calls of a function.
addHistory can be used to save a history of results of a
function. The history is stored as a matrix, so this works best if the
return value of fun is numeric.
addStorage will add a storage to a function. The storage is a
list in which each result is stored. The function will coerce the return
value into a numeric.
newtonRaphson finds zeroes of a function. The user can supply
the function and its first derivative. Note that the Newton Raphson
Algorithm is a special case of a fixed point algorithm thus it is
implemented using fixedPoint and is only a convenience.
## Not run:
vignette("fixedPoint", "saeRobust")
## End(Not run)
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