Evaluate a Monotone Functional Data Object
Evaluate a Monotone Functional Data Object
DESCRIPTION:
Evaluate a monotone functional data object at specified argument values,
or evaluate a derivative of the functional object.
USAGE:
eval.monfd(evalarg, Wfd, Lfd=0)
REQUIRED ARGUMENTS:
- evalarg
-
A vector of argument values at which the functional data object is to be
evaluated.
- Wfd
-
A functional data object that defines the monotone function to be
evaluated. Only univariate functions are permitted.
OPTIONAL ARGUMENTS:
- Lfd
-
A nonnegative integer specifying a derivative to be evaluated. AT
this time of writing, permissible derivative values are 0, 1, 2, or 3.
A linear differential operator is not allowed.
VALUE:
Returns an matrix containing the monotone function
values. The first dimension corresponds to the argument values in
evalarg and
the second to replications.
DETAILS:
A monotone function data object h(t) is defined by h(t) =
[D^{-1}
exp Wfd](t). In this equation, the operator D^{-1} means
taking the indefinite integral of the function to which it applies.
Note that this equation implies that the monotone function has a value
of zero at the lower limit of the arguments. To actually fit monotone
data, it will usually be necessary to estimate an intercept and a
regression coefficient to be applied to h(t), usually with the
least squares regression function lsfit.
The function Wfd that defines the monotone function is
usually estimated by monotone smoothing function
smooth.monotone.
REFERENCES: