Evaluate a positive functional data object at specified argument
values, or evaluate a derivative of the functional object.
Usage
eval.posfd(evalarg, Wfdobj, Lfdobj=int2Lfd(0), returnMatrix=FALSE)
## S3 method for class 'posfd'
predict(object, newdata=NULL, Lfdobj=0,
returnMatrix=FALSE, ...)
## S3 method for class 'posfd'
fitted(object, ...)
## S3 method for class 'posfd'
residuals(object, ...)
Arguments
evalarg, newdata
a vector of argument values at which the functional data object is
to be evaluated.
Wfdobj
a functional data object that defines the positive function to be
evaluated. Only univariate functions are permitted.
Lfdobj
a nonnegative integer specifying a derivative to be evaluated. At
this time of writing, permissible derivative values are 0, 1 or 2.
A linear differential operator is not allowed.
object
an object of class posfd that defines the positive function
to be evaluated. Only univariate functions are permitted.
returnMatrix
logical: If TRUE, a two-dimensional is returned using a
special class from the Matrix package.
...
optional arguments required by predict; not currently used.
Details
A positive function data object $h(t)$ is defined by $h(t) =[exp
Wfd](t)$. The function Wfdobj that defines the positive
function is usually estimated by positive smoothing function
smooth.pos
Value
a matrix containing the positive function values. The first dimension
corresponds to the argument values in evalarg and the second to
replications.