smooth.pos {fda} | R Documentation |
Smooth Data with a Positive Function
Description
A set of data is smoothed with a functional data object that only
takes positive values. For example, this function can be used
to estimate a smooth variance function from a set of squared residuals.
A function $W(t)$ is estimated such that that the smoothing
function is $exp[W(t)]$.
Usage
smooth.pos(argvals, y, WfdParobj, wt=rep(1,nobs),
conv=.0001, iterlim=20, dbglev=1)
Arguments
argvals |
a vector of argument values.
|
y |
a vector of data values. This function can only smooth
one set of data at a time.
|
WfdParobj |
a functional parameter object that provides an initial
value for the coefficients defining function $W(t)$,
and a roughness penalty on this function.
|
wt |
a vector of weights to be used in the smoothing.
|
conv |
a convergence criterion.
|
iterlim |
the maximum number of iterations allowed in the minimization
of error sum of squares.
|
dbglev |
either 0, 1, or 2. This controls the amount information printed out on
each iteration, with 0 implying no output, 1 intermediate output level,
and 2 full output. If either level 1 or 2 is specified, it can be
helpful to turn off the output buffering feature of S-PLUS.
|
Value
a named list of length 4 containing:
Wfdobj |
a functional data object defining function $W(x)$ that that
optimizes the fit to the data of the monotone function that it defines.
|
Flist |
a named list containing three results for the final converged solution:
(1)
f: the optimal function value being minimized,
(2)
grad: the gradient vector at the optimal solution, and
(3)
norm: the norm of the gradient vector at the optimal solution.
|
iternum |
the number of iterations.
|
iternum |
the number of iterations.
|
iterhist |
a iternum+1 by 5 matrix containing the iteration
history.
|
See Also
smooth.monotone
,
smooth.morph
Examples
#See the analyses of the daily weather data for examples.
[Package
fda version 1.2.3
Index]