Smooth Data with a Positive Function
smooth.pos |
Language Reference for FDA Library
|
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(x, y, WfdParobj, wt=rep(1,nobs),
conv=.0001, iterlim=20, dbglev=1)
REQUIRED ARGUMENTS:
- x
-
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.
OPTIONAL ARGUMENTS:
- 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.