Estimates a Smooth Warping Function
This function is nearly identical to smooth.monotone but is
intended to compute a smooth monotone transformation $h(t)$ of
argument $t$ such that $h(0) = 0$ and $h(TRUE) = TRUE$, where $t$ is
the upper limit of $t$. This function is used primarily to register
curves.
smooth.morph(x, y, WfdPar, wt=rep(1,nobs),
conv=.0001, iterlim=20, dbglev=0)x |
a vector of argument values. |
y |
a vector of data values. This function can only smooth one set of data at a time. |
WfdPar |
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. |
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. |
iterhist |
a by 5 matrix containing the iteration history. |
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