R: Estimates a Smooth Warping Function

smooth.morph {fda} | R Documentation

## Estimates a Smooth Warping Function

### Description

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.

### Usage

    smooth.morph(x, y, WfdPar, wt=rep(1,nobs),
                 conv=.0001, iterlim=20, dbglev=0)

### 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.
`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.

### 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.
`iterhist` | a by 5 matrix containing the iteration history.

### See Also

`smooth.monotone`, `landmarkreg`, `register.fd`

[Package fda version 2.4.8 Index]