This function computes 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 a range interval. This function is used to morph one probability density function into another having a possibly different domain.
smooth.morph(x, y, ylim, WfdPar, conv=1e-4, iterlim=20, dbglev=0)A named list of length eight containing:
a functional data object defining function $W(x)$ that that optimizes the fit to the data of the monotone function that it defines.
the optimal function value being minimized.
the gradient vector at the optimal solution
the Hessian matrix at the optimal solution
the norm of the gradient vector at the optimal solution.
a fine mesh of values of the estimated monotone function.
the number of iterations.
the iteration history.
a vector of argument values.
a vector of data values. This function can only smooth one set of data at a time.
a vector of length two containing the lower and upper limits of the target interval.
a functional parameter object that provides an initial value for the coefficients defining function $W(t)$, and a roughness penalty on this function.
a convergence criterion.
the maximum number of iterations allowed in the minimization of error sum of squares.
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.
cumfd,
smooth.monotone,
landmarkreg,
register.fd
# see the use of smooth.morph in cumfd.R and landmarkreg.R
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