This function is nearly identical to smooth.monotone
and but smooth.morph, 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 a range interval. This function is used to
morph one probability density function into another having a
possibly different domain.
smooth.morph2(x, y, ylim, WfdPar, wt=matrix(1,nobs,1),
conv=.0001, iterlim=20, dbglev=0)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 vector of weights to be used in the smoothing.
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.
A named list of length 4 containing:
a functional data object defining function $W(x)$ that that optimizes the fit to the data of the monotone function that it defines.
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.
the number of iterations.
a by 5 matrix containing the iteration history.
# NOT RUN {
# see example for function smooth.morph.R
# }
Run the code above in your browser using DataLab