monpol.fit: Monotone Polynomials
In MonoPoly: Functions to Fit Monotone Polynomials

Description Usage Arguments Value Author(s) References See Also

View source: R/00MonPol.R

This is the basic computing engine called by monpol used to fit monotonic polynomials. These should usually not be used directly unless by experienced users.

monpol.fit(x, y, w, K=1, start, trace = FALSE, plot.it = FALSE,
           control = monpol.control(),
           algorithm = c("Full", "Hawkins", "BCD", "CD1", "CD2"),
           ptype = c("Elphinstone", "EHH", "Penttila"),
           ctype = c("cge0", "c2"))
SOSpol.fit(x, y, w = NULL, deg.is.odd, K, start, a, b,
           monotone = c("increasing", "decreasing"),
           trace = FALSE, plot.it = FALSE, type,
           control = monpol.control())

`x`	vector containing the observed values for the regressor variable.
`y`	vector containing the observed values for the response variable; should be of same length as `x`.
`w`	optional vector of weights; should be of the same length as `x` if specified.
`deg.is.odd, K`	“deg.is.odd” is a logical, “K” is a non negative integer. If “deg.is.odd” is `TRUE` then a polynomial with highest power 2K+1 will be fitted to the data, otherwise the highest order will be 2K.
`start`	optional starting value for the iterative fitting.
`a,b, type`	polynomial should be monotone on the interval from a to b; “type” should be 0 if neither of the boundaries is finite, 1 if a if finite but not b and 2 if both boundaries are finite.
`monotone`	force the desired monotonicity in case the default choice is wrong.
`trace`	print out information about the progress of the interative fitting at the start and then every `trace` iterations.
`plot.it`	plot the data and initial fit, then plot current fit every `plot.it` iterations.
`control`	settings that control the iterative fit; see `monpol.control` for details.
`algorithm`	algorithm to be used; see `monpol` for details.
`ptype`	parameterisation to be used; see `monpol` for details.
`ctype`	parameterisation to be used; see `monpol` for details.

a list with components

`par`	the fitted parameters.
`grad`	the gradient of the objective function at the fitted parameters.
`beta`	the coefficients of the fitted polynomial in the ‘beta’ parameterisation; on the fitted scale.
`RSS`	the value of the objective function; on the fitted scale.
`niter`	number of iterations.
`converged`	indicates whether algorithm has converged.
`ptype`	input parameter `ptype`.
`ctype`	input parameter `cptype`.
`beta.raw`	the coefficients of the fitted polynomial in the ‘beta’ parameterisation; on the original scale.
`fitted.values`	the fitted values; on the fitted scale.
`residuals`	the residuals; on the fitted scale.
`K`	input parameter `K`.
`minx`	the minimum value in the vector `x`.
`sclx`	the difference between the maximum and minimum values in the vector `x`.
`miny`	the minimum value in the vector `y`.
`scly`	the difference between the maximum and minimum values in the vector `y`.
`algorithm`	input paramater `algorithm`.

Berwin A Turlach <Berwin.Turlach@gmail.com>

Murray, K., M<c3><bc>ller, S. and Turlach, B.A. (2016). Fast and flexible methods for monotone polynomial fitting, Journal of Statistical Computation and Simulation 86(15): 2946–2966, doi: 10.1080/00949655.2016.1139582.

Murray, K., M<c3><bc>ller, S. and Turlach, B.A. (2013). Revisiting fitting monotone polynomials to data, Computational Statistics 28(5): 1989–2005, doi: 10.1007/s00180-012-0390-5.

monpol which you should use for fitting monotonic polynomials unless you know better.

MonoPoly documentation built on May 2, 2019, 7:59 a.m.