MonoPoly (version 0.2-10)

monpol.fit: Monotone Polynomials

Description

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.

Usage

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"))

Arguments

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.

K

a polynomial with highest power $2K+1$ will be fitted to the data.

start

optional starting value for the iterative fitting.

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.

Value

• 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.

References

Murray, K., Müller{Mueller}, 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.

See Also

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