Description Usage Arguments Details Value Author(s) References Examples
Determine the leastsquares estimates of the parameters of a monotone polynomial
1 2 3 4 5 6 7 8 9 10  monpol(formula, data, subset, weights, na.action,
degree = 3, K, start,
a = Inf, b=Inf,
trace = FALSE, plot.it = FALSE,
control = monpol.control(),
algorithm = c("Full", "Hawkins", "BCD", "CD1", "CD2"),
ptype = c("SOS", "Elphinstone", "EHH", "Penttila"),
ctype = c("cge0", "c2"),
monotone,
model=FALSE, x=FALSE, y=FALSE)

formula 
an object of class 
data 
an optional data frame, list or environment (or object
coercible by 
subset 
an optional vector specifying a subset of observations to be used in the fitting process. 
weights 
an optional vector of weights to be used in the fitting
process. Should be 
na.action 
a function which indicates what should happen
when the data contain 
degree 
positive integer, a polynomial with highest power equal
to 
K 
nonnegative integer, a polynomial with highest power 2K+1 will be fitted to the data. 
start 
optional starting value for the iterative fitting. 
a,b 
polynomial should be monotone on the interval from a to b. If either parameter is finite, parameterisation “SOS” has to be used. 
trace 
print out information about the progress of the
interative fitting at the start and then every 
plot.it 
plot the data and initial fit, then plot current fit
every 
control 
settings that control the iterative fit; see

algorithm 
algorithm to be used. It is recommended to use either “Full” or “Hawkins”; see both papers in ‘References’ for details. 
ptype 
parameterisation to be used. It is recommended to use the “SOS” parameterisation; see the 2016 paper in ‘References’ for details. 
ctype 
parameterisation to be used; see paper in ‘References’ for details. 
monotone 
only used for parameterisation “SOS” to enforce the kind of monotonicity desired over the interval [a,b], should be “increasing” or “decreasing”. 
model, x, y 
logicals. If 
A monpol
object is a type of fitted model object. It has
methods for the generic function coef
,
fitted
, formula
,
logLik
, model.matrix
,
predict
, print
, residuals
.
The parameterisation type “SOS” with the “Full”
algorithm is currently the recommended fitting procedure and is
discussed in the 2016 paper in ‘References’. For this
parameterisation the argument ctype
is ignored.
The “Hawkins” algorithm is also recommended and discussed in both papers in the ‘References’.
The parameterisations “Elphinstone”, “EHH” and “Pentilla”, for which the argument “ctype” defines a further variation of parameterisation, work together with algorithms “Full”, “BCD”, “CD1” and “CD2”. These parameterisations and algorithms are discussed in the 2013 paper in ‘References’.
monpol
returns an object of class
"monpol"
Berwin A Turlach <[email protected]>
Murray, K., Müller, 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., Müller, S. and Turlach, B.A. (2013). Revisiting fitting monotone polynomials to data, Computational Statistics 28(5): 1989–2005, doi: 10.1007/s0018001203905.
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