inc: Fit a Monotone Curve Using a Cubic Spline

In assist: A Suite of R Functions Implementing Spline Smoothing Techniques

Fit a Monotone Curve Using a Cubic Spline

Description

Return a spline fit of a increasing curve.

Usage

inc(y, x, spar = "v", limnla = c(-6, 0), grid = x, prec = 1e-06, maxit = 50, verbose = F)

Arguments

y	a vecetor, used as the response data
x	a vector, used as the covariate. Assume an increasing relationshop of y on x
spar	a character string specifying a method for choosing the smoothing parameter. "v", "m" and "u" represent GCV, GML and UBR respectively. Default is "v" for GCV
limnla	a vector of length one or two, specifying a search range for log10(n*lambda), where lambda is the smoothing parameter and n is the sample size. If it is a single value, the smoothing parameter will be fixed at this value.
grid	a vector of x used to assess convergence. Default is x
prec	a numeric value used to assess convergence. Default is 1e-6
maxit	an integer represeenting the maximum iterations. Default is 50.
verbose	an optional logical value. If ‘TRUE’, detailed iteration results are displayed. Default is "FALSE"

Details

This function is to fit a increasing fucntion to the data. The monotone function is expressed as integral of an unknown function that a cubic spline is used to estimate.

Value

a split fit together with the convergence information

Author(s)

Yuedong Wang [e-mail] and Chunlei Ke [e-mail]

See Also

ssr

assist documentation built on Aug. 22, 2023, 9:12 a.m.