gamlss.fp: Support for Function fp()

In gamlss: Generalized Additive Models for Location Scale and Shape

Support for Function fp()

Description

Those are support for the functions fp() and pp. It is not intended to be called directly by users.

Usage

gamlss.fp(x, y, w, npoly = 2, xeval = NULL)
gamlss.pp(x, y, w)

Arguments

x	the x for function gamlss.fp is referred to the design matrix of the specific parameter model (not to be used by the user)
y	the y for function gamlss.fp is referred to the working variable of the specific parameter model (not to be used by the user)
w	the w for function gamlss.fp is referred to the iterative weight variable of the specific parameter model (not to be used by the user)
npoly	a positive indicating how many fractional polynomials should be considered in the fit. Can take the values 1, 2 or 3 with 2 as default
xeval	used in prediction

Value

Returns a list with

fitted.values	fitted
residuals	residuals
var
nl.df	the trace of the smoothing matrix
lambda	the value of the smoothing parameter
coefSmo	the coefficients from the smoothing fit
varcoeff	the variance of the coefficients

Author(s)

Mikis Stasinopoulos, Bob Rigby

References

Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion), Appl. Statist., 54, part 3, pp 507-554.

Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019) Distributions for modeling location, scale, and shape: Using GAMLSS in R, Chapman and Hall/CRC. An older version can be found in https://www.gamlss.com/.

Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, Vol. 23, Issue 7, Dec 2007, https://www.jstatsoft.org/v23/i07/.

Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC.

(see also https://www.gamlss.com/).

See Also

gamlss, fp

gamlss documentation built on May 29, 2024, 6:08 a.m.