gamlss: gamlss.ps – R documentation

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gamlss.ps

Support for Functions for smoothers

Description

Those functions are support for the functions pb(), pbo(), ps(), ridge(), ri(), cy(), pvc(), and pbm(). The functions are not intended to be called directly by users.

Usage

gamlss.pb(x, y, w, xeval = NULL, ...)
gamlss.pbo(x, y, w, xeval = NULL, ...)
gamlss.ps(x, y, w, xeval = NULL, ...)
gamlss.ri(x, y, w, xeval = NULL, ...)
gamlss.cy(x, y, w, xeval = NULL, ...)
gamlss.pvc(x, y, w, xeval = NULL, ...)
gamlss.pbm(x, y, w, xeval = NULL, ...)
gamlss.pbz(x, y, w, xeval = NULL, ...)
gamlss.pbc(x, y, w, xeval = NULL, ...)
gamlss.pbp(x, y, w, xeval = NULL, ...)

Arguments

`x`	the `x` for function `gamlss.fp` is referred to the design matric 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)
`xeval`	used in prediction
`...`	further arguments passed to or from other methods.

Value

All function return fitted smoothers.

Author(s)

Mikis Stasinopoulos d.stasinopoulos@londonmet.ac.uk, Bob Rigby

References

Eilers, P. H. C. and Marx, B. D. (1996). Flexible smoothing with B-splines and penalties (with comments and rejoinder). Statist. Sci, 11, 89-121.

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/).