additive.fit: Implementing Backfitting in GAMLSS

In gamlss: Generalized Additive Models for Location Scale and Shape

Implementing Backfitting in GAMLSS

Description

This function is not to be used on its own. It is used for backfitting in the GAMLSS fitting algorithms and it is based on the equivalent function written by Trevor Hastie in the gam() S-plus implementation, (Chambers and Hastie, 1991).

Usage

additive.fit(x, y, w, s, who, smooth.frame, maxit = 30, tol = 0.001, 
             trace = FALSE, se = TRUE, ...)

Arguments

x	the linear part of the explanatory variables
y	the response variable
w	the weights
s	the matrix containing the smoothers
who	the current smoothers
smooth.frame	the data frame used for the smoothers
maxit	maximum number of iterations in the backfitting
tol	the tolerance level for the backfitting
trace	whether to trace the backfitting algorithm
se	whether standard errors are required
...	for extra arguments

Details

This function should not be used on its own

Value

Returns a list with the linear fit plus the smothers

Author(s)

Mikis Stasinopoulos

References

Chambers, J. M. and Hastie, T. J. (1991). Statistical Models in S, Chapman and Hall, London.

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

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