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).
additive.fit(x, y, w, s, who, smooth.frame, maxit = 30, tol = 0.001,
trace = FALSE, se = TRUE, ...)the linear part of the explanatory variables
the response variable
the weights
the matrix containing the smoothers
the current smoothers
the data frame used for the smoothers
maximum number of iterations in the backfitting
the tolerance level for the backfitting
whether to trace the backfitting algorithm
whether standard errors are required
for extra arguments
Returns a list with the linear fit plus the smothers
This function should not be used on its own
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/).