Support for Function gnet()
This is support for the smoother function gnet() an interface for Tibshirani's glmnet() function.
It is not intended to be called directly by users.
gamlss.gnet(x, y, w, xeval = NULL, ...)
x |
the explanatory variables |
y |
iterative y variable |
w |
iterative weights |
xeval |
if xeval=TRUE then predicion is used |
... |
for extra arguments |
No return value, called for GAMLSS gnet procedure.
Florian Ziel, Peru Muniain and Mikis Stasinopoulos
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., and De Bastiani F., (2019) Distributions for Modeling Location, Scale and Shape: Using GAMLSS in R, Chapman and Hall/CRC.
Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.
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/).
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
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