Those are support for the functions fp()
and pp
.
It is not intended to be called directly by users.
gamlss.fp(x, y, w, npoly = 2, xeval = NULL)
gamlss.pp(x, y, w)
Returns a list with
fitted
residuals
the trace of the smoothing matrix
the value of the smoothing parameter
the coefficients from the smoothing fit
the variance of the coefficients
the x
for function gamlss.fp
is referred to the design matrix of the specific parameter model (not to be used by the user)
the y
for function gamlss.fp
is referred to the working variable of the specific parameter model (not to be used by the user)
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)
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
used in prediction
Mikis Stasinopoulos d.stasinopoulos@londonmet.ac.uk, Bob Rigby
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/).
gamlss
, fp