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.values
fitted

- residuals
residuals

- var
- nl.df
the trace of the smoothing matrix

- lambda
the value of the smoothing parameter

- coefSmo
the coefficients from the smoothing fit

- varcoeff
the variance of the coefficients

- x
the

`x`

for function`gamlss.fp`

is referred to the design matrix 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)- npoly
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

- xeval
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`