gamlss.scope {gamlss}

Generate a Scope Argument for Stepwise GAMLSS

Description

Generates a scope argument for a stepwise GAMLSS.

Usage

gamlss.scope(frame, response = 1, smoother = "cs", arg = NULL, form = TRUE)

Arguments

frame

a data or model frame

response

which variable is the response; the default is the first

smoother

what smoother to use; default is cs

arg

any additional arguments required by the smoother

form

should a formula be returned (default), or else a character version of the formula

Details

Each formula describes an ordered regimen of terms, each of which is eligible on their own for inclusion in the gam model. One of the terms is selected from each formula by step.gam. If a 1 is selected, that term is omitted.

Value

a list of formulas is returned, one for each column in frame (excluding the response). For a numeric variable, say x1, the formula is

~ 1 + x1 + cs(x1)

If x1 is a factor, the last smooth term is omitted.

Author(s)

Mikis Stasinopoulos: a modified function from Statistical Models in S

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

stepGAIC

Examples

data(usair)
gs1<-gamlss.scope(model.frame(y~x1+x2+x3+x4+x5+x6, data=usair))
gs2<-gamlss.scope(model.frame(usair))
gs1
gs2
gs3<-gamlss.scope(model.frame(usair), smooth="fp", arg="3")
gs3