gamlss.ggplots-package: Plotting Generalised Additive Model for Location, Scale and...

In gamlss.ggplots: Plotting Generalised Additive Model for Location, Scale and Shape

Plotting Generalised Additive Model for Location, Scale and Shape

Description

Plotting functions for Generalised Additive Models for Location Scale and Shape.

Details

The DESCRIPTION file: This package was not yet installed at build time.
Index: This package was not yet installed at build time.
The following convention has been used to name the functions:

fitted_NAME: plots concerning fitted values from a single fitted model

resid_NAME: plots concerning residuals from a single fitted model

predict_NAME: plots concerning prediction values from a single fitted model usually having newdata option.

model_NAME: plots concerning different fitted models

where NAME refer to different characteristics.

Author(s)

Mikis Stasinopoulos <[e-mail]>, Bob Rigby, Fernanda De Bastiani, Julian Merder

Maintainer: Mikis Stasinopoulos <[e-mail]>

References

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, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1201/9780429298547")}. 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, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v023.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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1201/b21973")}

Stasinopoulos, M. D., Rigby, R. A., and De Bastiani F., (2018) GAMLSS: a distributional regression approach, Statistical Modelling, Vol. 18, pp, 248-273, SAGE Publications Sage India: New Delhi, India.

(see also https://www.gamlss.com/).

See Also

gamlss, gamlss.family

Examples

library(gamlss)
m1 <- gamlss(y~pb(x), data=abdom)
resid_index(m1)

gamlss.ggplots documentation built on Sept. 3, 2023, 5:08 p.m.