tools:::Rd_package_description("gamlss.ggplots")
tools:::Rd_package_author("gamlss.ggplots")
Maintainer: tools:::Rd_package_maintainer("gamlss.ggplots")
The DESCRIPTION file: tools:::Rd_package_DESCRIPTION("gamlss.ggplots") tools:::Rd_package_indices("gamlss.ggplots") 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.
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, 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, 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. 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.
Stasinopoulos, M.D., Kneib, T., Klein, N., Mayr, A. and Heller, G.Z., (2024). Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications (Vol. 56). Cambridge University Press.
(see also https://www.gamlss.com/).
gamlss, gamlss.family
library(gamlss)
m1 <- gamlss(y~pb(x), data=abdom)
resid_index(m1)
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