summary.gamlss {gamlss} | R Documentation |
Summarizes a GAMLSS fitted model
Description
summary.gamlss
is the GAMLSS specific method for the generic function summary
which summarize
objects returned by modelling functions.
Usage
## S3 method for class 'gamlss'
summary(object, type = c("vcov", "qr"),
robust=FALSE, save = FALSE,
hessian.fun = c("R", "PB"),
digits = max(3, getOption("digits") - 3),...)
Arguments
object |
a GAMLSS fitted model |
type |
the default value |
robust |
whether robust (sandwich) standard errors are required |
save |
whether to save the environment of the function so to have access to its values |
hessian.fun |
whether when calculate the Hessian should use the "R" function |
digits |
the number of digits in the output |
... |
for extra arguments |
Details
Using the default value type="vcov"
, the vcov()
method for gamlss is used to get
the variance covariance matrix (and consequently the standard errors) of the beta parameters.
The variance covariance matrix is calculated using the inverse of the numerical second derivatives
of the observed information matrix. This is a more reliable method since it take into the account the
inter-correlation between the all the parameters. The type="qr"
assumes that the parameters are fixed
at the estimated values. Note that both methods are not appropriate and should be used with caution if smoothing
terms are used in the fitting.
Value
Print summary of a GAMLSS object
Author(s)
Mikis Stasinopoulos, Bob Rigby and Calliope Akantziliotou
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. 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
gamlss
, deviance.gamlss
, fitted.gamlss
Examples
data(aids)
h<-gamlss(y~poly(x,3)+qrt, family=PO, data=aids) #
summary(h)
rm(h)