summary.gamlss: Summarizes a GAMLSS fitted model

In gamlss: Generalized Additive Models for Location Scale and Shape

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 vcov uses the vcov() method for gamlss to get the variance-covariance matrix of the estimated beta coefficients, see details below. The alternative qr is the original method used in gamlss to estimated the standard errors but it is not reliable since it do not take into the account the inter-correlation between the distributional parameters mu, sigma, nu and tau.
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 optimHess() or a function based on Pinheiro and Bates nlme package, "PB".
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)

gamlss documentation built on May 29, 2024, 6:08 a.m.