summary.gcrq: Summarizing model fits for growth charts regression quantiles

In quantregGrowth: Non-Crossing Additive Regression Quantiles and Non-Parametric Growth Charts

Summarizing model fits for growth charts regression quantiles

Description

summary and print methods for class gcrq

Usage

## S3 method for class 'gcrq'
summary(object, type=c("sandw","boot"), digits = max(3, getOption("digits") - 3), 
    signif.stars =getOption("show.signif.stars"), ...)

Arguments

object	An object of class "gcrq".
type	Which covariance matrix should be used to compute the estimate standard errors? 'boot' means case-resampling bootstrap (see n.boot in gcrq()), 'sandw' mean via the sandwich formula.
digits	controls number of digits printed in output.
signif.stars	Should significance stars be printed?
...	further arguments.

Details

summary.gcrq returns some information on the fitted quantile curve at different probability values, such as the estimates, standard errors, values of check (objective) function values at solution. Currently there is no print.summary.gcrq method, so summary.gcrq itself prints results.

The SIC returned by print.gcrq and summary.gcrq is computed as \log(\rho_\tau/n) + \log(n) edf/(2 n), where \rho_tau is the usual asymmetric sum of residuals (in absolute value). For multiple J quantiles it is \log(\sum_\tau\rho_\tau/(n J)) + \log(n J) edf/(2 n J). Note that computation of SIC in AIC.gcrq relies on the Laplace assumption for the response.

Author(s)

Vito M.R. Muggeo

See Also

gcrq

Examples

## see ?gcrq
##summary(o)

quantregGrowth documentation built on July 9, 2023, 6:06 p.m.