| summary.gcrq {quantregGrowth} | R Documentation |
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 |
type |
Which covariance matrix should be used to compute the estimate standard errors? |
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
Examples
## see ?gcrq
##summary(o)