summary.Qest: Summarizing Q-estimators

In Qest: Quantile-Based Estimator

Summarizing Q-estimators

Description

Summary method for class “Qest”.

Usage

## S3 method for class 'Qest'
summary(object, covar = FALSE, ...)

Arguments

object	an object of class “Qest”.
covar	logical; if TRUE, the variance covariance matrix of the estimated parameters is returned.
...	for future methods.

Details

This function returns a summary of the most relevant information on model parameters, standard errors, and convergence status.

Value

The function summary.Qest computes and returns a list of summary statistics of the fitted model given in object, using the "call" and "terms" from its argument, plus

coefficients	a matrix with 4 columns reporting the estimated coefficients, the estimated standard errors, the corresponding z-values (coef/se), and the two-sided p-values.
obj.function	the value of the minimized loss function (see Qest for details).
n	the number of observations.
npar	the number of free parameters.
iter	the number of iterations.
covar	only if covar = TRUE, the estimated covariance matrix.
call	the matched call.
type	a character string defined as follows: "c" for right-censored data; "ct" for left-truncated, right-censored data; and "u" otherwise.

Author(s)

Gianluca Sottile <[e-mail]>

References

Sottile G, and Frumento P (2022). Robust estimation and regression with parametric quantile functions. Computational Statistics and Data Analysis. <doi:10.1016/j.csda.2022.107471>

See Also

Qest, for model fitting.

Examples

# Quantile function of an Exponential model
Qexp <- function(theta, tau, data){
  qexp(tau, exp(theta))
}

y <- rexp(100, exp(1))
m1 <- Qest(y ~ 1, Q = Qexp, start = NA)
summary(m1)
summary(m1, covar = TRUE)

Qest documentation built on May 29, 2024, 8:57 a.m.