gwer (version 2.1)

summary.elliptical: Summarizing Elliptical Model Fits.

Description

This function produce result summary of the result of the fitted elliptical regression model.

Usage

# S3 method for elliptical
summary(object, correlation = TRUE, ...)

Arguments

object

an object with the result of the fitted elliptical regression model.

correlation

a logical value to return the correlation for the estimated parameters. If FALSE (by default) not return the correlation matrix.

...

arguments to be used to form the default control argument if it is not supplied directly.

Value

returns an object of class “summary.elliptical”, a list with follow components:

coefficients

the matrix of coefficients, standard errors and significance values for parameters hypothesis test.

dispersion

either the supplied argument or the estimated dispersion with standard error.

residuals

the residuals from object.

cov.unscaled

the unscaled (dispersion = 1) estimated covariance matrix of the estimated coefficients.

corrrelation

the matrix of correlation for the estimated parameters.

family

family from object.

loglik

the likelihood logarithm value from object.

terms

the terms object used.

df

degrees of fredom from object.

inter

number of iterations of optimization process.

nas

a logical vector indicating if there is na in estimation of coefficients.

call

the matched call from object.

scale

values of the 4d_g for the specified distribution from object.

scaledispersion

values of the 4f_g for the specified distribution from object.

References

Cysneiros, F. J. A., Paula, G. A., and Galea, M. (2007). Heteroscedastic symmetrical linear models. Statistics & probability letters, 77(11), 1084-1090. https://doi.org/10.1016/j.spl.2007.01.012

See Also

glm, elliptical, family.elliptical

Examples

# NOT RUN {
data(luzdat)
y <- luzdat$y
x1 <- luzdat$x1 ; x1 <- factor(x1) ; x1 <- C(x1,treatment)
x2 <- luzdat$x2
x3 <- (luzdat$x2)^2
luz <- data.frame(y,x1,x2,x3)
elliptical.fitt <- elliptical(y ~ x1+x2+x3, family = Student(df=5)
,data=luz)
summary(elliptical.fitt)
# }