summary.sgtest {sgt}

Summary the Maximum-Likelihood Estimation with the Skewed Generalized T Distribution

Description

Summary the maximum-likelihood estimation.

Usage

## S3 method for class 'sgtest'
summary(object, ...)

Arguments

object

object of class 'sgtest', usually a result from maximum-likelihood estimation.

...

currently not used.

Value

summary.sgtest returns an object of class 'summary.sgtest' with the following components:

maximum

log-likelihood value of estimates (the last calculated value if not converged) of the method that achieved the greatest log-likelihood value.

estimate

estimated parameter value with the method that achieved the greatest log-likelihood value.

convcode

convcode returned from the optimx function in the optimx package of the method that achieved the greatest log-likelihood value. See ?optimx for the different convcode values.

niter

The amount of iterations that the method which achieved the the greatest log-likelihood value used to reach its estimate.

best.method.used

name of the method that achieved the greatest log-likelihood value.

optimx

A data.frame of class "optimx" that contains the results of the optimx maximization for every method (not just the method that achieved the highest log-likelihood value). See ?optimx for details.

gradient

vector, gradient value of the estimates with the method that achieved the greatest log-likelihood value.

hessian

matrix, hessian of the estimates with the method that achieved the greatest log-likelihood value.

varcov

variance/covariance matrix of the maximimum likelihood estimates

std.error

standard errors of the estimates

z.score

the z score of the estimates

p.value

the p-values of the estimates

summary.table

a data.frame containing the estimates, standard errors, z scores, and p-values of the estimates.

Author(s)

Carter Davis, [e-mail]

See Also

the optimx CRAN package

Examples

# SINGLE VARIABLE ESTIMATION:
### generate random variable
set.seed(7900)
n = 1000
x = rsgt(n, mu = 2, sigma = 2, lambda = -0.25, p = 1.7, q = 7)

### Get starting values and estimate the parameter values
start = list(mu = 0, sigma = 1, lambda = 0, p = 2, q = 10)
result = sgt.mle(X.f = ~ x, start = start, method = "nlminb")
print(result)
print(summary(result))