summary.MLE: Summary the Maximum-Likelihood Estimation with the Skewed Generalized T Distribution

Description

Summary the maximum-likelihood estimation including standard errors and t-values.

Usage

## S3 method for class 'MLE':
summary(object, \dots)
## S3 method for class 'mult.MLE':
summary(object, \dots)

Arguments

object

object of class 'MLE' or of class 'mult.MLE', usually a result from maximum-likelihood estimation.

...

currently not used.

Value

summary.MLE returns an object of class 'summary.MLE' with the following components:
parametersnames of parameters used in the estimation procedure.
typetype of maximisation.
iterationsnumber of iterations.
codecode of success.
messagea short message describing the code.
loglikthe loglik value in the maximum.
estimatenumeric matrix, the first column contains the parameter estimates, the second the standard errors, third t-values and fourth corresponding probabilities.
fixedlogical vector, which parameters are treated as constants.
NActiveParnumber of free parameters.
constraintsinformation about the constrained optimization. Passed directly further from maxim-object. NULL if unconstrained maximization.
summary.mult.MLE returns a list of class 'summary.mult.MLE' with components of class 'summary.MLE'.

Examples

Run this code

### Showing how to fit a simple vector of data to the skewed 
### generalized t distribution. 
require(graphics)
require(stats)
set.seed(123456)
x = rt(100, df=10)
X.f = X ~ x
start = list(mu = 0, sigma = 2, lambda = 0, p = 2, q = 12)
result = sgt.mle(X.f = X.f, start = start, finalHessian = "BHHH")
sumResult = summary(result)
print(result)
coef(result)
print(sumResult)
### Note that the t distribution is a special case of the 
### skewed generalized t distribution

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