sgt (version 1.0)

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:
• parameters
  names of parameters used in the estimation procedure.
• type
  type of maximisation.
• iterations
  number of iterations.
• code
  code of success.
• message
  a short message describing the code.
• loglik
  the loglik value in the maximum.
• estimate
  numeric matrix, the first column contains the parameter estimates, the second the standard errors, third t-values and fourth corresponding probabilities.
• fixed
  logical vector, which parameters are treated as constants.
• NActivePar
  number of free parameters.
• constraints
  information 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'.

See Also

the maxLik CRAN package

Examples

### 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