summarysgtest: Summary the Maximum-Likelihood Estimation with the Skewed...

In sgt: Skewed Generalized T Distribution Tree

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))

Example output

Loading required package: optimx
Loading required package: numDeriv
Skewed Generalized T MLE Fit
Best Result with nlminb Maximization
Convergence Code 0: Successful Convergence
Iterations: 24, Log-Likelihood: -2076.966 
Estimate(s):
     mu   sigma  lambda       p       q 
 1.9735  2.0075 -0.2761  1.7199  7.7856 
Skewed Generalized T MLE Fit
Best Result with nlminb Maximization
Convergence Code 0: Successful Convergence
Iterations: 24, Log-Likelihood: -2076.966 

          Est. Std. Err.       z  P>|z|    
mu      1.9735    0.0634 31.1041 0.0000 ***
sigma   2.0075    0.0594 33.7782 0.0000 ***
lambda -0.2761    0.0410 -6.7376 0.0000 ***
p       1.7199    0.2673  6.4333 0.0000 ***
q       7.7856    7.5662  1.0290 0.3035    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

sgt documentation built on May 2, 2019, 8:27 a.m.