Print (or summarise) the results of a starship estimation

Print (or summarise) the results of a starship estimation of the parameters of the Generalised Lambda Distribution

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

# S3 method for class 'starship'
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

x

An object of class starship.

object

An object of class starship.

digits

minimal number of significant digits, see print.default.

...

arguments passed to print

Details

summary Gives the details of the starship.adaptivegrid and optim steps.

References

Freimer, M., Mudholkar, G. S., Kollia, G. & Lin, C. T. (1988), A study of the generalized tukey lambda family, Communications in Statistics - Theory and Methods 17, 3547–3567.

Ramberg, J. S. & Schmeiser, B. W. (1974), An approximate method for generating asymmetric random variables, Communications of the ACM 17, 78–82.

King, R.A.R. & MacGillivray, H. L. (1999), A starship method for fitting the generalised \(\lambda\) distributions, Australian and New Zealand Journal of Statistics 41, 353–374

Owen, D. B. (1988), The starship, Communications in Statistics - Computation and Simulation 17, 315–323.

https://github.com/newystats/gld/

Examples

data <- rgl(100,0,1,.2,.2)
starship.result <- starship(data,optim.method="Nelder-Mead",initgrid=list(lcvect=(0:4)/10,
ldvect=(0:4)/10))
print(starship.result)
#> Starship estimate, gld type: FMKL 
#>  lambda1   lambda2   lambda3   lambda4  
#> -0.09456   1.49926  -0.02293  -0.08808  
summary(starship.result,estimation.details=TRUE)
#> Generalised Lambda Distribution FMKL type. Starship  estimate.
#> 
#> Optim (final) estimates:
#> Starship estimate, gld type: FMKL 
#>  lambda1   lambda2   lambda3   lambda4  
#> -0.09456   1.49926  -0.02293  -0.08808  
#> internal g-o-f measure at optim minimum: 0.1831044 
#> optim.details:
#> Counts: function gradient 
#>      231       NA 
#> Convergence: [1] 0
#> Message: NULL