StarshipClassDocs: Print (or summarise) the results of a starship estimation

Description Usage Arguments Details Author(s) References See Also Examples

Description

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

Usage

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## 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.

Author(s)

Robert King, robert.king@newcastle.edu.au, https://tolstoy.newcastle.edu.au/~rking/

Darren Wraith

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://tolstoy.newcastle.edu.au/~rking/gld/

See Also

starship, starship.adaptivegrid, starship.obj

Examples

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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)
summary(starship.result,estimation.details=TRUE)

Example output

Starship estimate, gld type: FMKL 
 lambda1   lambda2   lambda3   lambda4  
-0.23755   1.22406   0.15687  -0.03838  
Generalised Lambda Distribution FMKL type. Starship  estimate.

Adaptive Grid estimates:
 estimate, gld type: FMKL 
lambda1  lambda2  lambda3  lambda4  
-0.1584   1.2957   0.1000   0.0000  
internal g-o-f measure at grid minimum: 0.3289457 

Optim (final) estimates (starting from grid estimates):
Starship estimate, gld type: FMKL 
 lambda1   lambda2   lambda3   lambda4  
-0.23755   1.22406   0.15687  -0.03838  
internal g-o-f measure at optim minimum: 0.1785538 
optim.details:
Counts: function gradient 
     397       NA 
Convergence: [1] 0
Message: NULL

gld documentation built on Jan. 9, 2020, 1:06 a.m.

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