The starship is a computer intensive method of shape estimation. See this page for details on our paper which describes its use for the generalised lambda distribution. The programs in this directory use the starship method to fit the generalised lambda distribution to data.

The programs are © 1999 Robert King, and are made available under the Gnu General Public License. I recommend you use my gld package for R instead of this code.

These programs use the starship method to fit the generalised lambda distribution, as described in our paper "A starship method for fitting the generalized lambda distribution", Australian and New Zealand Journal of Statistics, 1999, to appear

Extract the files from the tar archive, using

tar xvzf starship-0.2.tar.gzif you have gnu tar, or uncompress the archive first and then untar it. The archive will extract into a directory called

starship-0.2

The directory should contain 6 .c files, and the makefile. Edit the makefile to reflect the location of your C compiler.

make allwill make the 4 executables

The 4 different executables make possible the choice of parameterisation and internal goodness-of-fit measure.

Program name | parameterisation | internal g-o-f |
---|---|---|

starfrmad | Freimer, Mudholkar, Kollia and Lin (FMKL) parameterisation | Anderson-Darling internal goodness-of-fit (g-o-f) |

starfrmks | FMKL parameterisation | Kolmogorov-Smirnov internal g-o-f |

starramad | Ramberg and Schmeiser (RS) parameterisation | Anderson-Darling internal goodness-of-fit (g-o-f) |

starramks | RS parameterisation | Kolmogorov-Smirnov internal g-o-f |

Call the progams with

(name) datafile searchfile (optionally full-data-size)

The error message from calling the programs with no arguments is quite informative:

The datafile (which should be called data.

Plain data should be in a file with one value per line.

"subset" data, meaning specific quantiles from the data should be in two columns, seperated by a space, with the rank of the data in the full subset in the first column and the data value in the second. If you use subset data, be sure to use the constraints on subsets discussed in the paper.

For example:

1 2.1 2 3.2 3 3.9 4 4.9 5 5 7 5.2 8 5.3 9 5.4 10 5.9 12 6

lambda_1 start lambda_1 step lambda_2 start lambda_2 step lambda_3 start lambda_3 step lambda_4 start lambda_4 stepThe region searched goes from the start value, across a grid of size 10 where each step is the given size, so that the largest value is start + 9*step. You can change this grid size in the code at the line

#define NOSTEP

| | | |

| | | |

comments to: robert.king@newcastle.edu.au