#Dave Woods, University of Southampton, UK, 2005
#email: dcw@maths.soton.ac.uk
#WWW: http://www.maths.soton.ac.uk/staff/woods

#This readme file briefly describes the input for the "coeffients.cpp" assessment algorithm 
for compromise designs under GLMs. The file "compare.in" has the input for the same 
example as this readme and will work with the algorithm.

Running the code
After compliation, run the executable. The first command is read from the default input and 
gives the name of the main input file (compare.in in this example). The input file 
specifies the designs under comparison, the parameters for the simulated annealing 
algorithm, the model under consideration and the parameter space of interest (see comments 
below).

Important: to change the random number seed, run the executable (coeff.run) with the
commandline

GSL_RNG_SEED=12 coeff.out

where "12" sould be replaced with your chosen seed. (The seed is not set dynamically, e.g.
using the current time, to aid parallel execution on multi=processor machines).

The output from the algorithm is a table of objective function values for each design and 
parameter vector. These can then be imported into your favourite spreadsheet program and 
manipulated.

Below is an annotated version of "compare.in"


2000 #the number of random parameter draws
compare.out #output file for
compare_des.out #output file for designs
sim_dist.in #input file for parameter space - currently, parameter values can only be drawn 
#independently and completely at random. The input takes the form of p rows of ranges for
#the uniform distribution corresponding to each parameter

16 #number of runs in each design
3  #number of variables
10 #number of parameters in each model

1 #number of designs to compare

#design 1
1 1 1
1 1 -1
1 -1 1
-1 1 1
1 -1 -1
-1 1 -1
-1 -1 1
-1 -1 -1
1.2872 0 0
-1.2872 0 0
0 1.2872 0
0 -1.2872 0
0 0 1.2872
0 0 -1.2872
0 0 0
0 0 0

#model - specified as a matrix - see input readme for the Simulated Annealing algorithm
0 0 0
1 0 0
0 1 0
0 0 1
1 1 0
1 0 1
0 1 1
2 0 0
0 2 0
0 0 2

#annealing parameters - see the readme for the Simulated Annealing algorithm
1000
0.95
100
20
0.75
1000000000
0.00001
logit
0


-1 1
-1 1
-1 1
