Description of Reway=Myway Options.

John M. Gipson
January 21, 1999

Overview.

If you do a VLBI solution using the raw correlator sigma as
the noise in the measurements you will find that Chi-square is
too small, which is indicative of unmodeled sources of error. For
many years there were two different programs which were used to
add noise to the observations.  Reway added the same amount of
noise to all observations.  Weigh added the same amount of
noise to all observations of a given baseline.  Both of these had
the goal of adjusting the noise so that Chi-square=1.

The program MYWAY combines reway and weigh into one program,
and adds another option. You now have the option of adding
station dependent noise to the observations.  Hence you have
three possible ways of adding noise:
 1.) Global--The same noise is added to all observations.
 2.) Station--The same noise is added to all observations
 involving a given station.
 3.) Baseline--The same noise is added to all observations
 involving a given baseline.
The goal of all three is the to have chi-sqaure=1.
Actually options 2 and 3 have a somewhat more stringent
requirement, namely that chi-square for each subset of the
data, for example all observations to a given station or with a
given baseline be equal to 1. When I say chi-square I mean the
correctly normalized post-fit chi-square, the "chi-square per
degree of freedom."

For reway to run correctly you need to first run a solution. Then
noise is added to the observations to make chi-square of this
solution (and the various subsets of the data) equal to 1.
Since adding noise changes the solution, there is no guarantee
that a new solution run with the new noise will have chi-square
equal to 1, although you can show that the chi-square should
be closer to 1 than the original solution.  Hence adding noise is
an iterative process.

In what follows I describe the options available in REWAY/MYWAY.


(I)terate to Unity.

This option will go through the following steps until
Chi-square is close to unity:
1.) Modify weights until we are close to Chi-square=1 for each
subset of the data.
2.) Run a solve solution.
3.) Recalculate chi-square.  If neccessary, goto 1.


(U)pdate weights once.

This modifies the weights based on the current solution until
we are close to chi-square=1 for each subset of the data. In
global mode, there is only one subset. In station mode we want
chi-square=1 for all observations involving a given station. In
baseline mode we want chi-square=1 for all observations involving
a given baseline.


(R)e-initialize weights to zero.

This sets all of the additive noise to 0. This means we just use
the correlator sigmas for the noise.


re-read the (N)amfil weights.

This reads in the weights from the namfil.


Good (C)hoice values 10 ps, 100 fs/s.

This makes the additive noise 10 ps for all delay observations
and 100 fs/s for all rate observations.


(E)nter specific weights.

This allows the user to enter in specific values for the weights.
The user is prompted to enter the weights one at a time.

Change to (G)lobal weights.
Change to (S)tation weights.
Change to (B)aseline weights.

Choose what kind of weights you want to use.


(D)isplay current weights.

Display the current delay and rate weights.


(M)aximum iterations.

This sets the maximum number of iterations for option (I)
above. Generally speaking, the iterative process converges
after a few (3-5) iterations.  In the case that it doesn't
converge this stops the process.

(T)olerance.

This allows you to set the tolerance for convergence. If
  |chi_square-1.| < tol
then we stop the iteration.  The default value is 0.005,
although this is certainly too stringent.

(P)ause in iteration.

This can be either YES or NO. If YES the program will pause
between computing weights and doing a solve solution.  This
allows the user to see how quickly things are converging.

(F)all back mode.

This can either On or OFF.  Occaisonaly it happens that it is
impossible to have chi-square=1 for all baselines, but it may
be possible to have this for all stations.  Or it may be
impossible to have this for all stations, although you can have
it for the dataset as a whole.  If fall back mode is on, and
MYWAY can't find acceptable values for the baseline weights, it
will change to station weights.  If it can't find acceptable
values for station weights it will change to global weights.

e(X)perimental new-weights: Off

This is a relic.  I tried experimenting with alternate ways of
updating the weights to see if I could come up with something
that converged faster, but never succeeded in doing so. If this
is turned on, you use the alternate technique. You should get the
same answers.

f(L)oor

This changes the minimum allowable value for the weights.

Ceiling (%)

This changes the maximum allowable value for the weights.

Find Baseline co(V)ariance.

This is a research option.  This calculates the covariance of
observations by baseline, and writes it out to a file called
bl_cov.tmp.  Explicitly, for each pair of baselines ij, kl,
it computes:
    SUM (res_ij/sig_ij)*(res_kl/sig_kl)/(num obs)
where the sum is over all observations.  If the observations were
really independent, you would find that that this quantity
should equal 1 when ij=kl, and 0 otherwise. Actually what you
find is that if the baselines have a common station there is
usually significant correlation, indicating that there is some
unmodeled systematic station dependent error. I believe this is
due to the atmosphere, but I haven't had a chance to follow up on
this. In any case, if we have significant correlation between
baselines the estimation process in solve is probably not
correct.

(A)bort without storing weights.

Return to optin without disturbing the namfil.


Return to (O)tion storing weights.

Store the weights we have found in the namfil, and return to
optin.











