Continuing with the graphical approach, the following diagrams illustrate the effects of using the ` SCALe` and
` TARGet` keywords as if they were adjustable parameters (which they should not).

Examination of these graphs shows that the effect of ` SCALe` is rather similar to changing the ` F000`. Actually,
their effects should be identical, ie giving a ` SCALe 2.0`, ` F000 50000` should give identical results with
` SCALE 1.0`, ` F000 25000`. The reason for this behaviour is that * GraphEnt* will NOT apply the scale factor
to the *F*_{000} term.

With the ` TARGet` keyword things are different. The difference of the two maps above (in terms of their sharpness)
has nothing to do with scaling or the *F*_{000} term. The argument in this case is that
reducing the target value is to a good approximation
equivalent to dividing the standard deviations of your measurements by a constant *c* > 1.0. In that case, * GraphEnt* will
fit your data closer, meaning that the high resolution data (which usually have the lowest
*F*/(*F*), will now be
reproduced more accurately and will contribute more to your map. Having said that, if the standard deviations were
correctly estimated in the first place, you will be fitting noise. Increasing the target value has the
opposite effect : * GraphEnt* will now fit your data less closely, and the * GraphEnt* map will be more uniform.
See page for an example of using the ` TARGet` keyword in the case of
macromolecular anomalous Patterson function calculations.

** Take home message : **
You need data on an absolute scale, with correctly estimated standard deviations. If you have an estimate
of a suitable --for your problem-- value for the *F*_{000} term, use it (edit the ` MAXENT_AUTO.IN`, add
a line with the ` F000` value, re-run with ` GraphEnt MAXENT_AUTO.IN`).