[R] bnlearn: how to compute boot strength with mmhc and a blacklist

[Marco Scutari]

Dear Leonore,

On Wed, Apr 10, 2013 at 5:51 PM, Leonore Wigger <leonore.wigger at unil.ch> wrote:
> Question: I have specified a blacklist. I would have expected this to
> completely disallow the arcs on the blacklist. But the result shows that
> some of the blacklisted arcs have a strength > 0 (rows 7,8,10,11). It
> seems that only the arc that was blacklisted in both directions was
> actually banned (x1-x2, in rows 9 and 12). What is the reason for this?
> Is there a way to completely disallow all blacklisted arcs, such that
> their strength is 0.0? Or is there a compelling reason why that should
> not be done?

Because by default boot.strength() runs with "cpdag = TRUE". This
means that reversible arcs can have positive strength in both
directions. You should set "cpdag = FALSE" to get the result you are
expecting. In that case the probabilities of the arc directions should
be taken with a grain of salt, as they can be influenced by many
things (optimized = TRUE/FALSE, order of the variables in the data
set) unless you are doing causal modelling.

> This code makes 50 different networks from the same data, then uses them
> as input for custom.strength. The networks are constructed using the
> algorithm "hc". A different network is produced every time "hc" is
> invoked because a random starting network is supplied to the parameter
> "start". I would like to do the same thing, but use "mmhc" instead of
> "hc". However, in my hands, the networks that are constructed by "mmhc"
> are all identical, and I am not sure how to introduce a random element
> into the construction. Question: Which parameters do I need to give to
> "mmhc" in order to obtain a different network every time it is run on
> the same data set?

This is not surprising, because mmhc() does not have a "start"
argument, so it's starting from the same network over and over. There
is no way to provide a random seed to mmhc(), so the only way to
perturb it is through bootstrap.

Marco

-- 
Marco Scutari, Ph.D.
Research Associate, Genetics Institute (UGI)
University College London (UCL), United Kingdom