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> [1] compiler_3.4.1 tools_3.4.1
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> Many thanks in advance
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mpute() in bnlearn
4.1, so you will have to upgrade to use it.
Cheers,
Marco
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Lecturer in Statistics, Department of Statistics
University of Oxford, United Kingdom
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u
showed above, I get an average an average of ~= 13 without considering
FFB. If I assume FFB is positive, then I can easily see E(y) ~= 15 and
E(y) - 1.96 * 0.96 s.d. ~= 13.5. So ABW < 11 has zero or almost zero
probability mass.
Cheers,
Marco
--
Marco Scutari, Ph.D.
Lecturer in Sta
), evidence=list(EST ='y', TR = c(9,
max(data$TR)), BU = c(15819, max(data$BU)), RF = c(2989,
max(data$RF)), n=10^6, method = "lw")
3) look at the parameters in your fitted network and diagnose why this
is happening.
Cheers,
Marco
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Marco Scutari
asses should both typically
> return a value for ABW that is very much higher than the threshold value.
That may be, but much depends on the specific sample the model was
fitted from. How does the fitted network look like?
Cheers,
Marco
--
Marco Scutari, Ph.D.
Lecturer in Statistics, D
ult logic sampling is the only option - likelihood
weighting
does not currently support unbounded intervals in the evidence.
Cheers,
Marco
--
Marco Scutari, Ph.D.
Lecturer in Statistics, Department of Statistics
University of Oxford, United Kingdom
[[al
of showing the network after
> training?
TAN does not do any sort of feature selection, so all the nodes should
be there. As for "showing the network after training", what kind of
plot are you looking for?
Cheers,
Marco
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Marco Scutari, Ph.D.
Lecturer in Statistics, Departmen
though it
is possible in theory to produce a graph + barplots plot from
Rgraphviz. I have never managed to make it work, though.
Cheers,
Marco
--
Marco Scutari, Ph.D.
Lecturer in Statistics, Department of Statistics
University of Oxford, United Kingdom
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the variables into intervals first.
Cheers,
Marco
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Lecturer in Statistics, Department of Statistics
University of Oxford, United Kingdom
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weight mass. Since no language
trickery is involved, this works reliably.
Cheers,
Marco
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Marco Scutari, Ph.D.
Lecturer in Statistics, Department of Statistics
University of Oxford, United Kingdom
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=eval(parse(text=(M=='s'))),
evidence=list(lag1.M1='s'),
method = lw)
passing str2 as a list.
Cheers,
Marco
--
Marco Scutari, Ph.D.
Lecturer in Statistics, Department of Statistics
University of Oxford, United Kingdom
,
Marco
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Marco Scutari, Ph.D.
Research Associate, Genetics Institute (UGI)
University College London (UCL), United Kingdom
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,
Marco
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will work.
Don't
hesitate to correct me if I'm wrong.
From your description, likelihood weighting should be fine.
Cheers,
Marco
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Marco Scutari, Ph.D.
Research Associate, Genetics Institute (UGI)
University College London (UCL), United Kingdom
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bugs in
cpquery(..., method = lw).
Cheers,
Marco
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Research Associate, Genetics Institute (UGI)
University College London (UCL), United Kingdom
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to reassemble observed and predicted class labels and compute your
metrics.
I also tried the *e1071* package, but I could not find a way to do
cross-validation.
You might be able to trick the tune() function to do it, but I am not sure.
Marco
--
Marco Scutari, Ph.D.
Research Associate, Genetics
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
in bnlearn,
naive.bayes()/tree.bayes(), which handle the concept of a response
variable more naturally than general-purpose BNs.
Hope it helps,
Marco
--
Marco Scutari, Ph.D.
Research Associate, Genetics Institute (UGI)
University College London (UCL), United Kingdom
package from Korbinian Strimmer targets exactly that kind
of appilication:
http://cran.r-project.org/web/packages/GeneNet/
Regards,
Marco
--
Marco Scutari, Ph.D.
Research Associate, Genetics Institute (UGI)
University College London (UCL), United Kingdom
,
Marco, author and maintainer of bnlearn.
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Department of Statistical Sciences
University of Padova, Italy
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Marco Scutari
author and maintainer of bnlearn
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University of Padova
Facts don't care if you feel good about them. Slashdot, 25/10/07
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