Biology Reference
In-Depth Information
Then we can call the ARTIVAnet function, specifying the target variables with the
targetData argument and parent variables with the parentData argument.
> DBN = ARTIVAnet(
+ targetData = simulatedProfiles[targets, ],
+ parentData = simulatedProfiles[parents, ],
+ targetNames = targets,
+ parentNames = parents,
+ niter = 50000,
+ savePictures = FALSE)
The number of iterations performed by the algorithm is set with the niter argu-
ment.
The return value of ARTIVAnet is a data frame containing, for each pair of
parent and target variables and for each phase, the estimated regression coefficient
and its posterior probability.
> head(ARTIVAtest1[, -7])
Parent Target CPstart CPend PostProb CoeffMean
1 TF1 1 2 10 0.0469 0.00000
2 TF2 1 2 10 0.0157 0.00000
3 TF3 1 2 10 0.0349 0.00000
4 TF4 1 2 10 0.0317 0.00000
5 TF5 1 2 10 0.0206 0.00000
6 TF1 1 11 30 0.9996 -1.52802
When savePictures = FALSE , ARTIVAnet produces several sets of plots
detailing the progress of the (RJ-)MCMC simulation. An example is shown in
Fig. 3.8 . If, on the other hand, savePictures is set to FALSE , the same plots
are saved in an output file (by default, a PDF file in an ad hoc subdirectory named
ARTIVAnet ).
Exercises
3.1. Consider the Canada data set from the vars package, which we analyzed in
Sect. 3.5.1 .
(a) Load the data set from the vars package and investigate its properties using the
exploratory analysis techniques covered in Chap. 1 .
(b) Estimate a VAR(1) process for this data set.
(c) Build the auto-regressive matrix A and the constant matrix B defining the
VAR(1) model.
(d) Compare the results with the LASSO matrix when estimating the L 1 -penalty
with cross-validation.
(e) What can you conclude?
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